bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Elliptical Diesel Nozzles on Spray Liquid-Phase Penetration under Evaporative Conditions
Authors: Yin Bifeng , Xu Bin , Jia Hekun , Yu Shenghao
Published: May 2020

Elliptical diesel nozzles affect the fuel–air mixing process, and thus combustion and exhaust emissions. Experiments were conducted to study biodiesel spray liquid-phase behaviors for elliptical and circular nozzles through the Mie-scattering method under evaporative conditions. Based on the measurements, the results show that the elliptical nozzle spray liquid-phase penetration is smaller than the circular one under steady-state conditions. The deformation and the axis-switching behaviors of the elliptical jet are helpful in accelerating the breakup of the liquid core. Moreover, the injection pressure has little impact on the penetration of the liquid-phase spray for either geometrical orifice. Additionally, increasing the ambient temperature can reduce the penetration of liquid-phase spray, because an increase in temperature increases the rate of evaporation. The differences in steady liquid-phase penetration between circular and elliptical sprays decrease as the ambient temperature increases. Additionally, increasing the backpressure can decrease the liquid-phase penetration. The differences in steady liquid-phase penetration between circular and elliptical nozzles are reduced with the increase in backpressure, probably due to the axis-switching and deformation behaviors of the elliptical jet being restrained under high-backpressure conditions. Finally, the application of an elliptical orifice is beneficial for decreasing the spray liquid-phase penetration, and thus avoiding the fuel impingement in small engine combustion chambers. The lower liquid-phase penetration for elliptical spray indicated higher fuel and air mixture quality, which is helpful for reducing the diesel engine exhaust soot emissions.
Assessment of Permian Zubers as the Host Rock for Deep Geological Disposal
Authors: Lankof Leszek
Published: May 2020

Together with renewable energy sources, nuclear power represents an important contribution to a sustainable energy mix in many countries and has an important impact on sustainable development. Nuclear energy production is also a source of high-level radioactive waste (HLW) and spent nuclear fuel (SNF), which require special concern. Disposal in deep geological formations is one of the solutions for the long-term management of HLW and SNF. It requires the development of a concept ensuring long-term safe isolation of waste and its validation applying the safety case methodology, which is a formal compilation of evidence, analyses and arguments that quantify and justify a claim that the repository will be safe. The results of laboratory testing of a potential repository host rock are an important component of the evidence that helps in the safety assessment of the deep geological disposal concept. This paper presents results of research focused on the physical, geomechanical and sorption properties of the Brown and Red Zuber unit rocks from the Kłodawa Salt Mine in Poland, which together with rock salt are an important component of Polish salt domes. The Brown and Red Zubers are typical evaporite lithostratigraphic units for the Polish part of the Zechstein Basin. They consist of halite (15–85%) and water-insoluble minerals, such as anhydrite, clay minerals, carbonates, quartz and feldspar, which occurred in varying proportions in the tested samples. The properties of the zuber rocks have been compared with those of rock salt, which is considered a suitable host rock for deep geological disposal of radioactive waste.
Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
Authors: del Real Alejandro J. , Dorado Fernando , Durán Jaime
Published: May 2020

This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.
A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid
Authors: Hafeez Ghulam , Alimgeer Khurram Saleem , Wadud Zahid , Shafiq Zeeshan , Ali Khan Mohammad Usman , Khan Imran , Khan Farrukh Aslam , Derhab Abdelouahid
Published: May 2020

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.
TRIC: A Thermal Resistance and Impedance Calculator for Electronic Packages
Authors: Codecasa Lorenzo , De Viti Francesca , d’Alessandro Vincenzo , Gualandris Donata , Morelli Arianna , Villa Claudio Maria
Published: May 2020

This paper presents the Thermal Resistance and Impedance Calculator (TRIC) tool devised for the automatic extraction of thermal metrics of package families of electronic components in both static and transient conditions. TRIC relies on a solution algorithm based on a novel projection-based approach, which—unlike previous techniques—allows (i) dealing with parametric detailed thermal models (pDTMs) of package families that exhibit generic non-Manhattan variations of geometries and meshes, and (ii) transforming such pDTMs into compact thermal models that can be solved in short times. Thermal models of several package families are available, and dies with multiple active areas can be handled. It is shown that transient thermal responses of chosen packages can be obtained in a CPU (central processing unit) time much shorter than that required by a widely used software relying on the finite-volume method without sacrificing accuracy.
A Survey on Enhanced Smart Micro-Grid Management System with Modern Wireless Technology Contribution
Authors: Tightiz Lilia , Yang Hyosik , Piran Mohammad Jalil
Published: May 2020

