Studies on human mobility have a long history with increasingly strong connections to multi-disciplines across social science, environmental science, information and technology, computer science, engineering, and health science. However, what is lacking in the current research is a summary of studies on human mobility to identify the evolutional pathway and future research directions. To address this gap, we conduct a systematic review of human mobility-related studies published from 1990 to 2020. Drawing on the selected publications retrieved from the Web of Science, we conduct a bibliometric analysis and network visualisation by CiteSpace and VOSviewer on publication years and numbers, authors and their countries and afflictions, citations, topics, abstracts, keyword, and journals. Our findings show that human mobility-related studies have become increasingly interdisciplinary and multi-dimensional, enhanced by the involvement of multi-source big data, the development of technologies, the innovation of modelling approaches, and the novel applications in various areas. We also summarise four future research directions proposed by top cited authors and mobility studies, in terms of data sources, modelling methods, applications, and technologies. We advocate in-depth research on human mobility to address real-world problems and contribute to social good as promising future orientations through integrating multi-source big data and advanced modelling methods facilitated by artificial intelligence, and machine and deep learning.

This study aims to understand the global environment of COVID-19 management and guide future policy directions after the pandemic crisis. To this end, we analyzed a series of the World Economic Forum’s COVID-19 response reports through text mining and network analysis. These reports, written by experts in diverse fields, discuss multidimensional changes in socioeconomic situations, various problems created by those changes, and strategies to respond to national crises. Based on 3,897 refined words drawn from a morphological analysis of 26 reports (as of the end of 2020), this study analyzes the frequency of words, the relationships among words, the importance of specific documents, and the connection centrality through text mining. In addition, network analysis helps develop strategies for sustainable response to and management of national crises through identifying clusters of words with a similar structural equivalence.

Prostate cancer (Pca) is a highly heterogeneous disease and the second more common tumor in males. Molecular and genetic profiles have been used to identify subtypes and guide therapeutic intervention. However, roughly 26% of primary Pca are driven by unknown molecular lesions. We use Principal Component Analysis (PCA) and custom RNAseq-data normalization to identify a gene expression signature which segregates primary PRAD from normal tissues. This Core-Expression Signature (PRAD-CES) includes 33 genes and accounts for 39% of data complexity along the PC1-cancer axis. The PRAD-CES is populated by protein-coding (AMACR, TP63, HPN) and RNA-genes (PCA3, ARLN1) sparsely found in previous studies, validated/predicted biomarkers (HOXC6, TDRD1, DLX1), and/or cancer drivers (PCA3, ARLN1, PCAT-14). Of note, the PRAD-CES also comprises six over-expressed LncRNAs without previous Pca association, four of them potentially modulating driver’s genes TMPRSS2, PRUNE2 and AMACR. Overall, our PCA capture 57% of data complexity within PC1-3. GO enrichment and correlation analysis involving major clinical features (i.e., Gleason Score, AR Score, TMPRSS2-ERG fusion and Tumor Cellularity) suggest that PC2 and PC3 gene signatures might describe more aggressive and inflammation-prone transitional forms of PRAD. Of note, surfaced genes may entail novel prognostic biomarkers and molecular alterations to intervene. Particularly, our work uncovered RNA genes with appealing implications on Pca biology and progression.

When all countries are battling Covid-19 by adopting different policies, it could help address the issue of government response policy efficiency. This study examines the efficiency of government intervention policies in 19 countries, the efficiency of preventing the spread, and also the mortality caused by the virus. The policies are categorized based on the Oxford COVID-19 Government Response Tracker, which combines various indicators, including Containment and closure, Economic response, and Health systems. Given that the governments apply a top-down approach to design these policies, the current research evaluates the efficiency of policies based on the same approach. Here, two different models have been used to calculate the efficiency of policies. We designed a combined index in both models and applied Window DEA method to determine the output quantities in the efficiency ratio. The results of our study show that in both models, Japan, Finland and Thailand had the highest level of government response efficiency. The countries with the lowest government response efficiency were the United States, Spain, and Russia in the first model, and China, Italy, and Spain in the second.

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image. (2) region adjacency graph conversion. (3) graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements such as walls, doors, and symbols as well as spatial elements such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

This study addresses an alternative use of viscous dampers (VDs) associated with buckling restrained braces (BRBs) as the innovative seismic protection devices. For this, 4, 8 and 12 storey steel frames were designed with 6.5 m equal span length and 4 m storey height. Thereafter, the VDs and BRBs were placed over the height of each frame considering three different configurations. The structures were modeled using SAP2000 finite element program and evaluated by the nonlinear time history analyses subjected to the six natural accelerograms (1976 Gazlı, 1978 Tabas, 1987 Superstition Hills, 1992 Cape Mendocino, 1994 Northridge and 1999 Chi-Chi). The structural response of the structures with and without VDs and BRBs were studied in terms of variation in the displacement, interstorey drift, absolute acceleration, maximum base shear, time history of roof displacement. The results clearly indicated that the application of VDs and BRBs had remarkable improvement in the earthquake performance of the case study frames by reducing the local/global deformations in the main structural systems and satisfied the serviceability.

The era of globalization of the industrial revolution 4.0 was also marked by rapid economic, social, scientific, and technological developments. Currently, the company was demanded to maximize all its potential to compete with other companies. For this reason, it was hoped that companies could understand the importance of knowledge management-based intellectual capital through outreach, externalization, internalization, and combinations that had implications for improving employee performance and competency. This study was to identify and describe the enhancement of performance and competency-based on the optimization of the intellectual capital model in terms of critical thinking analysis. This study used a qualitative approach with critical thinking analysis through literature study methods. The findings results of this study were to improve performance and competency were the basic measurement models of intellectual capital, new intellectual capital measurement models in empirical studies, intellectual capital measurement models with bottlenecks, and environmental-based intellectual capital models. This study concluded that intellectual capital measurement should be determined as accumulation and interpretation in the proposed model for a company that was an applied subject with a qualitative intellectual capital index system, to provide a good tool for companies to managed intellectual capital.

The technique of superposition of motions in the space of Lagrange variables is described, which allows us to obtain the equations of combined motion by replacing the Lagrange variables of superimposed (external) motion with Euler variables of nested (internal) motion. The components of velocity and acceleration in the combined motion obtained as a result of differentiating the equations of motion in time coincide with the results of vector addition of the velocities and accelerations of the particles involved in the superimposed motions at each moment of time. Examples of motion and superposition descriptions for absolutely solid and deformable bodies with equations for the main kinematic characteristics of motion, including for robot manipulators with three independent drives, pressing with torsion, bending with tension, and cross– helical rolling, are given. For example, given the fragment of calculation of forces in the kinematic pairs shown the advantages of the description of motion in Lagrangian form for the dynamic analysis of lever mechanisms, allows to determine the required external exposure when performing the energy conservation law at any time in any part of the mechanism.

The objective of this paper is to assess the potential for potable water savings due to rainwater use in a precast concrete factory in southern Brazil. The economic feasibility and the rainwater quality were also assessed. The current water consumption, future water demand and rainwater demand in the factory were estimated. The future demand considered was two times higher than the current water consumption since there are plans to increase the production. Three scenarios were then simulated using the computer programme Netuno. The ideal rainwater tank capacity, the potential for potable water savings and the economic feasibility analysis for each scenario were estimated. Samples of rainwater were collected in the factory and tested for quality for manufacturing precast concrete. For a rainwater tank capacity equal to 25,000 litres, the potential for potable water savings for the first scenario was 55.4%, but the first scenario was considered economically unfeasible. For the same tank capacity, the second and third scenarios presented viable results regarding potable water savings and payback. As for the rainwater quality, it was proven to be adequate for manufacturing precast concrete. The main conclusion is that rainwater can be used to manufacture precast concrete in the factory studied herein.

This study investigates the impact of Foreign Direct Investment (FDI) on economic growth and examines the causality between FDI and economic growth in Bangladesh during 1972-2013. Gross Domestic Product (GDP), export performance (EXP), Foreign Direct Investment (FDI), and Gross Fixed Capital Formation (GFCF) are considered to capture the objective of the study. The study methodology includes some systematic steps. As the data used in the study is time-series in nature, the author employs unit root tests, and in this case, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are used. Then Johansen’s cointegration test, Granger causality test, regression with Newey-West Standard Error and Vector Error Correction Model (VECM) are applied. By using the ADF and PP test the study reveals that the variables of four-time series are integrated of I (1) i.e. they are stationary at first difference. Regression analysis result demonstrates that FDI has a positive effect on economic growth. The Granger Causality test discloses that there is a unidirectional relationship between FDI and economic growth. But the VECM estimation finds that in the long run FDI negatively affects economic growth.

In order to reconstruct the origin and pathways of variola virus (VARV) dispersion, we analyzed 47 VARV isolates available in public databases and their SNPs. The mean substitution rate of the whole genomes was 9.41x10-6 (95%HPD:8.5-11.3x10-6) substitutions/site/year. The time of the tree root was estimated to be a mean 68 years (95%HPD:60.5–75.9). The phylogeographical analysis showed that the Far East and India were the most probable locations of the tree root and of the inner nodes, respectively, whereas for the outer nodes it corresponded to the sampling locations. The Bayesian Skyline plot showed that the effective number of infections started to grow exponentially in 1915-1920, peaked in the 1940s, and then decreased to zero. Our results suggests that the VARV major strains circulating between 1940s-1970s probably shared a common ancestor originated in the Far East; subsequently moved to India, which became the center of its dispersion to eastern and southern Africa, and then to central Africa and the Middle East, probably following the movements of people between south-eastern Asia and the other places with a common colonial history. These findings may help to explain the controversial reconstructions of the history of VARV obtained using long- and short- term calibrations.

The environmental safety of a car is currently one of the most important indicators of the vehicle competitiveness and quality in the consumer market. Currently, the assessment of the ecological properties of vehicles can be made based on various criteria. In the case of combustion-powered cars, the most attention is usually paid to the values characterizing their use, and in the environmental assessment, first of all to pollutant emissions and operational fuel consumption. The proposed article considers the possibility of using the life cycle assessment to analyze the ecological properties of a passenger car during its operation. A simplified LCA method of the vehicle was presented, which in strictly defined cases can be used for the analysis of environmental impacts and the assessment of the energy analysis related to its operation. For this purpose, a vehicle life cycle model was developed. Data on the operation of 33 passenger cars of different manufacturers with similar operational characteristics, coming from different production periods, were analyzed in detail. The obtained results were found to be highly sensitive to the assumptions made in the article.

