Artificial-intelligent-powered safety and efficiency improvement for controlling and scheduling in integrated railway systems

The multi-mode integrated railway system,anchored by the high-speed railway,caters to the diverse travel requirements both within and between cities,offering safe,comfortable,punctual,and eco-friendly transportation services.With the expansion of the railway networks,enhancing the efficiency and safety of the comprehensive system has become a crucial issue in the advanced development of railway transportation.In light of the prevailing application of artificial intelligence technologies within railway systems,this study leverages large model technology characterized by robust learning capabilities,efficient associative abilities,and linkage analysis to propose an Artificial-intelligent(AI)-powered railway control and dispatching system.This system is elaborately designed with four core functions,including global optimum unattended dispatching,synergetic transportation in multiple modes,high-speed automatic control,and precise maintenance decision and execution.The deployment pathway and essential tasks of the system are further delineated,alongside the challenges and obstacles encountered.The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system,ensuring a more effective alignment between transportation services and passenger demands.

Safety-efficiency trade-offs in the cotton xylem：acclimatization to different soil textures

The acclimatization of plant xylem to altered environmental conditions has attracted considerable attention from researchers over several decades. Plants growing in natural environments must seek a balance between water uptake and the water loss of leaves from evaporation. Thus, the adaptation of xylem to different soil textures is important in maintaining plant water balance. In this study, we investigated the xylem changes of cotton（Gossypium herbaceum L.） xylem in sandy, clay and mixed soils. Results showed that soil texture had a significant effect on xylem vessel diameter and length of stems and roots. Compared with G. herbaceum growing in the clay soil, those plants growing in the sandy soil developed narrower and shorter xylem vessels in their roots, and had a higher percentage of narrow vessels in their stems. These changes resulted in a safer（i.e. less vulnerable to cavitation）, but less-efficient water transport system when soil water availability was low, supporting the hydraulic safety versus efficiency trade-off hypothesis. Furthermore, in sandy and mixed soils, the root： shoot ratio of G. herbaceum increased twofold, which ensures the same efficiency of leaves. In summary, our finding indicates that the morphological plasticity of xylem structure in G. herbaceum has a major role in the acclimatization of this plant species to different soil textures.

Safety in Surgery:Evaluation of Safety and Efficiency in Use of Aminoglycosides in Acute Appendicitis

Background: Aminoglycosides are used as empirical antibiotic treatment of intraabdominal infections which are caused by Gram negative bacteria and for which the treatment of choice is surgery. Aminoglycosides maintain good efficacy against these bacteria and reduce the need for prescribing fluoroquinolone, cephalosporin and carbapenem antibiotics which contribute to the development of resistant bacterial strains. In recent years, several clinical trials and international guidelines have advised against the use of aminoglycosides owing largely to doubts about their effectiveness and to the concern for their known nephrotoxicity and ototoxicity. Aim: In our study, we aimed to prove whether aminoglycosides are appropriate agents in the treatment of acute appendicitis. Methods: Retrospectively, patients with acute appendicitis we included in the trial. Demographic characteristics, comorbidities, clinical signs and symptoms, the type of antibiotic and surgical treatment were analyzed. The effect of independent variables on the occurrence of complications was calculated using Student’s T-test and Fisher’s precise test. The effect of aminoglycosides on the loss of kidney function was determined by means of a linear regression method. Results: 300 patients proved acute appendicitis were included in the study. Univariate statistical analysis showed that the risk factors for postoperative complications in treating acute appendicitis were: age over 76 years (p Conclusion: Aminoglycoside antibiotics are a safe and effective treatment of acute appendicitis;our not published data are positive of AGs use in acute cholecystitis and left colon diverticulitis which requires surgery. If used for a limited time period, they do not increase the risk for kidney injury and remain a stable low level of all over complications.

Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios

Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence.While the UN R157 Regulation evaluates automated lane-keeping system(ALKS)performance baselines through safe collision plots(SCPs)in various scenario clusters,quantifying the specific ALKS safety efficiency remains challenging.We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios.First,we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process.Second,by utilizing Fourier analysis,a spectral analysis model was built to quantify and analyze the vehicle motion characteristics,providing insights into scenario safety.Finally,we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model.The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2.When the relative longitudinal distance and speed of the vehicle are the same,if the cut-in speed of the cut-in vehicle is larger,the normalized disturbance frequency is higher,indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.

Experimental study on stability control technology of surrounding rock of deep roadways in coal mine

In order to solve effectively the problems of deep mining with safety and high efficiency, the multi- pie factors influencing the stability of deep rock roadway and technical problems are analyzed in the light of the severe situation of effective mining for deep coal resource, and the stability control methods for deep rock road- way are provided, which are based on the idea of combined support with separated steps and integral control of surrounding rock of deep rock roadway. The suggested methods were applied to a deep rock roadway with -648 m depth in Gubei coal mine of Huainan area. The field test was carried out and the in-situ monitoring was imple- mented, and the support scheme was optimized and adjusted to improve the stability of the surrounding rock of the roadway based on the feedback analysis. The results showed that the stability can be improved greatly by the provided control methods tbr deep roadway. The present methods lbr stability control of deep rock roadway can be used to other deep rock roadways with the similar conditions.

Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections

Road intersection is one of the most complex and accident-prone traffic scenarios,so it’s challenging for autonomous vehicles(AVs)to make safe and efficient decisions at the intersections.Most of the related studies focus on the solution to a single scenario or only guarantee safety without considering driving efficiency.To address these problems,this study proposed a deep reinforcement learning enabled decision-making framework for AVs to drive through intersections automatically,safely and efficiently.The mapping relationship between traffic images and vehicle operations was obtained by an end-to-end decision-making framework established by convolutional neural networks.Traffic images collected at two timesteps were used to calculate the relative velocity between vehicles.Markov decision process was employed to model the interaction between AVs and other vehicles,and the deep Q-network algorithm was utilized to obtain the optimal driving policy regarding safety and efficiency.To verify the effectiveness of the proposed decision-making framework,the top three accident-prone crossing path crash scenarios at intersections were simulated,when different initial vehicle states were adopted for better generalization capability.The results showed that the developed method could make AVs drive safely and efficiently through intersections in all of the tested scenarios.

A Novel Routing Strategy to Provide Source Location Privacy in Wireless Sensor Networks

In order to secure the source location privacy when information is sent back to the base station in wireless sensor network, we propose a novel routing strategy which routes the packets to the base station through three stages： directional random routing, h-hop routing in the annular region and the shortest path routing. These stages provide two fold protections to prevent the source location from being tracked down by the adversary. The analysis and simulation results show that proposed scheme, besides providing longer safety period, can significantly reduce energy consumption compared with two baseline schemes.