Statistical Correlation Research Articles

Mutual information sums and relations with interaction energies in N-particle quantum systems.

The sums of position- and momentum-space mutual information measures are used to examine the pairwise and higher-order statistical correlation in the ground states of N-particle coupled oscillators. Analytical expressions for these measures are shown to be related to the logarithmic interaction energies of these states, plus those of mirror states where the intensities of the one- and two-body potentials are interchanged, and the nature of the attractive or repulsive interaction is opposite to that in the original state. The measures separate the contributions from the interactionsand those from the effective interactions due to marginalization into different terms. The pairwise mutual information sum is linearly related to the Shannon entropy sum in two particle systems, while the total correlation sum exhibits a similar relationship in three particle ones. In the latter instance, the interaction information sum can be related to entropy differences. This illustrates how entropy sums are connected to correlation measure sums in these systems. All measures approach zero with large N, when the magnitudes of the one- and two-body potentials are fixed. The pair mutual information and total correlation sums decay monotonically with N in the presence of an attractive potential and monotonically increase with a repulsive potential. On the other hand, the interaction information sum exhibits a minimum at small N, with an attractive potential. This is a consequence of the higher-order correlations governing behavior at smaller N while the pairwise ones dominate at larger N. Results are presented when the magnitude of the one-body potential is set to N.

Association between tumor size and prognosis in patients with small bowel adenocarcinoma-a SEER-based study

BackgroundThe association between small-bowel adenocarcinoma (SBA) tumor size and prognosis is unclear, and we used the Surveillance, Epidemiology, and End Results (SEER) database to assess the prognostic value of SBA tumor size. MethodsPatients with postoperative SBA were selected from the SEER database, and overall survival (OS) and cancer-specific survival (CSS) were used as outcome variables. Tumor size was used as a categorical and continuous variable, respectively, to adjust for confounders and analyze the association between SBA tumor size and prognosis using Cox proportional hazard regression, and the results were visualized using restricted cubic splines (RCS). Spearman correlation coefficient was used to evaluate the statistical correlation between tumor size and tumor invasion depth (T-stage). Kaplan-Meier survival curves were used to estimate OS at different T stages. ResultsWhen the tumor size was analyzed as a quantitative variable, the adjusted covariate model showed that the HR was 1.008 (P = 0.04) for OS and 1.021 (P = 0.03) for CSS. And regardless of OS or CSS, when the tumor size < 3–4 cm, there was a close linear relationship between tumor size and HR. What's more, in the SEER database, the 5-year survival rates of T1, T2, T3 and T4 patients were 81.8 %, 81.1 %, 66.0 % and 50.9 % (P < 0.001) according to AJCC T-stage. However, in the modified T-stage (mT), these rates were 82.8 %, 70.6 %, 60.7 % and 39.8 % (P < 0.001). When patients within each of the AJCC T stages were stratified by mT stages, significant survival heterogeneity was observed within each of the AJCC T1 to T4 stages(P < 0.001). ConclusionWhen tumor size is used in a quantitative way, tumor size is an independent predictor of poor outcome in patients with SBA. Furthermore, we established a modified T-stage based on tumor size and depth of invasion.

“Learn from Errors”: Post-traumatic growth among second victims

BackgroundSecond victims, defined as healthcare providers enduring emotional and psychological distress after patient safety incidents (PSIs). The potential for positive transformation through these experiences is underexplored but is essential for fostering a culture of error learning and enhancing patient care.ObjectiveTo explore the level and determinants of post-traumatic growth (PTG), applying the stress process model.MethodsThe study was conducted at a tertiary general hospital in Chongqing, China. A descriptive, cross-sectional study design was used. A total of 474 s victims were included. An online survey was conducted in November 2021 to assess various factors related to the second victim experience. These factors included PSIs (considered as stressors), coping styles, perceived threats, and social support (acting as mediators), as well as the outcomes of second victim syndrome (SVS) and PTG. Statistical description, correlation analysis, and structural equation modeling were utilized for the data analysis. A p-value ≤ 0.05 was considered to indicate statistical significance.ResultsThe participants reported moderate distress (SVS = 2.84 ± 0.85) and PTG (2.72 ± 0.85). The total effects on SVS of perceived threat, negative coping, social support, positive coping, and PSIs were 0.387, 0.359, -0.355, -0.220, and 0.115, respectively, accounting for 47% of the variation in SVS. The total effects of social support, positive coping, and PSIs on PTG were 0.355, 0.203, and − 0.053, respectively, accounting for 19% of the variation in PTG.ConclusionsThe study provides novel insights into the complex interplay between perceived threats, coping styles, and social support in facilitating PTG among second victims. By bolstering social support and promoting adaptive coping strategies, the adverse effects of PSIs can be mitigated, transforming them into opportunities for resilience and growth, and offering a fresh perspective on managing PSIs in healthcare settings.