Blueberries (Vaccinium section Cyanococcus) are perennial shrubs widely cultivated for their edible fruits. In this study, we used admixture and genetic relatedness analysis of northern highbush (NHB, V. corymbosum) and southern highbush (SHB, V. darrowii) blueberry genotypes and F2 progenies of the V. corymbosum × V. darrowii cross. Using genotyping-by-sequencing (GBS), we generated ~3.34 billion reads (75 bp). The GBS reads were aligned to the Vaccinium corymbosum cv. Draper v1.0 reference genome sequence, and ~2.8 million reads were successfully mapped. From the alignments, we identified 2,244,039 single nucleotide polymorphisms (SNPs), which were used for principal component, haplotype, and admixture analysis. PCA formed three main groups: 1) NHB cultivars, 2) SHB cultivars, and 3) BNJ16-5 progenies. The overall fixation index (FST) and nucleotide diversity for NHB and SHB, indicated wide genetic differentiation, and haplotype analysis revealed that SHB cultivars are more genetically diverse than NHB cultivars. The admixture analysis identified a mix of various lineages of parental genomic introgression. This study demonstrated the effectiveness of GBS-derived SNP markers in genetic and admixture analyses to reveal genetic relatedness and to examine parental lineages in blueberry, which may be useful for future breeding plans.

The article contains the results of experimental studies for a multi-opening orifice with substitute constriction factor of β = 0.5 (m = 0.25), mounted in a DN50 hydraulic measuring flume. Flow measurements were taken for a progressing turbulent flow within Reynolds numbers (Re = 4700...19500). Based on experimental data, flow characteristics as well as discharge coefficient C characteristics were determined. Relative expanded uncertainty of determining a discharge coefficient C was estimated within the changes of volume flow qv from 0.35 dm3/s to 0.68 dm3/s. The value does not exceed 1.25% within the changes of Reynolds numbers 9800 ≤ Re ≤ 19500.

In this article authors shows chosen problems of technical state diagnosis with the use of identification and technical diagnostics methods such as experimental modal analysis. Relations between methods of dynamic state evaluation and methods of technical state evaluation were indicated. Example modal analysis results illustrate the complexity of projecting dynamic state researches into diagnostic researches of state evaluation.

Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph, which is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactions. Initial findings show the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.

Student retention is one indicator of accountability in the implementation of educational programs. Achievement of student retention rates indicates the performance of the quality objectives of an institution or college. To get an accurate picture of the factors related to retention, we need to do modeling. The retention variable is the time response variable measured in semester units. One of the statistical analyzes that can be used to analyze response data in time is survival analysis. The selection of an accurate analytical method in modeling will produce valid conclusions and impact making policies that are right and on target. This paper presents alternative modeling of student retention in distance education using survival analysis. The method used is a literature review. This paper also briefly describes distance education, open and distance education, distance education students' characteristics, distance education student retention, and survival models for modeling student retention in distance education.

In this study, we used statistical models to analyze nonlinear behavior links with atmospheric teleconnections between hydrometeorological variables and Indian Ocean Dipole (IOD) mode over the East Asia (EA) region. The analysis of atmospheric teleconnections was conducted using principal component analysis and singular spectrum analysis techniques. Moreover, the nonlinear lag-time correlations between climate indices and hydrological variables were calculated using mutual information (MI) techniques. The teleconnection-based nonlinear correlation coefficients (CCs) were higher than the linear CCs in each lag time. Additionally, we documented that the IOD has a direct influence on hydro-meteorological variables, such as precipitation within the Korean Peninsula (KP). Moreover, during the warm season (June to September) the variation of hydro-meteorological variables in the KP demonstrated significantly decreasing patterns during positive IOD years and they have neutral conditions during negative IOD years in comparison with long-term normal conditions. Finally, the revealed relationship between climate indices and hydro-meteorological variables and their possible changes will allow better understanding of stakeholder decision-making regarding to manage of freshwater management over the EA region. It can also provide useful data for long-range water resources prediction, to minimize hydrological uncertainties in a changing climate.

The purpose of this study was to develop a mathematical model for non-contacting face seals to analyze how their performance is affected by thermoelastic phenomena. The model was used to solve thermal conductivity and thermoelasticity problems. The primary goal was to calculate the values of thermal deformations of the sealing rings in a non-contacting face seal with a flexibly mounted rotor (FMR) for a turbomachine. The model assumes conversion of mechanical energy into heat in the fluid film. The heat flux generated in the fluid film is transferred first to the sealing rings and then to the fluid surrounding them. An asymmetric distribution of temperature within the sealing rings leads to the occurrence of thermal stresses and, consequently, a change in the rings geometry. The model is solved analytically. The distributions of temperature fields for the sealing rings in the cross-sections are calculated using the Fourier-Bessel series as a superficial function of two variables (r,z). The thermoelasticity problems described by the Navier equations are solved by applying the Boussinesq harmonic functions and Goodier’s thermoelastic displacement potential function. The proposed method involves solving various theoretical and practical problems of thermoelasticity in FMR-type non-contacting face seals. The calculated thermal deformations of the sealing rings are used to determine the most important seal performance parameters such as the leakage rate and power loss.

This research seeks to interpret the component analysis of an innovative bio-asphalt binder using guayule resin in addition to crumb rubber modifier (CRM) at high concentrations. Such asphalt modification aims to minimize the dependency on virgin asphalt binder and provide new solutions concerning sustainable, flexible pavement industry. Guayule resin is a promising bioresource for asphalt binder replacement. By now, it could be considered a no value byproduct extracted during the guayule natural rubber production. CRM is a recycled material derived from scrap tires. The provided interpretation could help in understanding the asphalt-rubber-guayule interaction mechanism. Fourier transform infrared spectroscopy (FTIR), supported by thermo-gravimetric analysis (TGA), was used to investigate the component analyses of guayule resin composition, asphalt guayule interaction, and asphalt rubber guayule interaction, compared to corresponding asphalt rubber interaction. Additionally, the rheological properties at elevated temperatures were provided to link the microscale properties with the final product performance. The study clarified the distinct carbon and hydrogen compositional elements of guayule resin. Asphalt and guayule resin have similarities in chemical composition and rheological behavior with temperature susceptibility. The asphalt guayule binder had physical interaction. However, when both interacted with rubber, a chemical interaction was attributed, with no difference in rubber dissolution tendency, in asphalt rubber guayule, compared to asphalt rubber. A bio-binder composed of 62.5% asphalt, 25% guayule and 12.5% CRM had the potential to provide rheological properties better than base asphalt. Such behavior was interpreted by a high release of rubber components.

The generation of vortices around bridge piers can lead to removal of riverbed materials from around piers, especially during flood events and heavy rainfalls, which can compromise the stability of the bridge and consequently its failure, if not properly detected and mitigated. Bridge failures pose serious threats to the local socio-economic and public safety and can cost lives. As such, real-time monitoring and early warning systems for scour-induced bridge failure can serve as a vital tool to protect the community and civil infrastructure against disastrous events. Vibration-base monitoring of bridge scour is an attractive option due to its low cost and relatively easy installation without the need to block the road and close the bridge to traffic. While limited number of previous studies have shown the capabilities of acceleration-based monitoring techniques in this area of research, they generally lack a rigorous framework for data analysis within and relating those to evaluate scour. This paper is an attempt to provide such framework that would enable a fast and low-cost analysis of vibration data within a physical modelling study on a simplified bridge pier. To achieve this, three experiments were conducted on an ideal single-pier scaled model bridge in a hydraulic flume, where water flows at a velocity near the critical velocity for the sand bed which in turn generates a scour hole around the pier. Then, the vibration data recorded using two mounted wireless accelerometers, were used to conduct an operational modal analysis through which the natural frequencies are extracted. The extracted natural frequencies and measured scour depths are then used to provide a chart that relates these two parameters. The results of this study showed the promising capability of the vibration-based data analysis in finding this relationship, indicating an up to around 30-50% reduction in natural frequencies as a result of around 50% scour ratio (ratio of maximum scour depth to buried depth), and beyond 50% scour ratio the natural frequencies remain constant. While the data presented in this paper are preliminary, they clearly show a promising potential for application in real-time monitoring of bridge stability under the effect of local scour and further works are underway to enrich the experimental data and empower the proposed methodologies at the laboratory scale.

The overall cost of a hydropower plant is mainly due to the expenses for civil works, mechanical equipment (turbine and control units) and electrical components. The goal of a new draft tube design is to obtain a geometry that reduces investment costs, especially the excavation ones, but the primary driver is to increase the overall machine efficiency allowing for reduced payback time. In the present study, an optimization study of the elbow-draft tube assembly of a Kaplan turbine was conducted. A CFD model for the complete turbine has been developed and validated; next, an optimization of the draft tube alone was performed using a Design of Experiments technique; finally, several optimum solutions for the draft tube were obtained using a Response Surface technique aiming at maximizing pressure recovery and minimizing flow losses. A selection of optimized geometries was subsequently post-checked using the validated model of the entire turbine and a detailed flow analysis on the obtained results could make it possible to provide insight into the improved designs. It was observed that efficiency could be improved by 1% (in relative terms), and the mechanical power increased by 1,8% (in relative terms) with respect to the baseline turbine.

Metal additive manufacturing (AM) has gained much attentions in recent years due to its advantages including geometric freedom and design complexity, appropriate to a wide range of potential industrial applications. However, conventional metal AM methods have high-cost barriers due to the initial cost of the capital equipment, support and maintenance, etc. This study presents a unique low-cost metal material extrusion (MME) technology. The filaments used have polylactic acid (PLA) as the matrix and metal powders (copper, bronze, stainless steel, high carbon iron, and aluminum) as reinforcements. Using the proposed fabrication technology, test specimens were built by extruding polymer/metal composite filaments, which were then sintered in an open-air furnace to produce solid metallic parts. In this research, the mechanical and thermal properties of the built parts are examined using tensile tests, thermogravimetric-, thermomechanical- and microstructural analysis.

