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BackgroundMost of the studies on grading and classification of occupational health compre-hensive risks for specific employers still remain in the establishment and description of methodology, and practical application studies are rarely reported. ObjectiveTo explore the application of an occupational disease hazards comprehensive risk assessment method issued by the National Disease Control and Prevention Bureau in conjunction with the National Health Commission and a self-developed occupational health grading and classification method in petroleum refining enterprises, and to provide practical experience for the implementation of differentiated law enforcement by relevant regulatory authorities. MethodsTwo occupational health grading and classification methods were practiced in three petroleum refining enterprises in Guangdong Province. The occupational hazards comprehensive risk assessment method was provided by the Notice on Carrying out Pilot Work of Occupational Health Classification Supervision and Law Enforcement of the National Disease Control and Prevention Bureau and the National Health Commission. The principle was to derive the occupational health risk level according to nature of occupational hazards, exposure level, and number of workers exposed to them in an employer, and then to classify them into Class A, Class B, and Class C by combining with local occupational health management status level. The occupational health grading and classification method was self-developed according to available domestic and foreign occupational health risk assessment methods. Its principle was to calculate the risk level of each workstation in an employer based on published carcinogenicity classification or LD50/LC50 of chemical toxicants, level of noise, exposure parameters such as exposure level and exposure frequency, estimate the comprehensive risk Ro of the target employer by the Romanian comprehensive risk calculation method, and then calculate a comprehensive risk Ro' weighted by the occupational health management index of the target employer and classify it into class A, class B, and class C. Finally, assessment results, scope of application, inquired indicators, advantages,disadvantages and professional competence requirements of the two grading and classification methods were compared. ResultsThe occupational hazards that were evaluated in three enterprises in this study were benzene, toluene, xylene, gasoline, hydrogen sulfide, and noise. The exposure levels of benzene, toluene, xylene, and gasoline were all below 10% OEL (occupational exposure limit), and hydrogen sulfide and noise were disqualified. Occupational hazards such as benzene and hydrogen sulfide were serious occupational hazards in the three enterprises, and the number of workers exposed was 461, 912, and 224, respectively; the HRs (hazard ratings) of benzene, toluene, xylene, gasoline, hydrogen sulfide, and noise were level 5, 3, 2, 3, 5, and 3 respectively. The occupational health management status of the three enterprises was graded as B, A, and B, respectively by the occupational disease hazards comprehensive risk assessment method. The occupational health management index of the three enterprises was graded as B, A, and A, respectively by the occupational health grading and classification method. The comprehensive risk assessment results showed that two enterprises classified into as the highest class C and one into class B by the occupational disease hazards comprehensive risk assessment method, while all three enterprises were classified into class B by the occupational health grading and classification method. ConclusionThe two grading and classification methods are not consistent in the evaluation results of petroleum refining enterprises, and there are differences in the evaluation scope, indicators to be collected, and professionalism. It is recommended that occupational health regulators should fully consider the advantages and disadvantages of the two methods and choose the appropriate assessment method according to the actual regulatory purpose.

BackgroundOccupational hazards such as solvents and noise in the electronics industry are serious. Although various occupational health risk assessment models have been applied in the electronics industry, they have only been used to assess the risks of individual job positions. Few existing studies have focused on the total risk level of critical risk factors in enterprises.MethodsTen electronics enterprises were selected for this study. Information, air samples and physical factor measurements were collected from the selected enterprises through on-site investigation, and then the data were collated and samples were tested according to the requirements of Chinese standards. The Occupational Health Risk Classification and Assessment Model (referred to as the Classification Model), the Occupational Health Risk Grading and Assessment Model (referred to as the Grading Model), and the Occupational Disease Hazard Evaluation Model were used to assess the risks of the enterprises. The correlations and differences between the three models were analyzed, and the results of the models were validated by the average risk level of all of the hazard factors.ResultsHazards with concentrations exceeding the Chinese occupational exposure limits (OELs) were methylene chloride, 1,2-dichloroethane, and noise. The exposure time of workers ranged from 1 to 11 h per day and the frequency of exposure ranged from 5 to 6 times per week. The risk ratios (RRs) of the Classification Model, the Grading Model and the Occupational Disease Hazard Evaluation Model were 0.70 ± 0.10, 0.34 ± 0.13, and 0.65 ± 0.21, respectively. The RRs for the three risk assessment models were statistically different (P < 0.001), and there were no correlations between them (P > 0.05). The average risk level of all of the hazard factors was 0.38 ± 0.18, which did not differ from the RRs of the Grading Model (P > 0.05).ConclusionsThe hazards of organic solvents and noise in the electronics industry are not negligible. The Grading Model offers a good reflection of the actual risk level of the electronics industry and has strong practicability.