Micro-grid (MG) deployment has dramatically become more popular with the high penetration of renewable energy resources (RER). This trend brings with it the merits of independent power grid clean energy resource-based systems, and simultaneously the demerits of an unstable grid due to the intermittent nature of RER. Control and monitoring of MG through robust and ubiquitous communication system infrastructure is the solution to overcoming this intermittency. There has been an increasing focus in recent years on using wireless communication technologies as a prominent candidate in holistic proposal for the micro-grid management system (MGMS). The MGMS has been developed using the multi-agent system (MAS), multi-micro-grid (multi-MG), Internet of things (IoT) integration, and cloud concepts requiring new communication specifications, which can be satisfied by next-generation wireless technologies. There is, however, a lack of comprehensive corresponding investigation into management levels of MG interaction requirements and applied communication technologies, as well as a roadmap for wireless communication deployment, especially for the next generation. In this paper, we investigate communication technology applications in the MG and focus on their classification in a way that determines standard gaps when applying wireless for MG control levels. We also explore and categorize the literature that has applied wireless technologies to MG. Moreover, since MGMS has been revolutionized by attaching new technologies and applications to make it an active element of the power system, we address future trends for MGMS and offer a roadmap for applying new enhancements in wireless technologies, especially the fifth generation (5G) of wireless networks with its exclusive characteristics, to implement this novel approach.
Thermodynamic, Exergy and Environmental Impact Assessment of S-CO2 Brayton Cycle Coupled with ORC as Bottoming Cycle
Authors: Espinel Blanco Edwin , Valencia Ochoa Guillermo , Duarte Forero Jorge
Published: May 2020

In this article, a thermodynamic, exergy, and environmental impact assessment was carried out on a Brayton S-CO2 cycle coupled with an organic Rankine cycle (ORC) as a bottoming cycle to evaluate performance parameters and potential environmental impacts of the combined system. The performance variables studied were the net power, thermal and exergetic efficiency, and the brake-specific fuel consumption (BSFC) as a function of the variation in turbine inlet temperature (TIT) and high pressure (PHIGH), which are relevant operation parameters from the Brayton S-CO2 cycle. The results showed that the main turbine (T1) and secondary turbine (T2) of the Brayton S-CO2 cycle presented higher exergetic efficiencies (97%), and a better thermal and exergetic behavior compared to the other components of the System. Concerning exergy destruction, it was found that the heat exchangers of the system presented the highest exergy destruction as a consequence of the large mean temperature difference between the carbon dioxide, thermal oil, and organic fluid, and thus this equipment presents the greatest heat transfer irreversibilities of the system. Also, through the Life Cycle Analysis, the potential environmental impact of the system was evaluated to propose a thermal design according to the sustainable development goals. Therefore, it was obtained that T1 was the component with a more significant environmental impact, with a maximum value of 4416 Pts when copper is selected as the equipment material.
Assessing Energy-Based CO2 Emission and Workers’ Health Risks at the Shipbreaking Industries in Bangladesh
Authors: Mitra Nandita , Shahriar Shihab Ahmad , Lovely Nurunnaher , Khan Md Shohel , Rak Aweng Eh , Kar S. P. , Khaleque Md Abdul , Amin Mohamad Faiz Mohd , Kayes Imrul , Salam Mohammed Abdus
Published: May 2020

The study represents the estimation of energy-based CO2 emission and the health risks of workers involved in the shipbreaking industries in Sitakunda, Bangladesh. To calculate the carbon emission (CE) from three shipbreaking activities, i.e., metal gas cutting (GC), diesel fuel (FU) and electricity consumption (EC), we used the Intergovernmental Panel on Climate Change (IPCC) guidelines and Environmental Protection Agency (EPA)’s Emission and Generation Resource Integrated Database (eGRID) emission factors. Moreover, the geographic weighted regression (GWR) model was applied to assess the contribution of influencing factors of CE throughout the sampling points. To assess the workers’ health condition and their perceptions on environmental degradation, a semi-structured questionnaire survey among 118 respondents were performed. The results showed that total CO2 emissions from GC were 0.12 megatons (MT), 11.43 MT, and 41.39 MT for daily, monthly, and yearly respectively, and the values were significantly higher than the surrounding control area. Emissions from the FU were estimated as daily: 0.85 MT, monthly: 1.92 MT, and yearly: 17.91 MT, which were significantly higher than EC. The study also revealed that workers were very susceptible to accidental hazards especially death (91%), and pollution (79%). Environmental consequences and health risks of the workers in shipbreaking industry warrant more attention nationally and internationally at the industry-level.
Numerical and Optimal Study on Bending Moment Capacity and Stiffness of Mortise-and-Tenon Joint for Wood Products
Authors: Hu Wengang , Liu Na
Published: May 2020