The residential sector in the United States is in need of comprehensive policy-making reforms that concurrently address housing affordability and environmental sustainability. This study investigates the feasibility of state-wide zero-energy affordable housing by analyzing historical data on climate, energy use, and solar system costs in the Commonwealth of Virginia. The hypothesis examined is that the net present cost of implementation of rooftop residential solar systems to achieve zero-energy Low-Income Housing Tax Credit (LIHTC) buildings is lower than the discounted present cost of energy of otherwise identical conventional buildings that run without renewable energy generation systems. The authors propose a generalizable framework for analyzing the feasibility of achieving region- or state-wide zero-energy LIHTC developments. To validate the framework, the authors employ a longitudinal sample of monthly energy use data from 2013-2016 obtained from 310 residential units of 15 LIHTC developments across the state. Based on statistical regression analysis, energy simulation, and simulation-based risk analysis, the authors find that the net present value of zero-energy LIHTC investments can be positive with a low risk. The investment value varies often depending on the zero-energy building definition, weather characteristics, retail price of electricity, and incentive rate. This study can help housing policymakers and industry professionals analyze and benchmark the feasibility of innovative zero-energy housing policies and projects.

Hydrogen is useful as a fuel and could be produced by a variety of means. One approach uses artificial photosynthesis where energy from sunlight powers the splitting of water into hydrogen and oxygen. But, biological methods for producing hydrogen has emerged strongly over the past decades. In particular, specific microorganisms could use different substrates to produce hydrogen at differing yields. Such fundamental discoveries with industrial applications thus motivated the use of metabolic engineering approaches and methodologies in enhancing biological hydrogen production through a series of enzyme over-expression, pathway debottlenecking, and gene deletion. However, such approaches heavily rely on the selection of an appropriate microbial chassis for biohydrogen production. With the proper strain in hand, use of alternative substrates may engender greater hydrogen productivities. But learning from the bioprocessing field, co-culture of two compatible microorganisms have been sought after for improving biohydrogen production. In addition, thermophilic microbes may also be useful candidates for exploiting hydrogen production from composting. Future outlook in the field looks into filling our gaps in understanding of the metabolic network that feeds into hydrogen production in different organisms. But, more importantly, problems such as reduced growth rate in engineered microbes point to fundamental issues with using genetically engineered microorganisms for improved biohydrogen production, to which clever bioprocess engineering may yield solutions.

Cultivating research competencies facilitated by the use of technology in the university is crucial to boost acquisition of knowledge, educational innovation, and successful professional development. The objective of this article is to analyze the evolution and characteristics of scientific production that has had the greatest impact on the momentum of investigative competencies and their articulation within technological scenarios. Applying various selection criteria in the Scopus database, we utilized systematic, bibliometric mapping as a methodological strategy in which 250 articles related to the topic were identified, then analyzed with the semantic analysis software VOSViewer. The results indicate that there is an emerging line of research on the subject since 2016 and point to the underdevelopment of lines that associate research skills with digital literacy, information literacy, digital libraries databases, and the development of critical thinking.

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based studies using these datasets require thoughtful modification to sample selection and analytic strategies, relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings. This paper offers some practical guidance to the full analytical process, demonstrates methods for estimating causal treatment effects on several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying repeated measures outcomes, and aims to increase transparency and reproducibility. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research identifies emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to COVID-19, to identify dominant sentiment trends associated with the push to 'reopen' the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.

Gross strap muscle invasion (gSMI) in patients with differentiated thyroid cancer (DTC) was defined as high-risk recurrent group in the 2015 American Thyroid Association guidelines. However, controversy persists because several studies suggested gSMI had little effect on disease outcome. Herein, a systematic review and meta-analysis was conducted to investigate impact of gSMI on outcome of DTC. Methods: A systematic search of electronic databases (PubMed, EMBASE, Cochrane Library, and MEDLINE) for studies published until February 2020 was performed. Case-control studies and randomized controlled trials that studied the impact of gSMI on outcome of DTC were included. Results: Six studies (all retrospective studies) involving 13639 patients met final inclusion criteria. Compared with no extrathyroidal extension (ETE), patients with gSMI were associated with increased risk of recurrence (P=0.0004,OR, 1.46; 95% CI: 1.18 to 1.80) and lymph node metastasis (LNM) (P<0.00001,OR 4.19;95% CI. 2.53 to 6.96). For mortality (P=0.34,OR 1.47;95% CI:0.67 to 3.25), ten-year disease-specific survival (P=0.80, OR 0.91;95% CI:0.44 to 1.88) and distant metastasis (DM) (P=0.21, OR 2.94;95% CI. 0.54 to 15.93), there was no significant difference between gSMI and no ETE group. In contrast with maximal ETE, patients with gSMI were associated with decreased risk of recurrence (P<0.0001,OR, 0.58; 95% CI: 0.44 to 0.76) , mortality (P=0.0003,OR 0.20;95% CI:0.08 to 0.48), LNM (P=0.0003,OR 0.64;95% CI. 0.50 to 0.81) and DM (P=0.0009,OR 0.28;95% CI. 0.13 to 0.59). Conclusions: DTC patients with gSMI had a higher risk of recurrence and LNM than those without ETE. However, in contrast with maximal ETE, a much better prognosis was observed in DTC patients with only gSMI. The findings of our meta-analysis provide supportive evidence for the validity of the T category changes in the 8th edition American Joint Committee on Cancer system. The actual impact of gSMI should be re-evaluated and revised in the recurrent risk stratification system in the future.

Background: Outbreak of the Covid-19 is now an ongoing global health emergency. At the end of December 2019, the first infection was reported in Wuhan and the world did not pay attention to this extremely contaminated disease and plucked to react rapidly. The World is in an vulnerable state in disease spreading, facing a great loss of lives and socio-economic aspects also. That is why we have proposed a potential mathematical model with data analysis to predict and control the outcome of this pandemic. Methods: The model presented the epidemic dynamics of multiple compartments. We collected available online data of Spain. In primary step, we estimated the parameters using either the data analysis or reference papers. Then we did the data fitting analysis in comparison with the outcome of our mathematical results. The results of the system depended not only the parameters also on social consciousness. Results: It is found that disease progression in this model is determined by the basic reproductive ratio, $R_0$, the actual epidemic of $R_0$ and effective $R(t)$ of each day. If $R_0>1$, the number of latently infected individuals grows exponentially; endemic solution is stable while infection rate decays if $R_0<1$. The optimal control theory stated that vaccination and treatment strategies are highly effective for reducing both susceptible and infected population and to increase the recover rate high. In Spain, after state of alarm (quarantine) on 14 March 2020, reported cases increasing for 13 days only and from the 14th day, daily reported cases started to decline albeit with small fluctuation. Our proposed model approximates that the disease in Spain could be fully under control by after July 2020. Conclusion: Outbreak will be in control of health care system, reduce the death rate and will ensure social-economic stability.

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

Citation frequencies represent the most significant contributions in any respective field. This bibliometric analysis aimed to identify and analyze the 100 most-cited publications in the field of antibiotics and to highlight the trends of research in this field. “All databases” of Clarivate Analytics' Web of Science was used to identify and analyze the 100 publications. The articles were then cross-matched with Scopus and Google Scholar. The frequency of citation ranged from 940 to 11051 for the Web of Science, 1053 to 10740 for Scopus, and 1162 to 20041 for Google Scholar. Five hundred thirteen authors made contributions to the ranked list, and Robert E.W. Hancock contributed in six articles, which made it to the ranked list. Sixty-six scientific contributions originated from the United States of America. In contrast, five publications were linked to the University of Manitoba, Canada, that was identified as the educational organization, which made the most contributions (n=5). According to the methodological design, 26 of the most cited works were review-type closely followed by 23 expert opinions/perspectives. Eight articles were published in Nature journal, making it the journal with the most scientific contribution in this field. Correlation analysis between the publication age and citation frequency was found statistically significant (P = .012).

Triple negative breast cancers (TNBCs) are molecularly heterogeneous, and the link between their aggressiveness with African ancestry is not established. We investigated primary TNBCs for gene expression among self-reported race (SRR) groups of African American (AA, n=42) and European American (EA, n=33) women. Using The Cancer Genome Atlas (TCGA) approaches, we analyzed RNA sequencing data to measure changes in genome-wide expression and used logistic regressions to identify ancestry-associated gene expression signatures. To determine global ancestry, GATK best practices were followed for variant calling, and used the 1000 Genomes Project as reference data. We identified >150 African ancestry-associated genes and found that, compared to SRR, quantitative genetic analysis was a more robust method to identify racial/ethnic-specific genes that were differentially expressed. A subset of African ancestry-specific genes that were upregulated in TNBCs of our AA patients were validated in TCGA data. In AA patients, there was a higher incidence of basal-like 2 tumors and altered TP53, NFB1, and AKT pathways. The distinct distribution of TNBC subtypes and altered oncologic pathways show that the ethnic variations in TNBCs are driven by shared genetic ancestry. Thus, to appreciate the molecular diversity of TNBCs, tumors from patients of various ancestral origins should be evaluated.

This paper analyzes the robust dissipativity of uncertain stochastic generalized neural networks (USGNNs) with Markovian jumping parameters and time-varying delays. In practical applications most of the systems refer to uncertainties, hence, the norm-bounded parameter uncertainties and stochastic disturbance are considered. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF) and by employing integral inequalities LMI-based sufficient conditions of the considered systems are established. Numerical simulations are given to show the merit of the presented results.

A Life Cycle Analysis was performed considering three existing power plants of comparable size operating with different sources of renewable energy: geothermal, solar and wind. Primary data were used for building the life cycle inventories. The geothermal power plant includes emissions treatment for removal of hydrogen sulfide and mercury. The scenario about the substitution of natural emissions from geothermal energy, with specific reference to the greenhouse effect, is also investigated performing a sensitivity analysis. The results are characterized employing a wide portfolio of environmental indicators employing the Recipe 2016 and the ILCD 2011 Midpoint+ methods; normalization and weighting are also applied using the Recipe 2016 method at endpoint level. The results demonstrate a good eco-profile of geothermal power plant with respect to other renewable energy systems and allow for a critical analysis to support potential improvements of the environmental performances.

This Letter revisits the instability of jet produced by shaped charge. Scaling laws for shaped charge jet are derived by dimensional analysis. It shows that the scaling laws are universal when cross-sectional shrinkage rate is not included in the formulation.

