An Algorithm with Iteration Uncertainty Eliminate Based on Geomagnetic Fingerprint under Mobile Edge Computing for Indoor Localization.

Indoor localization problems are difficult due to that the information, such as WLAN and GPS, cannot achieve enough precision for indoor issues. This paper presents a novel indoor localization algorithm, GeoLoc, with uncertainty eliminate based on fusion of acceleration, angular rate, and magnetic field sensor data. The algorithm can be deployed in edge devices to overcome the problems of insufficient computing resources and long delay caused by high complexity of location calculation. Firstly, the magnetic map is built and magnetic values are matched. Secondly, orientation updating and position selection are iteratively executed using the fusion data, which gradually reduce uncertainty of orientation. Then, we filter the trajectory from a path set. By gradually reducing uncertainty, GeoLoc can bring a high positioning precision and a smooth trajectory. In addition, this method has an advantage in that it does not rely on any infrastructure such as base stations and beacons. It solves the common problems regarding the non-uniqueness of the geomagnetic fingerprint and the deviation of the sensor measurement. The experimental results show that our algorithm achieves an accuracy of less than 2.5 m in indoor environment, and the positioning results are relatively stable. It meets the basic requirements of indoor location-based services (LBSs).

[Glucosides of chaenomeles speciosa attenuate ischemia/reperfusion-induced brain injury by regulating NF-κB P65/TNF-α in mouse model].

OBJECTIVE: To investigate the effect and mechanism of glucosides of chaenomeles speciosa (GCS) on ischemia/reperfusion-induced brain injury in mouse model. METHODS: Fifty 8-week C57BL/C mice were randomly divided into five groups with 10 in each group:sham group, model group, GCS 30 mg/kg group, GCS 60 mg/kg group and GCS 90 mg/kg group, and the GCS was administrated by gavage (once a day) for 14 d. HE staining was performed to investigate the cell morphology; the Zea-Longa scores were measured for neurological activity; TUNEL staining was performed to investigate the cell apoptosis; ELISA was used to detected the oxidative stress and inflammation; Western Blot was performed to investigate the key pathway and neurological functional molecules. RESULTS: Compared with the sham group, the brain tissues in model group were seriously damaged, presenting severe cell apoptosis, oxidative stress and inflammation, associated with increased NF-κB P65 and TNF-α levels as well as decreased myelin associate glycoprotein (MAG) and oligodendrocyte-myelin glycoprotein (OMgp)levels (all P<0.01). Compared with the model group, the brain tissues in GCS groups were ameliorated, and cell apoptosis, oxidative stress and inflammation were inhibited, associated with decreased NF-κB P65 and TNF-α levels as well as increased MAG and OMgp levels (all P<0.01), which were more markedly in GCS 60 mg/kg group. CONCLUSIONS: GCS can inhibit the NF-κB P65 and TNF-α, reduce the oxidative stress and inflammation, decrease the cell apoptosis in mouse ischemia/reperfusion-induced brain injury model, and 60 mg/kg GCS may be the optimal dose.

RAQ-A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems.

Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.

Improving data quality with an accumulated reputation model in participatory sensing systems.

The ubiquity of mobile devices brings forth a sensing paradigm, participatory sensing, to collect and interpret sensory information from the environment. Participants join in multifarious sensing tasks and share their data. The sensing result can be obtained in light of shared data. It is not uncommon that some corrupted data is provided by participants, which makes sensing result unreliable accordingly. To address this nontrivial issue, we proposed the accumulated reputation model (ARM) to improve the accuracy of the sensing result. In ARM, participants' reputation will be computed and accumulated based on their sensing data. The sensing data from reputable participants make higher contributions to the sensing result. ARM performs well on calculating accurate sensing results, even in extreme scenarios, where there are many inexperienced or malicious participants.

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication.

Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

Comparison of short interval and low dose (SILD) with high dose of cyclophosphamide in the susceptibility to infection in SLE: a multicentrereal-world study.

OBJECTIVE: Infection is a major cause of death in patients with SLE. This study aimed to explore the infection rate in patients with SLE receiving a low dose of intravenous cyclophosphamide (IV-CYC). METHODS: Clinical parameters of 1022 patients with SLE from 24 hospitals in China were collected. Patients were divided into the short-interval and lower-dose (SILD, 400 mg every 2 weeks) IV-CYC group and the high-dose (HD, 500 mg/m2 of body surface area every month) IV-CYC group. The clinical data and infection rate between the two groups were compared. RESULTS: Compared with HD IV-CYC, the infection rate of the SILD IV-CYC group was significantly lower (13.04% vs 22.27%, p=0.001). Respiratory tract infection (10.28% vs 15.23%, p=0.046) and skin/soft tissue infection (1.78% vs 4.3%, p=0.040) were significantly decreased in the SILD IV-CYC group. Moreover, infections occurred most likely in patients with SLE with leucopenia (OR 2.266, 95% CI 1.322 to 3.887, p=0.003), pulmonary arterial hypertension (OR 2.756, 95% CI 1.249 to 6.080, p=0.012) and >15 mg/day of glucocorticoid (OR 2.220, 95% CI 1.097 to 4.489, p=0.027). CONCLUSIONS: SILD IV-CYC showed a lower frequency of infection events than high-dose IV-CYC in patients with SLE.