Divergent Areas Research Articles

Overall effects of topsoil elements on cancer mortality in rural Greece: A modeling approach.

This ecological study examines cancer mortality rates in 61 rural Greek municipalities, covering in total 7,305,554 person-years from 2000 to 2015, based on the Hellenic Statistical Authority data. Topsoil concentrations of Mn, Ni, Pb, Be, As and Cd in Greek grazing land samples were obtained from the GEMAS (Geochemical Mapping of Agricultural and Grazing land Soil) project. Municipalities of rural regions with population of up to 20,000 people were selected as the study area and were divided into four quartiles, according to their age-specific cancer mortality rates, to identify the most divergent areas of low/high mortality. Fusion of demographic data with topsoil measurements aimed to reveal their potential association. Towards a univariate analysis, comparing each element individually across the low/high mortality regions revealed significant overlaps in distributions of concentrations for all elements. Machine learning multi-element models were built to predict whether an area would have a low or high cancer mortality rate, based simultaneously and solely on the concentrations of the six elements of the region, for each group. The out-of-sample median accuracy of the model was very high (83.3%) for the 0-29 age group, high (66.7%) for the 30-39, 40-49 and 50-59 age groups, while the accuracy decreased significantly above age of 60. The interaction of Mn, As, Ni, and partially Be, contributes most to the model's prediction for the 0-29 age group, As becomes the main contributor in the 30-39 age group, while all six elements contribute almost equally for the 40-49 group, and the interaction of Ni, Mn and Be becomes important in the 50-59 age group. The findings of this study strongly suggest that topsoil elements may jointly influence the frequency and geographical distribution of cancer.

Comparative chloroplast genomics of Caryophyllaceae species: insights into sequence variations and phylogenetic evolution

BackgroundCaryophyllaceae contains 100 genera and 3000 species, many of which are valuable both ecologically and economically. However, as past research has shown, the fundamental phylogenetic relationships of Caryophyllaceae are still debatable, and molecular dating based on chloroplast genomes has not been thoroughly examined for the entire family.MethodsIn this study, the complete chloroplast genome sequences of Arenaria kansuensis Maxim., A. roborowskii Maxim., A. przewalskii Maxim., and Silene aprica Turcz were described. Additionally, the four newly generated along with eighteen published chloroplast genomes of Caryophyllaceae were included for comparative genomics analysis.ResultsThese 22 chloroplast genomes had typical quadripartite structure, with 128–134 distinct genes and lengths ranging from 133,621 bp to 153,957 bp. The 22 Caryophyllaceae chloroplast genomes showed significant variations in the number of long repeats and SSR types; mononucleotide repeats (A/T) and palindromic repeats were the most common types. Three substantially divergent areas containing atpB-rbcL, rbcL-accD, and accD were found by further comparative study, which could serve as effective molecular markers. The codon bias of chloroplast genomes in Caryophyllaceae were mainly affected by natural selection, but other factors such as mutation pressure could also affect the codon bias to some extent. Fourteen optimal codons were identified in the chloroplast genome of Caryophyllaceae. Phylogenetic analysis demonstrated that the monophyly of any of the three recognized subfamilies within Caryophyllaceae was not supported by our data. Meanwhile, seven well-supported clades correspond to 8 tribes were found in phylogenetic trees. The results of molecular dating demonstrated that the divergence between Caryophyllaceae and Amaranthaceae was estimated to occur in 69 Ma. The Paronychieae was the oldest tribe of the eight tribes included in this study, diverged at 59.92 Ma.ConclusionThis study provides resources for further investigations on the identification, genetic engineering, evolution, and phylogeny of Caryophyllaceae species.

Study on stochastic convergence of China’s carbon emission efficiency

ABSTRACT Carbon emission efficiency (CEE) and its stochastic convergence are important indicators for achieving the strategic goal of carbon peaking and carbon neutrality. Thus, this paper firstly estimates the CEE of 30 provinces in China from 1995 to 2019 with the super efficiency of the SBM model. Then the panel unit root test method is adopted to test its stochastic convergence. The results show that the overall CEE has not reached the effective level, and the CEE level varies greatly among regions. There is a stochastic divergence of CEE in most regions from 1995 to 2001. While the CEE of most regions changes to convergence after 2002. From 1995 to 2019, the CEE of the western, northeast, and southwest regions diverge. While the CEE of the national level, the eastern, central regions, and the other six regions in the eight major economic zones converged stochastically. Among the divergent areas, the CEE of Heilongjiang, Guangxi, and Sichuan provinces are the main contributors to the CEE divergence. Therefore, it is the key to narrowing the differences in regional low-carbon development in China by significantly improving the CEE of each province.