Mortise-and-tenon (M–T) joint is a traditional joint type commonly used in wood constructions and wood products. Bending moment capacity (BMC) is a critical criterion to evaluate the strength of the M–T joint. In order to design the M–T joint structure more rationally, many researchers have been devoted to studying on this topic. However, the factors influencing the BMC are too many to conduct comprehensive studies using experimental tests, especially for tenon size. In this study, the BMC and bending stiffness of the M–T joint were studied using a combination of finite element method (FEM) and response surface method to optimize the tenon size of the M–T joint. The results showed that (1) the proposed finite element model was capable of predicting BMC of M–T joints with the ratios of FEM to observed, ranging from 0.852 to 1.072; (2) the BMC and stiffness were significantly affected by tenon size, and tenon length had a more significant effect on BMC than tenon width, while the tenon width affected the bending stiffness more significantly; (3) the response surface model proposed to predict and optimize the BMC of the M–T joint relating to tenon length and tenon width was capable of providing an optimal solution; (4) it was recommended to make the ratio of tenon length to tenon width higher than 1 to get higher BMC of M–T joints. In conclusion, this study will contribute to reducing the cost of a huge amount of experimental tests by applying FEM and the response surface method to design M–T joint wood products.
The Relative Importance of Succession, Stand Age and Stand Factors on Carbon Allocation of Korean Pine Forests in the Northern Mt. Xiaoxing’anling, China
Authors: Wang Xuemei , Guo Zhiwen , Guo Xin , Wang Xiangping
Published: May 2020

Exploring carbon allocation pattern and its influencing factors is of great significance for estimating the carbon sequestration rate and potential of forest ecosystems. Here, we investigate all carbon pool components (including above and belowground biomass of tree, shrub and herb layers, and dead biomass and soil carbon pools) in four successional stages of broad-leaved and Korean pine (Pinus koraiensis Siebold & Zucc.) mixed forests in Northeast China. We explore the change of allocation among carbon pools with succession and examine the relative importance of succession, stand age, and stand factors on carbon allocation pattern. Our results illustrate that above- and belowground vegetation carbon increase as maximum tree height increases. Below- to aboveground vegetation carbon ratio (R/S ratio) decreases significantly with succession and increases significantly as mean diameter at breast height (DBH) increases, but does not significantly correlate with stand age. With succession and increasing stand age, understory (shrub, herb) to tree carbon ratio (understory/tree ratio) and soil to vegetation carbon ratio (soil/vegetation ratio) decrease significantly. The joint effect of succession, stand age, and stand factors have the largest contribution on above- and belowground vegetation carbon and understory/tree ratio (26.83%, 27.93%, and 49.48% of variations explained, respectively). As for the pure effects, stand factors explain the largest proportion of variations in vegetation aboveground carbon (11.25%) and soil carbon (20.18%). Meanwhile, succession is the variable with the largest contribution to vegetation belowground carbon (12.64%), R/S ratio (21.83%), understory/tree ratio (25.84%), and soil/vegetation ratio (6.68%). Overall, these results suggest that species composition change during forest succession, instead of stand factors and stand age, is the main driver of forest vegetation carbon allocation. In contrast, stand factors play a major role in soil carbon allocation. Our findings suggest more studies to better understand the role of species composition (in addition to stand factors and age) on biomass allocation, and the influence of stand factors and litterfalls on soil carbon sequestration, which are critical to improve forest management strategies (e.g., adjustment of species composition and forest structure) to increase the future ability of forest carbon sequestration.
Oak Resprouting Survival and Competition for 19 Years after Wildfire in the Republic of Korea
Authors: Kim Jeonghwan , Lim Joo-Hoon , Shin Moonhyun , Han Seung-Hyun , Kang Wonseok
Published: May 2020

In the Republic of Korea, forest fire occurred at a rate of more than 400 events yr−1 from 2000 to 2016, and the burned areas are concentrated in the eastern coastal region. In this region, pine forest is extensively distributed, and pine forest is converted to oak forest through natural succession or stump-sprout regeneration following forest fires. However, there is a lack of research on oak competition and survival during the regeneration process in burned areas. Therefore, this research was carried out to evaluate the effects of oak survival ratio of Quercus serrata (glandbearing oak), Q. variabilis (oriental oak), and Q. mongolica (Mongolian oak) in burned areas. The investigation plots were set to survey tree height, diameter at breast height, sprout survival, and composition in Goseong-gun, Kangwon province, Republic of Korea. The mean tree heights of Q. serrata and Q. variabilis were approximately 9.8 m and 9.1 m, respectively, which were higher than the approximately 5.8 m heights of Q. mongolica stands 18 years after a forest fire, and the trend for tree diameter at breast height was the same for all three species stands. In the early stage of regeneration after the forest fire, the survival probability of the Q. serrata sprouts decreased at a rate of 1.7–2.0 times higher than that of other oak species in the Q. serrata-dominant stands, and this tendency was found to the survival probability in Q. variabilis-dominant stands as well. The median survival time of Q. variabilis sprouts in the Q. variabilis-dominant stands was approximately 10.1 years, which was similar to Q. serrata-dominant stands. However, the dominant stand of Q. mongolica was different from that of other dominant stands since the stand mostly located in the ridge and the upper part of the mountain where topography and soil conditions were considerably dissimilar from the other stands. The decision of dominant species seems to be determined by the survival and occurrence of sprouts during the early stage in post-fire regenerated oak forests. Therefore, it would be more desirable to coppice sprout for dominant species productivity and rapid dominance after a forest fire.
Study on Evaluation of Timber Security in China Based on the PSR Conceptual Model
Authors: Li Qiujuan , Chen Shaozhi , Zhao Rong
Published: May 2020

