Number Of Similarity Research Articles

Oncology mental health providers' adaptation of an evidence-based intervention: A mixed-methods study.

Adaptations are intentional modifications maximizing the fit of an evidence-based intervention (EBI) in new context. Little is known about EBI adaptation within psychosocial oncology. Guided by the Framework for Reporting Adaptations and Modifications-Enhanced (FRAME), this mixed-methods study describes oncology mental health providers' planned adaptations to a psychosocial oncology EBI and examines the relationship between planned adaptations and longitudinal EBI usage. Providers (N=128) were social workers (47%) and psychologists (40%) practicing in community settings (44%) or academic medical centers (41%). They attended a 3-day training on a multicomponent psychosocial oncology EBI, the Biobehavioral Intervention (BBI). During training, providers prepared an "adaptation plan" describing necessary adaptations to BBI and rationales for change. Qualitative data from adaptation plans were analyzed using directed content analysis. Linear mixed models examined the relationship between adaptation characteristics (number, similarity to the manualized BBI) and EBI usage across 12months post-training. Three sets of qualitative themes reflecting FRAME elements emerged: (1) content modifications (e.g., shortening/condensing, selecting elements, adding/removing elements); (2) contextual changes (e.g., alternative group formats); and (3) reasons for adaptations (e.g., organization/setting, provider, and recipient factors). Neither number of adaptations nor adaptation similarity were associated with BBI usage across 12months post-training. To our knowledge, this study is the first to characterize oncology mental health providers' planned adaptations to a psychosocial oncology EBI. Planned adaptations did not increase usage, but importantly they did not decrease usage. The adaptation process enabled providers to make thoughtful adaptation choices, with implementation successful irrespective of setting constraints.

A LOCALLY AND GLOBALLY TUNED METAHEURISTIC OPTIMIZATION FOR OVERLAPPING COMMUNITY DETECTION

Many people use online social networks to share their opinions and information in this digital age. The number of people engaged and their dynamic nature pose a major challenge for social network analysis (SNA). Community detection is one of the most critical and fascinating issues in social network analysis. Researchers frequently employ node features and topological structures to recognize important and meaningful performance in order to locate non-overlapping communities. We introduce a locally and globally tuned multi-objective biogeography-based optimization (LGMBBO) technique in this research for detecting overlapping communities based on the number of connections and node similarity. Four real- world online social network datasets were used in the experiment to assess the quality of both overlapping and non-overlapping partitions. As a result, the model generates a set of solutions that have the best topological structure of a network with node properties. The suggested model will increase their productivity and enhance their abilities to identify significant and pertinent communities.

Molecular Evolution of Histone Methylation Modification Families in the Plant Kingdom and Their Genome-Wide Analysis in Barley.

In this study, based on the OneKP database and through comparative genetic analysis, we found that HMT and HDM may originate from Chromista and are highly conserved in green plants, and that during the evolution from algae to land plants, histone methylation modifications gradually became complex and diverse, which is more conducive to the adaptation of plants to complex and variable environments. We also characterized the number of members, genetic similarity, and phylogeny of HMT and HDM families in barley using the barley pangenome and the Tibetan Lasa Goumang genome. The results showed that HMT and HDM were highly conserved in the domestication of barley, but there were some differences in the Lasa Goumang SDG subfamily. Expression analysis showed that HvHMTs and HvHDMs were highly expressed in specific tissues and had complex expression patterns under multiple stress treatments. In summary, the amplification and variation of HMT and HDM facilitate plant adaptation to complex terrestrial environments, while they are highly conserved in barley and play an important role in barley growth and development with abiotic stresses. In brief, our findings provide a novel perspective on the origin and evolutionary history of plant HvHMTs and HvHDMs, and lay a foundation for further investigation of their functions in barley.

