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The purpose of this paper is to propose a grey clustering evaluation model based on analytic hierarchy process (AHP) and interval grey number (IGN) to solve the clustering evaluation problem with IGNs.

First, the centre-point triangular whitenisation weight function with real numbers is built, and then by using interval mean function, the whitenisation weight function is extended to IGNs. The weights of evaluation indexes are determined by AHP. Finally, this model is used to evaluate the flight safety of a Chinese airline. The results indicate that the model is effective and reasonable.

When IGN meets certain conditions, the centre-point triangular whitenisation weight function based on IGN is not multiple-cross and it is normative. It provides a certain standard and basis for obtaining the effective evaluation indexes and determining the scientific evaluation of the grey class.

The traditional grey clustering model is extended to the field of IGN. It can make full use of all the information of the IGN, so the result of the evaluation is more objective and reasonable, which provides supports for solving practical problems.

This paper presents a digital imaging method based on silhouette image analysis to visualize the 3D surface roughness of textile or other flexible materials. A prototype of imaging system consisting of five components was developed: visible lighting source (white backlighting), sample stage, progressive sample feeding equipment, detector (CCD camera) and software. A sequence of silhouette images of textile surface patches could be digitalized progressively when the sample passes through the sharp edge of crossbeam in bending status, so that the whole 3D surface profile could be generated by the combination of silhouette height profile of each surface patch in sequence. This non-destructive testing method is effective for surface roughness characterization or defects detection of flexible materials; it could reconstruct a pure 3D surface profile of textile surface while discarding the information of surface color so as to avoid the fusion of shadows and dark colors, which is impossible to be implemented based on the traditional 2-D image analysis.

The purpose of this paper is to focus on ecological consumption and test the effect of social value orientation on ecological consumption. What is more, this paper explores how Chinese consumers choose between prosocial and non-prosocial products under the influence of Chinese face culture.

The authors assume that social value orientation will change individual’s ecological consumption through the in-group identification, and simultaneously predict that the influence in pro-self and pro-social consumer groups will vary. Furthermore, Chinese face consciousness will moderate the relationship between ecological consumption and social value orientation. Online research and intercept survey are employed to collect data. In total, 600 questionnaires were distributed.

The results indicate that pro-social individuals prefer sustainable consumption than pro-self-individuals, and in-group identification mediates the effect of social value orientation on ecological consumption. Interestingly, pro-self-individuals’ behaviors have changed dramatically by the influence of face consciousness.

The authors discovered that social value orientation has a deep impact on ecological consumption through in-group identification. The authors tested and verified the dominance of Chinese face culture. Besides, four key elements of China’s “face” construct are proposed, namely, holism, synergy, synchronicity and dynamics, which enlarge the horizon of the theory of face.

The purpose of this research is to investigate how generalisable the public service motivation (PSM) observed in Western society is to China and to examine the effects of public service motivation on job satisfaction.

Exploratory factor analysis and confirmatory factor analysis techniques are applied to survey data of 191 public servants in China to investigate the generalisability of Western PSM. Using hierarchical regression analysis, the paper examines the effects of the dimensions of PSM on job satisfaction.

The results show that the public service motivation observed in the West exists in China, but the generalisability of the construct is limited. Three of the four dimensions of public service motivation (attraction to public policy making, commitment to the public interest, and self‐sacrifice) exist in China, but the fourth dimension (compassion) is unconfirmed.

The paper is the first to examine the generalisability and instrumentality of PSM as observed in Western society to China. The results indicate that the public service motivation observed in the West also exists in China, but that the generalisability is limited. Public service motivation emerges from the results as a positively significant predictor of job satisfaction in the public sector of China. It enhances the applicability and meaningfulness of the concept of public service motivation across political and cultural environments.

Provides a brief outline of the Philippines before looking at the values held by the culture and the management style commonly adopted. Outlines personnel management practices with references to short case studies. Concludes that one must first understand the culture in relation to feelings, honour and relationships and the successful company is one which has formed effective relationships with not only customers but employees, suppliers and dealers and also provides staff with development opportunities.

The purpose of this paper is to explore gas/liquid two-phase flow is widely existed in industrial fields, especially in chemical engineering. Electrical resistance tomography (ERT) is considered to be one of the most promising techniques to monitor the transient flow process because of its advantages such as fast respond speed and cross-section imaging. However, maintaining high resolution in space together with low cost is still challenging for two-phase flow imaging because of the ill-conditioning of ERT inverse problem.

In this paper, a sparse reconstruction (SR) method based on the learned dictionary has been proposed for ERT, to accurately monitor the transient flow process of gas/liquid two-phase flow in a pipeline. The high-level representation of the conductivity distributions for typical flow regimes can be extracted based on denoising the deep extreme learning machine (DDELM) model, which is used as prior information for dictionary learning.