The Influence of Financial Literacy on The Investment Behavior of Indonesian Migrant Workers in Japan

This study aims to analyze the influence of financial literacy on the investment behavior of Indonesian migrant workers in Japan, taking into account factors such as age, education level, and income. The research focuses on 50 Indonesian migrant workers in the Miyazaki area of Japan. Financial literacy is considered a key factor that can affect investment behavior, helping these workers make better investment decisions. A quantitative approach was employed, using descriptive statistical methods and correlation analysis based on questionnaire data that measured respondents' understanding of financial literacy and its impact on their investment behavior. The results show that financial literacy has a positive and significant effect on investment behavior. Higher levels of financial literacy are associated with improved investment decisions. Additionally, age, education, and income also have a significant impact. These findings highlight the importance of developing more effective financial literacy education and training programs for Indonesian migrant workers in Japan, as improved financial literacy can lead to better investment decisions, increased access to investment opportunities, and enhanced financial well-being.

Ethical ChatGPT: Concerns, Challenges, and Commandments

Large language models, e.g., Chat Generative Pre-Trained Transformer (also known as ChatGPT), are currently contributing enormously to making artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a “statistical correlation machine” (correlation instead of causality), and there are indeed ethical concerns associated with the use of AI language models including ChatGPT, such as bias, privacy, and abuse. This paper highlights specific ethical concerns about ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical recommendations for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These best practice examples are expected to motivate the ethical use of ChatGPT.

Determination of Biologically Active Compounds and Antioxidant Capacity In Vitro in Fruit of Small Cranberries (Vaccinium oxycoccos L.) Growing in Natural Habitats in Lithuania.

The composition of flavonols, proanthocyanidins, anthocyanins, triterpene compounds, and chlorogenic acid in small cranberry fruit samples collected in natural habitats in Lithuania and variation in the antioxidant capacity of cranberry fruit extracts was determined. This study showed that in the flavonol group, hyperoside and myricetin-3-O-galactoside predominated in cranberry fruit samples; in the anthocyanin group, the predominant compounds were cyanidin-3-O-galactoside, cyanidin-3-O-arabinoside, peonidin-3-O-galactoside, and peonidin-3-O-arabinoside, and in the group of triterpene compounds, ursolic acid was predominant. The highest total amounts of flavonols and anthocyanins were found in the samples collected in Čepkeliai State Strict Nature Reserve (2079.44 ± 102.99 μg/g and 6993.79 ± 350.22 μg/g, respectively). Cluster analysis of the chemical composition of small cranberry fruit samples revealed trends in the accumulation of bioactive compounds in cranberry fruit. Cranberry fruit samples collected in central Lithuania had higher levels of triterpene compounds. Statistical correlation analysis showed the strongest correlation between the quantitative composition of cyanidin-3-O-arabinoside and peonidin-3-O-arabinoside and the reducing capacity of the ethanolic extracts of the cranberry fruit samples assessed in vitro by the FRAP assay (r = 0.882, p < 0.01 and r = 0.805, p < 0.01, respectively). Summarizing the results, the geographical factor affects the variation of the quantitative composition of biologically active compounds in cranberry fruit samples.

Magnetic Resonance Elastography for Staging Liver Fibrosis in the Oncopig.