The aim of this study was to clarify degradation characteristics in each tissue of the knee complex of a medial meniscectomy (MMx)-induced knee osteoarthritis (KOA) animal model using classical methods and a new comprehensive evaluation method called contrast-enhanced X-ray micro-computed tomography (CEX-μCT), which was developed in the study. Surgical MMx was performed in the right knee joints of five male Wistar rats to induce KOA. At 4 wk post-surgery, the synovitis was evaluated using qPCR. Degradations of the articular cartilage of the tibial plateau were evaluated using classical methods and CEX-μCT. Evaluation of the synovitis demonstrated significantly increased expression levels of inflammation-associated marker genes in MMx-treated knees compared to that in sham-treated knees. Evaluation of the articular cartilage using classical methods showed that MMx fully induced degradation of the cartilage. Evaluation using CEX-μCT showed that local areas of the medial cartilage of the tibial plateau were significantly reduced in MMx-treated knees compared to that in sham-treated knees. On the other hand, total cartilage volumes were significantly increased in MMx-treated knees. Based on the findings of this study, the researchers in KOA research could be helped to select an optimal KOA model to discover new drugs.

Animal behavior analysis is a crucial tasks for the industrial farming. In an indoor farm setting, extracting Key joints of animal is essential for tracking the animal for longer period of time. In this paper, we proposed a deep network that exploit transfer learning to trained the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on hourglass stacked dense-net. In order to train the network, key frames are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.

Technological innovation has led to the development of increasingly efficient and complex industrial plants. To manage this complexity, it is necessary to define an integrated vision of the socio-technological system that includes: technological, human and organizational component. Petrochemicals can be considered one of the most complex socio-technical systems that deserve special attention to high risk management, especially during the emergency conditions. Traditional safety management models only consider static systems, while new resilience engineering models evaluate the performance variability developed between different actions. One of the recent development methods is the Functional Resonance Analysis Method (FRAM) that identifies the pairs between the functions. FRAM unfortunately is a qualitative model, this research integrates this model with the Performance Shaping Factors (PSFs) and with the Bayesian approach to identify the performance variability of the system. The analysis aims to develop a system that improves safety analysis. The proposed model is applied in a case study of an emergency in a petrochemical company.

Introduction: Patients’ satisfaction was extensively researched over the last decades, given its role in building loyalty, compliance to treatment, prevention, and eventually higher levels of wellbeing and improved health status. Patients’ feedback on the perceived quality of health services can be incorporated into practice; therefore, understanding factors and mechanisms responsible for patients’ satisfaction allows providers to tailor targeted interventions. Method: A questionnaire assessing patients’ perception of the quality of health services was administered to a country-representative sample of 1500 Romanian patients. Using a partial least squares—path modeling approach (PLS-PM), with cross-sectional data, we developed a variance-based structural model, emphasizing the mediating role of trust and satisfaction with various categories of health services. Results: We confirmed the mediating role of trust in shaping the relationship between the procedural accuracy of health professionals, along with the perceived intensity of their interaction with patients, and patients’ experienced quality of the health services. We confirmed the mediating role of satisfaction by the categories of services in the relationship between waiting time on the premises, attention received, and the perceived reliability of the information received, as predictors, and the experienced quality of the health services. In addition, indirect assessment of patients’ satisfaction is a good predictor for direct assessment, thereby affirming the idea that the results of the two types of evaluations converge. Discussions: One of the most efficient solutions to increase both patients’ satisfaction and their compliance is to empower the communication dimension between patients and health practitioners. Given the non-linear relationships among variables, we advocate that, unless the nature of the relationships between satisfaction and its predictors is understood, practical interventions could fail. The most relevant variable for intervention is the degree of attention patients perceive they received. We suggest three methods to turn waiting time into attention given to patients.

The objective was to model the teams’ style of play (SoP) in elite football and relate them to the match result. For this, the twenty Spanish first division teams in the 2016-17 season were analysed, using nine interaction performance indicators (IRi). A principal component (PC) analysis was applied. From two PCs four SoPs were established: deep or high-pressure defending, and elaborate or direct attack. The SoPs were distributed according to average performance obtained throughout the championship. The connection between the preferred SoP and the final result was estimated. Teams with elaborate offensive styles and teams defensively minded got better results. In addition, most of the teams showed variability in their SoP. The applications of the study are: 1) the IRi have served to identify SoP and can be used as a reference to optimize team performance; 2) teams should have a varied SoP repertoire, as well as being prepared to deal with different SoPs; 3) particular player profiles should be connected with the desired SoP when creating the squad. 4) clubs should develop a varied range of SoPs at their academies.

Corporate Sustainability (CS) literature has gone through a period of intense development. The moment is favorable to gathering these contributions to consistently advance the state of the art in CS and, also, discuss them to apply in real contexts. The main objective of the paper is to systematize, through a systematic literature review using content analysis of the 30 most cited articles from 2007 to 2017, the guiding pillars of CS management. The systematic search for papers was carried out in Scopus and Web of Science and the initial screening of the papers was assisted by the coding software MAXQDA 2018, through which the authors structured and analyzed their main insights, contributions and conclusions. After getting acquainted with the sample, an in-depth reading of the texts was conducted and 60 CS elements were identified. The elements cited in the relevant literature were grouped into 6 pillars related to Corporate sustainability strategy; Corporate governance; Human resources management; Knowledge and innovation management; Measurement, disclosure and independent assurance; and Management systems and Integrated management systems. The discussion of CS management pillars presented in this study provides understanding to researchers and managers on the main aspects that make up the integration of this construct in the companies, especially from a management point of view.

Precipitation processes are technologies commonly used in hydrometallurgical plants to recover metals or to treat wastewaters. Moreover, solid-liquid separation technologies, such as thickening or filtering, are relevant unit operations, included in the precipitation technologies. These methods are strongly dependent on the characteristics of the solid precipitates formed during the specific precipitation reaction. One of these characteristics is the particle size distribution (PSD) of the solid precipitates which are fed into a solid-liquid separation process. Therefore, PSD determination is a typical practice for the characterization of the slurries generated in a precipitation plant. Furthermore, the precipitates generated in these processes have a colloidal or aggregation behavior, depending on the operational conditions. Nevertheless, the conventional methods used to estimate PSD (e.g., laser diffraction and/or ciclosizer) have not been designed to measure particles that tend to aggregate or disaggregate, since they include external forces (e.g., centrifugal, agitation, pumping and sonication). These forces affect the true size of the aggregates formed in a unit operation, thereby losing representativity in terms of aggregates particle size. This study presents an alternative method of measuring the size distribution of particles with aggregation behavior, particularly, by using non-invasive microscopy and image processing and analysis. The samples used have been obtained from an experimental precipitation process by applying sulfidization to treat the cyanide-copper complexes contained in a cyanidation solution. This method has been validated with statistical tools and compared with a conventional analysis based on laser diffraction. Our results show significant differences between the methods analyzed, demonstrating that image processing and analysis by microscopy is an excellent and non-invasive alternative to obtaining size distribution of aggregates in precipitation processes.

Selection of efficient corrosion inhibitors requires detailed knowledge regarding interaction mechanism, which depends on the type and amount of functional groups within the inhibitor molecule. Position of functional groups between different isomers is often overlooked but not less important since factors like steric hinderance may significantly affect the adsorption mechanism. In this study we have presented how different dihydroxybenzene isomers interact with aluminium alloy 5754 surface, reducing its corrosion rate in bicarbonate buffer (pH = 11). We have shown the highest inhibition efficiency among tested compounds belongs to catechol at 10 mM concentration, although differences were moderate. Utilization of novel impedance approach to adsorption isotherm determination allowed to confirm that while resorcinol chemisorbs on aluminium surface, catechol and quinol follows ligand exchange model of adsorption. Unlike catechol and quinol, the protection mechanism of resorcinol is bound to interaction with insoluble aluminium corrosion products layer and was only found efficient at concentration of 100 mM (98.7%). The aforementioned studies were confirmed with scanning electron microscopy and x-ray photoelectron spectroscopy analyses. There is a significant increase of the corrosion resistance offered by catechol at 10 mM after 24 h exposure in electrolyte: from 63 to 98%, with only negligible changes in inhibitor efficiency observed for resorcinol at the same time. However, in the case of resorcinol a change in electrolyte color was observed. We have revealed that the differentiating factor is the keto-enol tautomerism. The NMR studies of resorcinol indicate the keto form in structure in presence of NaOH, while the chemical structure of catechol does not change significantly in alkaline environment.

The feedback collection and analysis has remained an important subject matter since long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the student expresses their feedback opinions on online social media sites, which need to be analyzed. This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews. Our technique computes the sentiment score of student feedback reviews and then applies fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level. The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

The EigenAnt algorithm has been proposed to find the shortest path between two nodes with a proof of convergence. Two novel features are noticeable in EigenAnt compared to the conventional ant colony optimization algorithms. Pheromone evaporation in the EigenAnt algorithm is done locally for the selected path and it does not use heuristic information in its selection phase. On the other hand, a simple version of ant colony optimization that also does not use heuristic information has been used for analyzing convergence, considering parameter impact analysis, in the problem of finding the shortest path for 1-node binary chain problems. In this paper, we propose to improve the EigenAnt algorithm by adding additional parameters in such a way as to be able to take advantage of the analysis developed for the simple ant colony optimization algorithm. We demonstrate through our analysis that since the Improved EigenAnt algorithm has one parameter more than ant colony optimization algorithms, tuning of convergence speed is decoupled from tuning of convergence features in the algorithm. Thus, IEigenAnt, which can be interpreted as having two uncorrelated intensification and diversification components, makes it possible to achieve a balance between intensification and diversification. In order to illustrate the advantages of this decoupling, we present experimental results for the routing network problem.

