Drawing Kafka’s Castle

We investigated the effects of narrative perspective on mental imagery by comparing responses to an English translation of Franz Kafka’s Das Schloß (The Castle) in the published version (narrated in the third person) versus an earlier (first-person) draft. We analysed participants’ pencil drawings of their imaginative experience for presence/absence of specific features (K. and the castle) and for image entropy (a proxy for image unpredictability). We also used word embeddings to perform cluster analysis of participants’ verbal free-response testimony, generating thematic clusters independently of experimenter expectations. We found no effects of text version on feature presence or overall entropy, but an effect on entropy variance, which was higher in the third-person condition. There was also an effect of text version on free responses: Readers of the third-person version were more likely to use words associated with mood and atmosphere. We offer conclusions on “Kafkaesque” aesthetics, cognitive realism, and the future of experimental literary studies.

Decreased m6A Modification of CD34/CD276(B7-H3) Leads to Immune Escape in Colon Cancer

Previous studies have reported that m6a modification promotes tumor immune escape by affecting tumor microenvironment (TME). Due to the complexity of TME, a single biomarker is insufficient to describe the complex biological characteristics of tumor and its microenvironment. Therefore, it is more meaningful to explore a group of effective biomarkers reflecting different characteristics of cancer to evaluate the biological characteristics of solid tumors. Here, the immune gene CD34/CD276 with different m6A peak was obtained by m6A sequencing (MeRIP-seq) of colon cancer (CRC)clinical samples and combined with MsIgDB database, which was used to perform cluster analysis on TCGA-COAD level 3 data. The CD34/CD276 as a molecular marker for CRC prognosis was confirmed by survival analysis and immunohistochemical assay. Further bioinformatics analysis was carried out to analyze the molecular mechanism of CD34/CD276 affecting the TME through m6a-dependent down-regulation and ultimately promoting immune escape of CRC.

Traffic Sign Detection and Recognition Based on Improved YOLOv4 Algorithm

Traffic sign detection and recognition play an important role in intelligent transportation. In this paper, a traffic sign detection framework based on YOLOv4 is proposed. The original CSPDarkNet53 backbone network model is replaced by RepVGG, and the SPP module is added in the feature pyramid part to improve the expression ability of information. The CCTSDB traffic sign data set is used to detect three categories of indication signs, prohibition signs and warning signs. In order to further improve the performance of YOLOv4 network, K-means++ algorithm was used to perform cluster analysis on the experimental data to determine the size of the priori box suitable for CCTSDB dataset. The experimental results show that the map value of the improved framework is increased by 4.1%, which indicates that the improved YOLOv4 network has a high practical value in traffic sign detection and recognition.

An Interband Registration Method for Hyperspectral Images Based on Adaptive Iterative Clustering

In the context of the problem of image blur and nonlinear reflectance difference between bands in the registration of hyperspectral images, the conventional method has a large registration error and is even unable to complete the registration. This paper proposes a robust and efficient registration algorithm based on iterative clustering for interband registration of hyperspectral images. The algorithm starts by extracting feature points using the scale-invariant feature transform (SIFT) to achieve initial putative matching. Subsequently, feature matching is performed using four-dimensional descriptors based on the geometric, radiometric, and feature properties of the data. An efficient iterative clustering method is proposed to perform cluster analysis on the proposed descriptors and extract the correct matching points. In addition, we use an adaptive strategy to analyze the key parameters and extract values automatically during the iterative process. We designed four experiments to prove that our method solves the problem of blurred image registration and multi-modal registration of hyperspectral images. It has high robustness to multiple scenes, multiple satellites, and multiple transformations, and it is better than other similar feature matching algorithms.

Eldercare in Japan: Cluster Analysis of Daily Time-Use Patterns of Elder Caregivers

Using the data of the 2006 Japanese Survey on Time Use and Leisure Activities, we perform cluster analysis and identify seven unique patterns of daily time-use patterns of co-resident family elder caregivers: (1) ‘Overworkers’, (2) ‘Full-time Workers’, (3) ‘Part-time Workers’, (4) ‘Intensive Caregivers’, (5) ‘Houseworkers’, (6) ‘Leisurely’, and (7) caregivers, who needed medical attention on the diary day (‘Emergency Diaries’). Our results show that the ‘Houseworkers’ and ‘Intensive Caregivers’ spend the most time on adult caregiving activities. Care activities for ‘Houseworkers’ are more likely to coincide with longer housework hours, increasing the total unpaid work volume. The analysis of demographic profiles suggests that similar daily patterns on weekdays and weekends do not belong to people with the same demographic characteristics. For instance, although on weekdays, ‘Leisurely Caregivers’ are mostly represented by the elderly taking care of other elderly, people of any age can belong to this category on weekends. Among all types of caregivers, only 'Intensive Caregivers' are as likely to be men as they can be women, suggesting that when the need for eldercare increases, family caregivers of any gender will step in.