This pilot study investigated the feasibility of using magnetic resonance elastography (MRE) for the non-invasive detection and quantification of liver fibrosis in the Oncopig cancer model. Seven 8-week-old Oncopigs underwent alcoholic liver fibrosis induction and serial MRE imaging and liver biopsy at 1, 2, and 3 months post procedure. MRE was utilized to quantify liver stiffness, and liver fibrosis was histologically graded using the METAVIR system. The primary outcome measure was the capability to detect and quantify liver fibrosis using MRE with radiologic-pathologic correlation. Liver fibrosis induction, MRE imaging, and liver biopsy were successfully performed. MRE liver fibrosis was evident in 57% (4/7), 50% (3/6), and 40% (2/5) animal subjects 1, 2, and 3 months after fibrosis induction, with mean liver stiffness of 2.94, 3.25, and 2.91 kPa, respectively. Histological liver fibrosis was noted in 71% (5/7), 100% (5/5), and 100% (5/5) of animal subjects with available tissue samples. There was no significant statistical correlation between the MRE-measured liver stiffness and the METAVIR fibrosis scores. In conclusion, quantifiable liver fibrosis may be induced in the Oncopig. MRE has potential utility in non-invasively detecting liver stiffness in this large-animal preclinical model, though tissue biopsy was more sensitive in demonstrating disease.

Information-based Gradient enhanced Causal Learning Graph Neural Network for fault diagnosis of complex industrial processes

By representing the embedded components and their interactions in industrial systems as nodes and edges in a graph, Graph Neural Networks (GNNs) have achieved outstanding results due to their ability to model statistical correlations. However, these correlations may not capture the true causal relationships within the data, thereby impairing the model’s performance in fault diagnosis.To address this issue, an Information-based Gradient enhanced Causal Learning Graph Neural Network (IGCL-GNN) is proposed for fault diagnosis of complex industrial processes. First, the information theory in graph representations is theoretically analyzed and the optimization objectives are derived separately for the causal and non-causal parts of the graph neural network, which decouple it into a multi-objective optimization problem. Then, to optimize such problem, a causal disentanglement layer is designed in the graph network that could effectively separate causal and non-causal information in graph representations. Thirdly, a novel gradient reactivation method is proposed to dynamically filter features from the disentangled layers, thereby capture the causal representations of graph data more accurately. For robust and efficient optimization, the multi-objective gradient descent algorithm is employed in this paper. Finally, comparative experiments were conducted on the three-phase flow facility (TFF) dataset, achieving a fault diagnosis accuracy of 98.42% for our proposed method.

Influence of Digital Financial Literacy on Investment Behaviour of Nepali Investors

This study investigates the influence of digital financial literacy on individual investment behaviour of Nepali investors. In order to explore the influences of digital financial literacy on individual investment behaviour, causal-comparative research design was used and responses from 358 investors were collected through a survey questionnaire. The convenience sampling method ensured statistical reliability and diversity in perspectives. Questionnaires were distributed through various platforms, ensuring anonymity and confidentiality. Descriptive statistics, regression, reliability and correlation were some of the tools used for a thorough study of the data. These methods enabled non-parametric testing and multiple regressions by providing a quantitative comprehension of the variables. The research establishes that digital financial literacy significantly influences investment behaviour in Nepal. The statistical correlation matrix and regression analysis, with an R Square of 0.589, highlight the interconnectedness of digital financial proficiency, financial literacy, financial goal management and individual investment behaviour. Investment behaviour is significantly influenced by financial literacy, digital financial literacy and financial goal management, having the p-value less than 0.05. However, cyber security and awareness did not show a significant relationship with investment behaviour. Policymakers in Nepal are focusing on enhancing digital financial literacy to empower individuals and cultivate a financially literate society. Financial institutions and educators should leverage digital platforms to enhance financial education and prepare individuals for informed investment decisions in Nepal’s evolving stock market landscape. Examining the digital financial literacy of Nepali investors can help in making more informed decisions regarding investments by looking at structures, comparing findings with those from other developing nations and assessing the effects of educational efforts.