Rendition on the impact of criteria for aspirates, needle size, preparation modalities of cytopathology, and edition of decision-making lexicons on divergent areas in Thyroidology.

Rendition on the impact of criteria for aspirates, needle size, preparation modalities of cytopathology, and edition of decision-making lexicons on divergent areas in Thyroidology.

Governance in regional sports organisations: An analysis of the Catalan sports federations

In recent decades there has been an increasing concern among stakeholders surrounding sport organisations regarding the implementation of governance principles and processes. It is believed that these can help them to overcome sustainability problems and to promote organisational success. This research aims to analyse the governance of Catalan sports federations (CSFs), an area that has not been analysed to date. The study, based on previous approaches in the sport management literature, proposes a model to measure three dimensions considered key to good governance in sport organisations: democracy and participation, ethics and integrity, and accountability and transparency, which are measured by quantitative performance indicators. 38 CSFs were assessed, and the results showed considerable room for improvement with respect to metrics in divergent areas of organisational governance. Six clusters were determined using the Hierarchical Ascending Classification, and statistical correlations were also found between the dimensions analysed and the size of the organisations. In addition to the interest for stakeholders in the context of Catalonia, the authors believe that this research supports recent calls for good governance in sport and can serve as a foothold for scholars to investigate other contexts.

Extrusion and rarefaction of lubricant in boundary layer is the key processes of adhesive wear of highly loaded tribocontacts

A comprehensive analysis of the Adhesion-Deformation, Elasto-hydrodynamic and Hydrodynamic friction models is presented, which describe different modes of lubrication in accordance with the Stribeck curve. The main provisions of these models are considered in conjunction with the Langmuir-BET theory of adsorption and Hertz's elastic-deformation theory of curvilinear contacts. It is shown that the revealed contradictions require their resolution, and the discovered multiple effects need a scientifically based interpretation. It is proposed to develop a more generalized model of friction and wear based on naturally occurring processes that have been hidden from direct observations for a long time. These are: Extrusion of lubricating layers in the convergent elastically deformed and Rarefaction in divergent elastically deformed regions of tribo-contacts. Understanding these processes makes it possible to predict the localization sites and causes of the occurrence of primary subsequent acts of adhesion of friction surfaces and their wear in the following cycle: “rarefaction and desorption of lubricating layers, which leads to deformation destruction of oxide films and adhesion of juvenile surface areas, after which to tearing of a fragment material from the bearing and the neoplasm of the protrusion on the shaft - in the divergent elastically deformed areas of the contact. Then microcutting by this fragment of the bearing surface occurs with the release of the wear product in the convergent elastically deformed region, which accordingly leads to a change in the actual geometry and tension of the tribo-contact. Further, in other areas of the renewed contact, adhesive interaction occurs in other divergent areas according to the same mechanism. A deep understanding of the reasons for the desorption of lubricating layers will make it possible to develop and apply new highly efficient technological and material science methods in order to increase the resource of highly loaded tribo-systems of machines and mechanisms.

Comparative analysis of two flow decomposition approaches and their applications in diagnosing water vapor in extreme precipitation events over Xinjiang province, China

Water vapor flux divergence under the terrain-following coordinate system is decomposed into slow and fast manifolds with the adoption of the Barnes filter method and the synoptic approach based on the Helmholtz theorem in this study. The performances and applications of both methods are evaluated in precipitating events over the Xinjiang province in China when traditional analysis fails to capture the evolutions of extreme precipitations caused by multi-scale systems and complicated terrains. A blizzard with a short duration under weak moisture environments and a persistent torrential rainfall event with abundant water vapor in the summer are selected as test examples for comparative analysis of the relationships between water vapor divergence and precipitation characteristics with the adoptions of both traditional synoptic diagnosis and two decomposition methods. It is found that divergent areas of the total water vapor flux divergence at the middle and high levels are blocked by false convergence at the low levels as the precipitation weakens in both cases, which leads to the failure of traditional synoptic diagnoses. Meanwhile, the two decomposition methods can provide superior indicators for extreme precipitation, especially before rainfall decreases or terminates. The synoptic approach performs better than the Barnes filter method as the latter sharply decreases in accuracy as the complexity of the flow fields and moisture conditions increase. Remedies for relieving this problem are proposed before being used for further applications.

