Similar Ranking Research Articles

Grafen Kaplı Kimyasal Sensörlerin Şişme Davranışları ve Matematiksel Modelleme

Bu çalışmada, grafen kaplı Langmuir-Blodgett (LB) ince filmlerin şişme davranışları Kuartz Kristal Mikrobalans (KKM) tekniği kullanılarak incelenmiştir. Bu filmler, bazı organik buharların (aseton, etanol ve metanol) difüzyon katsayı değerlerinin hesaplanması (Fick’in erken-zaman difüzyon kanunu kullanılarak) için oda sıcaklığında etkileşmeye bırakılmıştır. Difüzyon katsayı değerleri sırası ile aseton, etanol ve metanol için 20.43 x 10−16, 1.30 x 10−16 ve 0.91 x 10−16 cm2 s−1 olarak hesaplanmıştır. Benzer sıralama grafen kaplı KKM sensörünün tepkisi (frekans değişimi, Δf) için de aseton (342 Hz) > etanol (76 Hz) > metanol (51 Hz) olarak elde edilmiştir.

Inter- and Intra-individual Variability in Brain Oscillations During Sports Motor Imagery.

The aim of this work was to re-evaluate electrophysiological data from a previous study on motor imagery (MI) with a special focus on observed inter- and intra-individual differences. More concretely, we investigated event-related desynchronization/synchronization patterns during sports MI (playing tennis) compared with simple MI (squeezing a ball) and discovered high variability across participants. Thirty healthy volunteers were divided in two groups; the experimental group (EG) performed a physical exercise between two imagery sessions, and the control group (CG) watched a landscape movie without physical activity. We computed inter-individual differences by assessing the dissimilarities among subjects for each group, condition, time period, and frequency band. In the alpha band, we observe some clustering in the ranking of the subjects, therefore showing smaller distances than others. Moreover, in our statistical evaluation, we observed a consistency in ranking across time periods both for the EG and for the CG. For the latter, we also observed similar rankings across conditions. On the contrary, in the beta band, the ranking of the subjects was more similar for the EG across conditions and time periods than for the subjects of the CG. With this study, we would like to draw attention to variability measures instead of primarily focusing on the identification of common patterns across participants, which often do not reflect the whole neurophysiological reality.

Ultrasonographic cervical evaluation: A tool to select ewes for non-surgical embryo recovery.

This study assessed the cervical ultrasonography mapping as a tool to select donor ewes for non-surgical embryo recovery (NSER). Lacaune ewes had their cervix evaluated by ultrasonography 12hr after induced oestrus onset (Trial 1, n=24) or 30min before NSER (Trial 2, n=17). Cervical rings were longitudinally evaluated and classified by their degree of misalignment on ultrasonography (DMUS) into: DMUS-1-cervix rectilinear, DMUS-2-intermediate and DMUS-3-highly asymmetrical. For predicting cervical transposing, only DMUS-1 and DMUS-2 were considered suitable. Similar ranking was attributed to degree of misalignment on the cervical map (DMCM 1-3), established immediately before NSER, which was performed at days 6 to 7 after oestrus. In Trial 1, cervical retraction for NSER was not possible only in three ewes classified as DMUS-3 (3/14, 21.4%). No difference (p>.05) was observed in the cervical transposing rates between ewes with different DMUS (ranged from 80% to 100%). In Trial 2, DMUS-1 and DMUS-2 reached 100% of transposing, and the only DMUS-3 ewe has not been transposed. In Trial 1, the prediction performance for successful cervical transposing showed low sensitivity (45%) and no specificity due to a high incidence of false negatives (52%). However, in Trial 2, sensitivity and specificity were both 100%. The DMCM and DMUS were uncorrelated, probably due to cervical stretching required to perform NSER. In conclusion, cervical ultrasound assessment immediately before NSER was more efficient to predict the cervical transposing than at induced oestrus, allowing the classification and selection of ewes eligible for NSER.

Comparison of screening tools for optimizing fracture prevention in Canada.

