Future spacecraft communication subsystems will potentially benefit from software-defined radios controlled by artificial intelligence algorithms. In this paper, we propose a novel radio resource allocation algorithm leveraging multiobjective reinforcement learning and artificial neural network ensembles able to manage available resources and conflicting mission-based goals. The uncertainty in the performance of thousands of possible radio parameter combinations and the dynamic behavior of the radio channel over time producing a continuous multidimensional state-action space requires a fixed-size memory continuous state-action mapping instead of the traditional discrete mapping. In addition, actions need to be decoupled from states in order to allow for online learning, performance monitoring, and resource allocation prediction. The proposed approach leverages the authors' previous research on constraining decisions predicted to have poor performance through ”virtual environment exploration.” The simulation results show the performance for different communication mission profiles, and accuracy benchmarks are provided for the future research reference. The proposed approach constitutes part of the core cognitive engine proof-of-concept delivered to the NASA John H. Glenn Research Center's SCaN Testbed radios on-board the International Space Station.

3D-printing of cementitious materials is an innovative construction approach with which building elements can be constructed without the use of formwork. Despite potential benefits in the construction industry, it introduces various engineering challenges from the material point of view. This paper reviews the properties of extrusion-based 3D-printed cementitious materials in both fresh and hardened states. Four main properties of fresh-state printing materials are addressed: flowability, extrudability, buildability, and open time, along with hardened properties, including density, compressive strength, flexural strength, tensile bond strength, shrinkage, and cracking. Experimental testing and effective factors of each property are covered, and a mix design procedure is proposed. The main objective of this paper is to provide an overview of the recent development in 3D-printing of cementitious materials and to identify the research gaps that need further investigation.

Plasma medicine is an emerging field that applies the science and engineering of physical plasma to biomedical applications. Low-temperature plasma, also known as cold plasma, is generated via the ionization of atoms in a gas, generally via exposure to strong electric fields, and consists of ions, free radicals, and molecules at varying energy states. Plasmas generated at low temperatures (approximately room temperature) have been used for applications in dermatology, oncology, and anti-microbial strategies. Despite current and ongoing clinical use, the exact mechanisms of action and the full range of effects of cold plasma treatment on cells are only just beginning to be understood. Direct and indirect effects of plasma on immune cells have the potential to be utilized for various applications such as immunomodulation, anti-infective therapies, and regulating inflammation. In this review, we combine diverse expertise in the fields of plasma chemistry, device design, and immunobiology to cover the history and current state of plasma medicine, basic plasma chemistry and their implications, the effects of cold atmospheric plasma on host cells with their potential immunological consequences, future directions, and the outlook and recommendations for plasma medicine.

Construction workers executing manual-intensive tasks are susceptible to musculoskeletal disorders (MSDs) due to overexposure to awkward postures. Automated posture recognition and assessment based on wearable sensor output can help reduce MSDs risks through early risk-factor detection. However, extant studies mainly focus on optimizing recognition models. There is a lack of studies exploring the design of a wearable sensing system that assesses the MSDs risks based on detected postures and then provides feedback for injury prevention. This study aims at investigating the design of an effective wearable MSDs prevention system. This study first proposes the design of a wearable inertial measurement unit (IMU) sensing system, then develops the prototype for end-user evaluation. Construction workers and managers evaluated a proposed system by interacting with wearable sensors and user interfaces (UIs), followed by an evaluation survey. The results suggest that wearable sensing is a promising approach for collecting motion data with low discomfort; posture-based MSDs risk assessment has a high potential in improving workers’ safety awareness; and mobile- and cloud-based UIs can deliver the risk assessment information to end-users with ease. This research contributes to the design, development, and validation of wearable sensing-based injury prevention systems, which may be adapted to other labor-intensive occupations.

C₂⁺ hydrocarbons are selectively produced in one-step catalytic CO₂ conversion via designing the catalyst-bed configuration under non-thermal DBD plasma operating at low temperature and atmospheric pressure.