The evolution of nutrition intervention during the COVID-19 pandemic.

COVID-19 has challenged health professionals in widely divergent areas, including innovation of practice, communication, multidisciplinary activities, broader use of technology, and adaptability. The role of the dietitian and other health professionals in dealing with the evolving crisis might be considered essential in treating patients. Given the limited access to various food options, nutrition screening and assessment deserves a high priority to complete a comprehensive nutrition evaluation, identify nutrition risks, prioritize care, and provide early nutrition intervention and support to all patients with or who have had, COVID-19 and are experiencing ongoing symptoms. Such an intervention would benefit the patients and the health system by reducing the length of hospital stay, ameliorating further complications, limiting hospital readmission, enhancing recovery, and assisting in the management of comorbidities and their metabolic alterations. This brief overview outlines the essential role of nutrition intervention and support as part of an integrated, multidisciplinary treatment program for the care of COVID-19 patients during the pandemic. Restrictive movements have changed consultative approaches, and the importance of Telenutrition for the effective communication of health status and recommendations.

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties.

Several review studies have been performed on nano-silica-modified concrete, but this study adopted a new method based on scientometric analysis for the keywords’ assessment in the current research area. A scientometric analysis can deal with vast bibliometric data using a software tool to evaluate the diverse features of the literature. Typical review studies are limited in their ability to comprehensively and accurately link divergent areas of the literature. Based on the analysis of keywords, this study highlighted and described the most significant segments in the research of nano-silica-modified concrete. The challenges associated with using nano-silica were identified, and future research is directed. Moreover, prediction models were developed using data from the literature for the strength estimation of nano-silica-modified concrete. It was noted that the application of nano-silica in cement-based composites is beneficial when used up to an optimal dosage of 2–3% due to high pozzolanic reactivity and a filler effect, whereas a higher dosage of nano-silica has a detrimental influence due to the increased porosity and microcracking caused by the agglomeration of nano-silica particles. The mechanical strength might enhance by 20–25% when NS is incorporated in the optimal amount. The prediction models developed for predicting the strength of nano-silica-modified concrete exhibited good agreement with experimental data due to lower error values. This type of analysis may be used to estimate the essential properties of a material, therefore saving time and money on experimental tests. It is recommended to investigate cost-effective methods for the dispersion of nano-silica in higher concentrations in cement mixes; further in-depth studies are required to develop more accurate prediction models to predict nano-silica-modified concrete properties.

Difference in the general medicine in-training examination score between community-based hospitals and university hospitals: a cross-sectional study based on 15,188 Japanese resident physicians

BackgroundThe general medicine in-training examination (GM-ITE) is designed to objectively evaluate the postgraduate clinical competencies (PGY) 1 and 2 residents in Japan. Although the total GM-ITE scores tended to be lower in PGY-1 and PGY-2 residents in university hospitals than those in community-based hospitals, the most divergent areas of essential clinical competencies have not yet been revealed.MethodsWe conducted a nationwide, multicenter, cross-sectional study in Japan, using the GM-ITE to compare university and community-based hospitals in the four areas of basic clinical knowledge“. Specifically, “medical interview and professionalism,” “symptomatology and clinical reasoning,” “physical examination and clinical procedures,” and “disease knowledge” were assessed.ResultsWe found no significant difference in “medical interview and professionalism” scores between the community-based and university hospital residents. However, significant differences were found in the remaining three areas. A 1.28-point difference (95% confidence interval: 0.96–1.59) in “physical examination and clinical procedures” in PGY-1 residents was found; this area alone accounts for approximately half of the difference in total score.ConclusionsThe standardization of junior residency programs and the general clinical education programs in Japan should be promoted and will improve the overall training that our residents receive. This is especially needed in categories where university hospitals have low scores, such as “physical examination and clinical procedures.”