In case of a shortage of China’s domestic timber, the research of China’s timber security has become increasingly more important. Using the pressure-state–response (PSR) conceptual model and the entropy method, the timber security of China during 1997–2017 was evaluated and analyzed to understand and master the situation of timber security in this paper. The results showed that: (1) The pressure of timber security in China during 1997–2017 was increasing in waves, with the condition of timber imports as the main factor; (2) the state of timber security in China presented a downward-then-upward tendency during 1997–2017, the main influencing factors of which were the domestic timber supply and forest resources condition; (3) responses to ensure the timber security of China almost indicated a steep rising trend, because both the timber industry technical progress index and the waste paper recovery rate improved the safety of timber in China; and (4) the changing trend of the comprehensive evaluation of timber security in China approximately agrees with that of the state evaluation, which showed that state indicators were key factors affecting the timber security of China. The pressures influencing the timber security of China are rising, while the state of timber security and the responses of the high-tech industry have been improving at a higher range than the pressures, which has led to an improvement of China’s timber security.
Loading Dynamics of Cold Atoms into a Hollow-Core Photonic Crystal Fiber
Authors: Wang Yu , Chai Shijie , Xin Mingjie , Leong Wui Seng , Chen Zilong , Lan Shau-Yu
Published: May 2020

Cold atoms trapped and guided in hollow-core photonic crystal fibers provide a scalable diffraction-free setting for atom–light interactions for quantum technologies. However, due to the mismatch of the depth and spatial extension of the trapping potential from free space to the fiber, the number of cold atoms in the fiber is mainly determined by the loading process from free space to waveguide confinement. Here, we provide a numerical study of the loading dynamics of cold atoms into a hollow-core photonic crystal fiber. We use the Monte Carlo method to simulate the trajectories of an ensemble of cold atoms from free space trapping potential to optical potential inside a hollow-core fiber and calculate the temperature, loading efficiency, and geometry of the ensemble. We also study the noise sources that cause heating and a loss of atoms during the process. Our result could be used to design and optimize the loading process of cold atoms into a hollow-core fiber for cold atom experiments.
Fractional Kinetic Equations Associated with Incomplete I-Functions
Authors: Kumar Bansal Manish , Kumar Devendra , Harjule Priyanka , Singh Jagdev
Published: May 2020

In this paper, we investigate the solution of fractional kinetic equation (FKE) associated with the incomplete I-function (IIF) by using the well-known integral transform (Laplace transform). The FKE plays a great role in solving astrophysical problems. The solutions are represented in terms of IIF. Next, we present some interesting corollaries by specializing the parameters of IIF in the form of simpler special functions and also mention a few known results, which are very useful in solving physical or real-life problems. Finally, some graphical results are presented to demonstrate the influence of the order of the fractional integral operator on the reaction rate.
A Little Bit of That from One of Your Grandparents: Interpreting Others’ Direct-to-Consumer Genetic Ancestry Results
Authors: Bobkowski Piotr S. , Watson John C. , Aromona Olushola O.
Published: May 2020

With more than 25 million tests sold by early 2019, direct-to-consumer genetic ancestry tests expose the public to critical issues of genetics, ancestry, and identity. This study examines how individuals understand the results of a genetic ancestry test. Twenty undergraduate students viewed and interpreted an unfamiliar individual’s ancestry results. In in-depth interviews, students indicated that the results were easy to read and understand, but that they had difficulty articulating the meaning of the ancestry groups presented in the results. Participants could not accurately paraphrase the test’s scientific explanation. Those who engaged with the scientific explanation developed doubts about the test’s credibility. There was little consensus about the legitimacy of identity claims from low-proportion ancestry groups. Some students reserved judgment while others identified specific thresholds for what ancestry proportions legitimize identity claims. Results contribute to the literature on the public’s understanding of ancestry, genetics, and data interpretation.
Nonceliac Gluten Sensitivity—A Masquerading IBS or a Real Phenomenon?
Authors: Elitsur Yoram , Preston Deborah
Published: May 2020

