Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of FDI Motives in the Link between Institutional Distance and Subsidiary Ownership Choice by Emerging Market Multinational Enterprises

This study advances the limited generalizability of previous studies that have focused on developed market multinational enterprises and explores the link between institutional distance and ownership choice of emerging market multinational enterprises (EMNEs). Such studies in the EMNE context have been rare, and we provide key theoretical expla-nations for EMNEs’ distinct foreign direct investment motives to act as important contin- gencies in the link between institutional distance and EMNEs’ ownership choices. Analyses of longitudinal data of Chinese firms’ internationalization from 2001 to 2017 reveal that the higher the institutional distance, the lower the level of EMNE subsidiary ownership control with market-seeking motives; while the higher the institutional distance, the higher the level of EMNE subsidiary ownership control with knowledge-seeking motives.
Video Information recantation using picture characteristic method

Information withdrawal is one of the art for many researchers, especially extracting the images one of the most inspiring for many investigators nowadays, because the complexity of the data set. Image semantic features, because these features tell how to use the corresponding patterns during the process of editing the viedo. The video arrangements which is a formal and explicit specification of events. Specifically, an event is combination of Position, period, moving, and image capturing practise. Extracting the similar contentment from the stored data set is called interpretation template. Here, a semantic design is a mixture of little –smooth structures such color, motion and audio. This video arrangement bring the new set of information that not extracted previously.
The Use of Business Model Canvas to Formulate Business Model on Game Online Store “Asuna Store”

This study is aimed to formulate the business model of Asuna Store using canvas model business. Currently, the phenomenon of online games has been greatly popular in Indonesia. This phenomenon encourages entrepreneurs to establish online game business. Asuna Store is aware of this online game business opportunity. Asuna Store is an Online Game Store operating in Yogyakarta since 2017. The services offered by Asuna Store include the sales of game items, rank jockey services, and tournament. The research method used in this research is descriptive method with a case study approach. The object of the case study in this research is the Asuna store. The sampling technique used to determine the respondents in this study was a non-probability sampling technique, namely purposive sampling.The object of this case study is Asuna Store. The primary data is taken through deep interview. The easiest way to find business model is by using business model canvas. The use of business model canvas will make it easier to see how the replica of the business in Asuna Store is running. The values offered by Asuna Store is to give friendly services, fast responses, and stable and competitive prices for the convenience of consumers of Asuna Store. Asuna Store is an online game store so it does not need physical store. The use of online information media such as Instagram, Snapgram, and Line Official really helps in the business operational of Asuna Store
High parity is associated with increased risk of cervical cancer: Systematic review and meta-analysis of case–control studies

Background: Cervical cancer is the fourth most common cancer among women. High parity has long been suspected with an increased risk of cervical cancer. Evidence from the existing epidemiological studies regarding the association between parity and cervical cancer is variable and inconsistent. Therefore, the objective of this systematic review and meta-analysis was to synthesize the best available evidence on the epidemiological association between parity and cervical cancer. Methods: Case–control studies reporting the association between parity and cervical cancer were systematically searched in databases like MEDLINE/PubMed, HINARI, Google scholar, Science direct, and Cochrane Libraries. All studies fulfilling the inclusion criteria and published between 2000 and 7 March 2020 were included in this meta-analysis. This study reported according to PRISMA guideline. Cochran’s Q-statistics and I2 tests were performed to assess heterogeneity among included studies. Egger’s regression analysis was performed to assess publication bias. A random-effect meta-analysis model was used to compute pooled odds ratio of the association between parity and cervical cancer. Results: A total of 6685 participants (3227 patients and 3458 controls) were incorporated in the 12 studies included in this meta-analysis. The meta-analysis revealed that women with high parity had 2.65 times higher odds of developing cervical cancer compared to their counterparts (odds ratio = 2.65, 95% confidence interval = 2.08–3.38). Conclusion: High parity is positively associated with cervical cancer. Strong epidemiological studies are recommended to further explore the mechanisms and role of parity in the causation of cervical cancer.
Piezoelectric Response in WO3-x Thin Films by Aluminum Clustering

