Real Relationship Research Articles

Experimental investigation on dynamic mechanical characteristics of casing during hydrate decomposition using depressurization

During marine hydrate production, some potentially undesirable consequences of casing strength failure are prone to occur. However, conventional methods used in casing mechanical analysis is unable to handle casing failure accurately, as the macro constitutive model employed in current simulation could not reflect the real stress-strain relationship of hydrate reservoir. This paper aimed at proposing an experimental method to investigate dynamic mechanical responses of casing during hydrate decomposition based on the self-developed experiment system, in which overlying stress was simulated to construct a similar mechanical environment of casing. By conducting laboratory-scale tests, the effect of overlying stress on casing mechanical behaviors could be revealed, and comparative analysis of casing axial stress obtained from experiment and simulation was carried out. Experimental results indicated that axial stress and extrusion pressure of casing increased 20.91 % and 7.18 % with the increase of overlying stress, respectively. Casing axial stress increased first and then gradually reached stable in different depressurization stage, which agreed well with the results calculated by numerical simulation. The maximum error between experimental data and simulation results was 11.7 %, 11.1 % and 10.2 % in different depressurization stage, respectively. The proposed approach could provide reference for casing safety design of natural gas hydrate production.

Enhancing Psychotherapy Outcomes by Encouraging Patients to Regularly Self-Monitor, Reflect on, and Share Their Affective Responses Toward Their Therapist: Protocol for a Randomized Controlled Trial.

The quality of the therapeutic relationship is pivotal in determining psychotherapy outcomes. However, facilitating patients' self-awareness, reflection on, and sharing of their affective responses toward their therapist remains underexplored as a potential tool for enhancing this relationship and subsequent treatment outcomes. The primary objective of this study is to examine whether and how the patients' regular self-monitoring and self-reflection (fostered by the systematic compilation of a brief postsession battery) on their affective reactions toward the psychotherapist impact the quality of the therapeutic relationship and treatment outcomes in individual psychotherapy. Secondary objectives are to (1) explore whether and how the characteristics of the patient, the therapist, and the process moderate the effect of regular self-monitoring on the therapeutic relationship and outcomes; (2) examine the relationships between the affective response of the patient, the alliance, and the result of the therapy session outcome; and (3) explore how the affective responses of the patient unfold or change throughout the course of the therapy. We conducted a 1:1 randomized controlled trial of adults in individual psychotherapy versus individual psychotherapy plus self-monitoring. Participants will be enrolled through the web-based recruitment platforms "ResearchMatch" and "Research for Me," and data will be collected through web-based surveys. Participants in the control group will receive only their regular individual psychotherapy (treatment as usual) and will not complete postsession questionnaires. Participants in the intervention group will continue their regular individual psychotherapy sessions and complete the "in-Session Patient Affective Reactions Questionnaire" and the "Rift In-Session Questionnaire" following each therapy session in the 10 weeks of the trial. Additionally, after completion of the postsession battery, they will receive general written feedback encouraging them to discuss their feelings and reflections with their therapist. Participants in both groups will complete a comprehensive psychological assessment at baseline, midtrial (week 5), and end-of-trial (week 10). The primary outcome measure of the trial is the "Clinical Outcomes in Routine Evaluation-Outcome Measure," while the secondary outcomes are the "Real Relationship Inventory-Client-Short Form," the "Working Alliance Inventory-Short Revised," and the number of scheduled therapy sessions that the patient has missed or canceled. The trial was approved by the institutional review board of the University of North Carolina at Chapel Hill. Recruitment started in September 2023. A total of 475 individuals completed the baseline assessment. Data collection was completed in February 2024. The results are expected to be published in the autumn of 2024. This study could reveal key information on how regular self-monitoring and introspection can influence both the therapeutic relationship and treatment outcomes. Findings have the potential to shape interventions, enhance the efficacy of psychotherapeutic sessions, and possibly offer a cost-effective strategy for improving patients' well-being. ClinicalTrials.gov NCT06038747; https://classic.clinicaltrials.gov/ct2/show/NCT06038747. DERR1-10.2196/55369.

