Understanding the uneven spread of COVID-19 in the context of the global interconnected economy

The worldwide spread of the COVID-19 pandemic is a complex and multivariate process differentiated across countries, and geographical distance is acceptable as a critical determinant of the uneven spreading. Although social connectivity is a defining condition for virus transmission, the network paradigm in the study of the COVID-19 spatio-temporal spread has not been used accordingly. Toward contributing to this demand, this paper uses network analysis to develop a multidimensional methodological framework for understanding the uneven (cross-country) spread of COVID-19 in the context of the globally interconnected economy. The globally interconnected system of tourism mobility is modeled as a complex network and studied within the context of a three-dimensional (3D) conceptual model composed of network connectivity, economic openness, and spatial impedance variables. The analysis reveals two main stages in the temporal spread of COVID-19, defined by the cutting-point of the 44th day from Wuhan. The first describes the outbreak in Asia and North America, the second stage in Europe, South America, and Africa, while the outbreak in Oceania intermediates. The analysis also illustrates that the average node degree exponentially decays as a function of COVID-19 emergence time. This finding implies that the highly connected nodes, in the Global Tourism Network (GTN), are disproportionally earlier infected by the pandemic than the other nodes. Moreover, countries with the same network centrality as China are early infected on average by COVID-19. The paper also finds that network interconnectedness, economic openness, and transport integration are critical determinants in the early global spread of the pandemic, and it reveals that the spatio-temporal patterns of the worldwide spreading of COVID-19 are more a matter of network interconnectivity than of spatial proximity.

Construction and Validation of Social Citizenship Index

Background: Social citizenship means creating a situation in which everyone can develop their full potential. Objectives: This study aimed to determine the social citizenship index with its various dimensions in selected countries. Methods: This study used a mixed-methods approach consisting of two phases. In the first phase, social citizenship items were extracted based on a systematic review of previous studies and interviews with experts using direct content analysis. In the second phase, the standardized index was assessed by performing the validity and reliability tests. To combine the dimensions, their values were standardized using the Z score. To analyze the data, factor analysis and normality tests were used. Results: The social citizenship index was categorized into four main dimensions, including health and education, livelihood, economic-political prosperity, and open society. In this study, 125 countries were categorized based on the Social Citizenship Index. The selected countries were classified into three categories based on the opinions of the research group and the cutting point of statistical quartiles: high (32 countries), medium (62 countries), and low (31 countries). Conclusions: It can be concluded that the social citizenship index with four main dimensions and 26 variables is a new tool that allows countries to be compared in the areas of providing welfare services to their citizens.

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Multi-target recognition and positioning using robots in orchards is a challenging task in modern precision agriculture owing to the presence of complex noise disturbance, including wind disturbance, changing illumination, and branch and leaf shading. To obtain the target information for a bud-cutting robotic operation, we employed a modified deep learning algorithm for the fast and precise recognition of banana fruits, inflorescence axes, and flower buds. Thus, the cutting point on the inflorescence axis was identified using an edge detection algorithm and geometric calculation. We proposed a modified YOLOv3 model based on clustering optimization and clarified the influence of front-lighting and backlighting on the model. Image segmentation and denoising were performed to obtain the edge images of the flower buds and inflorescence axes. The spatial geometry model was constructed on this basis. The center of symmetry and centroid were calculated for the edges of the flower buds. The equation for the position of the inflorescence axis was established, and the cutting point was determined. Experimental results showed that the modified YOLOv3 model based on clustering optimization showed excellent performance with good balance between speed and precision both under front-lighting and backlighting conditions. The total pixel positioning error between the calculated and manually determined optimal cutting point in the flower bud was 4 and 5 pixels under the front-lighting and backlighting conditions, respectively. The percentage of images that met the positioning requirements was 93 and 90%, respectively. The results indicate that the new method can satisfy the real-time operating requirements for the banana bud-cutting robot.