We report piezoelectric response of d33 = 35 ± 5 pm V− 1 on aluminum doped tungsten trioxide thin films (Al-WO3 − x), prepared by RF-sputtering and post annealing treatment in air atmosphere. Using XPS characterization indicate a stoichiometry of WO2.7 and Raman a distorted octahedral tungsten vibration mode of monoclinic WO3 at 236.9 cm− 1, 691 cm− 1 and 803 cm− 1 corresponding to O-W-O chemical bonds. The grazing incidence X-ray diffraction revealed a non-centrosymmetric monoclinic (P21/c) and tetragonal (P4/nmm) mixed phases of WO3 − x with islands of piezoelectric domains as observed by atomic force microscope, additionally atom probe tomography revealed diffusion of aluminum ions from Al2O3 substrate.
Particulate organic carbon budget of the Gulf of Lion shelf (NW Mediterranean) using a coupled hydrodynamic-biogeochemical model

Abstract. The Gulf of Lion shelf (NW Mediterranean) is one of the most productive areas in the Mediterranean Sea. A 3D coupled hydrodynamic-biogeochemical model is used to study the mechanisms that drive the particulate organic carbon (POC) budget over the shelf. A set of observations, including temporal series from a coastal station, remote sensing of surface chlorophyll-a, and a glider deployment, is used to validate the distribution of physical and biogeochemical variables from the model. The model reproduces well the time and spatial evolution of temperature, chlorophyll, and nitrate concentrations and shows a clear annual cycle of gross primary production and respiration. Knowing the physical and biogeochemical inputs and outputs terms, the annual budget of the POC in the Gulf of Lion is estimated and discussed. We estimate an annual net primary production of ~200 104 tC yr−1 at the scale of the shelf. The primary production is marked by a coast-slope increase with maximal values in the eastern region. Our results show that the primary production is favored by the inputs of nutrients imported from offshore waters, representing 3 and 15 times the inputs of the Rhone in terms of nitrate and phosphate. Besides, the EOFs decomposition highlights the role of solar radiation anomalies and continental winds that favor upwellings, and inputs of the Rhone River, on annual changes in the net primary production. Annual POC deposition (19 104 tC yr−1) represents 10 % of the net primary production. The delivery of terrestrial POC favored the deposition in front of the Rhone mouth and the mean cyclonic circulation increases the deposition between 30 and 50 m depth from the Rhone prodelta to the west. Mechanisms responsible for POC export (24 104 tC yr−1) to the open sea are discussed. The export off the shelf in the western part, from the Cap de Creus to the Lacaze-Duthiers canyon, represented 37 % of the total POC export. Maximum values were obtained during shelf dense water cascading events and marine winds. Considering surface waters only, the POC was mainly exported in the eastern part of the shelf through shelf waters and Rhone inputs, which spread to the Northern Current during favorable continental wind conditions. The Gulf of Lion shelf appears as an autotrophic ecosystem with a positive Net Ecosystem Production and as a source of POC for the adjacent NW Mediterranean basin. The undergoing and future increase in temperature and stratification induced by climate change could impact the trophic status of the GoL shelf and the carbon export towards the deep basin. It is crucial to develop models to predict and assess these future evolutions.
Recognition of navel orange image with complex background based on residual network

In order to solve the problem that the detection effect of navel orange recognition in complex background is poor, we propose a navel orange recognition method based on residual network. In this study, the navel orange classification dataset was constructed and labeled, and the performance of five classic convolutional neural network models on this dataset was evaluated, including AlexNet, Improved LeNet, SqueezeNet, ResNet-18, GoogLeNet. The results present significant accuracy obtained by the ResNet-18 model, with accuracy of 98.27%, which is more suitable for navel orange image recognition in complex background.
A Detailed, Multi‐Scale Assessment of an Atmospheric River Event and Its Impact on Extreme Glacier Melt in the Southern Alps of New Zealand