The gluten-free diet has become popular among the public. People who are using this diet have reported symptom relief once gluten has been removed from their diet. Nonceliac gluten sensitivity (NCGS) has emerged as a new diagnosis for those patients who have tested negative for celiac disease. Although there are no diagnostic markers established for NCGS, its symptomatology ranges from gastrointestinal symptoms to neuropsychiatric symptoms. Indeed, some of these symptoms are also seen in patients with irritable bowel syndrome (IBS), such as abdominal pain, bloating, altered bowel movement, diarrhea, and constipation. It is important to add that unlike celiac disease, NGCS has never been associated with any long-term malignancy. We aim to review the recent clinical data available on this topic and address the overlap symptoms between NCGS and IBS. We concluded that despite the overlap symptoms between both diseases, NCGS is a real clinical phenomenon that awaits its own diagnostic clinical criteria and specific laboratory markers. We suggest that patients with gluten sensitivity who are negative for celiac disease should be considered for NCGS.
Impact of 2018 ESC/ESH and 2017 ACC/AHA Hypertension Guidelines: Difference in Prevalence of White-Coat and Masked Hypertension
Authors: Kim Byong-Kyu , Rhee Moo-Yong
Published: May 2020

Our study evaluated whether there were differences in the prevalence of white-coat hypertension (WH) and masked hypertension (MH) based on the 2018 ESC/ESH and 2017 ACC/AHA hypertension guidelines in Korea. The motivation was the lowering of the diagnostic threshold for hypertension in the 2017 ACC/AHA guidelines. Of 319 participants without antihypertensive drug history and with suspected hypertension based on outpatient clinic blood pressure (BP) measured by physicians, 263 participants (51.6 ± 9.6 years; 125 men) who had valid research-grade office BP and 24-h ambulatory BP measurements were enrolled. WH prevalence based on daytime ambulatory BP among normotensive individuals was lower with the ESC/ESH guidelines than the ACC/AHA guidelines (29.0% vs. 71.4%, p < 0.001). However, MH prevalence based on daytime ambulatory BP among hypertensive individuals was higher based on the ESC/ESH guidelines (21.6% vs. 1.8%, p < 0.001). Seventy percent of WH cases (2017 ACC/AHA guidelines) and 95.2% of MH cases (2018 ESC/ESH guidelines) occurred in individuals with systolic BP of 130–139 mmHg and/or diastolic BP of 80–89 mmHg. The diagnostic threshold of the 2017 ACC/AHA guidelines yielded a higher prevalence of WH compared to that of the 2018 ESC/ESH guidelines. However, the prevalence of MH was higher with the 2018 ESC/ESH guidelines than with the 2017 ACC/AHA guidelines. The high prevalence of WH and MH in people with a systolic BP of 130–139 mmHg or diastolic BP of 80–89 mmHg suggests the need for a more active out-of-office BP measurement in this patient group.
Does Food Insecurity Contribute towards Depression? A Cross-Sectional Study among the Urban Elderly in Malaysia
Authors: Mesbah Siti Farhana , Sulaiman Norhasmah , Mohd Shariff Zalilah , Ibrahim Zuriati
Published: May 2020

With the aging of the population worldwide, there is an increasing concern for the mental health status as well as physical health. Depression is a common mental health problem among the elderly populations. Since the elderly are susceptible to food insecurity, this cross-sectional study is aimed to determine an association between food insecurity and depression among elderly people. A total of 220 elderly people- aged 60 years and above, residing in the Petaling district of Selangor, were included in this study. Face-to-face interviews were conducted to obtain the pertinent information on demographic background, food security status (six items USDA FSSM), functional status (IADL, EMS), psychosocial status (LSNS-6), and depression status (GDS-15). Binary logistic regression was used to assess the factors that were associated with depression. The median age of the elderly sample was 65.5 years. The prevalence of depression and food insecurity that was recorded were 13.2% and 19.5%, respectively. Social isolation (AOR = 5.882, 95% CI: 2.221, 15.576), food insecurity (AOR = 3.539, 95% CI: 1.350, 9.279), and unsafe mobility (AOR= 3.729, 95% CI: 1.302, 10.683) increased the odds of depression. In conclusion, social isolation, food insecurity, and unsafe mobility are factors associated with depression among the elderly people. Plans such as health interventions as well as grocery and financial aid among the qualifying elderly are suggested to improve this depression and food insecurity.
Flu Vaccination Attitudes, Behaviours, and Knowledge among Health Workers
Authors: Arghittu Antonella , Dettori Marco , Azara Antonio , Gentili Davide , Serra Antonello , Contu Bruno , Castiglia Paolo
Published: May 2020