Building an Optimal Convolutional Neural Network Model for Solving Complex Character Recognition Problems

The purpose of the study is aimed at developing a lighter architecture of a convolutional neural network model that will cope with the narrowly focused task of recognizing complex characters better than large-scale and well-known ones. As the source data, the characters of the Japanese language are used, consisting of two syllabic alphabets: hiragana and katakana, which are the most complex, since their writing style is characterized by a large number of features and similarity of characters, which greatly complicates the task of their classification and recognition. The author's model of a convolutional neural network is designed in the article, consisting of four convolutional layers, three layers of subdiscretization and three layers of exclusion. The developed model was compared with one of the most popular models of the EfficientNetBO neural network from the point of view of their architecture and the results of work on the same data. To implement its own convolutional neural network model, the classic Keras + Tensorflow bundle was used, since these libraries provide the most convenient tools for working in the field of machine learning. The result of the conducted research is the developed technology of fast and accurate recognition of complex symbols based on a convolutional neural network, which can become the basis of a software product in the field of computer vision.

Using ChatGPT for Writing Articles for Patients' Education for Dermatological Diseases: A Pilot Study.

Patients' education is a vital strategy for understanding a disease by patients and proper management of the condition. Physicians and academicians frequently make customized education materials for their patients. An artificial intelligence (AI)-based writer can help them write an article. Chat Generative Pre-Trained Transformer (ChatGPT) is a conversational language model developed by OpenAI (openai.com). The model can generate human-like responses. We aimed to evaluate the generated text from ChatGPT for its suitability in patients' education. We asked the ChatGPT to list common dermatological diseases. It provided a list of 14 diseases. We used the disease names to converse with the application with disease-specific input (e.g., write a patient education guide on acne). The text was copied for checking the number of words, readability, and text similarity by software. The text's accuracy was checked by a dermatologist following the structure of observed learning outcomes (SOLO) taxonomy. For the readability ease score, we compared the observed value with a score of 30. For the similarity index, we compared the observed value with 15% and tested it with a one-sample t-test. The ChatGPT generated a paragraph of text of 377.43 ± 60.85 words for a patient education guide on skin diseases. The average text reading ease score was 46.94 ± 8.23 (P < 0.0001), and it indicates that this level of text can easily be understood by a high-school student to a newly joined college student. The text similarity index was higher (27.07 ± 11.46%, P = 0.002) than the expected limit of 15%. The text had a "relational" level of accuracy according to the SOLO taxonomy. In its current form, ChatGPT can generate a paragraph of text for patients' educational purposes that can be easily understood. However, the similarity index is high. Hence, doctors should be cautious when using the text generated by ChatGPT and must check for text similarity before using it.

A Novel Snore Detection and Suppression Method for a Flexible Patch With MEMS Microphone and Accelerometer

Sleep apnea impacts more and more people all over the world, and obstructive sleep apnea of which is the most frequent. Hence, research on snoring detection and related suppression methods is extremely urgent. In this article, a novel low-cost flexible patch with MEMS microphone and accelerometer is developed to detect snore event and sleeping posture, and a small vibration motor embedded in the patch is designed to suppress snoring. Theoretical analyses of short-time energy, piecewise average filtering (PAF), and Mel-frequency cepstral coefficients (MFCCs) processing are described in detail, and the improved MFCCs are put forward and used as the input of the convolutional neural network (CNN). Furthermore, the snore recognition method based on the combination of similarity analysis and CNN analysis is presented, followed by the snoring suppression method. Experimental results demonstrate that the main features of the sound signals can be extracted effectively by PAF and MFCCs processing, and the data compression ratio is about 99.41%. Besides, the locations of the eigenvectors can be found accurately based on short-time energy analysis. The numbers of high similarity of snoring signals within 30 s are larger than 3, while those of non-snoring signals are often less than 3. If the preliminary screening with similarity analysis is passed, CNN analysis will be conducted to judge whether there are snoring events. The accuracy of snore recognition with CNN analysis is calculated to be as high as 99.25%. Finally, the average snoring time measured by the smart patch with snoring suppression is reduced to 15 from 135 min, which indicates that the proposed snore recognition and suppression methods are effective.

