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Lyon consensus differentiates acid exposure time (AET) as physiological, borderline, and pathological. Mean nocturnal baseline impedance (MNBI) and post-reflux swallow-induced peristaltic wave index (PSPWi) are believed to increase diagnostic yield of gastroesophageal reflux disease (GERD) and correlate with symptom outcome of proton pump inhibitor (PPI) treatment. We aim to explore the clinical characteristics and the correlation of pH-impedance parameters with PPI response in Chinese patients with different AET levels.We retrospectively investigated 177 patients with typical reflux symptoms who received esophageal function tests. The demographics, GERD questionnaire scores, the proportion of esophagitis and PPI responders, and manometric and pH-impedance parameters were compared among patients with AET4%, 4-6%, and6%. In patients with AET ≥ 4%, manometric and pH-impedance parameters were compared between PPI responders and non-responders.Among 177 patients, 69 (39.0%) had AET 4-6%, and 53 (29.9%) had AET6%. The demographics, esophagogastric junction type, and occurrence of ineffective esophageal motility were similar between patients with AET 4-6% and6%, but different from AET4%. MNBI and PSPWi were different among different AET levels, but similar between PPI responders and non-responders in patients with AET ≥ 4%.It is reasonable to set 4% as a threshold to define pathological AET in Chinese patients. MNBI and PSPWi could identify GERD patients, but may not correlate with PPI response of Chinese GERD patients.

Background Although sleep disorder is thought as a risk factor for functional bowel disorders, its impact role in adolescents remains unknown and the contribution of different sleep dimensions may deserve further attention. This study aimed to evaluate the relationship between multiple sleep dimensions and functional bowel disorders among Chinese college freshmen. Methods A cross-sectional survey was conducted in college freshmen from Huazhong University of Science and Technology in Wuhan, China in September 2019 with random cluster sampling method. All participants completed questionnaires about living habits, sleep and digestive symptoms. Diagnosis of irritable bowel syndrome and functional constipation were based on the Rome IV criteria. Univariate and multivariate logistic regression models were applied to assess the association of sleep dimensions with irritable bowel syndrome or functional constipation. Results Based on the 3335 individuals who completed the questionnaire, the overall prevalence of irritable bowel syndrome and functional constipation in college freshmen were 2.5% and 1.7%, respectively. Multivariate analysis revealed that compared with individuals reporting good sleep quality, those reporting poor (OR = 7.269, 95%CI: 2.876–18.370) were associated with increased risk of irritable bowel syndrome. Similarly, those reporting fair (OR = 2.068, 95%CI: 1.010–4.236) and poor (OR = 5.664, 95%CI: 1.864–17.205) were associated with increased risk of functional constipation. There was no statistically significant association between other sleep dimensions (sleep duration, sleep timing, or sleep latency) and irritable bowel syndrome or functional constipation. Conclusion Self-reported poor sleep quality was a stronger independent predictor of functional bowel disorders than other sleep dimensions among Chinese college freshmen. Future intervention studies should consider the role of sleep quality for the prevention of FBDs in adolescents.

A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm’s performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.

The Internet of Things establishes the intimacy between the Internet and the physical world. Due to portable size, most IoT devices have limited computing and storage capabilities and are vulnerable to various malicious intrusions. Therefore, it is vital to have efficient approaches to distinguish the true IoT data from fake one, we term such methods as anomaly detection (AD). To detect anomalies accurately and efficiently, in this article a 3-hierarchy joint local and global anomaly detection framework, HADIoT, is proposed, in which IoT devices generate and transmit sensory data to their local edge servers for local AD after data refinement which includes re-framing, normalization, complexity reduction via Principal Component Analysis, and symbol mapping. High detection accuracy is achieved by jointly local and global ADs. The local AD focuses on the data pattern consistency of individual devices via the Gated Recurrent Unit, and the processed data is then forwarded from edge servers to the cloud server for global AD. The global AD focuses on the analysis of the data pattern correlations between different IoT devices, using the Conditional Random Fields. For the maintenance of cyber-security, the proposed anomaly detection framework HADIoT enables to provide an accurate and faster anomaly detection for IoT applications, compared with existing anomaly detection methods. The performance of the proposed method is also empirically evaluated through simulations, using a real dataset - the Information Security Center of Excellence (ISCX) 2012 dataset. Simulation results demonstrate the effectiveness of the proposed framework in terms of True Positive Rate, False Positive Rate, Precision, Accuracy and F_score, compared with three benchmark schemes.

BACKGROUND Beverage-drinking behavior could be a potential risk factor for gastroesophageal reflux disease (GERD) in young populations. However, GERD prevalence in this population has not been investigated, and beverage consumption's association with GERD remains inconclusive. This study aimed to evaluate the prevalence and beverage-related risk factors of GERD among Chinese college freshmen and in youth around the world. METHODS A cross-sectional survey was conducted in Chinese college freshmen in September 2019 using random cluster sampling method. Participants completed questionnaires on demographic information, food intake frequency, and GER symptoms. Multivariate logistic regression models were applied to assess the association between beverages and GERD. Studies were retrieved from multiple databases for systemic review. The prevalence of GERD in young populations and beverage-related risk factors were pooled using random-effect models. KEY RESULTS Based on the 3345 individuals who completed the questionnaires, GERD prevalence in Chinese college freshmen is 5.1%. Multivariate analysis showed students who drink green tea daily, and those who drink coffee regularly were more likely to develop GERD compared with those who never drink tea or coffee. The pooled prevalence of GERD in young populations is 18.0%, and frequent alcohol consumption is positively associated with GERD in general population. CONCLUSIONS AND INFERENCES The prevalence of GERD in Chinese college freshmen is significantly lower than that in worldwide youth populations. Alcohol, green tea, and coffee consumption could be potential risk factors for GERD. Future large-scale epidemiological studies are warranted for reliable identification of beverage-related risk factors for GERD in young populations.

