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This study aims to investigate the influence mechanism of thermal-mechanical deformations on the CO₂ mixture gases dry gas seal (DGS) flow field and compare the deformation characteristics and sealing performance between two-way and one-way thermal-fluid-solid coupling models.

The authors established a two-way thermal-fluid-solid coupling model by using gas film thickness as the transfer parameter between the fluid and solid domain, and the model was solved using the finite difference method and finite element method. The thermal-mechanical deformations of the sealing rings, the influence of face deformation on the flow field and sealing performance were obtained.

Thermal-mechanical deformations cause a convergent gap between the two sealing end faces, resulting in an increase in the gas film thickness, but a decrease in the gas film temperature and sealing ring temperature. The axial relative deformations of rotating and stationary ring end faces caused by mechanical and thermal loads in the two-way coupling model are less than those in the one-way coupling (OWC) model, and the gas film thickness and leakage rate are larger than those in the OWC model, whereas the gas film stiffness is the opposite.

This paper provides a theoretical support and reference for the operational stability and structural optimization design of CO₂ mixture gases DGS under high-pressure and high-speed operation conditions.

In this paper, a mixture of exponential and Rayleigh distributions in the proportions α and 1 − α and all the parameters in the mixture distribution are estimated based on fuzzy data.

The methods such as maximum likelihood estimation (MLE) and method of moments (MOM) are applied for estimation. Fuzzy data of triangular fuzzy numbers and Gaussian fuzzy numbers for different sample sizes are considered to illustrate the resulting estimation and to compare these methods. In addition to this, the obtained results are compared with existing results for crisp data in the literature.

The application of fuzziness in the data will be very useful to obtain precise results in the presence of vagueness in data. Mean square errors (MSEs) of the resulting estimators are computed using crisp data and fuzzy data. On comparison, in terms of MSEs, it is observed that maximum likelihood estimators perform better than moment estimators.

Classical methods of obtaining estimators of unknown parameters fail to give realistic estimators since these methods assume the data collected to be crisp or exact. Normally, such case of precise data is not always feasible and realistic in practice. Most of them will be incomplete and sometimes expressed in linguistic variables. Such data can be handled by generalizing the classical inference methods using fuzzy set theory.

The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

This paper seeks to address the following two research questions. RQ1: What are the influential user archetypes in the social question-answering (SQA) community? RQ2: To what extent does user feedback affect influential users in changing from one archetype to another?

Based on a sample of 13,840 influential users drawn from the Covid-19 community on Zhihu, the archetypes of influential users were derived from their ongoing participation behavior in the community using the Gaussian mixture model. Additionally, user feedback characteristics such as relevance and volume from 222,965 commenters who contributed 546,344 comments were analyzed using the multinomial logistic regression model to investigate the archetype change of influential users.

Findings suggest that influential users could be clustered into three distinctive archetypes: touch-and-go influential users, proactive influential users and super influential users. Moreover, feedback variables have various impacts on the influential user archetype change, including a shift toward creating higher-quality content and fostering increased interaction, a shift toward generating lower-quality content and decreased interaction but improved speed and having mixed effects due to differences in information processing among these archetypes.

This study expands the existing knowledge of influential users and proposes practical approaches to cultivate them further.

How do informal lending institutions affect entrepreneurship? This paper aims to investigates the role of formal and informal credit market institutions in the decision to become an entrepreneur over the life cycle.

The author developed a dynamic Roy model in which a decision to become an entrepreneur depends on the access to formal and informal credit markets, nonpecuniary benefits of entrepreneurship, career-specific entry costs, prior work experience, education, unobserved abilities and other labor market opportunities (salaried employment and nonemployment). Using detailed Russian panel microdata (the Russia longitudinal monitoring survey) and estimating a structural model of labor market decisions and borrowing options, the author assesses the impact of the development of informal and formal credit institutions.

The expansion of traditional (formal) credit market institutions positively impacts all workers’ categories, reduces the share of entrepreneurs who borrow from informal sources and incentivizes low-type entrepreneurs to switch to salaried employment. The development of the informal credit market reduces the percentage of high-type entrepreneurs who borrow from formal sources. In the case of default, a higher value of the social network or higher costs of losing social ties demotivate low-type entrepreneurs to borrow from informal sources. The author highlights the practical implications of estimates by evaluating policies designed to promote entrepreneurship, such as subsidies and accessibility regulations in credit market institutions.