Improved salp swarm algorithm based on the levy flight for feature selection

The fields of data science and data mining are enduring high-dimensionality issues because of a high volume of data. Conventional machine learning techniques give disgruntled responses to high-dimensional datasets. Feature selection is used to get the appropriate information from the dataset to reduce the dimensionality of the data. The recently proposed Salp Swarm Algorithm (SSA) is a population-based meta-heuristic optimization technique inspired by the Sea Salps Swarming technique. SSA failed to converge initial random solutions to the global optimum owing to its complete dependency on the number of iterations for the process of exploration and exploitation. The proposed improved SSA (iSSA) aims to enhance the ability of Salps to explore divergent areas by randomly updating its location. Randomizing the Salps location via Levy flight enriches the exploitation potential of SSA resulting in it converging the model toward the global optima. The performance of the proposed iSSA is investigated using six different high-dimensional microarray datasets. While comparing the ability to converge, it is understood that the proposed model outperforms SSA providing 0.1033% more confidence in the selected features. The results of the simulation revealed that the iSSA can provide better competitive and significant results compared to SSA.

Revanchism via Pedestrianism: Street‐level Bureaucracy in the Production of Uneven Policing Landscapes

AbstractThe emergence over the last decade of large numbers of vulnerable EU citizens begging on Swedish streets has led to ambivalent responses from the Swedish state, including from local police forces charged with policing public order. Based on research including interviews with vulnerable EU citizens and with police officials in two socio‐economically divergent areas of Stockholm, this paper seeks to understand how policing practices are motivated and enacted towards this group and how these practices are experienced by those targeted. The results reveal a set of policing practices which, whilst framed within a depoliticised logic of what Nicholas Blomley calls “pedestrianism”, work to produce spatially uneven punitive landscapes for those begging. The paper argues that understanding the role of police as “street‐level bureaucrats” (following Michael Lipsky), with the agency to escalate or soften revanchist landscapes, is fundamental to understanding the contingencies at the heart of punitive urbanisms.

An integrated framework of socio-economic and technological interventions for COVID-19 in different economies

COVID-19 outbreak has adversely affected the life of global population across the world. Efforts have been made to deal with the current grave situation from divergent areas viz. community, economic, science and medicine, digital technologies etc. In absence of potential remedial arrangements, best efforts could be directed towards minimizing the impact of COVID-19 on people’s health and economy. The present study focuses to provide a comprehensive model to reduce adverse impacts of COVID-19 disease to the minimum possible extent. A detailed review of scholarly works and web references is done on social, economic and technological interventions made to contain the impact of COVID-19. Relevant search key words were used on databases like Scopus, Ebsco, Google Scholar, etc. The second part of paper analyzes COVID-19 cases with respect to different economies of the world viz, upper income, middle income and lower income economies through Anova. Majority of the research works conducted on COVID-19 offer solutions from single perspectives. Present study suggested a conceptual model using multi-front approach considering social, economic and technological interventions. It also reveals mixed results about COVID-19 cases in different economies. There cannot be ‘one solution’ for controlling the pandemic and its aftermaths. Efforts are made to explore all possible ways ranging from display of socially accountable behaviors, economic reforms, medical research, and integration of AI and IoT with medical ecosystems. Surprisingly, COVID-19 has spread more in the high income economies of the world.

Variations in Pod and Seed Traits of <I>Dalbergia sissoo</I> in Different Seed Sources from Himachal Pradesh, India