Polydimethylsiloxane with hydroxy groups was functionalized to form functionalized polydimethylsiloxane, which subsequently underwent an addition reaction with isophorone diisocyanate to form the prepolymer. Next, 3-aminopropyltriethoxysilane (APTS) reacted with 3-glycidoxypropyltrimethoxysilane (GPTS) to produce bridged polysilsesquioxanes, and the sol–gel technology was employed to form hyperbranched polysiloxane nanoparticles with hydroxy groups, which was used as the additive. The hyperbranched polysiloxane and the prepolymer containing NCO functional groups then underwent an addition reaction to produce the hybrid materials. Fourier-transform infrared spectroscopy and 29Si nuclear magnetic resonance were used to characterize the structure of the polyurethane hybrid. Regarding thermal stability, after the hyperbranched polysiloxane nanoparticles was introduced, the integral procedural decomposition temperature increased from 348 ºC for polyurethane matrix to 859 ºC for the hybrid material. The results revealed that the thermal stability of the hybrid material substantially increased by approximately 247%.

Digital Elevation Models (DEMs) are essential in watershed delineation, but the sensitivity of simulated runoff to DEM resolution is poorly understood. This study investigates the impact of DEM resolution on topological attributes and simulated runoff in the Mahabad Dam watershed, Iran. To delineate the watershed, DEMs with 12.5 m, 30 m, and 90 m resolutions were acquired from the ALOS PALSAR, Space Shuttle Radar Topography Mission (SRTM), and ASTER global DEM data source, respectively. Watershed and streamlines were delineated in ArcGIS, with hydrologic analyses performed using the Soil and Water Assessment Tool (SWAT). Sensitivity analysis on parameters contributing to runoff was done using Sequential Uncertainties Fitting Ver-2 (SUFI-2) Algorithm, in SWAT Calibration and Uncertainty Procedures (SWAT-CUP) software. Results showed the watershed area, reach lengths, and elevations in the watershed varied due to DEM resolutions. Higher amounts of runoff were generated when DEMs with finer resolutions were implemented. The 12.5 m DEM generated 3.48% and 0.42% more runoff compared with 90 m and 30 m DEMs, respectively. SWAT-CUP results showed the sensitivity of parameters contributing to runoff changes under different DEM resolutions. Regardless of DEM resolution, surface properties, available water capacity, and moisture levels in the soil are the most sensitive parameters. As the distribution of slope changes in different DEM resolutions, surface parameters are most affected. The findings indicate to reduce computation time and speed up computation procedures, researchers may use DEMs with coarser resolutions at the expense of minor decreases in accuracy.

This report presents a framework for analysing the risk of alien taxa under South Africa's National Environmental Management: Biodiversity Act of 2004, and the Alien and Invasive Species Regulations of 2014. While the report was initially designed to meet a specific South Africa need, the risk analysis processes developed can, we believe, be transferred to any specified geographic region. In outlining a series of questions related to a taxon’s likelihood of invasion and the consequences thereof, i.e. the potential impacts, the report provides a structure for collating data relevant to the process of listing taxa as well as a process for developing recommendations that is both mathematically sound, transparent, and that explicitly takes uncertainty into account. The framework is based on collating information according to international standards in biological invasions (specifically the IUCN Environmental Impact Classification of Alien Taxa Scheme, the CBD's scheme for classifying invasion pathways, and the Unified Framework for Biological Invasions proposed by Blackburn et al. 2011). The risk analysis framework is currently being implemented in South Africa in an effort to underpin national regulatory lists of invasive species.

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Support Vector Machine (SVM), Naive Bayes (NB), and Decision Tree (DT).

Single-level Principal Components Analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”

Heat shock protein 47kDa (HSP47) serves as a client-specific chaperone, essential for collagen biosynthesis and its folding and structural assembly. To date, there is no comprehensive study on mutational hotspots and protein network for human HSP47. Using five different human mutational databases, we deduced a comprehensive list of human HSP47 mutations and we found 24 67, 50, 43 and 2 deleterious mutations from the 1000 genomes data, gnomAD, COSMICv86, cBioPortal, and CanVar. We identified thirteen top-ranked missense mutations of HSP47 with the stringent cut-off of CADD score (>25) and Grantham score (≥151) as Ser76Trp, Arg103Cys, Arg116Cys, Ser159Phe, Arg167Cys, Arg280Cys, Trp293Cys, Gly323Trp, Arg339Cys, Arg373Cys, Arg377Cys, Ser399Phe, and Arg405Cys with the arginine-cysteine change as the predominant mutation. We also found that HSP47 is up-regulated and down-regulated in 11 and 4 of cancers types. Upon constructing protein interactome map of human HSP47, we found that a set of molecular chaperones is interaction partners of HSP47, which included two copies each of CREB binding proteins, HSP27, HSP40, HSP70, HSP90, ubiquitin proteins and one copy each of cartilage associated protein (CRTAP), HSPH1, HSBP1, FK506-binding protein B (FKBP), kruppel-like factor (KLF13), peptidyl-prolyl isomerase PIPB and Prolyl 4-hydroxylase beta subunit (P4HB). This suggested a cocktail of different chaperones interact with HSP47. These findings will assist in the evaluation of roles of HSP47 in human disease including different types of cancers.

Structural vibration induced by dynamic load or natural vibration is a nonnegligible factor in failure analysis. Based on vibrating boundary condition, impact resistance of shape memory alloy reinforced composites is investigated. In this investigation, modified Hashin’s failure criterion, Brinson’s model and visco-hyperelastic model are implemented into the numerical model to charactering the mechanical behavior of glass fiber/epoxy resin laminates, SMAs and interphase, respectively. First, fixed boundary condition is maintained in simulation to verify the accuracy of material parameters and procedures by comparing with experimental data. Then, a series of vibrating boundaries with different frequencies and amplitudes are applied during the simulation process to reveals the effect on impact resistances. The statistics of absorbed energy and contact force indicate that impact resistance of the composite under high frequency and large amplitude is lower than that under low frequency and small amplitude, and summarized by a mathematical expression.

The steady streaming (SS) phenomenon is gaining increased attention in the microfluidics community, because it can generate net mass flow from the zero-mean vibration. We developed numerical simulation and experimental measurement tools to analyze this vibration induced flow, which has been challenging due to its unsteady nature. Validity of these analysis methods is confirmed by comparing the three-dimensional (3D) flow field induced around a cylindrical micropillar under circular vibration. In the numerical modeling, we directly solved the flow in the Lagrangian frame so that the substrate with a micropillar becomes stationary, and the result was converted to the Eulerian frame to compare them with the experimental results. The present approach enables to avoid the introduction of moving boundary or small perturbation approximation. The flow field obtained by the micro particle image velocimetry (PIV) measurement supported the three-dimensionality observed in the numerical results, which could be important for controlling the mass transport and manipulating particulate objects in the microfluidic systems.

Gluten is a complex mixture of storage proteins in cereals like wheat, barley and rye. Prolamins are the main components of gluten. Their high content in proline and glutamine makes them water-insoluble and difficult to digest in the gastrointestinal tract. Partial digestion generates peptide sequences which trigger immune responses in celiac and gluten-sensitive patients. Gluten detection in food is challenging because of the diversity, in various food matrices, of protein proportions and their immunogenicity. Attempts to develop standard reference materials have been unsuccessful. We present here a summary of recent studies reporting the detection of dominant Gluten Immunogenic Peptides (GIP) sharing epitopes presented in the α-gliadin 33-mer, the most important celiac disease-immunogenic sequence within gluten. GIP were not only detectable and quantifiable in very different kind of difficult to analyze food, but also in stool and urine of celiac patients on a supposedly gluten-free diet (GFD), providing the first simple and objective means to assess adherence to the GFD. Methods to specifically and sensitively detect the most active GIP in food and biological fluids are rational candidates may use similar analytical standard references for determination of the immunopathological risk of gluten exposure in gluten-related diseases.

B chromosomes (Bs) were discovered a century ago, and since then most studies have focused on describing their distribution and abundance using traditional cytogenetics. Only recently have attempts been made to understand their structure and evolution at the level of DNA sequence. Many questions regarding the origin, structure, function and evolution of B chromosomes remain unanswered. Here we identify B chromosome sequences from several species of cichlid fish from Lake Malawi by examining the ratios of DNA sequence coverage in individuals with and without B chromosomes. We examine the efficiency of this method, and compare results using both Illumina and PacBio sequence data. The B chromosome sequences detected in 13 individuals from 7 species were compared to assess the rates of sequence replacement. B-specific sequence common to at least 12 of the 13 datasets are identified as the “Core” B chromosome. The location of B sequence homologs throughout the genome provides further support for theories of B chromosome evolution. Finally, we identified candidate genes located on the B chromosome which may regulate the segregation and maintenance of the B chromosome.

The paper aims in assessing risks of mandible fractures consequent to impacts or sport accidents. The role of the structural stiffness of mandible, related to disocclusion state, is evaluated through numerical simulations using the finite element method (FEM). It has been assumed that the quasi-static stress field, due to distributed forces developed during accidents, could explain the common types of mandibular fractures. Geometric model of adolescent mandible was built, upon the basis of medical imaging, in CAD software with distinction between cortical layer and inner spongy bone. The finite element model of disoccluded mandible was next created. Mandibular condyles were supposed jammed in the maxillary fossae. The total force of 700 N, simulating an impact on mandible, has been sequentially applied in three distinct areas: centrally, at canine zone and at the mandibular angle. Clinically most frequent fractures of mandible were recognized through the analysis of maximal principal stress and maximal principal strain fields. Mandibular fracture during accidents can be analyzed at satisfactory level using linear quasi-static FE models for designing protections in sport and transport. The proposed approach can be improved by introducing more realistic interactions between condylar processes and fossae.

The active debate about the processes governing the organic-rich sediment deposition generally involves the relative roles of elevated primary productivity and enhanced preservation related to anoxia. However, other less spotlighted factors could have a strong impact on such deposits: e.g. residence time into the water column (bathymetry), sedimentation rate, transport behavior of organo-mineral floccules on the sea floor. They are all strongly interrelated and may be obscured in the current conceptual models inspired from most representative modern analogues (i.e. upwelling zones and stratified basins). To improve our comprehension of organic matter distribution and heterogeneities, we conducted a sensitivity analysis on the processes involved in organic matter production and preservation which have been simulated within a 3D stratigraphic forward model. The Lower-Middle Toarcian of the Paris Basin was chosen as a case study as it represents one of the best documented example of marine organic matter accumulation. The relative influence of the critical parameters (bathymetry, diffusive transport, oxygen mixing rate and primary production) on the output parameters (Total Organic Carbon, and oxygen level), determined performing a Global Sensitivity Analysis, shows that, in the context of a shallow epicontinental basin, a moderate primary productivity (> 175 gC.m^-².yr^-1) can led to local anoxia and organic matter accumulation. We argue that, regarding all the processes involved, the presence and distribution of organic-rich intervals is linked as a first-order parameter to the morphology of the basin (e.g. ramp slope, bottom topography). These interpretations are supported by very specific ranges of critical parameters which allowed to obtain output parameter values in accordance with the data. This quantitative approach and its conclusions open new perspectives about the understanding of global distribution and preservation of organic-rich sediments.