This study contributes to the literature in several ways. Unlike other studies that focus on individual characteristics in the selection for self-employment [Humphries (2017), Hincapíe (2020), Gendron-Carrier (2021), Dillon and Stanton (2017)], the paper models labor and borrowing decisions jointly. Previous studies discuss transitions between salaried employment and self-employment, taking into account entrepreneurial earnings, wealth, education and age, but do not consider the availability of financial institutions as a driving factor for the selection into self-employment. To the best of the author’s knowledge, this paper shows for the first time that the transition from salaried employment to self-employment is standard and consistent with changes in access to financial institutions. Another feature of this study is incorporating both types of credit markets – formal and informal. The survey by the European Central Bank on the Access to Finance of Enterprises (2018) shows 18% of small and medium enterprise in EU pointed funds from family or friends. Therefore, the exclusion from consideration of informal credit markets may distort the understanding of the role of the accessibility of credit markets.

The purpose of this paper is to investigate the effects of different surface structures, dimensional parameters and cavitation models on the lubrication characteristics of water-lubricated journal bearings.

In this paper, the coupling iteration method of ANSYS and MATLAB is established to calculate the journal orbits of water-lubricated bearing, and the differences between the journal orbits of the smoothed and the textured water-lubricated bearings are compared and analyzed, and the effects of different bearing materials, L/D ratios and clearance ratios on the lubrication performance of water-lubricated bearings are investigated. The effects of different cavitation models on the static equilibrium position and whirling trajectory of water-lubricated bearings are compared.

The results show that when the surface texture is distributed in the upper bearing or the bearing elastic modulus decreases, the bearing stability increases. Considering shear cavitation and noncondensing gas, the rotor journal orbits amplitude decreases at high speed with low clearance ratio. A water film test rig for water-lubricated bearings is built to measure the full-circle water film pressure of water-lubricated journal bearings, and the experimental results are compared with the simulation results, which are in good agreement.

The findings provide a theoretical basis for optimizing the structure of water-lubricated bearings.

Safety professionals' primary job is to execute safety control measures towards frontline personnel, and previous studies focus on the effectiveness of such controls. Rare research efforts, however, have been devoted to the effectiveness of management control measures towards safety professionals themselves. This study aimed to fill up this knowledge gap by examining whether safety professionals under differing management control configurations differ in their work attitudes, including affective commitment, job satisfaction, career commitment and intention to quit.

Drawing on a holistic view of control, five forms of management control, i.e. outcome control, process control, capability control, professional control and reinforcement, were investigated. A cross-sectional questionnaire survey targeting at construction safety professionals was conducted. The latent profile analysis approach was employed to identify how the five forms of management control are configured, i.e. identifying the distinctive patterns of control profiles. The Bolck–Croon–Hagenaars method was then used to examine whether safety professionals' work attitudes were different across the identified control profiles.

Seven distinct control profiles were extracted from the sample of 475 construction safety professionals. The overall test of outcome means showed that mean levels of affective commitment, job satisfaction and intentions to quit were significantly different across the seven profiles. The largest that was also the most desirable subgroup was the high control profile (n = 161, 33.9%). The least desirable subgroups included the low control profile (n = 75, 15.8%) and the low capability and professional control profile (n = 12, 2.5%). Pairwise comparison suggested that capability, professional and process controls were more effective than outcome control and reinforcement.

In theory, this study contributes to the burgeoning literature on how to improve the effectiveness of control measures targeted at safety professionals. The results suggested that effective management controls involve a fine combination of formal, informal, process and output controls. In practice, this study uncovers the ways in which managers leverage the efforts of safety professionals in achieving safety goals. Particularly, it informs managers that the control configurations, instead of isolated controls, should be executed to motivate safety professionals.

Learning organization (LO) concept has received much attention in the last decades. The purpose of an LO is to proactively shape its future by fostering a culture of continuous learning among its members. This approach empowers the organization to adapt, evolve and innovate, aligning with the needs and aspirations of both internal and external stakeholders. As a result, the assessment of an organization’s LO level, whether strong or weak, becomes a matter of significance. This study aims to use Senge’s LO Questionnaire (SLOQ) in conjunction with latent profile analysis (LPA) to identify latent classes within the organization. This approach seeks to enhance the organization‘s learning potential and facilitate its evolution.

This research was a cross-sectional study conducted at Hamadan University of Medical Sciences, Iran. The statistical population consisted of 451 managers and employees across seven departments based at the university headquarters. The sample included 295 individuals, and data was collected using the SLOQ.

The LPA has shown an excellent accuracy (97%) in identifying SLOQ cut-off points (three classes of good, moderate and low). Most of the participants are considered as a moderate LO (55.9%), which implies the possibility of improving learning, especially enhancing “common vision” and “systemic thinking.” Also, individuals belonging to the high-class category were more likely to be male and hold associate degree than individuals in other categories.

This study depends on self-report, and different perceptions of questions can cause percept bias. Another limitation is about the nature of this research, which is cross-sectional, which may bring back causality among variables. The other is only three demographic variables have been considered, and it is necessary to pay attention to other influential variables in future studies.

The originality of this study lies in its use of the SLOQ in combination with LPA to identify latent classes in an organization to improve learning potentials.