Popularity of Social Networks Among Armenian Users

Now there is active debate about the influence of virtual (online) communication on real (offline) communication. Do online and offline communications complement each other, or will online communication gradually completely replace real (offline) communication, including in the professional and personal life of a modern person? If people stay at home and go about their daily activities without interacting with other members of society and this suit them, then they will not make efforts to create social connections outside the online sphere, and the field of real (offline) relationships is completely or significantly transformed. The article discusses: the spread of social networks among Armenian users, online and/or offline communication preferences of Armenian users in one or another area of life, depending on age, gender and the subject of communication. As a result of the study, conducted by questionnaire, preferences were identified on the basis of which users build online communication, which determines the purposes of using social networks in Armenia.The research was carried out with the financial support of Yerevan State University according to the scientific project “Mechanisms of dissemination and consumption of fake news in social networks”, 2022-2024. The article was prepared according to the International Academic Mobility Program, organized by the National Research Institute of the Communications Development for young scientists from foreign countries.

The relationship of father's knowledge and support as a breastfeeding father on exclusive breastfeeding in the working area of Public Health Center Batanghari Hospital East Lampung

<p><strong>Background</strong><strong><em>:</em></strong><em> Exclusive breastfeeding coverage in Lampung Province in 2020 was 70.1%, yet it still did not meet the expected target. Many factors influenced the success of exclusive breastfeeding. In providing breast milk, the father played a major role for the success. Father's knowledge and support in breastfeeding issues were the first thing the fathers needed to have to support breastfeeding practices. The fathers' support for the breastfeeding process was still lacking as society believed that breastfeeding was only the mother's duty. The support that a father could provide as a Breastfeeding Father could be in the form of helping with child care, helping with household chores, and providing food or other needs related to the breastfeeding process. Preliminary studies showed a lack of support from fathers for breastfeeding mothers at the Batanghari Lampung Community Health Center</em></p><p><strong><em>Objective</em></strong><strong><em>:</em></strong><em> The purpose of this study was to determine the relationship between the knowledge and the father's support as a breastfeeding father towards exclusive breastfeeding.</em></p><p><strong><em>Method</em></strong><strong><em>:</em></strong><em> This research was conducted in the Work Area of the Batanghari Inpatient Health Center, East Lampung in January - February 2023. The sample used was the fathers of infants aged 6-12 months in the Work Area of the Batanghari Inpatient Health Center, East Lampung, as many as 63 respondents obtained using accidental sampling technique . This research was a quantitative study using the Cross Sectional Study approach, analyzed by using the chi square test (α=0.05) for bivariate analysis.</em></p><p><strong><em>Results</em></strong><strong><em>:</em></strong><em> The results of this study indicates that there was a real relationship between the father's knowledge as a breastfeeding father and the father's support as a breastfeeding father on exclusive breastfeeding in the Work Area of the Batanghari Inpatient Health Center, East Lampung with p-values of 0.038 and 0.010.</em></p><p><strong><em>Conclusions: </em></strong><em>There is a significant relationship between father's knowledge and the father's support as a breastfeeding father and exclusive breastfeeding in the Batanghari Inpatient Health Center, East Lampung working area.</em></p>

Statement of Retraction: Therapist Immediacy: The association with Working Alliance, Real Relationship, Session Quality and Time in Psychotherapy

Statement of Retraction: Therapist Immediacy: The association with Working Alliance, Real Relationship, Session Quality and Time in Psychotherapy

Causality-Driven Intra-class Non-equilibrium Label-Specific Features Learning

In multi-label learning, label-specific feature learning can effectively avoid some ineffectual features that interfere with the classification performance of the model. However, most of the existing label-specific feature learning algorithms improve the performance of the model for classification by constraining the solution space through label correlation. The non-equilibrium of the label distribution not only leads to some spurious correlations mixed in with the calculated label correlations but also diminishes the performance of the classification model. Causal learning can improve the classification performance and robustness of the model by capturing real causal relationships from limited data. Based on this, this paper proposes a causality-driven intra-class non-equilibrium label-specific features learning, named CNSF. Firstly, the causal relationship between the labels is learned by the Peter-Clark algorithm. Secondly, the label density of all instances is calculated by the intra-class non-equilibrium method, which is used to relieve the non-equilibrium distribution of original labels. Then, the correlation of the density matrix is calculated using cosine similarity and combined with causality to construct the causal density correlation matrix, to solve the problem of spurious correlation mixed in the label correlation obtained by traditional methods. Finally, the causal density correlation matrix is used to induce label-specific feature learning. Compared with eight state-of-the-art multi-label algorithms on thirteen datasets, the experimental results prove the reasonability and effectiveness of the algorithms in this paper.