The aim of this work is to evaluate the attitudes, behaviours, and knowledge of health workers employed at an Italian University Hospital on the topic of vaccinations and in regard to flu vaccination. To this end, the study provided for the articulation of a computerised questionnaire on the digital platform EUSurvey which was administered online via e-mail to a sample of 457 health workers, in the period between November 2018 and March 2019. The data were subjected to descriptive and inferential statistical analysis. In particular, a logistic regression analysis was carried out in order to evaluate the relationship between the variables collected and the dichotomous outcome (vaccinated/unvaccinated subjects in the 2018–2019 season). The results, in line with what has been reported by the literature, highlighted that vaccine hesitancy is prevalent also among health workers. Furthermore, according to our study, only 30.6% of the health care workers had the flu vaccination. The survey points out the need to plan educational and informative interventions aimed at changing the attitudes, behaviours, and knowledge of health workers in the field of flu vaccination, for the purpose of protecting the health of healthcare personnel and their patients.
Geographical Structural Features of the WeChat Social Networks
Authors: Ai Chuan , Chen Bin , Chen Hailiang , Dai Weihui , Qiu Xiaogang
Published: May 2020

Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.
Towards Automatic Points of Interest Matching
Authors: Piech Mateusz , Smywinski-Pohl Aleksander , Marcjan Robert , Siwik Leszek
Published: May 2020

Complementing information about particular points, places, or institutions, i.e., so-called Points of Interest (POIs) can be achieved by matching data from the growing number of geospatial databases; these include Foursquare, OpenStreetMap, Yelp, and Facebook Places. Doing this potentially allows for the acquisition of more accurate and more complete information about POIs than would be possible by merely extracting the information from each of the systems alone. Problem: The task of Points of Interest matching, and the development of an algorithm to perform this automatically, are quite challenging problems due to the prevalence of different data structures, data incompleteness, conflicting information, naming differences, data inaccuracy, and cultural and language differences; in short, the difficulties experienced in the process of obtaining (complementary) information about the POI from different sources are due, in part, to the lack of standardization among Points of Interest descriptions; a further difficulty stems from the vast and rapidly growing amount of data to be assessed on each occasion. Research design and contributions: To propose an efficient algorithm for automatic Points of Interest matching, we: (1) analyzed available data sources—their structures, models, attributes, number of objects, the quality of data (number of missing attributes), etc.—and defined a unified POI model; (2) prepared a fairly large experimental dataset consisting of 50,000 matching and 50,000 non-matching points, taken from different geographical, cultural, and language areas; (3) comprehensively reviewed metrics that can be used for assessing the similarity between Points of Interest; (4) proposed and verified different strategies for dealing with missing or incomplete attributes; (5) reviewed and analyzed six different classifiers for Points of Interest matching, conducting experiments and follow-up comparisons to determine the most effective combination of similarity metric, strategy for dealing with missing data, and POIs matching classifier; and (6) presented an algorithm for automatic Points of Interest matching, detailing its accuracy and carrying out a complexity analysis. Results and conclusions: The main results of the research are: (1) comprehensive experimental verification and numerical comparisons of the crucial Points of Interest matching components (similarity metrics, approaches for dealing with missing data, and classifiers), indicating that the best Points of Interest matching classifier is a combination of random forest algorithm coupled with marking of missing data and mixing different similarity metrics for different POI attributes; and (2) an efficient greedy algorithm for automatic POI matching. At a cost of just 3.5% in terms of accuracy, it allows for reducing POI matching time complexity by two orders of magnitude in comparison to the exact algorithm.
Molecular Markers Associated with Agro-Physiological Traits under Terminal Drought Conditions in Bread Wheat
Authors: Shokat Sajid , Sehgal Deepmala , Vikram Prashant , Liu Fulai , Singh Sukhwinder
Published: May 2020

Terminal drought stress poses a big challenge to sustain wheat grain production in rain-fed environments. This study aimed to utilize the genetically diverse pre-breeding lines for identification of genomic regions associated with agro-physiological traits at terminal stage drought stress in wheat. A total of 339 pre-breeding lines panel derived from three-way crosses of ‘exotics × elite × elite’ lines were evaluated in field conditions at Obregon, Mexico for two years under well irrigated as well as drought stress environments. Drought stress was imposed at flowering by skipping the irrigations at pre and post anthesis stage. Results revealed that drought significantly reduced grain yield (Y), spike length (SL), number of grains spikes−1 (NGS) and thousand kernel weight (TKW), while kernel abortion (KA) was increased. Population structure analysis in this panel uncovered three sub-populations. Genome wide linkage disequilibrium (LD) decay was observed at 2.5 centimorgan (cM). The haplotypes-based genome wide association study (GWAS) identified significant associations of Y, SL, and TKW on three chromosomes; 4A (HB10.7), 2D (HB6.10) and 3B (HB8.12), respectively. Likewise, associations on chromosomes 6B (HB17.1) and 3A (HB7.11) were found for NGS while on chromosome 3A (HB7.12) for KA. The genomic analysis information generated in the study can be efficiently utilized to improve Y and/or related parameters under terminal stage drought stress through marker-assisted breeding.
Utility of Animal Models to Understand Human Alzheimer’s Disease, Using the Mastermind Research Approach to Avoid Unnecessary Further Sacrifices of Animals
Authors: Qin Tian , Prins Samantha , Groeneveld Geert Jan , Van Westen Gerard , de Vries Helga E. , Wong Yin Cheong , Bischoff Luc J.M. , de Lange Elizabeth C.M.
Published: May 2020