A Retail Object Classification Method Using Multiple Cameras for Vision-Based Unmanned Kiosks

Several unmanned retail stores have been introduced with the development of sensors, wireless communication, and computer vision technologies. A vision-based kiosk that is only equipped with a vision sensor has significant advantages such as compactness and low implementation cost. Using convolutional neural network (CNN)-based object detectors, the kiosk recognizes an object when a customer picks up a product. In retail object recognition, the key challenge is the limited number of detections and high interclass similarity. In this study, these challenges are addressed by utilizing the “view-specific” feature of an object; specifically, an object class is divided into multiple “view-based” subclasses, and the object detectors are trained using these data. Further, the “view-aware feature” is defined by aggregating subclass detection results from multiple cameras. A superclass classifier predicts a superclass by utilizing an informative subclass detection result that distinguishes the target object from other similar-looking objects. To verify the effectiveness of the proposed approach, a prototype of the vision-based unmanned kiosk system is implemented. Experimental results indicate that the proposed method outperforms the conventional method, even on a state-of-the-art detection network. The dataset used in this study has been subsequently provided in the IEEE DataPort for reproducibility.

Additive impact of multiple point-of-sale tobacco control policies on youth tobacco-related outcomes

ObjectivesThe tobacco industry utilizes tactics to increase youth awareness, exposure, access and use of tobacco. To address these tactics, municipalities in Massachusetts have passed point-of-sale policies including: 1) restricting flavored tobacco (FTR), 2) restricting cigar package sizes and prices (CPPR), 3) banning tobacco in pharmacies (PB), and 4) raising the minimum legal sales age of tobacco to 21 (MLSA 21). This study evaluated whether more policies, and a combination of policies addressing all three industry tactics, are associated with more favorable youth tobacco-related outcomes. Study designThis study was a cross-sectional survey. MethodsMunicipalities were selected based on number of policies and similarity of municipality and tobacco retailer characteristics. The final sample included: Somerville with all four policies, Worcester with two policies (MLSA 21 and PB), and New Bedford with one policy (PB). Surveys were administered to youth in a public high school in each municipality. Multivariable models were used to compare tobacco-related outcomes between municipalities with varying numbers of policies. ResultsAfter adjusting for individual-level demographics, we observed a protective effect of having more policies on flavored tobacco initiation and tobacco exposure and awareness. A protective effect of number of policies on tobacco use was not found, but associations were primarily in the expected direction. Current tobacco users in Somerville had higher odds of menthol use compared to New Bedford. ConclusionsImplementing multiple policies addressing varied industry tactics may be effective for youth tobacco prevention. Including menthol in FTRs may help improve youth tobacco-related outcomes.

Are they really stronger? Comparing effects of semantic variables in speeded deadline and standard picture naming.

Investigations of effects of semantic variables on picture naming have often been inconclusive, with some studies reporting significant and others non-significant effects. One potential explanation may relate to the specific naming tasks used: While most previous studies have used standard picture naming, others have used speeded naming that requires participants to prioritise naming speed over accuracy. Speeded naming has been suggested to cause enhanced effects of item-inherent word characteristics due to disruptions of cognitive control and resulting modulations of responsiveness to input. Consequently, this study investigated whether effects are stronger in speeded compared to standard picture naming, focusing on six feature-based semantic variables: number of semantic features, intercorrelational density, number of near semantic neighbours, semantic similarity, typicality, and distinctiveness. The results showed few differences in the variables' effects between the two naming tasks: In the naming latency analysis, the inhibitory effect of distinctiveness was stronger in the speeded naming task, while in the accuracy analysis the effect of number of semantic features was stronger in the standard naming task. These findings cannot, therefore, be exclusively accounted for by increased responsiveness to input in speeded naming and we discuss possible underlying mechanisms. We conclude that, while some differences in effects of semantic variables between previous studies may have been caused by the specific naming task used, differences between studies more likely depend on statistical power and control of other influential variables in the experiment.

