Softwarized resource allocation in digital twins-empowered networks for future quantum-enabled consumer applications

Network softwarization (NetSoft), recognized as crucial attribute of 6G networks, promises to provide enhanced and advanced services, including future quantum-enabled consumer applications. Softwarized resource allocation is the core issue in NetSoft concept. Digital twins (DT) guarantees to generate the corresponding digital world that reflects and interacts with the original physical world seamlessly. With DT empowering, the digital replica of softwarized networks can be generated to predict, simulate, analyze the softwarized resource allocation in more economical, convenient and scalable methods.In this paper, we research the softwarized resource allocation of requested services, usually, called as slices, in DT-empowered networks for future quantum-enabled consumer applications. We focus on developing efficient softwarized resource allocation algorithm. At first, we present models of the DT-empowered networks and service requests by using graph theory and hypergraph theory. Then, we design one softwarized resource management framework, labeled as DT-Slice-Soft-6G. This framework has the functions of managing softwarized resources, calculating resource allocation solution in digital replica and sending the calculated solution back to softwarized 6G networks. Thereafter, one efficient and fine-grained softwarized resource allocation algorithm, inserted in DT-Slice-Soft-6G, is detailed. This algorithm is labeled as Heu-DT-Slice-6G and is proposed based on efficient heuristic methods. To validate the highlights of DT-Slice-Soft-6G and Heu-DT-Slice-6G, we conduct the simulation work in our self-developed simulator

Decision-Oriented Multi-Outcome Modeling for Anesthesia Patients

Anesthesia drugs have impact on multiple outcomes of an anesthesia patient. Most typical outcomes include anesthesia depth, blood pressures, heart rates, etc. Traditional diagnosis and control in anesthesia focus on a one-drug-one-outcome scenario. This paper studies the problem of real-time modeling for monitoring, diagnosing, and predicting multiple outcomes of anesthesia patients. It is shown that consideration of multiple outcomes is necessary and beneficial for anesthesia managements. Due to limited real-time data, real-time modeling in multi-outcome modeling requires low-complexity model strucrtures. This paper introduces a method of decision-oriented modeling that significantly reduces the complexity of the problem. The method employs simplified and combined model functions in a Wiener structure to contain model complexity. The ideas of drug impact prediction and reachable sets are introduced for utility of the models in diagnosis, outcome prediction, and decision assistance. Clinical data are used to evaluate the effectiveness of the method

Sec12 Binds to Sec16 at Transitional ER Sites

COPII vesicles bud from an ER domain known as the transitional ER (tER). Assembly of the COPII coat is initiated by the transmembrane guanine nucleotide exchange factor Sec12. In the budding yeast Pichia pastoris, Sec12 is concentrated at tER sites. Previously, we found that the tER localization of P. pastoris Sec12 requires a saturable binding partner. We now show that this binding partner is Sec16, a peripheral membrane protein that functions in ER export and tER organization. One line of evidence is that overexpression of Sec12 delocalizes Sec12 to the general ER, but simultaneous overexpression of Sec16 retains overexpressed Sec12 at tER sites. Additionally, when P. pastoris Sec12 is expressed in S. cerevisiae, the exogenous Sec12 localizes to the general ER, but when P. pastoris Sec16 is expressed in the same cells, the exogenous Sec12 is recruited to tER sites. In both of these experimental systems, the ability of Sec16 to recruit Sec12 to tER sites is abolished by deleting a C-terminal fragment of Sec16. Biochemical experiments confirm that this C-terminal fragment of Sec16 binds to the cytosolic domain of Sec12. Similarly, we demonstrate that human Sec12 is concentrated at tER sites, likely due to association with a C-terminal fragment of Sec16A. These results suggest that a Sec12–Sec16 interaction has a conserved role in ER export

A smart digital twin enabled security framework for vehicle-to-grid cyber-physical systems

The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin (DT) technologies have shown promise in mitigating system complexity and providing diverse functionalities for complex tasks such as system monitoring, analysis, and optimal control. This paper presents a resilient and secure framework for CCA detection and mitigation in V2G-CPSs, leveraging a smart DT-enabled approach. The framework introduces a smarter DT orchestrator that utilizes long short-term memory (LSTM) based actor-critic deep reinforcement learning (LSTM-DRL) in the DT virtual replica. The LSTM algorithm estimates the system states, which are then used by the DRL network to detect CCAs and take appropriate actions to minimize their impact. To validate the effectiveness and practicality of the proposed smart DT framework, case studies are conducted on an IEEE 30 bus system-based V2G-CPS, considering different CCA types such as malicious V2G node or control command attacks. The results demonstrate that the framework is capable of accurately estimating system states, detecting various CCAs, and mitigating the impact of attacks within 5 seconds.The work of H. Vincent Poor was supported by the U.S. National Science Foundation under Grant CNS-2128448 and Grant ECCS-2335876