A Correlation Study of Anthropometric Measurements and Body Compositions with Thyroid Hormones in Hypothyroidism Patients

Abstract Introduction: Hypothyroidism is often associated with weight gain. In this study, we assessed the correlation of anthropometric measurements and body compositions such as body mass index (BMI), waist circumference (WC), total fat, visceral fat, and triceps fold with different components of the thyroid profile. Methods: All the cases of clinical and subclinical hypothyroidism were evaluated. The anthropometric measurements were taken using skin calipers and the body composition was assessed using a Dexa scan. The blood samples were collected for the estimation of thyroid hormone levels. A statistical correlation was drawn between the various anthropometric measures and body composition with different components of thyroid profile. Results: The gender-wise comparison study revealed that BMI, total fat %, and visceral fat % were significantly higher in the female gender. Total fat and visceral fat have a direct significant association with BMI (P &gt; 0.005). An increase in age is associated with a rise in the values of triiodothyronine (T3) and thyroxine (T4) (P &gt; 0.05). A positive correlation between thyroid-stimulating hormone (TSH) levels and BMI (r = 0.294), total fat % (0.026), visceral fat % (r = 0.216), and triceps fold measurement (0.004). Conversely, a negative correlation was observed between TSH levels and WC (r = −0.178). There was a negative correlation observed between T4 levels and BMI (r = −0.327), visceral fat percentage (r = −0.152), and triceps fold measurements (r = −0.076). However, a positive correlation was found between T4 levels and WC (r = 0.174) and total fat percentage (r = 0.090). A negative correlation was observed between T3 levels and BMI (r = −0.108), while a positive correlation was observed between T3 levels and all other variables. Conclusion: Age and BMI, but not gender, were found to be significantly associated with thyroid profile. Among anthropometric measurements, the total fat percentage demonstrated a positive correlation with all thyroid hormones, suggesting that the total fat percentage may be a more accurate predictor of thyroid hormone levels than other anthropometric variables alone.

A snapshot audit of global flexible endoscopy practice among European Association of Endoscopic Surgeons (EAES) and Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) surgeons from the EAES Flexible Endoscopy Subcommittee survey.

Endoscopy is an essential skill for all surgeons. However, endoscopic competency, training, and practice may vary widelyamong them. The EAES Flexible Endoscopy Subcommittee is working towards a standardized set of fundamental endoscopic knowledge and skills. To best advise on current practice patterns of flexible endoscopy among surgeons worldwide, a snapshot audit was conducted on the training, use, and limitations of flexible endoscopy in practice. An online survey was distributed via email distribution and social media platforms for EAES, SAGES, and WebSurg members. Respondent demographics, training, and practice patterns were assessed. The main outcome measure was theannual endoscopic volume. Multivariate regression and machine learning models analyzed relationships between outcomes and independent variables of age, geographic region, laparoscopic surgery practice, and surgical specialization. A total of1486 surgeons from 195 countries completed the survey. Respondents were mainly general (n = 894/1486, 60.2%), colorectal (n = 189/1486, 12.7%), bariatric (n = 117/1486, 7.9%), upper gastrointestinal (GI)/foregut (n = 108, 7.3%), hepatobiliopancreatic/HPB (n = 59/1486, 4%), and endocrine surgeons (n = 11/1486, 0.7%) in active practice. Eighty-two percent (n = 1,204) mentioned having used endoscopy in theirpractice, and 64.7% (n = 961/1486) received formal flexible endoscopy training. Of those performing endoscopyannually, 64.2% (n = 660/1486) performed between0and20endoscopies, 15.2% (n = 156/1486) performed between20and50endoscopies, 10.1% (n = 104/1486) performed between50and100endoscopies, and 10.5% (n = 108/1486) performed over 100 endoscopies. From the regression analysis, there was no statistical correlation between the annual endoscopy volume and age, geographic region, laparoscopic surgery practice, or surgical specialization. Performing advanced endoscopy was directly related to thebariatric subspecialty and toperforming over 50% of cases in aminimally invasivefashion. This international snapshot audit revealed significant heterogeneity in endoscopic practices among surgeons worldwide. There wasa nonindependent relationship between endoscopy volumes and other variables tested. Barriers to practicing and receiving endoscopy training were common among respondents. The EAES Flexible Endoscopy Subcommittee will consider such results when developing an equitable and effective standardized flexible endoscopy curriculum.

Exploring How Stereotype Modification Mediates the Relationship between Social Dominance and Multicultural Acceptance.

