Pediatric Organ Dysfunction Information Update Mandate (PODIUM) Contemporary Organ Dysfunction Criteria: Executive Summary

Prior criteria for organ dysfunction in critically ill children were based mainly on expert opinion. We convened the Pediatric Organ Dysfunction Information Update Mandate (PODIUM) expert panel to summarize data characterizing single and multiple organ dysfunction and to derive contemporary criteria for pediatric organ dysfunction. The panel was composed of 88 members representing 47 institutions and 7 countries. We conducted systematic reviews of the literature to derive evidence-based criteria for single organ dysfunction for neurologic, cardiovascular, respiratory, gastrointestinal, acute liver, renal, hematologic, coagulation, endocrine, endothelial, and immune system dysfunction. We searched PubMed and Embase from January 1992 to January 2020. Study identification was accomplished using a combination of medical subject headings terms and keywords related to concepts of pediatric organ dysfunction. Electronic searches were performed by medical librarians. Studies were eligible for inclusion if the authors reported original data collected in critically ill children; evaluated performance characteristics of scoring tools or clinical assessments for organ dysfunction; and assessed a patient-centered, clinically meaningful outcome. Data were abstracted from each included study into an electronic data extraction form. Risk of bias was assessed using the Quality in Prognosis Studies tool. Consensus was achieved for a final set of 43 criteria for pediatric organ dysfunction through iterative voting and discussion. Although the PODIUM criteria for organ dysfunction were limited by available evidence and will require validation, they provide a contemporary foundation for researchers to identify and study single and multiple organ dysfunction in critically ill children.

Knowledge, Attitude, and Practices Associated With COVID-19 Among Healthcare Workers in Hospitals: A Cross-Sectional Study in India

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused phenomenal loss of lives and overburdened the health system in India. Low morale, fatigue, and inadequate knowledge among the healthcare workers (HCWs) are the perceived threats to pandemic control. We aimed to assess the COVID-19 related level of knowledge, attitude, and practices (KAP) among our HCWs. A cross-sectional, electronically distributed, questionnaire-based study was conducted which identified the demographics of HCWs and the current KAP related to coronavirus disease 2019 (COVID-19). The descriptive statistics were used to present the demographics of the participants and chi-square test was used to assess the differences in KAP among the participants. Of 1,429 total participants, 71.9% belonged to age group 21–40 years. Only 40.2% received any infection control training and 62.7% relied upon single source of information update. However, 82.9% of the participants had adequate knowledge. Being married, urban dwelling, and higher qualification were associated with knowledge adequacy (p < 0.001). Interestingly, the senior HCWs (age 41–50 years) were least likely to have adequate knowledge (74.1%). About 84% had positive attitude toward COVID-19, but 83.8% of the participants feared providing care to the patients with COVID-19. However, 93% of HCWs practiced safety precautions correctly most of the times and training had no influence on practice. In conclusion, more than 80% of HCWs in the study had adequate knowledge, positive attitude, and practiced safely most of the time. However, the pitfalls, such as poor training, knowledge uncertainties, and fear of disease acquisition among the HCWs need to be addressed.

Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication

The Third Generation Partnership Project (3GPP) Release 16 defines the sensing-based semi-persistent scheduling (SPS) as the resource allocation scheme for Sidelink Mode 2 in New Radio (NR)-based vehicle-to-everything (V2X) communication. A well-known issue in Mode 2 is the persistent packet collision that results from two or more vehicles repeatedly using the same resource for transmission. It may create serious safety problems when the vehicles are in a situation where only the broadcast safety beacons can assist in driving. To resolve this issue, a solution that relies on the feedback from neighboring vehicles is proposed, through which the vehicles suffering from persistent packet collisions can quickly part and select other resources. Extensive simulations show that the proposed broadcast feedback scheme reduces persistent packet collisions by an order of magnitude compared to SPS, and it is achieved without sacrificing the average packet reception ratio (PRR). Namely, it is the quality aspect (i.e., burstiness) of the packet collisions that the proposed scheme addresses rather than the quantity (i.e., total number of collision losses). By preventing extended packet loss events, the proposed scheme is expected to serve NR V2X better, which requires stringent QoS in terms of the information update delay thereby helping to reduce the chances of vehicle crashes.

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches. <br>

Elastic O-RAN Slicing for Industrial Monitoring and Control: A Distributed Matching Game and Deep Reinforcement Learning Approach

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches. <br>

Hardness of Learning in Rich Environments and Some Consequences for Financial Markets

This paper examines the computational feasibility of the standard model of learning in economic theory. It is shown that the information update technique at the heart of this model is impossible to compute in all but the simplest scenarios. Specifically, using tools from theoretical machine learning, the paper first demonstrates that there is no polynomial implementation of the model unless the independence structure of variables in the data is publicly known. Next, it is shown that there cannot exist a polynomial algorithm to infer the independence structure; consequently, the overall learning problem does not have a polynomial implementation. Using the learning model when it is computationally infeasible carries risks, and some of these are explored in the latter part of the paper in the context of financial markets. Especially in rich, high-frequency environments, it implies discarding a lot of useful information, and this can lead to paradoxical outcomes in interactive game-theoretic situations. This is illustrated in a trading example where market prices can never reflect an informed trader’s information, no matter how many rounds of trade. The paper provides new theoretical motivation for the use of bounded rationality models in the study of financial asset pricing—the bound on rationality arising from the computational hardness in learning.

Dynamic epistemic logics for abstract argumentation

This paper introduces a multi-agent dynamic epistemic logic for abstract argumentation. Its main motivation is to build a general framework for modelling the dynamics of a debate, which entails reasoning about goals, beliefs, as well as policies of communication and information update by the participants. After locating our proposal and introducing the relevant tools from abstract argumentation, we proceed to build a three-tiered logical approach. At the first level, we use the language of propositional logic to encode states of a multi-agent debate. This language allows to specify which arguments any agent is aware of, as well as their subjective justification status. We then extend our language and semantics to that of epistemic logic, in order to model individuals’ beliefs about the state of the debate, which includes uncertainty about the information available to others. As a third step, we introduce a framework of dynamic epistemic logic and its semantics, which is essentially based on so-called event models with factual change. We provide completeness results for a number of systems and show how existing formalisms for argumentation dynamics and unquantified uncertainty can be reduced to their semantics. The resulting framework allows reasoning about subtle epistemic and argumentative updates—such as the effects of different levels of trust in a source—and more in general about the epistemic dimensions of strategic communication.

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Defaultable sovereign debts with macroeconomic conditions and periodic news

This paper presents a default structural model of sovereign debt under macroeconomic conditions and periodic news. I model the macroeconomic conditions to be a finite state of Markov chain, and the periodic news to be a predictable factor in the drifting and the diffusion parts in the underlying value of the representative firm. The innovation of our model is to characterize the price of sovereign debt and the sovereign credit spread associated with macroeconomic conditions, and to model periodic news with both continuous factors and periodic factors. Both the defaultable yield-to-maturity, the sovereign credit spread and the duration are related to the finite state of Markov chain and periodic news. Furthermore, we obtain a closed-form solution for the two-state Markov chain associated to macroeconomic conditions and periodical information update.