North‐westerly airflow and associated atmospheric rivers (ARs) have been found to profoundly influence New Zealand’s west coasts, by causing flooding, landslides and extreme ablation and accumulation on glaciers in the Southern Alps. However, the response of local glacier mass balance to synoptic‐scale circulation, including events with ARs, has typically not been investigated by considering mesoscale processes explicitly. In this study, high‐resolution atmospheric simulations from the Weather Research and Forecasting model are used to investigate the mesoscale drivers of an extreme ablation event on Brewster Glacier (Southern Alps), which occurred on February 6, 2011 during the landfall of an AR on the South Island. The following processes were found to be crucial for transferring the high temperature and water vapor contained in the AR into energy available for melt on Brewster Glacier: First, the moist‐neutral character of the air mass enabled the flow to pass over the ridge, leading to the development of orographic clouds and precipitation on the windward side of the orography, and foehn winds on the leeside. These processes fueled melt through longwave radiation and strong turbulent and rain heat fluxes within the high‐condensation environment of the orographic cloud. Second, orographic enhancement occurred due to both cellular convection within the cloud and the combined effect of multiple precipitating systems by the seeder‐feeder‐mechanism. These results indicate the potential importance of AR dynamics for New Zealand’s glaciers. They also illustrate the benefit of mesoscale atmospheric modeling for advancing process understanding of the glacier‐climate relationship in New Zealand.
Analysis of aerosol-cloud interactions and their implications for precipitation formation using aircraft observations over the United Arab Emirates

Abstract. Aerosol and cloud microphysical measurements were collected by a research aircraft during August 2019 over the United Arab Emirates (UAE). The majority of science flights targeted summertime convection along the eastern Hajar mountains bordering Oman, while one flight sampled non-orographic clouds over the western UAE near the Saudi Arabian border. In this work, we study the evolution of growing cloud turrets from cloud base (9 °C) up to the capping inversion level (−12 °C) using coincident cloud particle imagery and particle size distributions from cloud cores under different forcing. Results demonstrate the active role of background dust and pollution as cloud condensation nuclei (CCN) with the onset of their deliquescence in the sub-cloud region. Sub-cloud aerosol sizes are shown to extend from submicron to 100 µm sizes, with higher concentrations of ultra-giant CCN (d >10 µm) from local sources closer to the Saudi border, compared to the eastern orographic region where smaller size CCN are observed. Despite the presence of ultra-giant CCN from dust and pollution in both regions, an active collision-coalescence (C-C) process is not observed within the limited depths of warm cloud (< 1000 m). The state-of-the-art observations presented in this paper can be used to initialize modelling case studies to study the influence of aerosols on cloud and precipitation processes in the region and to better understand the impacts of hygroscopic cloud-seeding on these clouds.
BRICS-T Ülkelerinde Sağlık Harcamaları Ekonomik Büyümeyi Etkiliyor mu? Ampirik Bir İnceleme

İçsel büyüme teorilerinde, beşeri sermaye ekonomik büyümeyi etkileyen unsurlar arasında yer almaktadır. Beşeri sermaye faktörlerinden biri olan sağlık düzeyindeki artışlar bir yandan, işgücü verimliliğini arttırırken, diğer yandan yaşam süresini arttırarak fiziksel yatırımlardaki artış yoluyla ekonomik büyüme üzerinde pozitif etki yaratmaktadır. Bu çalışmada harcamalarının için ele alınmıştır. Purpose : Human capital is among the factors affecting economic growth in endogenous growth theories. Increases in health, which is one of the human capital factors, not only increases labor productivity but also has a positive effect on economic growth by increasing life expectancy and increasing physical investments. In this study, the effect of health expenditures on economic growth is discussed for BRICS-T countries. causality relationship from the health expenditure variable to economic growth. These results support the validity of the health-oriented growth hypothesis in BRICS-T countries. Sınırlılıklar: Çalışmanın temel sınırlılığı örnekleme dahil edilen ülkeler ve 2000-2018 dönemleri olarak belirlenmiştir. Limitations : The main limitation of the study was determined as the countries included in the sampling and the period of 2000-2018. Özgünlük/Değer: Originality/Value : In this study, the effect of health expenditures, which is an important component of human capital, on economic growth was analyzed comparatively for BRICS countries and Turkey. Anahtar
Mapping of Flooded Heritage Structures Using SAR Polarimetry and Google Earth Engine