In the process of sustainable development in the future, human society faces problems such as severe population load, economic transition, and lack of educational measures. One of the root causes of these problems is the shortage of innovative talents. Therefore, how to cultivate learners with multi-disciplinary integration and innovation ability is the key point that should be paid attention to in promoting the concept of quality education and coping with the future sustainable development process. This paper uses questionnaire survey method and literature analysis method to analyze the development of many educational institutions with STEAM concept as the core in China since 2017. It is found that the existing STEAM educational institutions generally have the following problems: the lack of team composition、difficulty in independent research and development、single course content and insufficient validation of course effectiveness. In order to more effectively promote the sustainable development of China's future course, STEAM education institutions should focus on strengthening the following development strategy: gradient team building, continuous independent research and development, cutting-edge projects curriculum transformation mechanism and multi-angle course effectiveness verification.

Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textural document may reveal some additive information regarding the document that is not reflected in any other features from traditional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textural documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

Reliable and robust control of power converters is a key issue in the performance of numerous technological devices. In this paper we show a design technique for the control of a DC-DC buck converter with a switching technique that guarantees not only good performance but also global stability. We show that making use of the contraction theorem in the Jordan canonical form of the buck converter, it is possible to find a switching surface that guarantees stability but it is incapable of rejecting load perturbations. To overcome this, we expand the system to include the dynamics of the voltage error and we demonstrate that the same design procedure is not only able to stabilize the system to the desired operation point but also to reject load, input voltage and reference voltage perturbations.

Diverse factors may have an impact in Carbon dioxide (CO₂) emissions; thus, three main contributors, energy consumption, exergy indicator and gross domestic product (GDP) are examined in this work. This study explores the relationship between economic growth and energy consumption by means of the hypothesis postulated for the Environmental Kuznets Curve (EKC). Panel data for 10 countries, from 1971 to 2014 have been studied. Despite all this wide gamma of research, the role of an exergy variable has not been tested to find the EKC; then exergy analysis is proposed. Exergy analyses were developed to propose an exergetic indicator as a control variable and a comparative empirical study is developed to study a multivariable framework with the aim to detect correlations between them. High correlation between CO₂, GDP, energy consumption, energy intensity and trade openness are observed, conversely not statistically significant values for trade openness and energy intensity. The results do not support the EKC hypothesis, however exergy intensity opens the door for future research once it proves to be a useful control variable. Exergy provides opportunities to analyze and implement energy and environmental policies in these countries, with the possibility to link exergy efficiencies and the use of renewables.

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

Renewable sources and electric distribution network can produce or make available a surplus of electric and thermal energies. The Intermediate Temperature Solid Oxide Electrolyzer (IT-SOE) fed by CO2-steam mixtures can store these electric and thermal energies producing CO-H2 mixtures with high conversion efficiency. Inside the IT-SOE, the CO2-steam mixtures are converted to CO-H2 mixtures and O2 through the cathode and anode electrochemical reactions and reverse water gas shift chemical reaction. In this article an IT-SOE stack fed by different types of steam mixtures was tested at different operating temperatures and the stack polarization and electric power curves were detected experimentally. At the highest hydrogen production operating temperature of the stack fed by steam mixtures, the experimental polarization and electric power curves of the stack fed by steam and CO2-steam mixtures were compared. A simulation model of the IT-SOE system (stack and furnace) fed by CO2-steam mixtures was formulated ad hoc and implemented in a Matlab environment and experimentally validated. At the highest hydrogen production stack operating temperature, the IT-SOE system thermal equilibrium current was evaluated through the simulation model. Moreover, the influence of this current on the IT-SOE system efficiency and the CO-H2 mixture degree of purity was highlighted.

The rapid growth rate of the elderly population is a serious current issue in most countries, affecting them economically through needed medical treatment and healthcare planning. The priority concern is how to reduce the number of elderly people requiring long-term healthcare and raise the number who are able to live independently. This study executed a behavior assessment of elderly person’s self-reported use of electric scooters and analyzed their degree of acceptance of these assisted living tools, partly through a related factor analysis of our survey instrument. We used this questionnaire survey as our research method, applying SPSS22 software for factor analysis that revealed five survey facets.

This paper proposes a new non-radial biennial Luenberger energy and environmental performance index (EEPI) to measure the EEP change in various Chinese cities. The sources of EEP change, in terms of technical efficiency change and technological change, are examined by Luenberger EEPI. The contributions from specific undesirable outputs and energy inputs to the EEP change are identified by means of the non-radial efficiency measure. The proposed approach is applied to evaluate the EEP of the industrial sector in 283 cities in China over 2010-2014. Factors influencing the emission abatement potential are investigated by employing geographically weighted regression (GWR) model. We find that 1) changes in EEP can be attributed to technological progress but that technological progress slows down across the study period; 2) the soot emission performance experiences a downtrend among four specific sub-performances in line with the truth that severe haze happened frequently in China; 3) the best performers begin to move from the coastal to inland cities with the less resource consumption and higher ecological equality; 4) cities with the strongest positive effect in regards to pollution intensity on emission abatement potential are located in the areas around the Bohai Gulf, where air pollution is particularly severe.

The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.

We propose a new Mamdani fuzzy rule-based system in which the fuzzy sets in the antecedents and consequents are assigned in a discrete set of points and approximated by using the extended inverse fuzzy transforms, whose components are calculated by verifying that the dataset is sufficiently dense with respect to the uniform fuzzy partition. We test our system in the problem of spatial analysis consisting in the evaluation of the liveability of residential housings in all the municipalities of the district of Naples (Italy). Comparisons are done with the results obtained by using trapezoidal fuzzy numbers in the fuzzy rules.

Since autism was first recognised, prevalence has increased rapidly. The growing economic as well as social cost to society can only be mitigated by effective interventions and supports. It is therefore not surprising that most governments have developed public policy documents to address the management of autism. Over the past 40-50 years, meaningful evidence has accrued showing that interventions based on the scientific discipline of Applied Behaviour Analysis (ABA) can help people with autism reach their potential. In view of this, nearly all of North America has laws to mandate that ABA-based interventions are available through the health care systems. In contrast, across Europe there are no such laws. In fact, the National Institute for Health and Care Excellence (NICE), the body guiding health and social policy in the UK, concluded that it could not find any evidence to support ABA, and therefore could not recommend it. This paper addresses the reasons for these diametrically opposed perspectives. In particular, it examines what happens when health and social care policy is misinformed about effective autism intervention.

This research work is concerned in the exergy analysis of the continuous-convection drying of onion. The influence of temperature and air flow rate was studied in terms of exergy parameters. The energy and exergy balances were carried out taking into account the onion drying chamber. Its behaviour was analysed based on exergy efficiency, exergy loss rate, exergetic improvement potential rate and sustainability index. The exergy loss rates increase with the temperature and air flow rate augmentation. Exergy loss rate is augmented at higher drying air temperatures and flow rates because the overall heat transfer coefficient increase. On the other hand, the exergy efficiency increases with the air flow rate augmentation. This behavior is due to the energy utilization was improved because the most amount of supplied energy was utilized for the moisture evaporation. However, the exergy efficiency decreases with the temperature augmentation due to the free moisture is less, then, the moisture begins diffusing from the internal structure to the surface. The exergetic improvement potential rate values show that the onion drying process presents a high potential to improve the exergy efficiency. The sustainability index of the drying chamber varied from 1.9 to 5.1. To reduce the environmental impact of the process, the parameters must be modified in order to ameliorate the exergy efficiency of the process.

Fiber optic sensors cannot measure damage; for getting information about damage from strain measurements, additional strategies are needed, and several alternatives have been proposed. This paper discuss two independent concepts: the first one is based on detecting the new strains appearing around a damage spot; the structure does not need to be under loads; the technique is very robust, damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers a wider coverage of the structure, it is based on identifying the changes caused by the damage on the strains field in the whole structure for similar external loads. Damage location does not need to be known a priori, detectability is dependent upon the sensors network density, damage size and the external loads. Examples of application to real structures are given.

This paper proposes a crack recognition method based on high-resolution line array cameras and adaptive lifting algorithm. By defining the crack rate, this algorithm calculates the ratio of the crack area to the area of the entire collected image to characterize the damage extent of the current section. The algorithm first uses image preprocessing to reduce the image noise, then uses histogram equalization to enhance the feature of the crack region, divides the whole image into multiple sub-blocks, and extracts region features in the sub-block. At the same time, this algorithm defines related feature descriptors, and constructs weak classifiers according to each feature descriptor, and converts the weak classifiers into strong classifiers by using an adaptive lifting algorithm. Finally, this algorithm realizes the division of the crack regions. Experimental results show that the proposed algorithm can meet the actual needs and is better than other classical algorithms.

A monocular vision pose estimation and identification algorithm used on a small spacecraft for future orbital servicing is studied in this paper. A tracker spacecraft equipped with a short-range vision system is proposed to recover the 3D structural model of a space target in orbit and automatically identify its solar panels and main body using only visual information from an onboard camera. The proposed reconstruction and identification framework is tested using structure-from-motion and point cloud identification methods. The Efficient Perspective-n-Points (EPnP) descriptor is used for pose estimation. Triangulated points are used for component segmentation by means of orientation histogram descriptors. Experimental results based on laboratory images of a spacecraft model show the effectiveness and robustness of our approach.

This paper investigates existing mathematical morphology based techniques applied for performing malaria parasites detection and identification in both Giemsa and Leishman stained blood smears images. Malaria is an epidemic health disease and a rapid, accurate diagnosis is necessary for proper intervention. Generally, pathologists visually examine blood stained slides for malaria diagnosis; this kind of visual inspection is subjective, errorprone and time consuming. In order to cope with such issues, computer-aided methods have been increasingly evolved for abnormal erythrocyte and/or parasites detection, segmentation and semi/fully automated classification. The aim of this paper is to present a review of recent mathematical morphology based methods for malaria parasite detection.

