Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Work Stressors in the Health Care Sector by Combining External Observation and Health Professionals’ Self-report in a Cross-sectional Study Design

Objective: Health professionals are particularly affected by work stressors and various methods have already been used to assess them. Linking health professionals’ self-report and external observations can provide a more detailed assessment of stressors, since conclusions for interventions can be derived from their agreement. Since there is a lack of studies in the health sector linking both data sources, the aim of this study is to identify the convergence between health professionals’ self-reports and external observations. Methods: Data were collected in general hospitals, nursing homes, psychiatric institutions and home-care organizations in a cross-sectional study design. 110 health professionals were observed during one entire shift, by one of eight trained external observers. Health professionals and observer separately filled out a questionnaire on work stressors after the observation. For data analysis multiple regression models using bootstrap were calculated considering possible observer effects. Results: Convergent scores for 3 of 9 tested scales on ‘predictability’ of work, ‘social community’ and ‘social relations’ (p>0.05) at work, were identified. However, health professionals rated their ‘quantitative’ (p=0.001), ‘sensorial’ (p=0.001) and ‘physical demands’ (p=0.001) significantly higher than the external observers did. On the contrary, external observers perceived the ‘possibilities for development’ (p=0.007), ‘influence at work’ (p=0.032) and ‘social support at work’ (p=0.002) as lower than did the health professionals. Results also indicate a significant influence of different work settings (p<0.05) on the convergence of self-assessed and observed work stressors. Conclusion: This study results reveal that results on work stressors can be influenced by the chosen method for data collection, which should be considered when using one method only. Moreover, differences between the settings indicate that results on work stressors from one health-care setting cannot be easily transferred to another.
Characterisation and pathogenicity of Ditylenchus dipsaci isolated from Phlox subulata in Japan

Moss phlox, Phlox subulata L. , commonly known as ‘Shiba zakura’ (turf cherry), is one of the most important ornamental plants in Japan. Since 1956, thousands of visitors have gathered each spring in different prefectures to enjoy the beauty of this Phlox as part of a traditional festival. Recently, foliage dieback symptoms were observed in Phlox from many parks in the Chubu area of Japan, causing significant commercial losses (Fig. 1A-C). Dieback is a common symptom caused by Ditylenchus dipsaci (Kuhn) Filipjev (Hooper, 1972), which primarily infects onion and garlic but can also occur in many flowering plants, including Phlox, and is a quarantine species (Jones et al., 2013). The occurrence of this nematode in Japan is well documented on onion, narcissus, lily, tulip and hyacinth (Inagaki, 1985; Katsunori, 1987), but has never been documented on Phlox, although D. dipsaci was reported in 1993 by the Hokkaido Plant Protection Office (unpubl.). Herein, we report the first confirmed occurrence of D. dipsaci on Phlox from the Chubu area of Japan, the nematode identity being confirmed through morphological, molecular and phylogenetic methods. This finding is of quarantine importance for other areas of Japan.
WRF-GC: online two-way coupling of WRF and GEOS-Chem for regional atmospheric chemistry modeling

<p>We developed the two-way version of the WRF-GC model, which is an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem chemical transport model, for regional air quality and atmospheric chemistry modeling. WRF-GC allows the two parent models to be updated independently, such that WRF-GC can stay state-of-the-science. The meteorological fields and chemical variables are transferred between the two models in the coupler to simulate the feedback of gases and aerosols to meteorological processes via interactions with radiation and cloud microphysics. We used the WRF-GC model to simulate surface PM<sub>2.5</sub> concentrations over China during January 22 to 27, 2015 and compared the results to the outcomes from classic GEOS-Chem nested-grid simulations as well as the surface observations. For PM<sub>2.5</sub> simulations, both models were able to reproduce the spatiotemporal variations, but the WRF-GC (r = 0.68, bias = 29%) performing better than GEOS-Chem (r = 0.72, bias = 55%) especially over Eastern China. For ozone simulations, we found that including aerosol-chemistry-cloud-radiation interactions reduced the mean bias of simulated surface ozone concentrations from 34% to 29% compared to observed afternoon ozone concentrations. WRF-GC is computationally efficient, with the physical and chemical variables managed in distributed memory. At similar resolutions, WRF-GC simulations were three times faster than the classic GEOS-Chem nested-grid simulations, due to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. We envision WRF-GC to become a powerful tool for advancing science, serving the public, and informing policy-making. </p>
Cationic starches on cellulose surfaces : a study of polyelectrolyte adsorption