Development and validation of an 8-item version of the Real Relationship Inventory–Client form

Objective: To develop and validate a very brief version of the 24-item Real Relationship Inventory–Client (RRI-C) form. Method: Two independent samples of individual psychotherapy patients (Nsample 1 = 700, Nsample 2 = 434) completed the RRI-C along with other measures. Psychometric scale shortening involved exploratory factor analysis, item response theory analysis, confirmatory factor analysis (CFA), and multigroup CFA. Reliability and convergent and discriminant validity of the scale and subscales were also assessed. Results: The 8-item RRI-C (RRI-C-SF) preserves the two-factor structure: Genuineness (k = 4, α = .86) and Realism (k = 4, α = .87), which were correlated at r = .74. CFA provided the following fit indices for the bifactor model: X 2/df = 2.16, CFI = .99, TLI = .96, RMSEA = .07, and SRMR = .03. Multigroup CFA showed that the RRI-C-SF was invariant across in-person and remote session formats. The RRI-C-SF demonstrated high reliability (α = .91); high correlation with the full-length scale (r = .96); and excellent convergent and discriminant validity with measures of other elements of the therapeutic relationship, personality characteristics, current mental health state, and demographic-clinical variables. Clinical change benchmarks were calculated to serve as valuable tools for both research and clinical practice. Conclusion: The RRI-C-SF is a reliable measure that can be used for both research and clinical purposes. It enables a nuanced assessment of the genuineness and the realism dimensions of the real relationship.

Relationship between Knowledge Level and Dietary Adherence in Patients with Type II Diabetes Mellitus at Pajang Health Center Surakarta

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>Deficiency in glucose metabolism due to absolute or relative insulin deficiency leads to increased blood sugar levels beyond the established normal range; this condition is characterized by a collection of symptoms called diabetes mellitus. Dietary adherence in individuals with type II diabetes mellitus can be seen from their level of knowledge. The research aims to determine the relationship between the level of knowledge and dietary compliance in people with type II diabetes mellitus. This research is a quantitative research with a cross-sectional approach. This research was conducted in the Pajang Surakarta Community Health Center area on all Type II Diabetes Mellitus sufferers and was carried out in November 2023. The research population was all Diabetes Mellitus patients at the Pajang Surakarta Community Health Center, as many as 148 people. The research sampling technique was Consecutive Sampling, so the sample obtained was 60. Data was collected using questionnaires and interviews. Analysis of research data with bivariate analysis using the Chi-Square test. The research results show a real relationship between the knowledge level and diet adherence with a p-value = 0.040. The level of knowledge about Diabetes can influence adherence to a diabetes diet because patients need good knowledge regarding recognizing the signs and symptoms of DM, the risk of complications occurring, and care in treating Diabetes.</em> <em>Therefore, the role of health workers is needed to hold outreach activities to provide knowledge about diabetes mellitus, starting from the definition, signs and symptoms, risk factors, prevention methods and complications that occur by providing regular and scheduled counselling.</em><em></em></p><p> </p></td></tr></tbody></table></div><strong><em>Keywords: Dietary Adherence, Diabetes Mellitus, Knowledge</em></strong>

Revelation of Specific Ferocity Genes in Escherichia coli Isolated from Patient with Urinary Tract Infections (UTIs)

Background: The purpose of this research is to detect the occurrence of ferocity genes in uropathogenic E. coli that isolated from urinary tract infections by Polymerase chain reaction technique. Method: A whole of five strains of E.coli were obtained from patient with urinary tract infections. Identification of E.coli was depended on colonial morphology in culture media, gram stain, and conventional biochemical tests. ferocity factors genes (cnf-1, hly,urea A and ndv B) of E.coli strain were revelated by molecular technique. Results: ferocity genes were detected successfully by molecular technique in five isolates of E.coli, the predominance of genes in isolates are seen in percentage 100% Conclusion: Urinary tract infections still the major universal infection in the worldwide. The one of the most prevalent infectious diseases induce renal failure in patients is UTI that generated by UPEC. The presence of ferocity factors are critical features where the grade of pathogenicity of UPEC strains relied on it. This study indicate that was a real relationship between the ferocity factor genes of E.coli and clinical symptoms of UTI.