To inform clinical practice guidelines in Canada, we compared multiple screening tools using the population-based Manitoba BMD Program registry. The study populations consisted of (a) 28,906 individuals > 50 years or older, and (b) 15,429 women age > 65 years undergoing baseline BMD assessment (2010-2018). We considered two treatment qualifications: Treatment Approach 1: prior high-risk fracture, high fracture probability (FRAX-MOF with BMD > 20%), or vertebral fracture on VFA; Treatment Approach 2: Approach 1 or an osteoporotic BMD T score. Candidate screening tools were FRAX-MOF without BMD, age alone, weight alone, SCORE, ORAI, SOFSURF, OSIRIS, ABONE, and OST. Healthcare records were assessed for the presence of incident fracture diagnoses. Among all individuals, FRAX-MOF without BMD demonstrated the best ability to identify those satisfying Treatment Approach 1 (area under the curve [AUC 0.863]) and was significantly better than all other screening tools (P < 0.001). For identification of individuals satisfying Treatment Approach 2, FRAX-MOF without BMD showed moderate stratification (AUC 0.735), slightly lower than OSIRIS (AUC 0.752, P < 0.05), similar to SCORE (AUC 0.739, P > 0.05) and significantly better than all other screening tools (P < 0.05). For prediction of incident MOF, FRAX-MOF without BMD achieved the highest performance (AUC 0.652), and was significantly better than all other screening tools except OSIRIS. AUCs among women age > 65 years tended to be greater with a similar ranking, and no tool outperformed FRAX-MOF without BMD. Based upon a summary score, the highest ranked strategy was FRAX-MOF without BMD using a cutoff of 10%. All screening tools show some ability to identify individuals qualifying for treatment and stratify risk for incident fracture. For treatment based upon indicators of high fracture risk, the best performing strategy was FRAX-MOF without BMD using a cutoff of ≥ 10%.

Document Recommendations and Feedback Collection Analysis within the Slovenian Open-Access Infrastructure

This paper presents a hybrid document recommender system intended for use in digital libraries and institutional repositories that are part of the Slovenian Open Access Infrastructure. The recommender system provides recommendations of similar documents across different digital libraries and institutional repositories with the aim to connect researchers and improve collaboration efforts. The hybrid recommender system makes use of document processing techniques, document metadata, and the similarity ranking function BM25 to provide content-based recommendations as a primary method. It also uses collaborative-filtering methods as a secondary method in a cascade hybrid recommendation technique. We also provide a real-world data feedback collection analysis for our hybrid recommender system on an academic digital repository in order to be able to identify suitable time-frames for direct feedback collection during the year.

Knowledge, Attitudes, and Perceptions Associated With Antimicrobial Stewardship Among Veterinary Students: A Multi-Country Survey From Nigeria, South Africa, and Sudan.

In African countries, antimicrobial resistance (AMR) issue remains pertinent. Despite this, little efforts have been made to assess the future veterinary prescribers on their knowledge, attitudes and practices (KAP) related to antimicrobial usage. This multi-country survey attempts to explore the KAP of future veterinarians on stewardship of antimicrobial and identify knowledge gaps. Eight veterinary schools participated from Nigeria, Sudan and South Africa. Data regarding perceptions and knowledge were analyzed using Chi-square χ2 test, Spearman's (Rho) Rank order correlation and factor analysis using principal component factoring extraction method. Fifty-two percent of the study participants were final year veterinary students, respectively, and majority (77.2%) had no previous knowledge of biomedical sciences. Majority age were 22–27 years (24.7 ± 2.8) 79% and multiple career fields post-graduation were preferred. Overall, poor perceptions and knowledge of antimicrobial stewardship were observed with variations among countries and only 36.3% (n = 123) of the students were confident in their ability to choose the ideal antimicrobial agents for a specific patient/group of animals. The majority of the final year students were confident of their knowledge regarding AMR (68%), making of Gram staining (69.2%) and in choosing the most ideal route for administering a specific antimicrobial (74.7%). The final year students had significantly (p < 0.05) higher confidence level for knowledge compared with the pre-final year students. Tetracyclines, penicillins, and sulphonamides represent the three most abused veterinary antimicrobials with similar ranking across countries. South African (69.7 ± 20.5) and Sudanese (68.1 ± 15.4) had significantly (p < 0.0001) higher mean scores compared to the Nigerian students (44.3 ± 6.8) in the student's ability to correctly match some specific antimicrobials against their classes but Nigerian students performed better in ranking antimicrobials. This survey revealed poor to average knowledge of antimicrobial stewardship among veterinary students with significant knowledge gaps across the countries. It is recommended that the relevant regulatory and standardization authorities should make concerted efforts and interventions to regularly review curricula to ensure the delivery of targeted formative and normative training, and improved lectures on antimicrobial usage and stewardship in order to improve the awareness and behaviors of future prescribers. The identified knowledge gaps of veterinary medical students on antimicrobial stewardship must be bridge to safeguard the future.

