OBJECTIVE: To demonstrate a framework for calculating daily dose distributions for proton therapy in a timeframe amenable to online evaluation using CT-on-Rails.
METHODS: Tasks associated with calculation of daily dose are fully automated. A rigid registration between daily and planning images is used to propagate beams and targets for calculation of daily dose; additionally, risk structures are propagated using deformable registration to facilitate online evaluation. An end-to-end constancy test was carried out using a pelvis phantom containing a simulated target and bladder contour. 97 Daily fan-beam CT data sets associated with 10 clinical patients were processed to demonstrate feasibility and utility of online evaluation. Computing times and dosimetric differences are reported.
RESULTS: The phantom constancy test took 62 s to complete with no notable discrepancies in the registrations or calculated dose. Max doses were identical for target and bladder contours on initial and repeat scans (359 and 310 cGy (RBE) respectively). Total processing time for 97 daily patient images averaged 154.6 s (73.0 - 222.0 s; SD = 31.8 s). On average, dose calculation accounted for 35 % of total processing time. Average differences in D95 for target contours was 1.5 % (SD = 1.6 %) with a max decrease of 5.9 % on a single daily image.
CONCLUSIONS: Daily dose can be automatically calculated in a timeframe amenable to online evaluation using scanner utilities in conjunction with the scripting API of a commercial treatment planning system. Online evaluation of dose in proton therapy is useful to detect clinically relevant changes, guide setup, and facilitate treatment or replanning decisions.

UNASSIGNED: Online review system contains multiple components, such as ratings, review text, product pictures, and video uploads, that could affect consumer loyalty. However, how the affordance of such components influences perceptions and behaviors of consumers remains unclear. We extend stimulus-organism-response (S-O-R) theory to the online review system. Specifically, we combine affordance theory and the technology acceptance model (TAM) to investigate the relations among the affordance of review systems, consumers\' perceived beliefs, and their loyalty.
UNASSIGNED: We surveyed 320 customers on their online shopping experiences in China. We tested our hypotheses using the partial least squares path structural equation modeling (PLS-SEM) method. We report the direct effect of affordances of review components on consumer loyalty and its indirect effects on consumer loyalty through perceived beliefs.
UNASSIGNED: Our results show that integrity and social interaction affordance of review components have significant relations with perceived ease of use, perceived usefulness, and perceived enjoyment. Intelligent topic mining reveals a positive relation on perceived enjoyment. Operability has a positive relation with perceived ease of use and perceived usefulness. These three consumer-perceived beliefs can mediate, to different degrees, the relationship between affordance of review components and consumer loyalty.
UNASSIGNED: This research takes an innovative approach to offer insights into the relationships between IT affordances and consumer perceptions. We examine S-O-R theory through the lens of information technology and extend S-O-R theory by integrating IT affordances. Our research findings pave the way for businesses to design and implement more effective online review systems.

Since the outbreak of the COVID-19 pandemic in 2020, China has adopted a zero-clearing policy under closed control. It is rather common for residents who are quarantined at home to buy fresh agricultural products online, when COVID-19 spread in big cities. Many e-commerce platforms are trying to develop online shopping channels for fresh agricultural products. However, negative comments and news about those platforms have been increasing because of several reasons, such as the difference in the quality of fresh products, inadequate categories of commodity and inefficient delivery caused by the shortage of personnel and so on. The smooth daily supply of online fresh agricultural products is conducive to soothing the pessimistic emotions and to encouraging their active obedience to epidemic prevention and control policy. Therefore, it is of great importance to explore the preference characteristics of consumers\' online purchase of fresh agricultural products under this critical situation. In this paper, firstly, Pycharm software is used to collect online comment texts of fresh agricultural products on the online platforms with a total of 34,546 pieces of evaluation data. Secondly, the collected data is preformed into the text preprocessing. To be specific, the obtained online comments are processed by Python, including the process of text duplication between sentences, text duplication within sentences and short sentence filtering. After that, processed texts are subjected to Jieba Text Segmentation to form the final word frequency ranking, involving two procedures, part-of-speech tagging and stop-words removal. Lastly, the results of the LDA model indicate the factors that influence consumers\' preferences when they purchase fresh agricultural products online. This study could not only identify the typical features of residents\' online shopping preference in the context of the spread of COVID-19, but also provide pragmatic suggestions for the local government to appease the residents\' negative emotions for the prevention of widespread complaints at the social level.

Previous research has mostly focused on Internet use behaviors, such as usage time of the Internet or social media after individuals experienced offline social exclusion. However, the extant literature has ignored online response behaviors, such as online review responses to social exclusion. To address this gap, drawing on self-protection and self-serving bias, we proposed three hypotheses that examine the effect of offline social exclusion on online reviews, which are verified by two studies using different simulating scenarios with 464 participants. The results show that when individuals are socially excluded offline, regardless of where the exclusion comes from (businesses or peers), they will be more likely to give negative online reviews. In addition, brand awareness moderates the effect of offline social exclusion on online reviews. Specifically, if the brand is less known, compared with social inclusion, offline social exclusion will lead individuals to give more negative online reviews; conversely, for well-known brands, no significant difference exists in the online reviews between social exclusion and inclusion.