The popular gene of hot event: A hot event propagation mechanism based on information coupling and information energy interaction

Recently, the research on the propagation of hot events has received widespread attention. By analyzing the data of hot events and the data of the common events in the same period on the network, we found that hot events usually break out quickly and opinion leaders and cluster behaviors exist in their propagation process. At the same time, the media public opinion fields of reporting hot events overlap and promote each other. Based on the common factors that drive an event to become a hot event, we used the heat calculation formula and entropy method to put forward the propagation model of hot events based on information coupling and information energy interaction. In the model, the coupling values of different event information are quantified based on the information fragment coupling effect. The heat calculation formula is used to dynamically adjust the propagation probability of different individuals in the propagation process of hot event, and the sensational effect threshold is introduced based on the overall energy value of the event. Finally, we used the real social relationship networks to simulate the evolution propagation process of the hot events, and compared it with the crawling real propagation curve of the events. The proposed model provides a good supplement for the study of the hot events.

Metropolitan Marvels : To Forge Seamless Possibilities for Urban Discoverability and Connectivity

Embodies a particular approach with the devising set out to enrich the quality of the city living experience incorporating technology and real human relationships in applying information in the city. The research journey is going to trace dynamic pathways underpinning in software development through powerful tools like Android Studio among others that helps in designing simple and user-friendly interfaces that easily adjust themselves to the diversity of the residents who live in cities and visitors come to the city. It now focuses to build proper links of implementation not on the technological strides but only by increasing the spotlight. Reliability as well as availability is ensuring that hustling has a strong back-end that would handle the data smoothly by the use of applications like Firebase and Room Persistence. Added to it, the approach makes use of offline capabilities as well part of validation of strict security precursors for performing through real-user trials while designing the likes of the application in conformance to the changing user preferences of the urban communities. This methodology visualizes transformative fostering of people-centric cities in successfully intricacy technology by richness of human experiences within innovative city information application, and elevated urbanlifestyles.

Spatiotemporal correlation based self-adaptive pose estimation in complex scenes

In the current digital era, large-scale Artificial Intelligence (AI) models have fundamentally changed the landscape of the AI field. These models excel at extracting complex patterns and information from massive datasets, making them indispensable in a wide range of domains. With the advancement of large-scale AI models, pose estimation has achieved improved performance in managing large data sets and complex computations. However, most existing pose estimation methods suffer from the problem of inaccurate pose graph extraction in complex scenes. Moreover, the existing methods all focus on the message, ignoring the fact that it may not be optimal for the graph structure. This fixed graph structure is difficult to reflect the real dependency relationship between joint points. This study proposes a Character Redetection Model (CRM) to dynamically adjust the foreground extraction strategies to solve these problems. The CRM locates the target position based on the rich spatiotemporal information between the adjacent frames, effectively solving the problem of inaccurate foreground acquisition caused by occlusion and blur in complex scenes. In addition, an Uncertainty Graph Structure (UGS) is designed, which adaptively learns the topological structure of graphs with different Graph Convolutional Network (GCN) layers and skeleton samples in an end-to-end manner and mines the hidden space dependency between data to accurately model the different poses of the characters. The experimental results show that the proposed method achieves a superior performance compared to that of the State-Of-The-Art (SOTA) method, with a Percentage of Correct Keypoints (PCK) value of 94.6% at the Max Planck Institute for Informatics (MPII), along with the mean Average Precision (mAP) values of 83.3% and 82.1% on the PoseTrack2017 and classroom pose datasets, respectively.

Multivariate assumptions and effect of model parameters in path analysis in oat crop

Context Path analysis (PA) is a widely used multivariate statistical technique. When performing PA, the effects of the parameters of the mathematical model relating to the experimental design are disregarded, working only with the average effects of the treatments. Aims We aimed to analyse the implications of statistical assumptions, and of removing mathematical model parameters, on the PA results in oat. Methods A field study was conducted in southern Brazil in five crop years. The experimental design employed was a two-factor 22 × 5 randomised complete block design, characterised by 22 cultivars and five fungicide applications, with three repetitions. Six explanatory variables were measured, panicle length, panicle dry mass, panicle spikelet number, panicle grain number, panicle grain dry mass, and harvest index, and the primary variable yield. Initially, normality and multicollinearity diagnoses were carried out and correlation coefficients were calculated. The PA was performed in three ways: traditional, with measures to address multicollinearity (ridge), and traditional with eliminating variables. Key results and conclusions The occurrence of multicollinearity resulted in obtaining path coefficients without biological application. Removing the model’s parameters modifies the path coefficients, with average changes of 10.5% and 13.3% in the direction, and 24.7% and 23.0% in the magnitude, of the direct and indirect effects, respectively. Implications This new approach makes it possible to remove the influences of treatments and experimental design from observations and, consequently, from path coefficients and their interpretations. Therefore, the researcher will reduce possible bias in the coefficient estimates, highlighting the real relationship between the variables, and making the results and interpretations more reliable.