This research examines the mediating role of stereotype modification in the relationship between social dominance orientation (SDO) and multicultural acceptance in South Korea's increasingly diverse society. We obtained a sample of 402 participants between the ages of 20 and 65 through an online survey. We used SPSS 26.0 for statistical analyses, including frequency, correlation, and regression analyses. Employing Hayes' Model 4, we examined the mediation effect with a bootstrap sample of 10,000 iterations, determining the significance of the effect with a 95% confidence interval (CI). The results revealed nuanced relationships among the variables, shedding light on the complex dynamics of social cognition and intergroup relationships in the South Korean context. The research concludes that individuals with a higher social dominance orientation tend to have lower acceptance of multiculturalism and are more likely to hold prejudiced attitudes toward outgroups. This finding suggests that SDO is a significant factor in integrating and adapting migrants into host societies and can lead to social conflict. The study implies that addressing SDO is crucial for fostering positive attitudes toward multiculturalism and reducing discrimination.

“The correlation between histopathological pattern and surgical treatment for palatal pleomorphic adenoma. Can we choose a more conservative approach?”

Pleomorphic adenoma (PA) is the most prevalent benign salivary gland tumor. Although rare, among the minor salivary glands, palatal PA exhibits the highest incidence. Unlike other benign tumors, PA infiltrates the surrounding tissues, posing challenges for complete removal through conservative measures. Surgeons often resort to aggressive surgical procedures involving resection of adjacent tissue to ensure clear margins and prevent recurrence.This study aims to analyze diverse histological characteristics of palatal PA, seeking statistical correlations for early prediction of tumor aggressiveness. The goal is to facilitate the preservation of the periosteum during surgical resection and attain conservative surgical margins. A retrospective histopathological investigation encompassed 18 patients diagnosed with palatal PA who underwent surgical treatment at Hadassah Medical Centre, Jerusalem, Israel. Evaluated parameters included tumor size, pseudocapsule thickness, tumor-periosteum distance, and the presence of pseudopodia and satellite nodules indicating tumor penetration. Statistical significance was set at P < 0.05.Tumors of varying sizes, whether large or small, lack consistent features. Neither tumor size, pseudocapsule thickness, nor tumor-periosteum distance displayed correlations with tumor penetration features. Palatal PA exhibits varied histological attributes impacting surgical technique. The absence of correlations among these attributes impedes early prediction of tumor aggressiveness, casting doubt on periosteum preservation. The periosteum is sufficiently robust to contain the tumor and should be excised. There is no data to support either ostectomy or a through-and-through surgical resection as part of the treatment.

Deep spatial–spectral difference network with heterogeneous feature mutual learning for sea fog detection

Multispectral remote sensing image-based sea fog detection (SFD) is both important and challenging. Deep learning methods for SFD have become mainstream due to their powerful nonlinear learning capabilities and flexibility. However, existing methods have not fully utilized the physical difference priors in multispectral images (MSI) for SFD, making it difficult to skillfully capture the shape and appearance characteristics of sea fog, thus leading to uncertainties in SFD. We propose the spatial–spectral difference network (S2DNet), a deep encoding–decoding framework that merges inter-spectral and intra-spectral heterogeneous difference features. Specifically, inspired by physics-based difference threshold methods, we developed a physics-inspired inter-spectral difference module (PIDM) that combines feature-level difference with deep neural networks to capture the shape characteristics of sea fog. We designed the intra-spectral difference module (ISDM) using difference convolution to represent sea fog’s fine-grained and dynamic appearance information. Furthermore, inspired by multi-view learning, we propose heterogeneous feature mutual learning (HFML) that seeks robust representations by focusing on semantically invariant aspects within heterogeneous difference features, adapting to the dynamic nature of sea fog. HFML is achieved through global feature mutual learning using an adversarial procedure and local feature mutual learning supported by a novel information-theoretic objective function that links maximizing statistical correlation with expectation maximization. Experiments on two SFD datasets show that integrating physical difference priors into deep learning improves SFD. In both continuous temporal and high spatial resolution SFD tasks, S2DNet outperforms existing advanced deep learning methods. Moreover, S2DNet demonstrates stronger robustness under degraded remote sensing image conditions, highlighting its potential usefulness and practicality in real-world applications.