New public management (NPM) has transformed the public sector auditing context, although in quite different ways. Further, investigations into NPM’s impact on public sector auditors and audit institutions have been largely unconnected, with the exception of the critical examination of performance audits. We investigate the question of how public sector auditors’ roles and activities have changed as a result of NPM and later reforms.

We examine and synthesise public sector audit research examining reforms since the year 2000. The research presented considers changes to external and internal public sector audits as well as the development of public sector audit institutions – known as supreme audit institutions (SAIs).

Considerable changes have occurred. Many were influenced by NPM, but others have evolved from the eco-system of accounting, auditing and public sector management. External auditors have responded to an increase in demand for accountability. Additional management and governance techniques have been introduced from the private sector, such as internal auditing and audit committees. NPM has also led to conflicting trends, particularly when governments introduced competition to public sector auditing by contracting out but then chose to centralise to improve accountability. There is also greater international influence now through bodies like the International Organisation of Supreme Audit Institutions (INTOSAI) and similar regional bodies.

NPM reforms and the eco-system have impacted public sector auditing. Sustainability reporting is emerging as an area requiring more auditing attention; auditors also need to continue to develop better ways to communicate with citizens. Further, research into auditing in non-Western nations and emerging technologies is also required, especially where it provides learnings around more valuable audit practices. Empirical evidence is required of the strengths and weaknesses of SAIs’ structural variety.

With the rapid advancement of lifestyle and technology, human lives are becoming increasingly threatened. Accidents, exposure to dangerous substances and animal strikes are all possible threats. Human lives are increasingly being harmed as a result of attacks by wild animals. Further investigation into the cases reported revealed that such events can be detected early on. Techniques such as machine learning and deep learning will be used to solve this challenge. The upgraded VGG-16 model with deep learning-based detection is appropriate for such real-time applications because it overcomes the low accuracy and poor real-time performance of traditional detection methods and detects medium- and long-distance objects more accurately. Many organizations use various safety and security measures, particularly CCTV/video surveillance systems, to address physical security concerns. CCTV/video monitoring systems are quite good at visually detecting a range of attacks associated with suspicious behavior on the premises and in the workplace. Many have indeed begun to use automated systems such as video analytics solutions such as motion detection, object/perimeter detection, face recognition and artificial intelligence/machine learning, among others. Anomaly identification can be performed with the data collected from the CCTV cameras. The camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. Many cases have been recorded where wild animals enter public places, causing havoc and damaging lives and property. There are many cases where people have lost their lives to wild attacks. The conventional approach of sifting through images by eye can be expensive and risky. Therefore, an automated wild animal detection system is required to avoid these circumstances.

The proposed system consists of a wild animal detection module, a classifier and an alarm module, for which video frames are fed as input and the output is prediction results. Frames extracted from videos are pre-processed and then delivered to the neural network classifier as filtered frames. The classifier module categorizes the identified animal into one of the several categories. An email or WhatsApp notice is issued to the appropriate authorities or users based on the classifier outcome.

Evaluation metrics are used to assess the quality of a statistical or machine learning model. Any system will include a review of machine learning models or algorithms. A number of evaluation measures can be performed to put a model to the test. Among them are classification accuracy, logarithmic loss, confusion matrix and other metrics. The model must be evaluated using a range of evaluation metrics. This is because a model may perform well when one measurement from one evaluation metric is used but perform poorly when another measurement from another evaluation metric is used. We must utilize evaluation metrics to guarantee that the model is running correctly and optimally.

The output of conv5 3 will be of size 7*7*512 in the ImageNet VGG-16 in Figure 4, which operates on images of size 224*224*3. Therefore, the parameters of fc6 with a flattened input size of 7*7*512 and an output size of 4,096 are 4,096, 7*7*512. With reshaped parameters of dimensions 4,096*7*7*512, the comparable convolutional layer conv6 has a 7*7 kernel size and 4,096 output channels. The parameters of fc7 with an input size of 4,096 (i.e. the output size of fc6) and an output size of 4,096 are 4,096, 4,096. The input can be thought of as a one-of-a-kind image with 4,096 input channels. With reshaped parameters of dimensions 4,096*1*1*4,096, the comparable convolutional layer conv7 has a 1*1 kernel size and 4,096 output channels. It is clear that conv6 has 4,096 filters, each with dimensions 7*7*512, and conv7 has 4,096 filters, each with dimensions 1*1*4,096. These filters are numerous, large and computationally expensive. To remedy this, the authors opt to reduce both their number and the size of each filter by subsampling parameters from the converted convolutional layers. Conv6 will use 1,024 filters, each with dimensions 3*3*512. Therefore, the parameters are subsampled from 4,096*7*7*512 to 1,024*3*3*512. Conv7 will use 1,024 filters, each with dimensions 1*1*1,024. Therefore, the parameters are subsampled from 4,096*1*1*4,096 to 1,024*1*1*1,024.