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Background: The COVID-19 pandemic has had a profound global impact, although the majority of recently infected cases have presented with mild to moderate symptoms. Previous clinical studies have demonstrated that Shufeng Jiedu (SFJD) capsule, a Chinese herbal patent medicine, effectively alleviates symptoms associated with the common cold, H1N1 influenza, and COVID-19. This study aimed to assess the efficacy and safety of SFJD capsules in managing symptoms of mild to moderate COVID-19 infection. Methods: A randomized, double-blind, placebo-controlled trial was conducted from May to December 2022 at two hospitals in China. Mild and moderate COVID-19-infected patients presenting respiratory symptoms within 3 days from onset were randomly assigned to either the SFJD or placebo groups in a 1:1 ratio. Individuals received SFJD capsules or a placebo three times daily for five consecutive days. Participants were followed up for more than 14 days after their RT-PCR nucleoid acid test for SARS-CoV-2 turned negative. The primary outcome measure was time to alleviate COVID-19 symptoms from baseline until the end of follow-up. Results: A total of 478 participants were screened; ultimately, 407 completed the trial after randomization (SFJD, n = 203; placebo, n = 204). No statistically significant difference in baseline parameters was observed between the two groups. The median time to alleviate all symptoms was 7 days in the SFJD group compared to 8 days in the placebo group (p = 0.037). Notably, the SFJD group significantly attenuated fever/chills (p = 0.04) and headache (p = 0.016) compared to the placebo group. Furthermore, the median time taken to reach normal body temperature within 24 h was reduced by 7 hours in the SFJD group compared to the placebo group (p = 0.033). No deaths or instances of serious or critical conditions occurred during this trial period; moreover, no serious adverse events were reported. Conclusion: The trial was conducted in a unique controlled hospital setting, and the 5-day treatment with SFJD capsules resulted in a 1-day reduction in overall symptoms, particularly headache and fever/chills, among COVID-19-infected participants with mild or moderate symptoms. Compared to placebo, SFJD capsules were found to be safe with fewer side effects. SFJD capsules could potentially serve as an effective treatment for alleviating mild to moderate symptoms of COVID-19. Clinical Trial Registration: https://www.isrctn.com/, identifier ISRCTN14236594.
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In this paper, we present an estimation-based route planning (ERP) method for chemical source searching using a wheeled mobile robot and validate its effectiveness with outdoor field experiments. The ERP method plans a dynamic route for the robot to follow to search for a chemical source according to time-varying wind and an estimated chemical-patch path (C-PP), where C-PP is the historical trajectory of a chemical patch detected by the robot, and normally different from the chemical plume formed by the spatial distribution of all chemical patches previously released from the source. Owing to the limitations of normal gas sensors and actuation capability of ground mobile robots, it is quite hard for a single robot to directly trace the intermittent and rapidly swinging chemical plume resulting from the frequent and random changes of wind speed and direction in outdoor field environments. In these circumstances, tracking the C-PP originating from the chemical source back could help the robot approach the source. The proposed ERP method was tested in two different outdoor fields using a wheeled mobile robot. Experimental results indicate that the robot adapts to the time-varying airflow condition, arriving at the chemical source with an average success rate and approaching effectiveness of about 90% and 0.4~0.6, respectively.
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Maintaining contact between the robot and plume is significant in chemical plume tracing (CPT). In the time immediately following the loss of chemical detection during the process of CPT, Track-Out activities bias the robot heading relative to the upwind direction, expecting to rapidly re-contact the plume. To determine the bias angle used in the Track-Out activity, we propose an online instance-based reinforcement learning method, namely virtual trail following (VTF). In VTF, action-value is generalized from recently stored instances of successful Track-Out activities. We also propose a collaborative VTF (cVTF) method, in which multiple robots store their own instances, and learn from the stored instances, in the same database. The proposed VTF and cVTF methods are compared with biased upwind surge (BUS) method, in which all Track-Out activities utilize an offline optimized universal bias angle, in an indoor environment with three different airflow fields. With respect to our experimental conditions, VTF and cVTF show stronger adaptability to different airflow environments than BUS, and furthermore, cVTF yields higher success rates and time-efficiencies than VTF.
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Algoritmos , TemperaturaRESUMEN
This paper investigates the problem of locating a continuous chemical source using the concentration measurements provided by a wireless sensor network (WSN). Such a problem exists in various applications: eliminating explosives or drugs, detecting the leakage of noxious chemicals, etc. The limited power and bandwidth of WSNs have motivated collaborative in-network processing which is the focus of this paper. We propose a novel distributed least-squares estimation (DLSE) method to solve the chemical source localization (CSL) problem using a WSN. The DLSE method is realized by iteratively conducting convex combination of the locally estimated chemical source locations in a distributed manner. Performance assessments of our method are conducted using both simulations and real experiments. In the experiments, we propose a fitting method to identify both the release rate and the eddy diffusivity. The results show that the proposed DLSE method can overcome the negative interference of local minima and saddle points of the objective function, which would hinder the convergence of local search methods, especially in the case of locating a remote chemical source.