Unsupervised Deep Multi-Similarity Hashing With Semantic Structure for Image Retrieval

With the advance of Convolutional Neural Network, deep hashing methods have shown the great promising performance in large-scale image retrieval. Without depending on extensive human-annotated data, unsupervised hashing is more applicable to image retrieval tasks compared to supervised methods. However, due to the lack of fine-grained supervised signals and multi-similarity constraints, most state-of-the-art unsupervised deep hashing algorithms cannot ensure the correct fine-grained similarity ranking for image pairs. In this paper, we propose a novel unsupervised deep multi-similarity hashing framework to learn compact binary codes by jointly exploiting global-aware and spatial-aware representations, called Unsupervised Deep Multi-Similarity Hashing with Semantic Structure (UDMSH). Specifically, to obtain distinguishing characteristics, we develop a sub-network by jointly learning global semantic structures from Convolutional Neural Network (CNN) and inherent spatial structures from Fully Convolutional Network (FCN). By computing the cosine distance for deep features from image pairs, we construct a similarity matrix with semantic structure, then utilize this matrix to guide hash code learning process. Based on it, we carefully design a multi-level pairwise loss to preserve the correct fine-grained similarity ranking. Furthermore, we introduce Hamming-isometric mapping into unsupervised hashing framework to decrease the quantization errors. Extensive experiments on three widely used benchmarks prove that our proposed UDMSH outperforms several state-of-the-art unsupervised hashing with respect to different evaluation metrics.

PrGCN: Probability prediction with graph convolutional network for person re-identification

Robust similarity measurement is an important issue for person re-identification (ReID). Most existing ReID models estimate the similarity between query and gallery images by computing their Euclidean distances while ignoring the rich context information contained in the image space. In this paper, we propose a graph convolutional network (GCN) based method to improve the similarity measurement in ReID, which regards the ReID task as a prediction problem of the link probability between node pairs. Our method is named as PrGCN (Probability GCN), in which each person is regarded as an instance node. Firstly, an Instance Centered Sub-graphs (ICS) is constructed for each instance node to depict its rich local context information. Secondly, the constructed ICS is input to a GCN to infer and predict the link probability of node pairs, followed by a similarity ranking between the query and gallery images according to the predicted probabilities. Extensive experiments show that the proposed method improves the mAP and Top-1 accuracy of ReID significantly, yielding better or comparable results to the state-of-the-art methods on various benchmarks (Market1501, DukeMTMC-ReID and CUHK03). In addition, we validate that the proposed PrGCN can be easily embedded into other deep learning architectures to replace Euclidean distance metric and achieve significant performance improvements.