The growing flood events and the associated risk in heritage structures are an increasingly crucial issue for India, which possesses heritage richness. However, it is more critical for developing countries where often the case is poorly understudied. Hampi, also referred to as the Group of Monuments, is a UNESCO World Heritage Site located in east-central Karnataka, India. Several monuments at the heritage site of Hampi along the Tungabhadra river are inundated several times within a year. After every flood, the river changes its course, inundating areas that were supposed to be safe from floods. The post-flood silt deposition over structures makes them more vulnerable to erosion and distortion. So, to restore, mapping of flooded structures is crucial. The changes in the cultural landscape should be monitored on a spatial and temporal basis. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in spatial and temporal measurements to monuments.
Chemical Analysis of Ginger Root

Ginger root( Zingiber officinal Rose) was analysed to identify its nutritional and anti- nutritional contents. The results showed that Ginger has 34.13% crude protein, 4.07% Ether Extract, 4.02% crude fibre content, 13.75% moisture content, 7.64% Ash content and 1.036% vitamin C. Furthermore, ginger contains major minerals like: Zn 64.0 mg/l , Mn 5.90 mg/l, Fe 279.7 mg/l, Cu 8.80 mg/l, Ca 280.0 mg/l and P 8068.0 mg/l. The result obtained confirmed the usefulness of ginger root as a potential functional food and could be explored further in new product and formulation. I. Introduction Ginger (Zingiber officinal rose) is an underground stem or rhizome of the plant Zingiber officinate. It has ben used as traditional medicine in China, India, Malaysia and Arabic countries since ancient times. Ginger has been used to treat stomach upset, nausea, diarrhea, colic, arthritis, heart conditions, flu- like symptoms and painful menstral periods 1 . It was found that Ginger extracts have antimicrobial properties against E.Coli, sslmonella typhi and bacillus substilis that are common cause of gastro intestinal tract infections 2 . The important active component of ginger root is the volatile oil and pungent phenol compound such as gingerol, which is a very potent anti-inflammatory compound. Mordern scientific research has revealed that ginger possesses numerous therapeutic like anti-oxidant effects, an ability to inhibit the formation of inflammatory compounds and direct anti-inflammatory effects 3 . Ginger has aso been found to reduce all symptoms associated with motion sickness like dizziness, cold sweating, nausea and vomiting 4 .
fmdtools: A Fault Propagation Toolkit for Resilience Assessment in Early Design

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one's ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many analysis frameworks have been presented for early hazard assessment, these frameworks are difficult to apply without reference implementations, and most currently-available fault modelling tools are meant for the later stages of design. This paper describes fmdtools, a Python-based resilience-based design and analysis environment that solves these problems by enabling the designer to represent the system in the early design process, simulate the effects of faults, and quantify corresponding resilience metrics. This toolkit is then demonstrated in the hazard analysis and architecture design of a multi-rotor drone.
Integrative Statistics, Machine Learning and Artificial Intelligence Neural Network Analysis Correlated CSF1R with the Prognosis of Diffuse Large B-Cell Lymphoma