Development of Psychological First Aid Guidelines for People Who Have Experienced Disasters

This study developed guidelines for psychological first aid. This guideline promotes core response and disaster capabilities for disaster mental-health professionals, such as mental-health nurses and counseling psychologists at disaster sites. A research team composed of a first-aid professor and counseling psychology professor developed this psychological first-aid guideline to promote the psychological response required at disaster sites. The team verified each question’s content adequacy at each guideline-development stage to determine the appropriateness of response to a disaster. The PFA performance stage and achievement objectives were moved to the next stage only when the research team fully agreed upon them. This guideline revised and supplemented the six steps suggested in the handbook to five steps through expert meetings. The modified part was made into one step, without separating the first rapport formation and safety check. The checklist for evaluation was developed after verification by a total of four people, including one emergency-rescue-department professor, one counseling psychology professor, one paramedic, and one health educator. Based on previous studies, the cutting point is 24 points. The final completed psychological first aid consists of five stages: rapport formation and safety verification, psychological stabilization, information collection, problem resolution, and recovery, with details to be carried out at each step. These guidelines contribute to the promotion of disaster-response capabilities of disaster psychologists. Continuous training and practical exercises based on the five stages will provide fundamental data for a disaster-simulation psychological-first-aid educational development.

A peduncle detection method of tomato for autonomous harvesting

For automating the harvesting of bunches of tomatoes in a greenhouse, the end-effector needs to reach the exact cutting point and adaptively adjust the pose of peduncles. In this paper, a method is proposed for peduncle cutting point localization and pose estimation. Images captured in real time at a fixed long-distance are detected using the YOLOv4-Tiny detector with a precision of 92.7% and a detection speed of 0.0091 s per frame, then the YOLACT + + Network with mAP of 73.1 and a time speed of 0.109 s per frame is used to segment the close-up distance. The segmented peduncle mask is fitted to the curve using least squares and three key points on the curve are found. Finally, a geometric model is established to estimate the pose of the peduncle with an average error of 4.98° in yaw angle and 4.75° in pitch angle over the 30 sets of tests.

Predicting of job burnout of female nurses based on effort – reward imbalance and characteristics of positive psychology

Introduction: The present study aimed to predict job burnout based on effort-reward imbalance and characteristics of positive psychology among female nurses of Shahid Rahnemoon Hospital of Yazd. Materials and Methods: The research method was descriptive; correlation type. 132 female nurses working in the different parts of Shahid Rahnemoon Hospital in Yazd were selected by random sampling. Three questionnaires (Muslesh & Jackson`s job burnout, Seligman`s positive psychological characteristics scale, and effort-reward imbalance of Seigersit) were completed by nurses. Results: The findings of the one-sample t-test showed that the job burnout level of nurses in the components of emotional exhaustion and depersonalization was moderate, but personal accomplishment is severe and statistically significant. Also, the score of effort-reward imbalance in female nurses is 1.09, which is higher than the cutting point and showed the imbalance of effort-reward in nurses. The situation of the meaning life component and the engagement life component (in the characteristics of positive psychology) among nurses was higher than the average and statistically significant, but the situation of the pleasant life was lower than the average and insignificant. The findings also showed that a positive and significant correlation between effort-reward imbalance with job burnout(r=0.458, p<0.01), as well as a negative and significant correlation between the positive psychological characteristics with job burnout (r=-0.65, p<0.01). In addition, the results of multiple regression analysis showed that 40% of variances of job burnout were predicted by the meaning of life, the effort-reward imbalance, and the engagement life. Conclusion: According to the results of this study, job burnout of nurses is moderate. Serious action should be taken by health policymakers and managers to increase the effort-reward balance improve the characteristics of positive psychology and reduce the burnout of nurses.