Analyzing the Performance of Mega Construction Projects in Oman: A Study of Selected Projects from Oil &amp; Gas Sector

This study investigates Omani O&amp;G project’s performance and analyzes the most common time and cost overrun causes. In addition, the effects of delays are examined further along with the key enablers for efficient project delivery. This study adopts a mix method of research to improve the level of credibility of the study. A sample of 160 respondents is employed following both quantitative and qualitative method where a well-structured questionnaire was used for interviews with the concerned personnel. Following the quantitative method, this study revealed that 8% of the respondents, says the projects have completed ON time where the response rate was 80%. Besides, the qualitative part backed up this data as most of them have experienced delay of 3 months to 2 years of delay. For cost, 3% of the respondents have experienced projects finishing ON budget and the interviewees have expressed that the average cost overrun is between 10%-30%. In terms of time overrun group factors, the top group time-overrun factors among all the three parties are management factors, where it is ranked first by all stakeholders. The sub-group factor that affects more is the project planning and control. Poor understanding of scope of work during tendering factor is ranked first with main contractor and second with both the client and engineering firms. This factor is most factors that has similar ranking between all. As matter of fact is the only factor that exists between in the top 10 among all stakeholders. This study claimed that understanding the scope of work of the project properly and giving its time to be grasped by the contractors is one of the key solutions to tackle the top factor of this research. Also, the SOW must be detailed as possible to avoid any conflict between the parties, one of the recommended details to have WBS included.

Volatility forecasts, proxies and loss functions

Abstract The main problem in volatility forecasting is that the variable of interest is unobservable, which complicates not only the construction of forecasts but also their comparison. This article challenges the common practice of using only proxy-robust loss functions, which have the nice property that they lead to the same ranking of forecasts regardless whether the unobservable true volatility is used or some unbiased proxy. It is shown that two proxy-robust loss functions need not necessarily produce similar rankings but may even produce completely contradictory rankings. Two likelihood-based loss functions are proposed instead, which are not exactly proxy-robust but are still robust in the classical sense. The first is based on a t-distribution and is meant for daily data. The second is based on an F-distribution and is meant for high-frequency data. In the latter case, the squared error loss function may also be used when a logarithmic transformation is applied to the realized variances in order to achieve approximate normality. An alternative transformation is proposed which allows the adaptation to the degree of non-normality. The forecasting procedures that are compared by the different loss functions include GARCH, HAR, HARQ, and MIDAS models as well as nonparametric techniques. Finally, the economic relevance of choosing the right forecast is illustrated with the problem of establishing the intertemporal risk–return tradeoff. All theoretical arguments are backed up with empirical evidence obtained from daily data as well as from high-frequency data.

Rainfall induced soil erosion assessment, prioritization and conservation treatment using RUSLE and SYI models in highland watershed of Ethiopia

Soil erosion and the subsequent sedimentation are the major problem in Ethiopia. We assessed the impact of rainfall-induced soil erosion and prioritize the sub-watersheds using RUSLE and SYI models. We procured mean annual precipitation, Abay Basin soil map, Digital Elevation Model (DEM) and LANDSAT 8 OLI/TIRS, 2017 datasets. Results show that annual estimated soil loss was ≤270 and ≤120 t ha−1 yr−1 whereas, mean rates were 37.72 and 23.92 t ha−1 yr−1 for RUSLE and SYI models respectively. Both the models have identified ∼25% area of sub-watersheds (SW19 to SW23) has similar average soil loss (>20 t ha−1 yr−1) at higher elevations. Interestingly, similar erosion severity, priority level and ranks were observed through RUSLE and SYI. Findings indicate a strong relationship between erosion rate and topography. The severe erosion prone area requires immediate action for conservation treatment proposed in study.

Deep learning of immune cell differentiation

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

The effects of aliphatic alcohols and related acid metabolites in zebrafish embryos - correlations with rat developmental toxicity and with effects in advanced life stages in fish