To diagnose and treat early-stage (preclinical) Alzheimer’s disease (AD) patients, we need body-fluid-based biomarkers that reflect the processes that occur in this stage, but current knowledge on associated processes is lacking. As human studies on (possible) onset and early-stage AD would be extremely expensive and time-consuming, we investigate the potential value of animal AD models to help to fill this knowledge gap. We provide a comprehensive overview of processes associated with AD pathogenesis and biomarkers, current knowledge on AD-related biomarkers derived from on human and animal brains and body fluids, comparisons of biomarkers obtained in human AD and frequently used animal AD models, and emerging body-fluid-based biomarkers. In human studies, amyloid beta (Aβ), hyperphosphorylated tau (P-tau), total tau (T-tau), neurogranin, SNAP-25, glial fibrillary acidic protein (GFAP), YKL-40, and especially neurofilament light (NfL) are frequently measured. In animal studies, the emphasis has been mostly on Aβ. Although a direct comparison between human (familial and sporadic) AD and (mostly genetic) animal AD models cannot be made, still, in brain, cerebrospinal fluid (CSF), and blood, a majority of similar trends are observed for human AD stage and animal AD model life stage. This indicates the potential value of animal AD models in understanding of the onset and early stage of AD. Moreover, animal studies can be smartly designed to provide mechanistic information on the interrelationships between the different AD processes in a longitudinal fashion and may also include the combinations of different conditions that may reflect comorbidities in human AD, according to the Mastermind Research approach.
A Single Amino Acid Change in Nramp6 from Sedum Alfredii Hance Affects Cadmium Accumulation
Authors: Lu Zhuchou , Chen Shuangshuang , Han Xiaojiao , Zhang Jin , Qiao Guirong , Jiang Yugen , Zhuo Renying , Qiu Wenmin
Published: May 2020

SaNramp6 in Sedum alfredii encodes a membrane-localized metal transporter. We isolated the SaNramp6h allele from the hyperaccumulating ecotype (HE) of S. alfredii. When this allele was expressed in transgenic yeast and Arabidopsis thaliana, it enhanced their cadmium (Cd) sensitivity by increased Cd transport and accumulation. We isolated another allele, SaNramp6n, from a nonhyperaccumulating ecotype (NHE) of S. alfredii. Amino acid sequence comparisons revealed three amino acid differences between SaNramp6h and SaNramp6n. We investigated the Cd transport activity of the Nramp6 allele, and determined which residues are essential for the transport activity. We conducted structure-function analyses of SaNramp6 based on site-directed mutagenesis and functional assays of the mutants in yeast and Arabidopsis. The three residues that differed between SaNramp6h and SaNramp6n were mutated. Only the L157P mutation of SaNramp6h impaired Cd transport. The other mutations, S218N and T504A, did not affect the transport activity of SaNramp6h, indicating that these residues are not essential for metal selectivity. Transgenic plants overexpressing SaNramp6hL157P showed altered metal accumulation in shoots and roots. Our results suggest that the conserved site L157 is essential for the high metal transport activity of SaNramp6h. This information may be useful for limiting or increasing Cd transport by other plant natural resistance associated macrophage protein (NRAMP) proteins.
DRUGPATH: The Drug Gene Pathway Meta-Database
Authors: Jaundoo Rajeev , Craddock Travis J. A.
Published: May 2020

The complexity of modern-day diseases often requires drug treatment therapies consisting of multiple pharmaceutical interventions, which can lead to adverse drug reactions for patients. A priori prediction of these reactions would not only improve the quality of life for patients but also save both time and money in regards to pharmaceutical research. Consequently, the drug-gene-pathway (DRUGPATH) meta-database was developed to map known interactions between drugs, genes, and pathways among other information in order to easily identify potential adverse drug events. DRUGPATH utilizes expert-curated sources such as PharmGKB, DrugBank, and the FDA’s NDC database to identify known as well as previously unknown/overlooked relationships, and currently contains 12,940 unique drugs, 3933 unique pathways, 5185 unique targets, and 3662 unique genes. Moreover, there are 59,561 unique drug-gene interactions, 77,808 unique gene-pathway interactions, and over 1 million unique drug-pathway interactions.
The Role of Chemokines and Chemokine Receptors in Diabetic Nephropathy
Authors: Chang Ting-Ting , Chen Jaw-Wen
Published: May 2020