Theoretical Derivation of Mean Cutting-Point Space of Grinding Wheel

In a surface grinding process, a successive cutting-point space of grinding wheel affects the maximum abrasive grain depth of cut, which is a major factor affecting grinding characteristics such as the grinding forces and temperature. These characteristics degrade the productivity and machining accuracy. Therefore, we have to clearly define the successive cutting-point space. There are, however, few reports on the derivation method of the theoretical formula since abrasive grains inside the wheel are randomly distributed. This study aimed to theoretically derive the mean cutting-point space and to clarify the successive cutting-point space. We proposed a new derivation method for the mean cutting-point space, which was measured by mapping the diamond wheel surface using an EPMA. The theoretically derived mean cutting-point space was then compared with the measurement results.

A higher cut-off for Thyroid-stimulating immunoglobulin (TSI) could better predict relapse in Graves’ disease?

Background: TSH receptor (TSHr)-stimulating immunoglobulins (Igs) can be used as diagnostic markers of Graves’ disease (GD). Thyroid-stimulating immunoglobulin (TSI) assays exclusively detect these specific Igs. Materials and Methods: This was a prospective longitudinal study in which hyperthyroid patients with GD and toxic nodular goitres were evaluated at diagnosis. GD patients were also evaluated at antithyroid drug (ATD) withdrawal. An automated chemiluminescent assay measured TSI. According to the manufacturer TSI less than 0.55 IU/L was a non-reactive result. The authors evaluated the Se and Sp of the cutoff point provided by the TSI assay manufacturer, and tested other cutting points through a ROC curve, to assess relapse risk of Graves’ disease. Results: At diagnosis, were evaluated 92 (85.2%) GD patients aged 41.2 ± 2.0 years, and 16 patients (14.8%) with toxic multinodular goiter (TMNG) or toxic adenoma (TA), aged 60.8 ± 4.8 years. They were re-evaluated after 18 ± 4 months with methimazole (MMI) treatment. The follow-up after treatment suspension was of 20 ± 6 months. At diagnosis, the TSI (Se) and (Sp) were 98.9% and 100%, respectively. At ATD withdrawal, despite a high Se (95.5%), Sp was low (59.6%). By adjusting the cut-off to 1.11 (TSI <1.11 IU/L non-reactive), TSI presented the best Sp (89.4%) with a small decrease in Se (93.3%) in predicting GD relapse. Conclusions: TSI had high Se and Sp in GD differential diagnosis with nodular goiters. In the assessment for GD relapse, by raising the cutting point to 1.11 IU/L, a better Sp was obtained at the expense of a small drop in Se. A larger sample is needed to support a higher TSI cut-off point in the clinical routine to assess GD relapse after ATD.

Track Behavior and Crash Risk Analysis of Passenger Cars on Hairpin Curves of Two-Lane Mountain Roads

Hairpin curves are often employed in alignment layout and an important feature that identifies dangerous driving conditions for mountain roads. However, driving behaviors at hairpin curves remain ambiguous. Field driving tests were conducted in this study on one two-lane mountain road with 11 hairpin curves. Vehicle-mounted equipment was utilized to collect track and lateral distance between the wheels and the lane markings under naturally driving conditions. Track morphology and patterns, risks, and road crash mechanisms were analyzed. The main findings are as follows. Curve cutting was a typical method for negotiating hairpin curves, was observed for left and right turns, and can be classified into three types based on the location of the cutting point, namely, cutting at curve entry, cutting at curve middle, and cutting at curve exit. Based on the lateral positional relationships between tracks and lane markings, six track patterns are determined for left turns and four track patterns for right turns. When passing a right turn by cutting the curve, a driver occupied the right shoulder of the turn; therefore, there is a risk of colliding with the mountain or the guardrail. When making a left turn into hairpin curves, a driver occupied the right shoulder on curve exit, resulting in running off the road or colliding with the guardrail. More than 70% and 60% of drivers occupied the opposite lane when turning right and turning left, respectively, into a hairpin turn, which led to intertwining between the tracks in the two driving directions and therefore a risk of potential collisions.