The zebrafish embryo toxicity test (ZFET) is a simple medium-throughput test to inform about (sub)acute lethal effects in embryos. Enhanced analysis through morphological and teratological scoring, and through gene expression analysis, detects developmental effects and the underlying toxicological pathways. Altogether, the ZFET may inform about hazard of chemical exposure for embryonal development in humans, as well as for lethal effects in juvenile and adult fish. In this study, we compared the effects within a series of 12 aliphatic alcohols and related carboxylic acid derivatives (ethanol, acetic acid, 2-methoxyethanol, 2-methoxyacetic acid, 2-butoxyethanol, 2-butoxyacetic acid, 2-hydroxyacetic acid, 2-ethylhexan-1-ol, 2-ethylhexanoic acid, valproic acid, 2-aminoethanol, 2-(2-hydroxyethylamino)ethanol) in ZFET and early life stage (ELS, 28d) exposures, and compared ZFET results with existing results of rat developmental studies and LC50s in adult fish. High correlation scores were observed between compound potencies in ZFET with either ELS, LC50 in fish and developmental toxicity in rats, indicating similar potency ranking among the models. Compounds could be mapped to specific pathways in an adverse outcome pathway (AOP) network through morphological scoring and gene expression analysis in ZFET. Similarity of morphological effects and gene expression profiles in pairs of alcohols with their acid metabolites suggested metabolic activation of the parent alcohols, although with additional, metabolite-independent activity independent for ethanol and 2-ethylhexanol. Overall, phenotypical and gene expression analysis with these compounds indicates that the ZFET can potentially contribute to the AOP for developmental effects in rodents, and to predict toxicity of acute and chronic exposure in advanced life stages in fish.

Using Parallel, Narrative-Based Measures to Examine the Relationship Between Listening and Reading Comprehension: A Pilot Study.

Purpose The purpose of this pilot study was to determine whether parallel measures of narrative-based listening comprehension and reading comprehension reflected the same construct and yielded comparable scores from a diverse sample of second- and third-grade students. One hundred ten students participated in this study. Method Three listening and three reading comprehension narrative retells and subsequent responses to story questions and vocabulary questions were collected using the Narrative Language Measures Listening and Reading subtests of the CUBED assessment. Results Results indicated a strong correlation between the listening comprehension and reading comprehension measures. Confirmatory factor analysis indicated that the listening and reading comprehension measures loaded onto one factor. Mean scores were not significantly different between the listening and reading comprehension measures, and the equipercentile analyses indicated that the two measures yielded scores that aligned with similar percentile rankings for a diverse sample of students, suggesting symmetry and equity. Conclusion Oral narrative language retells and responses to story and vocabulary questions could potentially serve as proxy measures for reading comprehension for young students.

Parallel Valuation of the EQ-5D-3L and EQ-5D-5L by Time Trade-Off in Hungary.

The wording of the Hungarian EQ-5D-3L and EQ-5D-5L descriptive systems differ a great deal. This study aimed to (1) develop EQ-5D-3L and EQ-5D-5L value sets for Hungary from a common sample, and (2) compare how level wording affected valuations. In 2018 to 2019, 1000 respondents, representative of the Hungarian general population, completed composite time trade-off tasks. Pooled heteroscedastic Tobit models were used to estimate value sets. Value set characteristics, single-level transition utilities from adjacent corner health states, and mean transition utilities for all possible health states were compared between the EQ-5D-3L and EQ-5D-5L. Health utilities ranged from -0.865 to 1 for the EQ-5D-3L and -0.848 to 1 for the EQ-5D-5L. The relative importance of the 5 EQ-5D-5L dimensions was as follows: mobility, pain/discomfort, self-care, anxiety/depression, and usual activities. A similar preference ranking was observed for the EQ-5D-3L with self-care being more important than pain/discomfort. The EQ-5D-5L demonstrated lower ceiling effects (range of utilities for the mildest states: 0.900-0.958 [3L] vs 0.955-0.965 [5L]) and better consistency of mean transition utilities across the range of scale. Changing "confined to bed" (3L) to "unable to walk" (5L) had a large positive impact on utilities. Smaller changes with more negative wording in the other dimensions (eg, "very much anxious/feeling down a lot" [3L] vs "extremely anxious/depressed" [5L]) had a modest negative impact on utilities. This study developed value sets of the EQ-5D-3L and EQ-5D-5L for Hungary. Our findings contribute to the understanding of how the wording of descriptive systems affects the estimates of utilities.