The Effectiveness of English in Supporting Communication with Customers to Provide the Best Service in the World of Work

This research aims to determine the description of workers' ability to speak English, English is needed to communicate via online media/internet both with fellow actors and with consumers consisting of domestic consumers and tourists. The total sample technique used in this research was 70 people. The description of the characteristics of MSME actors consists of age, education, length of business, and media ownership, while the characteristics of MSMEs are presented based on micro, small and medium business scale. English language skills are measured based on the ability to read, understand and interpret messages on social media. Based on the formulation of research objectives and the results of data analysis tests, it can be concluded that the description of the characteristics of MSME actors mostly includes those of productive age (80%), most of the respondents are male (40%), the education of MSME actors is dominated by undergraduate graduates (60%), and has Most have more than 5 years of experience and most have more than 1(77.14%) media experience. The characteristics of business scale include small scale (50%), only 1.43% of medium scale ones were in the research sample. The description of the English language skills of MSME players is quite good with an average score of 3.32, while the ability to use media has a better average score than English language skills, namely 3.42. The results of the correlational analysis show that there is a very real positive relationship between English language ability and media use ability, which is expressed by an r value of 0.993, meaning that the higher the English language ability, the higher the media use ability.

HUBUNGAN KELENGKAPAN DOKUMEN REKAM MEDIS DENGAN AKURASI CODING ICD -10 DI RUMAH SAKIT ISLAM MASYITOH BANGIL

One of the authorities of medical recorders is to carry out a system of clinical classification and codification of diseases and must refer to regulations that have been issued by WHO. The purpose of this study was to analyze the factors that affect the accuracy of coding outpatient medical record documents at RSI Masyitoh Bangil. The independent variable in this study is the completeness of medical record documents consisting of patient identity, history, diagnosis, physical examination, treatment and action, supporting examinations, service plans. The type of research used is quantitative research, using bivariate analysis. The results and conclusions obtained there is a clearly proven relationship between the accuracy of ICD 10 outpatient document codification with physical examination at p value = 0.302. And there is a relationship between the accuracy of ICD 10 codefication of outpatient documents with supporting examinations with results with p value = 0.002. And there is a real relationship between the accuracy of ICD 10 codefication of outpatient documents with treatment and action with a p value = 0.006. The results of statistical tests with a Sig (P-Value) value of <0.25 in bivariate chi square analysis can enter the multivariate analysis stage.

God, Religion and History: The Significance of Schelling’s Philosophy of Religion for Determining the Concept of Religion

This article discusses Schelling’s contribution to the definition of the concept of religion in post-Kantian philosophy. In three lines of argument, it is shown that Schelling’s late lectures on the philosophy of mythology and revelation place religion in a history of development in which religion is successively understood as religion. Schelling assumes that religion is independent of reason and is based on a real relationship with God that is connected to the nature of man. This makes the philosophy of religion an independent academic discipline. Schelling links the historical development of religion and the history of God in his concept of monotheism. This is the content of Schelling’s formula that God is the Lord of being.

Динамика и структура смысловых базовых установок кыргызстанцев

This article presents a study of the meaningful basic sets dynamics (hereinafter referred to as MBS) among Kyrgyzstanis, as well as an attempt to determine the structure of MBS. In this paper, we rely on the level theory of the set was offered by A. G. Asmolov. Shifts in meaningful sets are always determined by changes in a person's real life relationship with social reality. The purpose of the study is to examine the dynamics and structure of the Kyrgyz people's MBS. The study is longitudinal in nature and covers the period from 2010 to 2021. It involved men and women who were on average 15 years old in 2010 and 26 years old in 2021. The main methodology was the questionnaire “Meaningful basic sets” (adaptation by A. D. Ishkov and N. G. Miloradova of the questionnaire “Dysfunctionality Scale” by D. Burns). It was revealed that the dynamics of MBS occurs in both male and female samples. Statistically significant differences were found depending on achievement, self-demanding behavior, and responsibility for others. The structure of MBS changes over time due to the loss of connections between MBS, which can speak about the formation of relative independence of MBS in the meaning sphere of personality. The prospects for this study are: expansion of the methodological tools, recruitment of an additional sample, for example, Russians, as well as inclusion in the analysis of data obtained by non-standardized methods.