"The first man I saw was of a meagre aspect, with sooty hands and face, his hair and beard long, ragged and singed in several places. His clothes, shirt, and skin were all of the same colour. He had been eight years upon a project for extracting sunbeams out of cucumbers, which were to be put into vials hermetically sealed, and let out to warm the air in raw inclement summers. He told me, he did not doubt in eight years more, that he should be able to supply the Governor's gardens with sunshine at a reasonable rate; but he complained that his stock was low, and entreated me to give him something as an encouragement to ingenuity, especially since this had been a very dear season for cucumbers. I made him a small present, for my Lord had furnished me with money on purpose, because he knew their practise of begging from all who go to see them. "
Modelling the water-system in the Pyrenean Aure-Louron Valley

The increasing demand for renewable energy renders the optimal management of the waterenergy nexus highly important, and the complexity of global change compromises the sustainability of current water use. Therefore, models representing human interventions on water resource are crucial. This work takes the multipurpose reservoir system of the Aure-Louron Valley in the center of the Pyrenees as a typical study case to establish an integrated hydrological modelling framework. Hydropower and downstream consumption represent the main water uses in the study case. The work is a scientific contribution to the Interreg PIRAGUA project (https://www.opcc-ctp.org/en/piragua). Detailed work aims to develop a modelling chain that integrates a water resource model, a water demand model, and a water management model. This study focuses on the water resource model and the water demand model for energy. Water resource is characterized by the hydrological model GR6J (Riboust et al., 2019), calibrated with the SAFRAN surface reanalysis (Vidal et al., 2010) with a dedicated Pyrenean 2.5 km resolution version, and gap-filled MODIS data (Gascoin et al., 2015) for better robustness of snowpack modelling. The energy demand model is based on the air temperature of France and calendar day (Hendrickx and Sauquet, 2013). It is validated with the historical data of water used for hydropower production over the 2001-2018 period. Tools are being developped to make the models transposable to a wide range of water management contexts. The next steps of the study will focus on establishing a water demand model for downstream consumption, and a water management model. Finally, the modelling chain will be applied under various global change scenarios to assess the vulnerability of the system.
A Digital Signal-Conditioner for Resistive Sensors and its Utility for Linearizing GMR-based Magnetometer

In this paper, a novel relaxation-oscillator based resistance to digital converter is proposed. The converter processes the resistive sensors with a circuit employing a single reference voltage and possesses low execution time. The circuit is simple and it provides many advantages like independence from amplifier gain, switch resistance, etc. The performance of the circuit is simulated and emulated for different types of resistive sensor configurations, and results are presented in this paper. The maximum nonlinearity observed from the above studies is 0.2 %. The possible error sources are brought out to show the accuracy of the proposed work. Finally, the proposed circuit is interfaced with the commercially available GMR sensor to develop a GMR-based magnetometer. Results show that the proposed circuit provides better performance than the existing works.
On Wind‐Driven Energetics of Subtropical Gyres

The flow of energy in the wind‐driven circulation is examined in a combined theoretical and numerical study. Based on a multiple‐scale analysis, we find the mesoscale field in the ocean interior is strongly affected by, but does not feed back onto, the ventilated thermocline. In the western boundary region, the associated currents first appear as coastal jets, conserving mean energy, and later as separated jet extensions where the mesoscale is energized by the mean field. It is in the separated jet zone where the primary loss of general circulation energy to the mesoscale occurs. These ideas are tested by an analysis of a regional 1/12° primitive equation numerical model of the North Atlantic. The predictions of the theory are generally supported by the numerical results. The one exception is that topographic irregularities in the coastal jet spawn eddies, although these eddies contribute modestly to the energy budget. We therefore conclude the primary sink of wind input into the mean circulation is in the separated jet, and not the interior. The analysis also shows wind energy input to be much smaller than the interior energy fluxes; thus, the general circulation largely recirculates energy.
Cerimetric determination of ranitidine in pharmaceuticals based on redox and complexation reactions.

One titrimetric and 2 spectrophotometric methods are described for the detn. of ranitidine in bulk drug and in formulations using cerium (IV) sulfate as the oxidimetric agent and orthophenanthroline and thiocyanate as complexing agents. In titrimetry (method A), ranitidine is treated with a measured excess of cerium (IV) sulfate in acid medium and the unreacted oxidant is back titrated with std. ammonium ferrous sulfate using ferroin indicator. In the spectrophotometric method also, ranitidine is treated with a known excess of cerium (IV) sulfate and the residual oxidant is detd. via different reaction schemes. In the 1st method (method B), the unreacted oxidant is reduced by a fixed amt. of iron (II) and the residual iron (II) is complexed with orthophenanthroline at a raised pH and absorbance measured at 510 nm. The 2nd procedure (method C) involves redn. of unreacted oxidant by iron (II) and complexing the resultant iron (III) with thiocyanate and measurement of absorbance at 470 nm. In all the 3 methods, the amt. of cerium (IV) sulfate corresponds to the amt. of ranitidine. Titrimetry is applicable over 8-14 mg range and in the spectrophotometric methods, calibration curves are linear over the concn. range of 2.5-12.5 I¼g ml-1 (method B) and 1.25-6.25 I¼g ml-1 (method C). The methods were satisfactorily applied to the detn. of ranitidine in tablet and injection formulations and no interferences from excipients were noticed. [on SciFinder(R)]
Reversal of Antibiotic Resistance by Phage Resistant Pseudomonas aeruginosa PA01