The prospective relationship between loneliness, life satisfaction and psychological distress before and during the COVID-19 pandemic in the UK

AimMental wellbeing in the UK seems to have deteriorated significantly during the COVID-19 pandemic, with the rates of loneliness, life satisfaction and psychological distress taking longer to return to the pre-pandemic levels than elsewhere. Nevertheless, there is little knowledge about the interactions between these outcomes, or the factors that played a role in the rates of change. The current study aims to address this gap by simultaneously investigating changes in loneliness, life satisfaction and psychological distress in the UK from pre-pandemic levels to those between April and November 2020, while critically assessing the role of a range of social ecological influencing factors.Subject and MethodsLongitudinal data from Understanding Society (N=3475) were used to explore the changes in loneliness, life satisfaction and psychological distress from pre-pandemic levels (2017-2019) through November 2020, the interactions between these outcomes, and the role of individual, social, community and geographic factors in the rates of change, using multivariate latent growth curve model.ResultsLoneliness, life satisfaction and psychological distress deteriorated minimally between April and November 2020, compared to the pre-pandemic levels (2017–2019), while the rate of change in each outcome influenced the rates of change in the other two. Key individual (age, gender, physical health), social (number of friends and similarity to them), and environmental (neighbourhood quality) variables influenced baseline scores and the rates of change.ConclusionConsidering significant dynamic associations between loneliness, life satisfaction and psychological distress, we argue that interventions to tackle any one of the outcomes may have beneficial effects on others, while highlighting malleable factors and individual and community-level interventions to tackle loneliness.

Detecting Ottokar II’s 1248–1249 uprising and its instigators in co-witnessing networks

We provide a detailed case study showing how social network analysis allows scholars to detect an event affecting the entire historical network under consideration and identify the responsible actors. We study the middle 13th century in Czech lands, where a rigid political structure of noble families surrounding the monarchs led to the uprising of part of the nobility. Having collected data on approximately 2,400 noblemen from 576 charters, we attempted to uncover social network features pointing to the rebellion and expose the noblemen who joined it. We observed, among other such quantifiable features, assortativity increasing before and resetting to random after the rebellion, a drop in the number of stable connections and subgraph similarity between yearly networks and regional titles (burgraves) rising in centrality above royal court officials in that period. The presented methods can be directly translated to other person-document data of comparable or larger sizes, and we hope it can help detect or disambiguate the timing of similar major events and the roles of people involved in them.

Prospective Study of PET/MRI Tumor Response During Chemoradiotherapy for Patients with Low and Intermediate Risk p16-positive Oropharynx Cancers