The evaluations of seed and pod parameters are important criteria for quality seedling production for out planting and to maintain the fertility of soil for longer time. The present study was conducted to elucidate the pattern of variation in pods, seed characteristics and germination behavior among Dalbergia sissoo seed sources to develop criteria for improvement of tree seedlings in future programmes. Five seed sources from Himachal Pradesh (Solan, Hamirpur, Kangra, Bilaspur and Una) were selected to represent widely divergent areas. All the characterstics of pod (pod weight, length and width), seed (No. of seeds, length, width, thickness, weight and volume) and germination parameters (germination percentage, germination value, germination period) were observed for variation in different seed sources. Among the selected five seed sources, Bilaspur from Himachal Pradesh exhibited highest pod characters viz., 1000 pod weight (52.86 g), pod length (8.30 cm), pod width (1.15 cm), number of seeds/pod (2.33), seed length (8.37mm), seed width (4.55mm), seed weight (20.46g), seed thickness (1.07mm), seed volume (0.99cc), 85.94% germination, germination value 266.04, germination energy index 9.64, vigour index of 499.26 and showed least germination period of 6.66 days. Hence Bilaspur seed source have best pod and seed source and can be used for quality seedling production with better germination.

Student Classification Based on Cognitive Abilities and Predicting Learning Performances using Machine Learning Models

Education is the vital parameter of the country for development in divergent areas like cultivation, economic, political, health and so on. Any educational Institute’s (universities, colleges, schools) main goal is to increase the student’s learning capabilities and their skills for their full contribution towards the society. In these days, “student’s learning process and skill development” research topic requires much needed attention for the betterment of the society. The student’s performance depends on his/her learning ability and is influenced by many factors. In this paper, we analyze the different categories of student’s leanings that are very fast, fast, moderate and slow. For this, we conducted the training and tests for attributes like ability, knowledge level, reasoning and core subject abilities for the 313 engineering students in AITAM, Tekkali, affiliated to JNTUK, India from 2017 to 2019. We gathered information about personal, academic, cognitive level and demographic data of students. In this experiment, we are conducting statistical analysis as well as classification of students into 4 types of learners and applying the different Machine Learning (ML) techniques and choose the best ML algorithm for predicting students learning rates. This leads to conducting the remedial classes with new teaching methods for moderate and slow leaning students. The proposed paper accommodates the individual differences of the learners in terms of knowledge level, learning preferences, cognitive abilities etc. For this, we apply 5 ML algorithms that are Naive Bayes, classification Trees (CTs), k-NN, C4.5 and SVM. As per ML analysis, the k-Nearest Neighborhood (k-NN) algorithm is more efficient than other algorithms where the accuracy and prediction values are nearer to 100%.

Comprehensive assessment of spatial soil variability related to topographic parameters in sugarcane fields

Landscapes are intrinsically related to spatial soil variability. Understanding soil chemical fertility based on topographic parameters is essential to ensuring sustainable agronomic management through the rational use of inputs. The aim of this study was to perform a comprehensive assessment of soil organic matter (OM), pH, sum of bases (SB) and cation exchange capacity (CEC) according to topographic parameters, with the goal of identifying spatial patterns and better understanding the relationship between them to capture the soil variability in sugarcane fields. A soil dataset from nine sugarcane experimental fields was evaluated. Approximately 3000 soil samples collected between 2008 and 2017 were evaluated. The topographic parameters of vertical and horizontal curvatures were related to the variability in the soil attributes. The results showed that the horizontal morphometric classes (HConv, HPlan and HDiv) associated with vertical concave areas (VConc) presented higher levels of OM, SB and CEC, which indicated that these areas have higher soil chemical fertility. The VConcHDiv showed the highest soil chemical fertility; regardless of site, topographic level and inherent soil fertility. For all vertical morphometric classes (VConc, VRet and VConv), soil pH levels were higher in horizontal divergent areas (HDiv) and lower in convergent areas (HConv), suggesting that stricter soil acidity management was needed in the HConv areas. The VConvHConv areas, where lower soil chemical fertility was observed, should be sampled with greater accuracy for adequate spatial soil characterization because it had a higher coefficient of variation (CV) than the other morphometric classes. The results showed that the detected spatial patterns were temporally stable. With high spatial and temporal stability, topographic parameters could be excellent sources of economically feasible and easily assessed information to support farmers in soil chemical fertility characterization. Finally, topographic attributes proved to be an excellent source about soil spatial variability for farmers to use in their fields aiming a site-specific management or an additional layer of data for management zones delineation.

