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Microbial experimental systems provide a platform to observe how networks of groups emerge to impact plant development. We applied selection pressure for microbiome enhancement ofBrassica rapabiomass to examine adaptive bacterial group dynamics under soil nitrogen limitation. In the 9th and final generation of the experiment, selection pressure enhancedB. rapaseed yield and nitrogen use efficiency compared to our control treatment, with no effect between the random selection and control treatments. Aboveground biomass increased for both the high biomass selection and random selection plants. Soil bacterial diversity declined under highB. rapabiomass selection, suggesting a possible ecological filtering mechanism to remove bacterial taxa. Distinct sub-groups of interactions emerged among bacterial phyla such asProteobacteriaandBacteroidetesin response to selection. Extended Local Similarity Analysis and NetShift indicated greater connectivity of the bacterial community, with more edges, shorter path lengths, and altered modularity through the course of selection for enhanced plant biomass. In contrast, bacterial communities under random selection and no selection showed less complex interaction profiles of bacterial taxa. These results suggest that group-level bacterial interactions could be modified to collectively shift microbiome functions impacting the growth of the host plant under soil nitrogen limitation.

 

Time-Sensitive Networking (TSN) is designed for real-time applications, usually pertaining to a set of Time-Triggered (TT) data flows. TT traffic generally requires low packet loss and guaranteed upper bounds on end-to-end delay. To guarantee the end-to-end delay bounds, TSN uses Time-Aware Shaper (TAS) to provide deterministic service to TT flows. Each frame of TT traffic is scheduled a specific time slot at each switch for its transmission. Several factors may influence frame transmissions, which then impact the scheduling in the whole network. These factors may cause frames sent in wrong time slots, namely misbehaviors. To mitigate the occurrence of misbehaviors, we need to find proper scheduling for the whole network. In our research, we use a reinforcement-learning model, which is called Deep Deterministic Policy Gradient (DDPG), to find the suitable scheduling. DDPG is used to model the uncertainty caused by the transmission-influencing factors such as time-synchronization errors. Compared with the state of the art, our approach using DDPG significantly decreases the number of misbehaviors in TSN scenarios studied and improves the delay performance of the network. 

AFDX (Avionics Full Duplex Switched Ethernet) is developed to support mission-critical communications while providing deterministic Quality of Service (QoS) across cyber-physical avionics systems. Currently, AFDX utilizes FP/FIFO QoS mechanisms to guarantee its real-time performance. To analyze the real-time performance of avionic systems in their design processes, existing work analyzes the deterministic delay bound of AFDX using NC (Network Calculus). However, existing analytical work is based on an unrealistic assumption leading to assumed worst cases that may not be achievable in reality. In this paper, we present a family of algorithms that can search for realistic worst-case delay scenarios in both preemptive and non-preemptive situations. Then we integrate the proposed algorithms with NC and apply our approach to analyzing tandem AFDX networks. Our reality-conforming approach yields tighter delay bound estimations than the state of the art. When there are 100 virtual links in AFDX networks, our method can provide delay bounds more than 25% tighter than those calculated by the state of the art in our evaluation. Moreover, when using our reality-conforming method in the design process, it leads to 27.2% increase in the number of virtual links accommodated by the network in the tandem scenario. 

Low temperature rechargeable batteries are important to life in cold climates, polar/deep‐sea expeditions, and space explorations. Here, this work reports 3.5–4 V rechargeable lithium/chlorine (Li/Cl₂) batteries operating down to −80 °C, employing Li metal negative electrode, a novel carbon dioxide (CO₂) activated porous carbon (KJCO₂) as the positive electrode, and a high ionic conductivity (≈5–20 mS cm⁻¹from −80 °C to room‐temperature) electrolyte comprised of aluminum chloride (AlCl₃), lithium chloride (LiCl), and lithium bis(fluorosulfonyl)imide (LiFSI) in low‐melting‐point (−104.5 °C) thionyl chloride (SOCl₂). Between room‐temperature and −80 °C, the Li/Cl₂battery delivers up to ≈29 100–4500 mAh g⁻¹first discharge capacity (based on carbon mass) and a 1200–5000 mAh g⁻¹reversible capacity over up to 130 charge–discharge cycles. Mass spectrometry and X‐ray photoelectron spectroscopy probe Cl₂trapped in the porous carbon upon LiCl electro‐oxidation during charging. At −80 °C, Cl₂/SCl₂/S₂Cl₂generated by electro‐oxidation in the charging step are trapped in porous KJCO₂carbon, allowing for reversible reduction to afford a high discharge voltage plateau near ≈4 V with up to ≈1000 mAh g⁻¹capacity for SCl₂/S₂Cl₂reduction and up to ≈4000 mAh g⁻¹capacity at ≈3.1 V plateau for Cl₂reduction.

 

This paper studies the current status and future directions of RTOS (Real-Time Operating Systems) for time-sensitive CPS (Cyber-Physical Systems). GPOS (General Purpose Operating Systems) existed before RTOS but did not meet performance requirements for time sensitive CPS. Many GPOS have put forward adaptations to meet the requirements of real-time performance, and this paper compares RTOS and GPOS and shows their pros and cons for CPS applications. Furthermore, comparisons among select RTOS such as VxWorks, RTLinux, and FreeRTOS have been conducted in terms of scheduling, kernel, and priority inversion. Various tools for WCET (Worst-Case Execution Time) estimation are discussed. This paper also presents a CPS use case of RTOS, i.e. JetOS for avionics, and future advancements in RTOS such as multi-core RTOS, new RTOS architecture and RTOS security for CPS.