<h3>Purpose/Objective(s)</h3> To compare the intratreatment PET/MRI tumor response in patients with low-risk (LR) and intermediate-risk (IR) p16-positive oropharynx cancer during chemoradiotherapy. <h3>Materials/Methods</h3> Patients with non-metastatic, p16-positive oropharynx cancer were treated with definitive chemoradiotherapy. LR disease was defined as cT0-3 and cN0-2b and </= 10 pack-years and IR disease as cT4 or cN2b-3 or > 10 pack-years. Patients underwent a PET/MRI scan pre-treatment and at fraction 10. PET/MRI data used for analysis of the primary tumor and largest node included changes in volume, SUVmax, SUVmedian, SUVpeak, and diffusion. Radiographic differences between LR and IR patients were compared using the standard t-test. Principal component analysis, with K-means and Euclidean distance functions were used for patient clustering. Silhouette width was used to determine the optimal number of clusters similarity of objects within each cluster. Linear regression was performed on all radiographic metrics using patient and disease characteristics. <h3>Results</h3> Twenty-four patients were enrolled with 7 LR and 11 IR patients available for analysis. Cluster analysis defined two cohorts that exhibited a similar intratreatment response. Cohort 1 contained 7 of 7 LR patients and 8 of 11 IR patients. Cohort 2 contained 3 of 11 IR patients. There was a trend for patients in Cohort 2 to be older (mean 68 years ± 5 versus 59 years ± 7; p = 0.057) and have a higher smoking history (mean 30 pack-years ± 40 s.d. versus 12 pack-years ± 17 s.d.; p = 0.2). Cluster 2 exhibited significant differences compared to cluster 1 in the change in primary tumor SUVpeak (0.46 mean ± 0.15 s.d. versus -0.22 mean ± 0.24 s.d.; p < 0.001) and largest lymph node SUVmedian (0.43 mean ± 0. s.d. versus -0.00 mean ± 0.9 s.d.; p = 0.001) after 10 fractions of chemoradiotherapy. <h3>Conclusion</h3> We demonstrate that intermediate-risk p16-positive oropharynx cancers exhibit heterogeneity in their radiographic response to chemoradiotherapy. These data support further study of intratreatment image response as a mechanism for identifying patients with intermediate-risk disease suitable for therapy de-intensification.

Effects of dietary ARA, DHA, and carbohydrates levels on gilthead sea bream liver and intestine oxidative stress, tissue histomorphology, and gut microbiota

The present study aims evaluate the oxidative status, histomorphology of liver and intestine, and intestinal microbiota modulation of gilthead sea bream juveniles (initial body weight = 47.5 g) fed four isoproteic and isolipidic diets (47% protein; 18% lipids) with high or low gelatinized starch levels (20% or 5%; diets HS or LS, respectively) and different dietary ARA or DHA content (2.3/0.3 or 0/2.6, diets ARA or DHA, respectively).ARA-rich diets reduced liver and intestine lipid peroxidation (LPO) circa 12 and 66%, respectively. Dietary ARA also increased liver glutathione redox status (total, reduced (GSH) and oxidized glutathione (GSSG)) circa of 35% but only in the HS diets. Independently of dietary starch level, dietary ARA also reduced GSSG content (80 and 63 nmol g−1 tissue) and oxidative stress index (OSI) (8.7 and 7%) in the intestine. Fish fed DHA diets had increased hepatic superoxide dismutase (SOD) (749.1 and 489.1 U mg protein−1) and glutathione reductase (GR) (3.7 to 1.9 mU mg protein−1) activities. In the intestine, dietary ARA or DHA did not induce alterations in the activity of the antioxidant enzymes. Dietary starch improved liver OSI (1.5%), increased intestinal GSSG (80 and 128 nmol g−1 tissue), and decreased intestinal LPO values (104 nmol MDA g−1 tissue) in fish fed the DHA diets. Fish fed the HS diets had increased hepatic glucose-6-phosphate dehydrogenase (G6PDH) (171.4 and 166 mU mg protein−1), GR (2 and 3.71 mU mg protein−1), and glutathione peroxidase (GPX, only in DHA diets) (63.8 mU mg protein−1) activities, but decreased GR (27.2 and 27.6 mU mg protein−1) and SOD (2089.7 and 2158.8 U mg protein−1) activities in the intestine.Diet composition did not induce major histomorphological alterations in the distal intestine. Nevertheless, the HS diets, especially when combined with DHA, promoted hepatocyte hypertrophy and alterations in nuclei position.Compared to the DHA diets, in fish fed the ARA diets, mucosa bacterial profile had a reduced number of operational taxonomic units (10.3 and 12.0), richness (0.6 and 0.7), and diversity (2.2 and 2.3), and higher similarity between bacterial communities (85.0 and 80.4%).In conclusion, fish fed high dietary ARA presented lower hepatic and intestinal LPO and fewer hepatocyte morphological alterations than fish fed high DHA diets. Furthermore, interactive effects between dietary starch and DHA on liver and intestine GSH metabolism, antioxidant enzymes, LPO susceptibility, and liver histomorphology may be attenuated by a balanced ARA/DHA dietary ratio.