The Causal Relationship Between Gastroesophageal Reflux Disease and Chronic Obstructive Pulmonary Disease: A Bidirectional Two-Sample Mendelian Randomization Study.

Gastroesophageal reflux disease (GERD) and Chronic Obstructive Pulmonary Disease (COPD) often coexist and have been associated in observational studies. However, the real potential causal relationship between GERD and COPD is unknown and not well established. In this study, we conducted a bidirectional two-sample Mendelian randomization(MR) to estimate whether GERD and COPD are causal. The GERD genetic data is from summary level data of a genome-wide association (GWAS) meta-analysis (Ncases = 71,522, Ncontrol=26,079). The COPD GWAS are available from the FinnGen (Ncases=16,410, Ncontrol=283,589). MR-Egger regression, Weighted Median, and Inverse-variance weighted (IVW) were used for MR analysis from the R package "TwoSampleMR", and IVW was the dominant estimation method. Additionally, the MR pleiotropy residual sum and outlier (MR-PRESSO), Cochran Q statistic, and leave-one-out analysis were used to detect and correct for the effect of heterogeneity and horizontal pleiotropy. MR analysis indicated that GERD was causally associated with an increased risk of COPD (IVW odds ratio (OR): 1.3760, 95% confidence interval (CI): 1.1565-1.6371, P=0.0003), and vice versa (IVW OR: 1.1728, 95% CI:1.0613-1.2961, P=0.0018). The analyses did not reveal any pleiotropy or heterogeneity. Our study revealed possible evidence for a bidirectional causal relationship between GERD and COPD. Implementing screening and preventive strategies for GERD in individuals with COPD, and vice versa, will be crucial in future healthcare management. Further studies are needed to elucidate the mechanisms underlying the causal relationship between GERD and COPD.

A Cooperative Silicon Content Dynamic Prediction Method With Variable Time Delay Estimation in the Blast Furnace Ironmaking Process

Online assessment of the molten iron quality of a blast furnace ironmaking process strongly depends on reliable measurement of silicon content (Si). This article proposes a novel cooperative strategy to train the data-driven prediction model and estimate variable time delay (VTD) values. First, a dynamic deep network that stacks denoising autoencoders (D-SDAE) with the ability to describe process nonlinearity and time-varying is designed for Si estimation. Then, time delay between the process variables and Si, which may disrupt the data distribution pattern (i.e., the real input-output relationship), is recovered in the modeling process. It is an overlooked data feature that occurs due to material transportation times, installation location and the analysis period of sampling equipment. The VTD values are considered to be the parameters of the D-SDAE model and obtained in the training process by the proposed double-scale collaborative search particle swarm optimization algorithm. The effectiveness of the proposed VTD-based D-SDAE model is validated in a numerical simulation and an industrial ironmaking plant, and a marked improvement in the prediction performance is achieved when VTD information is considered.

Pick-and-Place Transform Learning for Fast Multi-View Clustering.

To manipulate large-scale data, anchor-based multi-view clustering methods have grown in popularity owing to their linear complexity in terms of the number of samples. However, these existing approaches pay less attention to two aspects. 1) They target at learning a shared affinity matrix by using the local information from every single view, yet ignoring the global information from all views, which may weaken the ability to capture complementary information. 2) They do not consider the removal of feature redundancy, which may affect the ability to depict the real sample relationships. To this end, we propose a novel fast multi-view clustering method via pick-and-place transform learning named PPTL, which could capture insightful global features to characterize the sample relationships quickly. Specifically, PPTL first concatenates all the views along the feature direction to produce a global matrix. Considering the redundancy of the global matrix, we design a pick-and-place transform with ℓ2,p-norm regularization to abandon the poor features and consequently construct a compact global representation matrix. Thus, by conducting anchor-based subspace clustering on the compact global representation matrix, PPTL can learn a consensus skinny affinity matrix with a discriminative clustering structure. Numerous experiments performed on small-scale to large-scale datasets demonstrate that our method is not only faster but also achieves superior clustering performance over state-of-the-art methods across a majority of the datasets.