Emergence of antibiotic resistance is a serious cause of concern worldwide. Limitation in the discovery of new antibiotics and emergence of resistant infectious microorganisms has directed the attention of researcher toward the phage therapy. Phages are obligate parasite of bacteria and they are highly specific for their host. Phage therapy is an ideal strategy for the control of such infectious pathogens. Pseudomonas aeruginosa PA01 is multiple drug resistant (MDR) bacteria, biofilm forming nosocomial pathogen. Phage therapy of PA01 was studied using bacteriophage φs1 isolated from Ganga water, India. During this study, we observed PA01 variants that had evolved resistant mechanism against phage φs1 infection. These phage resistance cells showed variation in their pigment production from green to brown and also showed reversion of antibiotic resistance for- erythromycin (ERT), norfloxacin (NRF), kanamycin (KAN), gentamicin (GEN), gatifloxacin (GAT), and imipenem (IMP) class of antibiotics. Biofilm forming ability of prPA01 also decreased by up to 90% and they became more susceptible for imipenem antibiotic as compare to PA01.
Epidemiological Prediction using Deep Learning

Accurate and real-time epidemic disease prediction plays a signiﬁcant role in the health system and is of great importance for policy making, vaccine distribution and disease control. From the SIR model by Mckendrick and Kermack in the early 1900s, researchers have developed a various mathematical model to forecast the spread of disease. With all attempt, however, the epidemic prediction has always been an ongoing scientiﬁc issue due to the limitation that the current model lacks ﬂexibility or shows poor performance. Owing to the temporal and spatial aspect of epidemiological data, the problem ﬁts into the category of time-series forecasting. To capture both aspects of the data, this paper proposes a combination of recent Deep Leaning models and applies the model to ILI (inﬂuenza like illness) data in the United States. Speciﬁcally, the graph convolutional network (GCN) model is used to capture the geographical feature of the U.S. regions and the gated recurrent unit (GRU) model is used to capture the temporal dynamics of ILI. The result was compared with the Deep Learning model proposed by other researchers, demonstrating the proposed model outperforms the previous methods.
Impacts of a New Supermarket on Dietary Behavior and the Local Foodscape in Kisumu, Kenya: Protocol for a Mixed Methods, Natural Experimental Study

Background Access to healthy food is considered a key determinant of dietary behavior, and there is mixed evidence that living near a supermarket is associated with a healthier diet. In Africa, supermarkets may contribute to the nutrition transition by offering both healthy and unhealthy foods and by replacing traditional food sellers. In Kisumu, Kenya, a planned hypermarket (ie, a supermarket combined with a department store) will form the basis for a natural experimental evaluation. Objective The aim of this study is to explore the impacts of a new hypermarket on food shopping practices, dietary behaviors, physical activity patterns, and body composition among local residents and to identify concurrent changes in the local foodscape. We also aim to explore how impacts and associations vary by socioeconomic status. Methods We employ a mixed methods, longitudinal study design. Two study areas were defined: the hypermarket intervention area (ie, Kisumu) and a comparison area with no hypermarket (ie, Homabay). The study is comprised of 4 pieces of primary data collection: a quantitative household survey with local residents, a qualitative study consisting of focus group discussions with local residents and semistructured interviews with government and private sector stakeholders, an audit of the local foodscape using on-the-ground data collection, and an intercept survey of shoppers in the hypermarket. Assessments will be undertaken at baseline and approximately 1 year after the hypermarket opens. Results Baseline assessments were conducted from March 2019 to June 2019. From a total sampling frame of 400 households, we recruited 376 of these households, giving an overall response rate of 94.0%. The household survey was completed by 516 individuals within these households. Across the two study areas, 8 focus groups and 44 stakeholder interviews were conducted, and 1920 food outlets were geocoded. Conclusions This study aims to further the understanding of the relationship between food retail and dietary behaviors in Kenya. Baseline assessments for the study have been completed. International Registered Report Identifier (IRRID) DERR1-10.2196/17814
Bayesian Adaptive Independence Sampling with Latent Variables