Researchers\u2019 Perceptions of a Responsible Research Climate: A Multi Focus Group Study

The research climate plays a key role in fostering integrity in research. However, little is known about what constitutes a responsible research climate. We investigated academic researchers’ perceptions on this through focus group interviews. We recruited researchers from the Vrije Universiteit Amsterdam and the Amsterdam University Medical Center to participate in focus group discussions that consisted of researchers from similar academic ranks and disciplinary fields. We asked participants to reflect on the characteristics of a responsible research climate, the barriers they perceived and which interventions they thought fruitful to improve the research climate. Discussions were recorded and transcribed at verbatim. We used inductive content analysis to analyse the focus group transcripts. We conducted 12 focus groups with 61 researchers in total. We identified fair evaluation, openness, sufficient time, integrity, trust and freedom to be mentioned as important characteristics of a responsible research climate. Main perceived barriers were lack of support, unfair evaluation policies, normalization of overwork and insufficient supervision of early career researchers. Possible interventions suggested by the participants centered around improving support, discussing expectations and improving the quality of supervision. Some of the elements of a responsible research climate identified by participants are reflected in national and international codes of conduct, such as trust and openness. Although it may seem hard to change the research climate, we believe that the realisation that the research climate is suboptimal should provide the impetus for change informed by researchers’ experiences and opinions.

Evaluating susceptibility of Ni-base alloys to solidification cracking by transverse-motion weldability test

The transverse-motion weldability (TMW) test, in which the lower sheet in lap welding is moved at a constant speed V in the transverse direction of welding to induce solidification cracking, was applied to Ni-base alloys. Alloys 600, 625, 718 and 800 were used as example materials for demonstration. The relative susceptibility ranking of the alloys was similar to those reported. A similar ranking was calculated based on |dT/d(fS )1/2| near (fS )1/2 = 1 (T: temperature; fS : fraction solid). Based on the transverse micrograph of the weld and V, the critical local strain rate near the crack was estimated. It was significantly higher for 718 than 800, consistent with the significantly lower susceptibility of 718 shown by the TMW test.

Perceived programmatic and institutional fit for faculty in public administration, policy, and affairs: An empirical study

ABSTRACT This study undertakes an evaluation of candidates and their hireability within programmatic and institutional contexts. The focus is on applicant qualifications, characteristics, and perceived fit within the program and institution. A survey of program contacts for graduate programs in public affairs analyzes candidate hireability. Results indicate clear differences between programs with similar ranking and institutional differences from Carnegie Classifications, but with little clarity on the rationale for the differences. Likewise, findings emerge relating to publishing in discipline-specific academic journals and obtaining a doctorate from online institutions. Discussions focus on implications for programs, institutions, and applicants seeking their first appointment in higher education.

Agreement between ranking metrics in network meta-analysis: an empirical study

ObjectiveTo empirically explore the level of agreement of the treatment hierarchies from different ranking metrics in network meta-analysis (NMA) and to investigate how network characteristics influence the agreement.DesignEmpirical evaluation from...

Comparison of Multi Criteria Decision Making Methods SAW and ARAS: An Application to Performance of Indian Pharmaceutical Companies

While assessing the performances of companies, the decision makers to take not only a single criterion for making the right decisions in to account, but also a number of other relevant criteria that could affect the performance. Because when it is necessary to make the best selection among several option, Multiple-Criteria Decision Making (MCDM) methods are used. This study is to provide insight in to the applicability of method Simple Additive Weightings Method (SAW) and Additive Ratio Assessment (ARAS) method under MCDM techniques to evaluate the performance of Indian Pharmaceutical companies during the study period 2006-2019. The seventeen evaluation criteria’s were used in the application. The constructed model was analysed using both SAW and ARAS method. The study results showed that the best performance belongs to Glico Smith Kline Pharma Limited in SAW method and Sun Pharmaceutical Industries Ltd in ARAS method and worst performance belongs to Ranbaxy Laboratories Limited in both methods. By comparison, both methods revealed the similar rankings of companies during the study period.