Viral infections have long been the biggest threat to human survival, and from a medical perspective, the development of noninvasive high-throughput screening methods that target the incubation period to either treat diseases or limit viral spread would be strikingly effective. Using this technology to target viral incubation periods would also be inexpensive to perform. The current study proposes to transform pulse signals into a rapid diagnostic test using “coincidence analysis” in the hope of preventing or reducing the symptoms of viral infections. The heart plays a critical role in calculating and supplying the needs of all tissues of the body. Pulse waves are pressurized signals in response to heart’s calculations and include all phases of the cardiac cycle, which maintains life and provides energy needed to perform tasks. Any small movement gives a corresponding signal to pulse waves. The current study investigated conclusive data on self-limiting infections, such as common cold. We used pulse wave, coincidence analysis technology to capture signals from individuals with common cold during the incubation period and investigated if particular characteristic signals could be applied to influenza during the incubation period. Preliminary work demonstrated that pulse waves could generate signals using this technology that would be worthwhile for future research. A small amount of analytical data from common cold existed previously. The data structure is based on the idea that a single pulse wave at differing physiological conditions would have slight modifications that would be amplified and presented by various geometrical shapes after extensive data are accumulated. These geometric shapes can then be sliced vertically or horizontally to extract data during different illness stages. The significance of these findings are impressive; from a personal or a public hygiene perspective, this analytical technology provides many benefits, such as rapid and precise decision-making that can be directly visualized or can be analyzed using software programs. Also, this technology also uses futuristic wearable technology that brings practical problem solving to physiology.

Project portfolio configuration (PPC) is an important approach to maintain the sustainable development of enterprises and achieve organizations’ strategy. However, the synergetic efficacy of PPC which determines the degree of the project's strategic objectives achieved is a fuzzy problem and hard to be measured. To solve this problem, this paper takes the data envelopment analysis (DEA) as the tool to measure the efficacy of PPC under deterministic conditions. First, a portfolio evaluation index system which takes financial indicators and non-financial indicators into consideration is developed based on the review of the literature; Second, an evaluation model based on DEA is built to reduce the number of decision making-unit with the perspective of synergetic theory; Then, a computational experiment is studied to verify the feasibility of this proposed model. The results of this computational experiment show that this model can effectively narrow scope of decision-making, improve the decision-making level and provide a reference to decide the DEA effective project portfolio decision-making unit. To our knowledge, this study is the first time to apply the notion of synergetic efficacy and DEA to the PPC domain. It is hoped that this paper may shed lights on any further study about PPC and enterprise competitiveness of sustainable development.

Studies on the structure of economic systems are, most frequently, carried out by the methods of informational statistics. These methods, often accompanied by a wide range of indicators (Shannon entropy, Balassa coefficient, Herfindahl specialty index, Gini coefficient, Theil index etc.) around which a wide literature has been created over time, have a major disadvantage. Such weakness is related to the imposition of the system condition, therefore the need to know all the components of the system (as absolute values or as weights). This restriction is difficult to accomplish in some situations, and in others, this knowledge may be irrelevant, especially when there is an interest in structural changes only in some of the components of the economic system (either we refer to the typology of economic activities - NACE or of territorial units – NUTS). This article presents a procedure for characterizing the structure of a system and for comparing its evolution over time, in the case of incomplete information, thus eliminating the restriction existent in the classical methods. The proposed methodological alternative uses a parametric distribution, with subunit values for the variable. The application refers to Gross Domestic Product values for five of the 28 European Union countries, with annual values of over 1,000 billion Euros (Germany, Spain, France, Italy and United Kingdom) for the years 2003 and 2015. A form of the Wald sequential test is applied to measure changes in the structure of this group of countries, between the years compared. The results of this application validate the proposed method.

Due to the demands of personal travels and entertainments, light airplanes and small business aircrafts are developing rapidly. Light airplane structure is simple; however, it lacks crashworthiness design, especially the considerations on the impact of energy absorption. Therefore, in an event of accident, significant damage to passengers will be usually incurred. Airplanes made of composite materials structurally have high specific strength and good aerodynamic configuration. These materials have become the primary choice for new airplane development. This study mainly explores the topology optimization analysis of the light aircraft’s cockpit made of carbon fiber reinforced composites. This paper compares the compression amounts in the original models of composite material and aluminum alloy fuselages with the models after optimization during the crash-landing, in order to investigate the safety of fuselages made of different materials after structural optimization under the dynamic crashing. This study found that the energy absorbed by the aluminum alloy fuselage during crash-landing is still higher than that by the carbon fiber reinforced composites fuselage. On the other hand, the aluminum alloy fuselage after topology optimization could have an energy absorption capability enhanced by 40%, as compared to the that of the original model of aluminum alloy fuselage.

The European Commission has been striving to achieve sustainable development in its rural areas for more than 25 years through funds aimed at modernizing the agricultural and forestry sectors, protecting the environment and improving the quality of life. But is sustainable rural development really being accomplished? This study sets out to answer this question in the case of Extremadura, a Spanish territory with Low Demographic Density and a Gross Domestic Product still below 75 % of the European average. Both qualitative and quantitative methodology have been employed, using a Principal Component Analysis the result of which has provided us with a model which shows how various behaviors coexist in the region in view of the distribution of current funding from the EAFRD. The most dynamic areas have received the largest amounts of funding and these are linked to the agricultural sector and to the protection of the environment, leaving aside the more depressed areas and the implementation of the LEADER Approach as well. Therefore, we have come to the conclusion that the current rural development in Extremadura is not sustainable enough.

We evaluated the potential of using fish species and functional traits as indicators of land use impacts to fish assemblages. We used environmental data collected at multiple spatial scales (local, reach, and upstream catchment) for 19 tributary and main stem sites in the Nolichucky River watershed in Tennessee. Canonical correspondence analyses showed that temperature, elevation, specific conductivity, sediment yield, impervious surfaces, and row crop cover at the catchment scale were strongly associated with fish assemblage structure, as well as forest cover from all three spatial scales. Blocked indicator species analysis, with stream size as the block, showed that significantly strong indicators of the least-impacted riparian land use condition (≥60% forest cover) were Saffron Shiner (Notropis rubricroceus), Rainbow Trout (Oncorhyncus mykiss), Longnose Dace (Rhynichthys cataractae), Creek Chub (Semotilus atromaculatus), and Mottled Sculpin (Cottus bairdi). Traits indicative of the least-impacted sites were the herbivorous trophic guild, mean female age-at-maturity, longevity, rock-gravel spawners, montane geology and pelagic swimmers. Specific conductivity was strongly related to multiple catchment-scale land use variables, and was a strong local-scale influence on fish assemblage structure. Our results show promise for using a relatively common but endemic southern Appalachian fish species, the Saffron Shiner, as an indicator for land-use related impacts to these streams.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

Capital structure decision plays an imperative role in firm’s performance. Recognizing the importance, there has been many studies inspected the rapport of capital structure with performance of firms and findings of those studies are inconclusive. In addition, there is relative deficiency of empirical studies examining the link of capital structure with performance of banks in Bangladesh. This paper attempted to fill this gap. Using panel data of 22 banks for the period of 2005-2014, this study empirically examined the impacts of capital structure on the performance of Bangladeshi banks assessed by return on equity, return on assets and earnings per share. Results from pooled ordinary least square analysis show that there are inverse impacts of capital structure on bank’s performance. Empirical findings of this study is of greater significance for the developing countries like Bangladesh because it will call upon concentration of the bank management and policy makers to pursue such policies to reduce reliance on debt and to accomplish optimal level capital structure. This research also contributes to empirical literatures by reconfirming (or otherwise) findings of previous studies.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

Pollen tubes grow by spatially and temporally regulated expansion of new material secreted into the cell wall at the tip of the tube. A complex web of interactions among cellular components, ions and small molecule provides dynamic control of localized expansion and secretion. Cross-correlation studies on oscillating lily (Lilium formosanum Wallace) pollen tubes showed that an increase in intracellular calcium follows an increase in growth, whereas the increase in the alkaline band and in secretion both anticipate the increase in growth rate. Calcium, as a follower, is unlikely to be a stimulator of growth, whereas the alkaline band, as a leader, may be an activator. To gain further insight herein we reversibly inhibited growth with potassium cyanide (KCN), and followed the re-establishment of calcium, pH and secretion patterns as growth resumed. While KCN markedly slows growth and causes the associated gradients of calcium and pH to sharply decline, its removal allows growth and vital processes to fully recover. The calcium gradient reappears before growth restarts, however it is preceded by both the alkaline band and secretion, in which the alkaline band is slightly advanced over secretion. Thus the pH gradient, rather than the tip-focused calcium gradient, may regulate pollen tube growth.

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

Industries, which are mainly responsible for high energy consumptions, need to invest in research projects in order to develop new managing systems for rational energy use and to tackle the devastating effects of climate change caused by human behavior. The study reported in this paper concerns the forging industry, where the production processes generally start with the heating of the steel in furnaces and continue with other processes, such as heat treatments and mechanical machining. One of the most critical operations, in terms of energy loss, is the opening of the furnace doors for the insertion and extraction operations. During this time, the temperature of the furnaces decreases by hundreds of degrees in a few minutes. Because the dispersed heat needs to be supplied again through the combustion of fuel, increasing the consumption of energy and the pollutant emissions, the evaluation of the amount of the lost energy is crucial for the development of operating or mechanical systems able to contain this dispersion. To perform this study, CFD simulation software was used. Results show that at the door opening, because of temperature and pressure differences between the furnace and the ambient, turbulences are generated. Results also show that the amount of energy lost for an opening of 10 minutes for radiation, convection and conduction is equal to 5606 MJ where convection is the main contributor with 5020 MJ. The model created, after being validated, has been applied to perform other simulations in order to improve the energy performance of the furnace. Results show that a reduction of the opening time of the door allows energy savings and limits pollutant emissions.

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.