Statistical Methodology for the Definition of Standard Model for Energy Analysis of Residential Buildings in Korea

This study was conducted to propose an optimal methodology for deriving a standard model from existing residential buildings. To strategically improve existing residential buildings, it is necessary to identify standard models that can be used as quantitative standards. In this study, a total of six methods were established for different algorithms in the dimensionality reduction and clustering stage of the data preprocessing stage. In addition, a total of 22,342 households’ data were analyzed, and a total of 26 variables were used to perform cluster analysis. The process of method 6 (data pre-processing, principal components analysis, clustering [K-medoids], verification) was proposed as a way to derive the standard model from the existing Korean housing. The method proposed in this study is capable of deriving a number of standard models considering all variables (n) in a single analysis. The representative building derived in this study contains a lot of building data, so it can be effectively used for planning and research related to buildings on a regional and national scale. In addition, this process can be applied to various buildings to derive representative buildings.

To perceive or not to perceive? Individual differences in perception predict rule induction and response generalization

When novel stimuli trigger a previously learned response, this can be due to failure to perceive the novel stimulus as different from the trained stimulus (perception), or active extrapolation of learned properties from the trained stimulus (induction). To date, there has been little investigation of how individual differences in perceptual ability relate to differences in induction. In this paper, we perform cluster analysis in six datasets (four published datasets and two unpublished datasets, N = 992 total) to examine the relationship between individual differences in perception and induction, as well as the utility of perception in predicting generalization gradients. The datasets were obtained from predictive learning tasks where participants learned associations between different colored cues and the presence or absence of a hypothetical outcome. In these datasets, stimulus perception and response generalization (expectancy ratings) were assessed in separate phases. Using cluster analyses, we identified similar subgroups of good and bad perceivers in all six datasets, with distinct patterns of response generalization between these subgroups. Based on the differences in stimulus perception, we could predict where across the stimulus range generalized responses would differ between subgroups as well as the direction of the difference. Furthermore, participants classified as good perceivers were more likely to report a similarity generalization rule than a relational or linear rule, providing evidence that individual differences in perception predict differences in induction. These findings suggest that greater consideration should be given to inter-individual variability in perception and induction and their relationship in explaining response generalization.

Analysis of Minimum Cost of Supply Chain Emergencies Based on Diffusion Path Processing

In view of the complexity and severity of the impact of supply chain emergencies on enterprise economy, this paper proposes modular processing to improve the design structure matrix (DMS), and the designed clustering algorithm is used to perform cluster analysis of the improved DMS, to predict the possible diffusion path of emergencies, and to establish the critical event diffusion path planning model by designing the critical event diffusion path storage method. As in the case data of a certain type of servo motor of the H Company, after data screening, the diffusion path is classified and stored by analyzing the relationship between each member of the supply chain network. Secondly, the same group of data is put into the method of this paper and other scholars’ to calculate the minimum cost of emergency response in time.

Genomic Comparison of Salmonella Enteritidis Strains Isolated from Laying Hens and Humans in the Abruzzi Region during 2018

Salmonellosis is a major cause of bacterial foodborne infection. Since 2016, an increased number of cases of gastroenteritis caused by Salmonella enterica serovar Enteritidis linked to eggs produced in Poland has been reported in Europe. In Italy, S. Enteritidis is one of the three most commonly reported serotypes, associated mainly with the consumption of contaminated eggs and derived products. In our work, we analysed 61 strains of S. Enteritidis obtained from humans and farms in the Abruzzi region, Italy, in 2018. We used Multiple-Loci Variable-Number Tandem Repeat (VNTR) analysis (MLVA)-based typing and Whole-Genome Sequencing (WGS) tools to identify closely related strains and perform cluster analysis. We found two clusters of genetically similar strains. The first one was present in the local farms and isolated from human cases and had single-linkage distance of no more than two core genes and less than five Single-Nucleotide Polymorphisms (SNPs). The second cluster contained strains isolated from humans and from a dessert (tiramisù) sample that shared identical core genome and were assigned the same SNP address. Cluster 2 isolates were found to be genetically similar to an S. Enteritidis strain from a multi-country outbreak linked to Polish eggs.