Pengaruh Motivasi Belajar Mahasiswa PAI UNP melalui Pergaulan

Sociability is a continuation of the process of social interaction that exists between individuals and their social environment, then motivation is one of the things that affects the success of learning activities, without the learning process will not run well. This study aims to determine the effect of association on the learning motivation of PAI students class of 2020 UNP. This research includes quantitative research with the type of correlation and descriptive statistical analysis techniques. The data collection method uses primary data. In this study using validity test and reliability test. There were 60 PAI 2020 UNP students who were sampled in this study. Data data were collected through instrument questionnaires from socialization and learning motivation variables. The results of the data data are processed through statistical calculations and average correlations obtained through the use of SPSS 16.0. The data shows that there is a positive and significant influence between socialization on learning motivation by 59.3%. The results showed that association partially had a positive and significant effect on learning motivation, which means that learning motivation is influenced by association. Researchers recommend that be smart in choosing associations.

Hubungan Kepuasan Tenaga Kesehatan dalam Penggunaan Sistem Informasi Management Puskesmas (Simpus) di Puskesmas Pakuniran Kecamatan Pakuniran Kabupaten Probolinggo

Introduction: Simpus puskesmas management application that simplifies and speeds up services to manage patient information starting from registration, registration, examination (diagnosis) and patient care. The satisfaction of health workers, the response or feedback from users about the use of the system, as well as the assessment of the good and bad sides of the system, is the starting point for system developers to improve the performance of the information system built. Purpose of the study: To determine the relationship between satisfaction of health workers in the use of the puskesmas management information system (Simpus) at the Pakuniran health center. Method: This study uses a quantitative descriptive approach using Nonprobability sampling technique using Total sampling technique using statistical test Spearman's rho correlation, with data collection instruments using questionnaires. This research was conducted at the Pakuniraan health center in July 2023. Research Results: The results of the spearman rank correlation statistical test obtained results namely (p = 0.000) because the p value &lt;0.05 then Ho was rejected and Ha was accepted Conclusion: that there is a relationship between health worker satisfaction in using the puskesmas management information system (SIMPUS), which means that the better and more appropriate the puskesmas management information system, the higher the level of health worker satisfaction.

Correlations between sewage sludge composting physicochemical parameters and emissions of greenhouse gases and ammonia: A statistical analysis

Mitigating the environmental impact of composting by the reduction of greenhouse gases (N2O, CH4) and ammonia (NH3) emissions is a major challenge. To meet this challenge, the understanding of the relationships between composted substrates initial physicochemical parameters and gas emissions is essential. From a long-term perspective, it will allow to guide the recipe formulation of the initial mixture to be composted, with a view to reducing gas emissions during composting. This study gathered literature data targeting sewage sludge composting and performed statistical correlation analyses between cumulative gas emissions and the following parameters: sewage sludge, bulking agent and composted mixture initial physicochemical parameters (pH, dry matter, total carbon, total nitrogen, C/N), the dry mass ratio of sewage sludge to bulking agent and the duration of composting. The average values of cumulative emissions show a large variability: 1.37 ± 2.71 gC.kg initial mix DM−1, 0.13 ± 0.17 gN.kg initial mix DM−1 and 2.23 ± 2.79 gN.kg initial mix DM−1 for CH4, N2O and NH3 emissions respectively. Although the correlation analysis highlighted some significant interesting correlations between initial physicochemical parameters and gas emissions (p.value < 0.05), reliable multiparametric model could not fit the data, meaning that the actual literature data are not sufficient to explain most part of gas emissions. Among the most interesting relationships, the study showed that the dry matter of the composted mixture is negatively correlated to N2O emissions, while the ratio of sewage sludge to bulking agent and the duration of composting are positively correlated to N2O emissions. It was also shown that the pH of the bulking agent is positively correlated to NH3 emissions. Considering the large part of the emission variability that is not explained and the difficulty to link the correlation with their causality, it will be interesting to improve composting gas emissions knowledge in future research by analyzing free air space, bulking agent adsorption capacity and available and biodegradable organic matter. These parameters are of particular interest in solving the main problems associated with sewage sludge composting, namely porosity and nitrogen retention. This study also highlighted the necessity to extend the duration of the composting studies over 40 days in order to measure possible N2O late release and better identify parameters influencing N2O emissions.