In this paper, we introduce a new formulation based on Floquet (Fourier) spectral analysis combined with a spectral modulation technique (and its spatial form) to study strongly coupled sublattices predefined in the infinite and large finite extent of almost periodic antenna arrays (e.g metasurfaces). This analysis is very relevant for dense massive MIMO, intelligent surfaces, 5G, and 6G applications (used for very small areas with a large number of elements such as millimeter and terahertz waves applications). The numerical method that is adopted to model the structure is the method of moments simplified by equivalent circuits MoM GEC. Other numerical methods (as the ASM array scanning method and windowing Fourier method) used this analysis in their kernel that to treat periodic and pseudo-periodic (or quasi-periodic) arrays.

Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Zs’ perceptions to-ward plant-based foods and diets, and explore the relationship between attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet with willingness to adopt green-eating practices. Using cross-sectional data from uni-versity students in Greece, India and UK, various tools were employed to determine the factors influ-encing youths’ consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students’ viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Zs’ willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents toward plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of ani-mal-based foods, dissatisfaction with plant-based foods attributes and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a rather «homogeneity» in the factors influencing youths’ intention to adopt plant-based diets, with attitude components, meal prep-aration indicators, perceived barriers to eating “green” and personal factors, viz. self-assessed knowledge on healthy eating and physical activity, being strongly associated with students’ willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths’ transition to a more sustainable lifestyle.

This study presents the interaction with human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a Protein-Protein-Interaction Networks analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2,753 proven interactions and 1,708 interactors, specific to ORF7b. We used these proteins as functional seeds and we got a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. Seven high ranked proteins as hub and seven as bottleneck following a hub-and-spoke network-architectural-model were found. Through this interaction model, we identified significant GO-processes (5,057 terms in 15 categories) induced in human metabolism by ORF7b. High statistical significance processes of dysregulated molecular cell mechanisms by the action of ORF7b were discovered. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems and the molecular mechanisms that supervise to programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant-Connected-Components core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b mainly acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation, transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes, within the limits of the poor knowledge that these sectors still impose.

Teledermatology, the dermatological consultation and diagnosis via digital communication technologies, has become increasingly important in modern healthcare. The integration of teledermatology into clinical practice has gained significant interest, especially during the COVID-19 pandemic. Numerous studies have shown the effectiveness and reliability of teledermatology, with high levels of patient satisfaction and diagnostic accuracy. Pathpoint® eDerma is a cloud-based dermatology platform that aims to streamline the documentation and management of dermatological conditions. It offers several clinical benefits, including faster diagnosis, reduced unnecessary procedures, improved access to care, and cost savings. The platform follows a comprehensive risk management plan and complies with safety regulations. It undergoes regular recertification and reviews to ensure its safety and effectiveness. The clinical evaluation of eDerma confirms its ability to deliver significant clinical benefits while effectively mitigating risks. The benefit-risk ratio of eDerma is highly acceptable, supporting its use in clinical settings to enhance dermatological care.

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues on steel pipes, the demand for glass fiber reinforced plastics (GFRP) pipes is increasing in the oil and gas gathering and transmission systems. However, the medium transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-、Ca2+、Mg2+、SO42-、CO32- and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped GFRP pipes with depths of 2mm and 5mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis.In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5,502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface, accessible at https://bacspad.altrabio.com.

Eotaxin-3 is a key chemokine with a relevant role in eosinophilic esophagitis, a rare chronic immune/antigen-mediated inflammatory disorder. Eotaxin-3 is a potent activator of eosinophil emergence and migration, which may lead to allergic airway inflammation. We investigated by bioinformatics tools the protein structure and the possible effects of the known variations reported in public databases. Following a procedure already established, we created a 3D model of the whole protein and modelled the structure of 105 protein variants due to known point mutations. The effects of the amino acid substitution at the level of impact on protein structure, stability, and possibly function, were detected by the bioinformatics procedure and described in detail. A web application was implemented to browse the results of the analysis and visualize the 3D models, with the opportunity of downloading the models and analyze by the own software. Among 105 amino acid substitutions investigated, the study evidenced in 44 cases at least one change in any of the investigated structural parameters. Other 6 variations are also relevant, although a structural effect is not detected by our analysis, because they substituted amino acids highly conserved, which suggests a possible function role. All these variations should be the object of particular attention, as they may induce loss of functionality in the protein.

In this work, we propose a generalized partially functional linear regression model with an interaction term between functional predictors. The estimators of the regression coefficient of scalar predictors and regression coefficient functions of functional predictors and their interactions are obtained. The asymptotic property of the estimators is established. Extensive simulation studies illustrate the asymptotic results. Finally, the proposed model was applied to investigate the influence of air qualities, climate factors, medical and social indicators and the interactions on cancer incidence which is a binary response.

The Klaten Regency, Central Java Province, Indonesia is an leptospirosis endemic area. This study’s purpose is to spatially describe the abiotic and biotic environmental factors with the Leptospirosis incidence in the Klaten Regency in 2018. This was a descriptive observational with cross-sectional approach conducted in the Klaten Regency, Central Java, in 2019 with 59 respondents. The results revealed that the percentage of abiotic environment factors such as poor waste disposal facilities, poor gutter conditions, river <200 meters, and flooding history, namely 35.6%; 41.2%; 54.2%; and 6.8%, respectively. The highest leptospirosis cases occurred in April 2018 with 325 mm of rainfall, average temperature of 270C, average humidity of 82.3%, and altitude of 100-200 MASL (79.7%). Meanwhile, biotic factors included rat nest existence (100%), having pets at risk (32.2%), and ≥3 types of vegetation (79.7%). In conclusion, most respondents' dominant environmental factors were presence of rat nests, ≥3 vegetation types, and river availability <200 meters.

The aims of this systematic review and meta-analysis were to evaluate the risk of inmates with mental disorder of being placed into disciplinary confinement (DC) and its effect on mental health. A systematic search of studies was performed in PubMed, PsycINFO, Web of Science, and Google Scholar. The meta-analysis was conducted using random-effects models. Heterogeneity among study point estimates was assessed with Q statistics and quantified with I2 index. Publication bias was assessed with Egger’s test. Quality assessment was based on the GRADE Checklist for observational studies. Guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were followed throughout. First, a meta-analysis of 5 articles including 27,455 inmates showed than incarcerated individuals with a mental disorder were 1.23 times (OR=1.23, CI=1.10; 1.38) more likely to be placed in DC than incarcerated individuals without a mental disorder. Particularly, having a severe mental disorder (OR=1.31, p&lt;0.001), a personality disorder (OR=1.66, p&lt;0.001) and having previously received mental health services (OR=1.16, p=0.024) increased the risk of being placed in DC. Secondly, a systematic review of 5 articles including 171,300 inmates showed more psychological distress, psychiatric symptoms (self-harm, thought disorders, obsessive-compulsive symptoms), need for mental health services and hospitalizations in DC than the general correctional population. Considering the increased risk of placement in DC for incarcerated persons with a mental disorder and its deleterious effect on mental state, it is essential that correction officials create new safe interventions to manage these inmates and offer them proper mental health care to limit its use.

Medical datasets may be imbalanced and contain errors due to subjective test results and clinical variability. The poor quality of original data affects classification accuracy and reliability. Hence, detecting abnormal samples in the dataset can help clinicians make better decisions. In this study, we propose an unsupervised error detection method using patterns discovered by the Pattern Discovery and Disentanglement (PDD) model, developed in our earlier work. Applied to the large data, the eICU Collaborative Research Database for sepsis risk assessment, the proposed algorithm can effectively discover statistically significant association patterns, generate an interpretable knowledge base for interpretability, cluster samples in an unsupervised learning manner, and detect abnormal samples from the dataset. As shown in the experimental result, our method outperformed K-Means by 38% on the full dataset and 47% on the reduced dataset for unsupervised clustering. Multiple supervised classifiers improve accuracy by an average of 4% after removing abnormal samples by the proposed error detection approach. Therefore, the proposed algorithm provides a robust and practical solution for unsupervised clustering and error detection in healthcare data.

(1) Background: São Paulo, Brazil, exhibits high mortality rates from pediatric solid tumors. This study analyzed the survival trends of patients aged 0-19 diagnosed with the state's five most prevalent solid tumors between 2000-2022. (2) Methods: An epidemiological, descriptive study utilized data from the Oncocentro Foundation of São Paulo, classified according to the International Classification of Childhood Cancer. The time between the first consultation and diagnosis, between diagnosis and treatment initiation, and patient survival were assessed using the Pe-to-Peto test. (3) Results: Analysis involved 11,067 cases: 53.5% male, with 89.6% diagnosed via microscopic confirmation. Tumor distribution comprised 34.3% central nervous system, 21.1% bone, 18.6% soft tissue, 14.2% germ cell, and 11.9% retinoblastomas. Main treatments included surgery with chemotherapy (26.5%), surgery alone (20.5%), and chemotherapy alone (15.6%). The average time between consultation and diagnosis was 22.94 ± 69.93 days, significant for treatments and recurrences, except for germ cell tumors (p=0.0178). The time between diagnosis and treatment was 25.46 ± 39.71 days, not significant for germ cell tumor treatments (p=0.0793). (4) Conclusions: Survival curves varied among neoplasm groups, with patients experiencing delays beyond recommended times, despite advanced healthcare services in the state.

Background: Positive person-centered attributes of inpatient rehabilitation need to be identified from the patient perspective to be further developed and sustained. Purpose: To identify which attributes patients openly evoke as being great care experiences, using an open appreciative inquiry during the inpatient rehabilitation stay. Methods: Qualitative secondary analysis of appreciative patient comments during a bedside patient experience rounding, facilitated by a neutral party. Two independent analysts employed an inductive, summative form of content analysis. Results: Among 150 patients rounded, 122 provided categorizable appreciative accounts. Over two thirds of the patients (67.2%) focused on “staff attributes” in their great-experience accounts. Those attributes were mostly interpersonal such as being “attentive & caring - beyond clinical duty” and being “encouraging (but not too hard) & reassuring”. These interpersonal staff attributes were reported with words showing deep levels of personal significance or patient appreciation. Beyond staff attributes, the perceived quality of the “patient care” (31.1%) and opportunities for “leisure and social activities” (9.0%) were also frequently evoked. Amenities like food or costumer service were the least evoked, rarely so as an exclusive attribute (0.8% for each). Conclusions: The human(e) factor, especially the interpersonal qualities of staff, emerged as greatly appreciated from the patient experience perspective during inpatient rehabilitation. These experiences help identify which person-centered attributes of care might be further developed and sustained.