Genome-wide identification and expression profiles of AP2/ERF transcription factor family in mung bean (Vigna radiata L.).

Mung bean (Vigna radiata L. Wilczek) is an economically important grain legume crop in Asia, with high nutritional quality and potential in other parts of the world particularly arid and semiarid regions. Considering the potential adverse effects of drought, high salt, and other abiotic stresses on crop yield, significant efforts have been made to understand the underlying molecular mechanisms of tolerance to these abiotic stresses in legumes. In this study, a total of 186 putative AP2/ERF genes were identified, which were named VrERF1-186. These VrERF genes were classified into four main subfamilies according to the number of AP2 domains and sequence similarity, including 24 AP2 gene members, 81 ERF gene members, 79 DREB gene members, and 2 RAV members. VrERF genes are scattered across all 11 chromosomes and form small gene clusters on chromosomes due to segmental or tandem duplication. Promoter analysis revealed various cis-acting elements related to light, hormones, and stress responsiveness processes. The expression profiles of the VrERF genes in tissues during development and in response to abiotic stresses were assessed by transcriptome sequencing, and the selected reference genes were validated by qRT-PCR. A total of 174 VrERF genes were expressed in at least one of five tissues, while others showed distinct expression patterns in different tissues or under specific abiotic stress treatments, which indicates that VrERF genes are involved in developmental and environmental stress responses in V. radiata. In conclusion, the genome localization, genome-wide characterization, gene duplication, phylogenetic relationships, and expression pattern of VrERF genes in V. radiata were analyzed, and these results will lay the foundation for further functional analysis of these genes and improve stress tolerance to adverse conditions in plants.

Semantic variables both help and hinder word production: Behavioral evidence from picture naming.

This research investigated how word production is influenced by six feature-based semantic variables (number of semantic features, intercorrelational density, number of near semantic neighbors, semantic similarity, typicality, and distinctiveness). We simultaneously investigated effects of the six semantic variables on spoken picture naming in a large group of participants (n = 87), while controlling for other psycholinguistic variables. Across analyses, number of semantic features was the most consistent predictor with a facilitatory effect on naming latency and accuracy. In addition, inhibitory effects were found on naming accuracy for intercorrelational density and on naming latency for distinctiveness. The facilitatory effect of number of semantic features is suggested to stem from stronger semantic activation with an increasing number of semantic features, which results in facilitated selection of the word's lexical representation. In contrast, the inhibitory effect of intercorrelational density is most easily accounted for by increased competition at the lexical level. The mechanism underlying the inhibitory effect of distinctiveness is unclear. These findings indicate that future research on factors affecting word retrieval should also control for effects of number of semantic features, intercorrelational density, and distinctiveness. They also suggest that effects of the other semantic variables (e.g., semantic neighbors) reported in the literature were potentially overestimated due to insufficient control of other semantic and/or psycholinguistic variables. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Signals of resilience and change in tidepool fish communities on the Pacific coast of Vancouver Island, Canada

