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Abstract This paper adopts the “baseline scenario method” to construct a comprehensive model for calculating and reducing carbon emissions generated by employee travel, including the accounting of carbon emissions from commuting and business travel, as well as the assessment of green travel for carbon reduction. The study employs methods such as questionnaires and on-site interviews to collect travel data from employees of a research institute in Beijing as a case study. The results show that employees’ commuting methods are diverse, with the subway being the primary mode of travel; however, business travel generates higher carbon emissions, particularly among employees with higher education levels. The research concludes that the model proposed in this paper provides a framework for preliminary carbon emission estimation, but to improve the accuracy of the estimates, more variables and factors need to be considered, and the limitations of the model are pointed out. The research findings have significant implications for policy and institutional practices, suggesting the adoption of more targeted measures to reduce the use of high-carbon-emission travel methods and to encourage the use of green travel options. With the continuous advancement of data collection technologies in the future, it will be possible to further establish a more refined carbon emission accounting model and obtain more accurate and comprehensive travel data, thereby providing solid data support for the development of more effective carbon reduction strategies and policies.

Abstract Typical public institutions such as government offices, hospitals, and schools play important leading and exemplary roles in the green and low-carbon development of the whole society. This paper analyzes the business and energy consumption characteristics of public institutions, as well as the characteristics of personnel travel, and constructs a carbon accounting model for public institutions. It innovatively proposes a methodology for carbon accounting of personnel travel related to public institutions and validates and analyzes it using a comprehensive hospital as an example. The paper analyzes the carbon emission characteristics of various energy types, systems, and types of travelers in hospitals, provides schemes for the transformation of main energy systems, and corresponding emission reduction effects, thereby providing technical support for the full-chain carbon accounting of public institutions. Additionally, this paper explores the carbon reduction pathways for hospitals to support the peak carbon and carbon neutrality goals of public institutions, and promote the high-quality development of public institutions in China.

Abstract Public institutions, emblematic of public infrastructure, exhibit extensive reach and operational scope, positioning them as vanguards in China’s dual carbon initiatives and serving as exemplars. Electricity and natural gas predominantly fuel the operations of public institutions. Notably, the fixed commute routes and consistent procurement patterns of office personnel yield a standardized energy consumption profile within these entities. Researching carbon emissions related to commuting and evaluating procurement strategies for reducing carbon footprints in public institutions demonstrate a precision-tailored approach. This paper, through an analysis of the energy consumption characteristics, utilization structure of public institutions, and the commuting behaviors and procurement practices of office personnel, establishes a bespoke carbon accounting model specifically designed for public institutions, seamlessly embedded within a comprehensive platform. By providing fundamental methodological frameworks and advanced technological foundations for carbon accounting in public institutions across China, this work propels the nation’s efforts towards carbon peak and ultimately carbon neutrality.

[Introduction] The 3.7 GHz high-power TH2103A klystrons are widely used in the tokamak experimental device. To simplify the HV power supply system (removing the vacuum tetrode in the anode modulation), a power supply mode based on voltage division mode is introduced into the TH2103A klystron of the HL-2A device. To operate the klystron safely in this power supply mode, a system for measuring and protecting the anode current of TH2103A klystron is developed. [Method] According to the analysis of the characteristics of the anode current in this power supply mode, the anode current showed the condition of normal, forward overcurrent, or reverse overcurrent. In the case of forward overcurrent or reverse overcurrent, it meant that many electrons were bombarding or flowing out of the anode. They were very dangerous to the klystron, which usually may cause overcurrent of the klystron titanium pump, or even damage the klystron. Therefore it was important to accurately measure the anode current and cut off the high voltage to protect the klystron in case of overcurrent. The principle and power supply mode and the principle and implementation plan of the anode protection system were described in this paper. [Result] In this paper, the effectiveness and reliability of the anode current measurement and protection system were confirmed. [Conclusion] The power supply mode and the anode current measurement and protection system of this paper are of important reference significance for the operation and maintenance of TH2103A klystron.

IntroductionWe aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features.MethodsUltrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve.ResultsWe found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value.DiscussionThe Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

This study investigated the effects of replacing commercial feeds with fresh black soldier fly larvae (BSFL) on the immune enzyme activities, water quality in the culturing environment, and intestinal microbiota of Pacific white shrimp (Litopenaeus vannamei). Five different feeding regimes were designed, in which 0% (control group), 25%, 50%, 75%, and 100% commercial feeds were replaced with equal wet weight of fresh BSFL, respectively. The experiment lasted for 45 days, and the results showed that the serum T-NOS (total nitric oxide synthase), AKP (alkaline phosphatase), and ACP (acid phosphatase) activity were significantly increased in the BSFL25% group. However, the immune enzymes of the hepatopancreas decreased significantly in all the BSFL-containing groups compared to the control group. Total ammonia nitrogen concentrations throughout the experimental phase and nitrate concentrations in the middle and later phases of the experiment were negatively correlated with the replacement rate of fresh BSFL in the diet, whereas pH was positively correlated with the replacement rate of fresh BSFL in the diet. Proteobacteria and Bacteroidetes were dominant phyla in the intestines of shrimps, but the relative abundances of Proteobacteria and Firmicutes decreased, with those of Bacteroidetes and Planctomycetes increased in the BSFL-containing groups. Among them, higher relative abundances of potential probiotics such as Motilimonas, Shimia, Pseudoalteromonas, and Shewanella and lower relative abundance of genus Vibrio were observed in the fresh BSFL-containing groups. Furthermore, shrimps fed with BSFL-containing diets had higher bacterial richness and diversity in the intestines. In conclusion, a proper replacement of commercial feed with fresh BSFL had a positive effects on the immune-related enzyme activities, water quality, and intestinal health in the L. vannamei.

Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one unlabeled point cloud. Thus, the whole training requires no manual annotation and manual selection of patches. In addition, we propose to involve keypoint sampling into the pipeline, which further improves the performance of our model. Our experiments demonstrate the capability of our self-supervised local descriptor to achieve even better performance than the supervised model, while being easier to train and requiring no data labeling.