ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports

Time apart while together: A smart trip design for group travelers

Family or friends traveling in a group often have different preferences and some of which may conflict with each other. Existing smart tourism recommender systems have not adequately addressed this important issue. This study attempts to tackle the problem by incorporating a “joining and forking” strategy into the system design, which allows members to have certain “time apart” to enjoy their treat separately and “time together” to co-create shared experience and memory. A comparison test was conducted based on a total of 50 groups of tourists in an island destination of Kulangsu in China. The results indicate that our new design outperforms five state-of-the-art baseline methods and realize a good balance between individual experience and co-experiences with group members

Adaptive Prognostic of Fuel Cells by Implementing Ensemble Echo State Networks in Time-Varying Model Space

International audiencePrognostic plays an important role in improving the reliability and durability performance of fuel cells (FCs); although it is hard to realize an adaptive prognostic because of complex degradation mechanisms and the influence of operating conditions. In this paper, an adaptive data-driven prognostic strategy is proposed for FCs operated in different conditions. To extract a feasible health indicator (HI), a series of linear parameter-varying models are identified in sliding data segments. Then, virtual steady-state stack voltage is formulated in the identified model space and considered as the HI. To enhance the adaptability of prognostic, an ensemble echo state network is then implemented , given the extracted HI data. Long-term tests on a type of low-powerscale proton-exchange membrane FC stack in different operating modes are carried out. The performance of the proposed strategy is evaluated using the experimental data. Index Terms-Adaptability, data-driven prognostic, echo state network (ESN) ensemble, health indicator (HI), model space, PEMFC

Efficiency and Durability Improvement of Photovoltaic Panels by DC/DC Converter Integrated Impedance Measurements

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Maximizing the Contact Opportunity for Vehicular Internet Access

Abstract—With increasing popularity of media enabled handhelds, the need for high data-rate services for mobile users is evident. Large-scale Wireless LANs (WLANs) can provide such a service, but they are expensive to deploy and maintain. Open WLAN access-points (APs), on the other hand, need no new deployments, but can offer only opportunistic services with no guarantees on short term throughput. In contrast, a carefully planned sparse deployment of roadside WiFi provides an economically scalable infrastructure with quality of service assurance to mobile users. In this paper, we propose to study deployment techniques for providing roadside WiFi services. In particular, we present a new metric, called Contact Opportunity, as a characterization of a roadside WiFi network. Informally, the contact opportunity for a given deployment measures the fraction of distance or time that a mobile user is in contact with some AP when moving through a certain path. Such a metric is closely related to the quality of data service that a mobile user might experience while driving through the system. We then present an efficient deployment method that maximizes the worst case contact opportunity under a budget constraint. We further show how to extend this concept and the deployment techniques to a more intuitive metric – the average throughput – by taking various dynamic elements into account. Simulations over a real road network and experimental results show that our approach achieves more than 200 % higher minimum contact opportunity, 30%-100 % higher average contact opportunity and a significantly improved distribution of average throughput compared with two commonly used algorithms. I

A review of the applications of fuel cells in microgrids: opportunities and challenges

International audienceSince the last two decades, microgrid, as one typical structure in smart grid framework, has been receiving increasing attention in the world. Meanwhile, fuel cell (FC), as one promising power source, has redrawn the attention of both academia and industry since the beginning of 21th century. Some encouraging achievements in FC technology have been realized thanks to the efforts taken in the last years. Due to this, it is seen that FC, as a clean and efficient energy source, is penetrating into different fields. Among the applications, integrating FCs into microgrids has shown interesting advantages on improving the performance of microgrids and promoting the use of the hydrogen energy. Some ongoing projects have shown that FCs of different power scales can be integrated into microgrids smartly and in different manners. Along with the advantages carried by the combination of the two technologies, many challenges lying on multiple domains are faced in the process. The challenges can be from the FC, the microgrid, and the integration of these two technologies. In this review paper, the advantages of integrating FCs into microgrids are summarized after recalling the knowledge background of FC. The challenges and ongoing researches on FCs and FCs based microgrids are then reviewed. Based on the analysis, the research directions are then extracted in view of the challenges

Challenges of the Remaining Useful Life Prediction for Proton Exchange Membrane Fuel Cells

International audienceWith the advantages of high efficiency, light weight and non-pollution, Proton Exchange Membrane Fuel Cells (PEMFCs) can be used in portable devices, transportation and distributed power supply systems. Nevertheless, the durability and cost are two key barriers for their large-scale commercialization in light-duty vehicle transportation applications. It is necessary to predict the future State of Health (SoH) and future behaviors. Then the operating parameters can be optimized with time or Condition-based Maintenance (CBM) can be activated to extend its life. Prognostics have the ability to estimate the Remaining Useful Life (RUL) before the failure occurs. It seems to be a great solution to deal with the durable issue of PEMFCs. PEMFCs have wide range of applications. Besides, PEMFCs have the property of multi-physics, multi-scales, and nonlinearity. Moreover, the degradation phenomenon has a relationship with the mission profiles and the external disturbances. Then all the degradation mechanisms of the various fuel cells components can hardly be completely understood. Three kinds of prognostic methods are commonly distinguished: model-based, data-driven, and hybrid method. Developing novel and efficient methods to improve the prognostic accuracy, to decrease the computational burden, and to reinforce the robustness and dynamics are what should be done in the next steps