Kidney function decline is one of the complications of diabetes mellitus and may be indicated as diabetic nephropathy (DN). DN is a chronic inflammatory disease featuring proteinuria and a decreasing glomerular filtration rate. Despite several therapeutic options being currently available, DN is still the major cause of end-stage renal disease. Accordingly, widespread innovation is needed to improve outcomes in patients with DN. Chemokines and their receptors are critically involved in the inflammatory progression in the development of DN. Although recent studies have shown multiple pathways related to the chemokine system, the specific and direct effects of chemokines and their receptors remain unclear. In this review, we provide an overview of the potential role and mechanism of chemokine systems in DN proposed in recent years. Chemokine system-related mechanisms may provide potential therapeutic targets in DN.
Estradiol-Induced Epigenetically Mediated Mechanisms and Regulation of Gene Expression
Authors: Kovács Tamás , Szabó-Meleg Edina , Ábrahám István M.
Published: May 2020

Gonadal hormone 17β-estradiol (E2) and its receptors are key regulators of gene transcription by binding to estrogen responsive elements in the genome. Besides the classical genomic action, E2 regulates gene transcription via the modification of epigenetic marks on DNA and histone proteins. Depending on the reaction partner, liganded estrogen receptor (ER) promotes DNA methylation at the promoter or enhancer regions. In addition, ERs are important regulators of passive and active DNA demethylation. Furthermore, ERs cooperating with different histone modifying enzymes and chromatin remodeling complexes alter gene transcription. In this review, we survey the basic mechanisms and interactions between estrogen receptors and DNA methylation, demethylation and histone modification processes as well as chromatin remodeling complexes. The particular relevance of these mechanisms to physiological processes in memory formation, embryonic development, spermatogenesis and aging as well as in pathophysiological changes in carcinogenesis is also discussed.
Current Synthesis and Systematic Review of Main Effects of Calf Blood Deproteinized Medicine (Actovegin®) in Ischemic Stroke
Authors: Firan Florentina Carmen , Romila Aurelia , Onose Gelu
Published: May 2020

Background: Stroke is one of the largest problems and clinical-social challenges within neurology and, in general, pathology. Here, we briefly reviewed the main pathophysiological mechanisms of ischemic stroke, which represent targets for medical interventions, including for a calf blood deproteinized hemodialysate/ultrafiltrate. Methods: We conducted a systematic review of current related literature concerning the effects of Actovegin®, of mainly the pleiotropic type, applied to the injury pathways of ischemic stroke. Results: The bibliographic resources regarding the use of Actovegin® in ischemic stroke are scarce. The main Actovegin® actions refer to the ischemic stroke lesion items’ ensemble, targeting tissue oxidation, energy metabolism, and glucose availability through their augmentation, combating ischemic processes and oxidative stress, and decreasing inflammation (including with modulatory connotations, by the nuclear factor-κB pathway) and apoptosis-like processes, counteracting them by mitigating the caspase-3 activation induced by amyloid β-peptides. Conclusion: Since no available therapeutic agents are capable of curing the central nervous system’s lesions, any contribution, such as that of Actovegin® (with consideration of a positive balance between benefits and risks), is worthy of further study and periodic reappraisal, including investigation into further connected aspects.
Shared Genomic and Proteomic Contribution of Amyloid and Tau Protein Characteristic of Alzheimer’s Disease to Brain Ischemia
Authors: Pluta Ryszard , Ułamek-Kozioł Marzena , Januszewski Sławomir , Czuczwar Stanisław J.
Published: May 2020

Post-ischemic brain damage is associated with the deposition of folding proteins such as the amyloid and tau protein in the intra- and extracellular spaces of brain tissue. In this review, we summarize the protein changes associated with Alzheimer’s disease and their gene expression (amyloid protein precursor and tau protein) after ischemia-reperfusion brain injury and their role in the post-ischemic injury. Recent advances in understanding the post-ischemic neuropathology have revealed dysregulation of amyloid protein precursor, α-secretase, β-secretase, presenilin 1 and 2, and tau protein genes after ischemic brain injury. However, reduced expression of the α-secretase in post-ischemic brain causes neurons to be less resistant to injury. In this review, we present the latest evidence that proteins associated with Alzheimer’s disease and their genes play a key role in progressive brain damage due to ischemia and reperfusion, and that an ischemic episode is an essential and leading supplier of proteins and genes associated with Alzheimer’s disease in post-ischemic brain. Understanding the underlying processes of linking Alzheimer’s disease-related proteins and their genes in post-ischemic brain injury with the risk of developing Alzheimer’s disease will provide the most significant goals for therapeutic development to date.