Analytical tools for evaluating customer experience in the context of marketing management of service companies

The purpose of this study was to theoretically elaborate the model of formation of customer experience of service organisations at the conceptual level and to conduct a study using the developed tools for its evaluation on the example of the hotel business. The main results were based on the principle of holistic personalism and are obtained using the methods of field marketing research, questionnaire survey of respondents, scoring and index method. The data were processed and analysed using statistical correlation analysis, graphical and matrix approaches. The Customer development methodology was used to form a chain of positive customer reactions. The article substantiates a conceptual model of formation of customer experience in service organizations based on the principles of behavioural theory. The article allocates 2 groups of factors on the example of hotels that form the customer experience: explicit and implicit, and includes 6 factor attributes. Three types of customer experience are distinguished: before the moment of consumption of the product – the expected experience (CX ex en.), at the moment of consumption – as the perceived customer experience (CX at the time), and intentions for future consumption of the service product (CX ex post). To assess them, 4 marketing metrics were proposed to evaluate each type of customer experience. To interpret the results, the values of the indicators are scaled. The carried out testing of the proposed methodology on a sample of 14 four-star hotels confirmed the hypotheses of the study. The proposed theoretical concept and evaluation tools can be further used to develop marketing programmes for Customer Experience Management (CXM)

Influence of initial correlations on evolution over time of an open quantum system

A novel approach to accounting for the influence of initial system–bath correlations on the dynamics of an open quantum system, based on the conventional projection operator technique, is suggested. To avoid the difficulties of treating the initial correlations, the conventional Nakajima–Zwanzig inhomogeneous generalized master equations (GMEs) for a system’s reduced statistical operator and correlation function are exactly converted into the homogeneous GMEs (HGMEs), which take into account the initial correlations in the kernel governing the evolution of these HGMEs. In the second order (Born) approximation in the system–bath interaction, the obtained HGMEs are local in time and valid at all timescales. They are further specialized for a realistic equilibrium Gibbs initial (at t=t0) system+bath state (for a system reduced statistical operator an external force at t>t0 is applied) and then for a bath of oscillators (Boson field). As an example, the evolution of a selected quantum oscillator (a localized mode) interacting with a Boson field (Fano-like model) is considered at different timescales. It is shown explicitly how the initial correlations influence the oscillator evolution process. In particular, it is shown that the equilibrium system’s correlation function acquires at the large timescale the additional constant phase factor conditioned by survived initial system–bath correlations.

Multi-parameterised surface texture characterisation for ultra-precision machined surfaces

Abstract In surface metrology, the multi-parameterised characterisation of surface texture measurement is beneficial not only for surface quality evaluation but also for manufacturing process inspection. To bridge this gap for ultra-precision machined surfaces, a white light interferometer was firstly employed for measuring surface texture generated by orthogonal ultra-precision machining experiments. Next, surface texture was filtered by the zero-order Gaussian regression filter to the limited scale bandwidth. Then, twenty-one surface texture parameters were calculated based on seventy-five S-L surfaces according to the ISO 25178-2. Finally, the outlier effect of surface measurement was investigated by the 95%-99% rule and the Spearman correlation coefficient matrix was proposed to determine their statistical correlation. The results revealed that most of the height parameters (Sp, Sv, Sz, Ssk, and Sku), several function and related parameters (Vmp, Vvv, Spk, and Svk), and the spatial parameter (Str) and hybrid parameter (Sdr) presented a strong sensitivity to the outlier effect. The height parameters (Sa, Sq, Sp, Sv, and Sz), the function and related parameters (Vmp, Vmc, Vvv, Vvc, Spk, Svk, and Sk), and the spatial parameters (Sdq and Sdr) showed a strong correlation to each other, while the miscellaneous parameter Std had a weak correlation to the other parameters. This study provides a systematic multi-parameterised surface texture characterisation for ultra-precision machined surfaces to promote the advancement of nanotechnology and nanometrology.