AbstractAimOcean warming and marine heatwaves have emerged as strong predictors of biodiversity change in coastal systems. Here, we test for systematic change in tidepool fish communities between recent and historical data sets, which include the North Pacific marine heatwave of 2014–2016.LocationPeriodic sampling occurred since 1966 at study site isolated from human activities on the Pacific west coast of Vancouver Island, Canada.MethodsRepeated fish sampling at seven wave‐exposed rocky shore tidepools was conducted on 8 (historical: 1966–1993) and 5 (recent: 2012–2016) occasions during low tide using methods that capture all individuals present (including cryptic and rare species). Species were subsequently identified using standard taxonomic approaches and abundances recorded. These data comprised the basis for analyses of both community‐level and species‐specific responses to test for differences in the historical and recent time periods.ResultsWe did not detect systematic increases or declines in species density, total abundance of fishes within tidepools and community similarity between historical and recent sampling events in high‐intertidal pools. However, fish communities from low‐shore pools decreased in the number of species present, overall abundance and community similarity. Those species lost from the lower pools were non‐resident rare species typical of subtidal environments. By contrast, a warm‐affinity crevice kelpfish was observed only recently. We also found that six species had higher or lower abundances between historical and recent sampling events, and one species occurred at higher vertical heights.Main conclusionFish communities in high‐intertidal pools were resilient between historical and recent sampling periods, and lacked evidence of invasion. However, some signals of community and species changes were evident in the lower shore. Overall, these communities may reach a threshold with ongoing ocean warming and continued monitoring is required to reveal any major changes as they unfold.

Calibration and validation of predicted genomic breeding values in an advanced cycle maize population

Key messageModel training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets.The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.

Energy Analysis of the Propulsion Shaft Fatigue Process in a Rotating Mechanical System Part III Dimensional Analysis

Abstract This article presents the third and last part of the problem of diagnosing the fatigue of marine propulsion shafts in terms of energy with the use of the action function, undertaken by the authors. Even the most perfect physical models of real objects, observed under laboratory conditions and developed based on the results of their research, cannot be useful in diagnostics without properly transferring the obtained results to the scale of the real object. This paper presents the method of using dimensional analyses and the Buckingham theorem (the so-called π theorem) to determine the dimensionless numbers of the dynamic similarity of the physical model of the propulsion shaft and its real ship counterpart, which enable the transfer of the results of the research on the energy processes accompanying the ship propulsion shaft fatigue from the physical model to the real object.

Affix-based Distractor Generation for Tamil Multiple Choice Questions using Neural Word Embedding

Assessment plays an important role in learning and Multiple Choice Questions (MCQs) are quite popular in large-scale evaluations. Technology enabled learning necessitates a smart assessment. Therefore, automatic MCQ generation became increasingly popular in the last two decades. Despite a large amount of research effort, system generated MCQs are not useful in real educational applications. This is because of the inability to produce the diverse and human alike distractors. Distractors are the wrong choices given along with the correct answer (key) to confuse the examinee. Especially, in educational domain (grammar learning) the MCQs deal with affix-based or morphologically transformed distractors. In this paper, we present a method for automatic generation of affix-based distractors for fill-in-the-blanks for learning Tamil Vocabulary. Affix-based distractor generation relies on certain regularities manifest in high dimensional spaces. We investigate the quality of distractors generated by a number of criteria, including Part-Of-Speech, difficulty level, spelling, word co-occurrence, semantic similarity and affixation. We evaluated our proposed method in grammar based Multiple Choice Questions (MCQs) dataset. The result shows that affix-based distractors, yield significantly more plausible outcomes in certain grammar based questions.

Differential Consistency Analysis: Which Similarity Measures can be Applied in Drug Discovery?

Similarity measures are widely used in various areas from taxonomy to cheminformatics. To this end, a large number of similarity and distance measures (or, collectively, comparative measures) have been introduced, with only a few studies directed to revealing their inner relationships. We present a thorough analytical study of the conditions leading to two comparative measures providing equivalent results over a given set of molecules. A key part of this work is the introduction of a novel way to study the consistency between comparative measures: the differential consistency analysis (DCA). This tool reveals how the consistency can be established in an analytical way with minimal (or no) assumptions. We found that the consensus between Tanimoto and the Cosine coefficients improved by choosing a reference whose similarity to the rest of the molecules varies less, or by representing the molecules in a way that does not depend strongly on their size (i. e. bit frequency in the chosen fingerprint representation). The presented derivations are just some generic examples; DCA can be applied widely and for all binary similarity coefficients introduced so far, independently from the molecular representations.