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

We consider a scenario where the pandemic infection rate is inversely proportional to the power of the distance between the infected region and the non-infected region. In our study, we analyze the case where the exponent of the distance is 2, which is in accordance with Reilly’s law of retail gravitation. One can test for infection but such tests are costly so one seeks to determine the region of infection while performing few tests. Our goal is to find a boundary region of minimal size that contains all infected areas. We discuss efficient algorithms and provide the asymptotic bound of the testing cost and simulation results for this problem.

Building emission reduction is an important way to achieve China's carbon peaking and carbon neutrality goals. Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system, a multi-time scale optimal scheduling strategy based on model predictive control (MPC) is proposed under the consideration of load optimization. First, load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature, and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost. Second, considering inter-day to intra-day source-load prediction error, an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption. Finally, taking an office building on a summerwork day as an example, the effectiveness of the proposed scheduling strategy is verified. The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%, improves the carbon emission reduction by 7.99%, and the photovoltaic consumption rate reaches 98.57%, improving the system's low-carbon and economic performance. [ABSTRACT FROM AUTHOR]

The goal of this research is to apply diagrams to environmental color studies. The experience of environmental color is not only visual experience, but also the relationship of the color and the form of the object, the material, the place, the human perception experience. Diagrams records these factors along with the human perception experiences. These diagrams make it possible to study and analyze environmental color more deeply and more easily. In this paper, we first reviewed the "case studies of environmental color workshops" and "theory of information processing by rearranging matrix of Jacques Bertin" which are related to this research. Next, we conducted an environmental color workshop and evaluated the application of diagrams at workshop by students and experts (design teachers and designers). As a result, we confirmed that the diagrams proposed by us are effective for the environmental color workshop. In particular, it was confirmed that these diagrams are effective for group discussion (analysis). The workshop was held at the South China University of Technology (Guangzhou, China) on October 14 - October 17, 2016 (Figure.1). The participants were 5 students. First, students walked along a path near the natural lake in the campus and recorded the observed color objects and their perception experiences. Next, the students (A, B, C, D, E) entered the observation results into the matrix type diagram (a) (Figure.2). In diagram (a), the colors observed by each student were displayed on the left side of the observation route, and the photographs of the color objects, the perception experiences on the color objects, and the landscape photographs of the observation place were arranged on the right side of the observation route. Next, according to Jacques Bertin's theory (visual variables and rearrangement of the matrix), diagram (a) was transformed into diagram (b) (Figure.3) and group discussion and information analysis were carried out. In diagram (b), color objects are represented as "rows", and various attributes of color objects as "columns". The attributes of color objects are the keywords extracted from the "perception experiences for color objects" in diagram (a). In the notation of visual variables, ordinary impression was displayed as gray dot, good impression was displayed as orange dot, bad impression was indicated as blue dot, and the number of observers was indicated by the size of the dot. Looking at Figure.3-left, the most common thing is finding color objects by "brightness", and the number of observers who recorded the "brightness" of bicycle is the largest. By rearranging the matrix in diagram (b), we found that the color objects can be divided into "gray", "white" and "yellow" (Figure.3-middle), there were many good impressions for "yellow and white (flowers)", and there were many bad impressions for "gray and white (street lights)" (Figure.3-right). [ABSTRACT FROM AUTHOR]