This dissertation introduces an adaptive Markov chain Monte Carlo technique called Bayesian Adaptive Independence Sampling with Latent variables (BAIS+L). BAIS+L, which extends an earlier technique known as Bayesian Adaptive Independence Sampling, has been designed with multimodal target distributions in mind. The dissertation discusses the development of BAIS+L, including an approximation that makes it possible, before assessing its performance through a comparison to the Equi-Energy Sampler and an application to spin glass simulation. It finishes with a discussion of a modification of BAIS+L, which avoids the use of the approximation, at the expense of greater computational complexity.
Discriminant Function Analysis to Distinguish the Performance of Information and Communication Technology (ICT) Companies (A Study of U.S. Companies Listed in U.S. Stock Market)

The growing important role of ICT companies in digital era has attracted many institutions and researchers to conduct studies to measure the value creation created by digitalization. However, not many of them emphasize the importance of financial information as a performance measurement for ICT companies that are useful for their sustainability in the rapid pace of technology. Therefore, this study aims to find the importance of financial ratios in assessing the performance of ICT companies. This study uses discriminant function analysis to find the best financial ratios that distinguish the ICT companies’ performance based on their grade in the credit ratings. The scope of this study is 70 US-based companies listed in US stock market within ICT groups with 35 companies in each group of Investment Grade and Non-investment Grade. There are 4 financial ratios that best discriminate the performance between the two groups which are ROA, CFO to current liabilities, total debt to EBITDA, and CFO to net sales. This model has a predictive accuracy or early warning ability of 87.1% in the latest full-year financial statements prior to rating date and 80% in the longer period (up to 3rd last full-year financial statements prior to rating date). Keywords: Discriminant function analysis, financial ratio, performance, information and communication technology
A Practical Monochrome Video Colorization Framework for Broadcast Program Production

Techniques of using convolutional neural networks (CNNs) to colorize monochrome still images have been widely researched. However, the results of automatic colorization are often different from the user’s intentions and historical fact. A lot of color correction work still needs to be done in order to produce a colorized video. This is a major problem in situations such as broadcasting production where footage must be appropriately colorized in accordance with historical fact. In this article, we propose a practical video colorization framework that can easily reflect the user’s intentions. The proposed framework uses a combination of two CNNs—a user-guided still-image-colorization CNN and a color-propagation CNN—that allows the correction work to be performed efficiently. The user-guided still-image-colorization CNN produces key frames by colorizing several monochrome frames from the target video on the basis of user-specified colors and color-boundary information. The color-propagation CNN automatically colorizes the entire video on the basis of the key frames, while suppressing discontinuous changes in color between frames. A quantitative evaluation showed that it is possible to produce color video reflecting the user’s intention with less effort than with earlier methods.
Design and Analysis of a Robot Clamping Mechanism for Power Tower

In the paper,design and analysis of a robot clamping mechanism,which is mainly composed of a gripping hand, an abduction mechanism and an ejection mechanism, for power tower is presented. According to the climbing environment of the power tower climbing robot, the three-dimensional model of the mechanism is drawn, the rationality of the centering and the size of the mechanism is verified. The feasibility of the clamping mechanism was preliminarily verified by the prototype development and test.
Modeling Calcium Concentration and Biochemical Reactions

urn:nbn:de:0009-3-2245 Abstract. Almost all regions of the brain receive one or more neuromodulatory inputs, and disrupting these inputs produces deficits in neuronal function. Neuromodulators act through intracellular second messenger pathways to influence the electrical properties of neurons, integration of synaptic inputs, spatio-temporal firing dynamics of neuronal networks, and, ultimately, systems behavior. Second messengers pathways consist of series of bimolecular reactions, enzymatic reactions, and diffusion. Calcium is the second messenger molecule with the most effectors, and thus is highly regulated by buffers, pumps and intracellular stores. Computational modeling provides an innovative, yet practical method to evaluate the spatial extent, time course and interaction among second messenger pathways, and the interaction of second messengers with neuron electrical properties. These processes occur both in compartments where the number of molecules are large enough to describe reactions deterministically (e.g. cell body), and in compartments where the number of molecules is small enough that reactions occur stochastically (e.g. spines). - In this tutorial, I explain how to develop models of second messenger pathways and calcium dynamics. The first part of the tutorial explains the equations used to model bimolecular reactions, enzyme reactions, calcium release channels, calcium pumps and diffusion. The second part explains some of the GENESIS, Kinetikit and Chemesis objects that implement the appropriate equations. In depth explanation of calcium and second messenger models is provided by reviewing code, both in XPP, Chemesis and Kinetikit, that implements simple models of calcium dynamics and second messenger cascades.