The results from simulation and dynamic experiments indicate that the proposed algorithm efficiently improves the quality of reconstructed images as compared to some typical algorithms such as Landweber and SR-discrete fourier transformation/discrete cosine transformation. Furthermore, the SR-DDELM has also used to estimate the important parameters of the chemical process, a case in point is the volume flow rate. Therefore, the SR-DDELM is considered an ideal candidate for online monitor the gas/liquid two-phase flow.

This paper fulfills a novel approach to effectively monitor the gas/liquid two-phase flow in pipelines. One deep learning model and one adaptive dictionary are trained via the same prior conductivity, respectively. The model is used to extract high-level representation. The dictionary is used to represent the features of the flow process. SR and extraction of high-level representation are performed iteratively. The new method can obviously improve the monitoring accuracy and save calculation time.

Prisoners are at greater risk of infectious diseases compared to the general population. While imprisoned, it is often difficult to observe Coronavirus 2019 (COVID-19) prevention strategies such as social distancing. To the authors’ knowledge, no study has been conducted worldwide to examine the condition of female prisoners with COVID-19. Therefore, this study aims to investigate the epidemiological, clinical and imaging characteristics of prisoners with COVID-19 in Ahvaz Women’s Prison (southwest, Iran).

The data for this descriptive cross-sectional study was collected using a checklist including epidemiological information, clinical symptoms, X-ray and computed tomography scan findings of the chest, underlying diseases and the final status of all female prisoners whose COVID-19 test was positive.

This study included 139 female prisoners with COVID-19 with a mean age of 37.19 ± 12.67 years. The most common underlying diseases were hypertension (14.4%), obesity (10.8%) and chronic obstructive pulmonary disease (9.4%). The most common symptoms at the onset of the disease were myalgia (59.0%), cough (41.0%) and dyspnea (37.4%). The most common radiological symptoms were ground-glass opacity (12.9%) and atelectasis (7.2%). In terms of extension of involvement, both lungs were involved in 8.6% of patients. In terms of zonal involvement, the lower lobes were more involved (8.6%). In terms of involvement position, the most common was sub-pleural (10.1%). None of the patients died.

Because the incidence, morbidity and mortality rate of COVID-19 in the prison population are likely to differ from those of the public. This study sought to investigate the situation of prisoners with COVID-19 in Ahvaz Prison, Khuzestan Province. To the best of the authors’ knowledge, this is the first worldwide study in this regard in women’s prisons.

This study aims to examine the relationship between neutrophil to lymphocyte ratio (NLR), handgrip strength (HGS) and nutritional status in non-critically ill patients and outpatients.

A cross-sectional study was conducted on 80 geriatric patients. Nutritional status was evaluated using the Mini Nutritional Assessment Short Form, Geriatric Nutritional Risk Index, some anthropometric and biochemical parameters. NLR was calculated from the complete blood count results. Data were analyzed using SPSS 24.0.

While 38.3% of patients were malnourished or at risk of malnutrition, 61.7% had normal nutritional status. Mini Nutritional Assessment Short Form was not correlated with NLR, whereas it showed a weak positive correlation with HGS. According to the logistic regression analysis, age, HGS, hemoglobin and platelet to lymphocyte ratio were significant independent factors for predicting malnutrition or risk of malnutrition. The receiver operator characteristic curve analysis showed that the optimum HGS cut-off point for patients with malnourished or at risk of malnutrition was 13.2. In conclusion, HGS was associated with the nutritional status. NLR was not associated with nutritional status but associated with nutritional risk.

It is well known that malnutrition is a serious health problem among older adults, and it is important to assess the nutritional status of older adults because of the adverse health effects. In addition, to the best of the authors’ knowledge, this is the first study to determine the relationship between NLR, HGS and nutritional status in non-critically ill patients and outpatients.

This paper aims to propose a suitable atomizing solidification chitosan (CS) gel liquid extrusion molding technology for the three dimensional (3D) printing method, and experiments verify the feasibility of this method.

This paper mainly uses experimental means, combined with theoretical research. The preparation method, solidification forming method and 3D printing method of CS gel solution were studied. The CS gel printing mechanism and printing error sources are analyzed on the basis of the CS gel ink printing results, printing performance with different ratios of components by constructing a gel print prototype, experiments evaluating the CS gel printing technology and the effects of the process parameters on the scaffold formation.

CS printing ink was prepared; the optimal formula was found; the 3 D printing experiment of CS was completed; the optimal printing parameters were obtained; and the reliability of the forming prototype, printing ink and gel printing process was verified, which allowed for the possibility to apply the 3 D printing technology to the manufacturing of a CS gel structure.

This study can provide theoretical and technical support for the potential application of CS 3 D printed gels in tissue engineering.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.