The comprehensively completed BDS-3 short-message communication system, known as the short-message satellite communication system (SMSCS), will be widely used in traditional blind communication areas in the future. However, short-message processing resources for short-message satellites are relatively scarce. To improve the resource utilization of satellite systems and ensure the service quality of the short-message terminal is adequate, it is necessary to allocate and schedule short-message satellite processing resources in a multi-satellite coverage area. In order to solve the above problems, a short-message satellite resource allocation algorithm based on deep reinforcement learning (DRL-SRA) is proposed. First of all, using the characteristics of the SMSCS, a multi-objective joint optimization satellite resource allocation model is established to reduce short-message terminal path transmission loss, and achieve satellite load balancing and an adequate quality of service. Then, the number of input data dimensions is reduced using the region division strategy and a feature extraction network. The continuous spatial state is parameterized with a deep reinforcement learning algorithm based on the deep deterministic policy gradient (DDPG) framework. The simulation results show that the proposed algorithm can reduce the transmission loss of the short-message terminal path, improve the quality of service, and increase the resource utilization efficiency of the short-message satellite system while ensuring an appropriate satellite load balance.

Due to the limited on-board resources, when large number of ground users have call requests, the call blocking probability will be high in satellite communication. Based on the deep study of the working process of multi-beam low earth orbit satellite, a probability-based channel reservation strategy (PCRS) is proposed. The proposed strategy takes into the consideration of user's communication time and current location, and believes that part of users will end the communication tasks without the next spotbeam handover, thereby releasing the reserved channels to access more users. The simulation results show that the proposed strategy can raise the handover success rate, which improves the quality of service.

Background Psychosocial factors are seemed as important causes of functional gastrointestinal disorders (FGIDs). However, the role of stress in FGIDs in high school students under the pressure of college entrance examination is largely unknown. The aim of this study is to investigate the association between the stress and FGIDs in high school graduates. Methods A cross-sectional survey was conducted in randomly selected high school fresh graduates. Questionnaires concerned health condition, living habits, gastrointestinal symptoms and life stress were given out and be finished voluntarily. Participants were diagnosed as FGIDs based on the Rome IV criteria. Results Stress level of FGIDs population was higher than control group and stress was independent predicted factor of high risk of FGIDs. The stressor “changes” was significantly correlated with functional gastroduodenal disorders (OR1.118(1.011-1.238)). Stressor “frustration” was significantly correlated with functional bowel disorders (OR1.038(1.006-1.071)). “Physiological reaction” was correlated with functional bowel disease and functional gastroduodenal disorders + functional bowel disorders (OR1.027(1.007-1.046) and OR1.055(1.000-1.113)). Students with more than one gastrointestinal symptom exhibited higher stress level. Moreover, there was mediation effect of stress in the association between gender, sleep quality, allergies and FGIDs. Limitations This was a cross-section study and the sample included in the study were only from Wuhan, China. Conclusions Our findings illustrated the predicted and mediated role of stress in FGIDs in high school fresh graduates. Different stressors and reactions to stressors contributed to different FGIDs. Intervening measures aimed at stress coping strategies were warranted for students in daily school life.

Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR).

In the automatic measurement of water level, the hydrological satiation also uses a traditional staff gauge to calibrate the water depth measured by the sensor. And in the most of water-covered areas, such as underpass overpasses, low-lying roads and tunnels, staff gauges have been installed to monitor road water. Therefore, the automatic water level measurement method based on staff gauge image, has drawn more and more attention. So we propose a method for the water level estimation based on images of staff gauge in this paper. First, we proposed a two-step method for segmenting the staff gauge. We implemented the initial positioning by HSV color space and morphological processing, and then we defined a component map to achieve precise positioning. Next, we split the characters on the staff gauge image and recognized those characters through a Convolutional Neural Network. Finally, we measured the water level by using a quadratic function to determine the mapping relationship between pixels and the metric values. Compared with the traditional method of extracting water line, the two-step positioning method improves the water level detection accuracy. The measurement result in a real world image of a pond shows more accurate detection with the algorithm and the feasibility of applying it to road water monitoring.

Wireless sensor networks (WSN) have been in widely use due to its prominent capability of data sensing and processing. However, the lifetime of WSN is constrained because of the limited battery energy in each sensor node. Fortunately, recent breakthrough in the area of wireless energy transfer technology has created a new dimension to prolonging the lifetime of WSN. In this paper, we firstly introduce the conception and requirements of cyclic energy conservation in conditions of rechargeable WSN via Wireless Charging Vehicle (WCV). Then, in order to enhance the utility of charging, optimization of the minimal occupied time of WCV achieved by Cuckoo Search is carried out, hence leading to the prior solution of TSP and essential properties of cyclic energy conservation. Furthermore, the optimal solution of cyclic energy conservation in WSN is presented and the simulation result of the proposed scheme is given. Lastly, a conclusion of our research is drawn and a potential outlook of research is given as well.