Buffalo's West Side Story: Migration, Gentrification, and Neighborhood Change

Using a multi–methods approach, we examine socioeconomic and demographic change in Buffalo, New York's, West Side neighborhood. We do this by performing a systematic case study of the neighborhood analyzing census tract data, crime data, key informant interview data from community leaders and organizational representatives, and content analysis data from local newspaper articles. Results suggest that although the neighborhood has shifted dramatically over the last forty–five years, the changes have been uneven across the West Side. Two divergent areas have emerged: one neighborhood fueled by white gentrifiers and another driven by international migrants.

An investigation of micro aerial vehicles (µAV)

Quadcopter UAV also known as quadrotor is the another form of helicopters having more spirited firmness than helicopters. They play a paramount role in divergent areas like military operations, surveillance, fire sensing and some important areas having many complications. Quadcopters are UAV's with capability of perpendicular takeoffs, arrivals and drift at a crave location. This survey paper addresses the delineation and evolution of a proclivity arm quadcopter for mini payload and longtime endurance.

Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review.

ObjectiveThis integrative review identifies convergent and divergent areas of need for collecting and using patient-generated health data (PGHD) identified by patients and providers (i.e., physicians, nurses, advanced practice nurses, physician assistants, and dietitians).MethodsA systematic search of 9 scholarly databases targeted peer-reviewed studies published after 2010 that reported patients’ and/or providers’ needs for incorporating PGHD in clinical care. The studies were assessed for quality and bias with the Mixed-Methods Appraisal Tool. The results section of each article was coded to themes inductively developed to categorize patient and provider needs. Distinct claims were extracted and areas of convergence and divergence identified.ResultsEleven studies met inclusion criteria. All had moderate to low risk of bias. Three themes (clinical, logistic, and technological needs), and 13 subthemes emerged. Forty-eight claims were extracted. Four were divergent and twenty were convergent. The remainder was discussed by only patients or only providers.ConclusionAs momentum gains for integrating PGHD into clinical care, this analysis of primary source data is critical to understanding the requirements of the 2 groups directly involved in collection and use of PGHD.

Selection of optimal scales for soil depth prediction on headwater hillslopes: A modeling approach

Landform attributes derived from digital elevation models (DEMs) are the most commonly used factors to predict soil depths on hillslopes. However, the selection of an appropriate algorithm to calculate terrain attributes and identify an optimal DEM resolution remains ambiguous. In this study, we propose a method to use high-resolution DEM and spatial soil depth data to obtain terrain attributes and identify the optimal DEM resolution to predict soil depths at hillslope scales. A geophysical method (ground penetrating radar, GPR) was used to investigate the reasons for the optimal DEM resolution findings. Point-scale soil depth from 116 sites and elevation data were collected from two adjacent headwater hillslopes (H1: 0.42ha and H2: 0.31ha) at the Hemuqiao hydrological experimental station in Southeast China. The elevation datasets were collected using a total station at a variable spacing level and were then used to derived DEMs at nine spatial resolutions: 0.25, 0.50, 0.75, 1.00, 2.00, 3.50, 5.00, 7.50 and 10.00m. Nine primary and secondary topographic attributes using the nine spatial resolution DEMs were then derived. Two different algorithms (D8 and D∞) for calculating contributing areas and related secondary topographic attributes were compared. We used and compared both linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN) models for soil depth prediction. Results demonstrated that the two models performed well for predicting soil depth. Specifically, MLR performed better than the non-linear model of ANNs. Additionally, we found that the multiple-direction algorithm (D∞) allowed flow divergence and avoided abrupt changes in soil depth predictions (orphan cells) and should be adopted for model construction. The D∞ algorithm performed better in divergent areas, such as ridges and side slopes, and the D∞ algorithm also worked well in convergent areas, such as valleys. Moreover, our results demonstrated that moderate (e.g., 2.00m) resolution topographic attributes, instead of the finest resolution, achieved the best prediction with the lowest root mean square error (RMSE) and mean absolute error (MAE) and the highest values of the coefficient of determination (R2). Moreover, the GPR results indicated that the valley accumulated more soils than side-slope areas, and a sharp increase in soil depth was found in areas adjacent to the valley. Comparing the optimal DEM resolution and valley width obtained by GPR, we found that average valley width (AVW) should be considered a good measure for choosing the optimal DEM resolution for soil depth prediction.