Tumor-associated macrophages (TAMs) of the immune microenvironment play an important role in the Diffuse Large B-cell Lymphoma (DLBCL) pathogenesis. This research aimed to characterize the expression of macrophage colony-stimulating factor 1 receptor (CSF1R) at the gene and protein level in correlation with survival. First, the immunohistochemical expression of CSF1R was analyzed in a series of 198 cases from Tokai University Hospital and two patterns of histological expression were found, a TAMs, and a diffuse B-lymphocytes pattern. The clinicopathological correlations showed that the CSF1R + TAMs pattern associated with a poor progression-free survival of the patients, disease progression, higher MYC proto-oncogene expression, lower MDM2 expression, BCL2 translocation, and a MYD88 L265P mutation. Conversely, a diffuse CSF1R + B-cells pattern was associated with a favorable progression-free survival. Second, the histological expression of CSF1R was also correlated with 10 CSF1R-related markers including CSF1, STAT3, NFKB1, Ki67, MYC, PD-L1, TNFAIP8, IKAROS, CD163, and CD68. CSF1R moderately correlated with STAT3, TNFAIP8, CD68, and CD163 in the cases with the CSF1R + TAMs pattern. In addition, machine learning modeling predicted the CSF1R immunohistochemical expression with high accuracy using regression, generalized linear, an artificial intelligence neural network (multilayer perceptron), and support vector machine (SVM) analyses. Finally, a multilayer perceptron analysis predicted the genes associated with the CSF1R gene expression using the GEO GSE10846 DLBCL series of the Lymphoma/Leukemia Molecular Profiling Project (LLMPP), with correlation to the whole set of 20,683 genes as well as with an immuno-oncology cancer panel of 1790 genes. In addition, CSF1R positively correlated with SIRPA and inversely with CD47. In conclusion, the CSF1R histological pattern correlated with the progression-free survival of the patients of the Tokai series, and predictive analytics is a feasible strategy in DLBCL.
The Impact of Emotional Intelligence on the Professional Orientations of Secondary School Students in Jordan

This study aimed to identify the relationship between emotional intelligence and professional dispositions among secondary stage students in Jordan, and the study relied on the relational descriptive approach, and the study sample consisted of (1500) secondary stage students in Jordan. The emotional intelligence scale and the professional dispositions scale were used to collect data. The results showed that the level of emotional intelligence among secondary school students in Jordan was of a moderate degree. The results indicated that there is a close correlation between professional dispositions and emotional intelligence, whenever a higher level of emotional intelligence, the level of professional dispositions towards motor professions or manual skills were increased, but the relationship decreases with emotional intelligence whenever the professional dispositions go towards software, discoveries, and media matters. In light of the results of the study, a set of recommendations were presented.
Toward Hybrid Physics -Machine Learning to improve Land Surface Model predictions

1 1. Title: Toward Hybrid Physics-Machine Learning to improve Land Surface Model predictions 2. Authors/Affiliations: Geza, M. , Assistant professor, Hydrology, SD Mines Tesfa T. , Scientist, Hydrology, Pacific Northwest National Laboratory (PNNL) Liangping Li, Assistant professor, Hydrogeology, SD Mines Qiao, M. Associate Professor, Data-Scientist/Machine Learning, SD Mines 3. Focal Area(s): Improving land surface model predictions using physics informed machine learning. 4. Science Challenge: Improving the E3SM Land Model (ELM) predictions using integrated Physics Informed Machine Learning (PIML) modeling. 5. Rationale: A critical challenge for Land Surface Models (LSMs) is to simulate processes at the surface and the subsurface and their feedbacks to the atmosphere. Even using the same climate forcings, different LSMs predict different surface fluxes and soil moisture conditions due to differences in the formulations of individual processes, parameterizations, and representation of spatial heterogeneity. Ultimately, these differences contribute to the LSM prediction errors and uncertainty. This research seeks to address this challenge by coupling physics-based modeling with state-of-the-art machine learning (ML) techniques to describe complex physical and biogeochemical processes and narrow the gap between model predictions and observations.