The Central Bank Shift to Market Maker of Last Resort: The Unintended Consequences of Unconventional Monetary Policies

We analyze the transition of central banks from lenders to market makers of last resort. The adoption of unconventional monetary policies characterizes this transition. In their new role as market makers, central banks engage in the latter by extending and reinforcing interventions in other markets than the traditional bank reserves market. We then explain that the difference between the two roles is one of degree rather than kind. In both cases, the prevention of liquidity shortages is a primary concern. As conventional policies become inadequate, central banks resort to unconventional policies to escape a general liquidity shortage at the zero lower bound. However, these unconventional policies do not solve the structural problems in financial and real markets. Both conventional and unconventional monetary policies cause price distortions, in particular on asset markets. The policies of the market maker of last resort prevent necessary readjustments of cyclical divergences between real and financial markets.

Soft Robotic Deployable Origami Actuators for Neurosurgical Brain Retraction

Metallic tools such as graspers, forceps, spatulas, and clamps have been used in proximity to delicate neurological tissue and the risk of damage to this tissue is a primary concern for neurosurgeons. Novel soft robotic technologies have the opportunity to shift the design paradigm for these tools towards safer and more compliant, minimally invasive methods. Here, we present a pneumatically actuated, origami-inspired deployable brain retractor aimed at atraumatic surgical workspace generation inside the cranial cavity. We discuss clinical requirements, design, fabrication, analytical modeling, experimental characterization, and in-vitro validation of the proposed device on a brain model.

Bayesian Hierarchical Models can Infer Interpretable Predictions of Leaf Area Index From Heterogeneous Datasets

Environmental scientists often face the challenge of predicting a complex phenomenon from a heterogeneous collection of datasets that exhibit systematic differences. Accounting for these differences usually requires including additional parameters in the predictive models, which increases the probability of overfitting, particularly on small datasets. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to incorporate information about the structure of the dataset explicitly. To this end, we look at a typical application in remote sensing: the estimation of leaf area index of white winter wheat, an important indicator for agronomical modeling, using measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach show that Bayesian hierarchical models are a versatile tool for the prediction of leaf area index, particularly in scenarios where the available data sources are heterogeneous.

Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multiagent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.

Room-Level Ventilation in Schools and Universities

Ventilation is of primary concern for maintaining healthy indoor air quality and reducing the spread of airborne infectious disease, including COVID-19. In addition to building-level guidelines, increased attention is being placed on room-level ventilation. However, for many universities and schools, ventilation data on a room-by-room basis are not available for classrooms and other key spaces. We present an overview of approaches for measuring ventilation along with their advantages and disadvantages. We also present data from recent case studies for a variety of institutions across the United States, with various building ages, types, locations, and climates, highlighting their commonalities and differences, and examples of the use of this data to support decision making.

Reflections on service-learning in european higher education

Over the last two decades, there has been a growing emphasis on the transformation of higher education within Europe. The promotion of active and democratic citizenship through formal higher education is a primary concern. Service-learning (SL) has proven to be a powerful didactic methodology for achieving these ideals. The objective of this paper is to highlight some reflections that permeate SL european experiences, published in the European Observatory of Service-Learning in Higher Education, between January 2020 and September 2021. The counting and percentages of the valued attitudes of SL experiences were collected to explore the guiding philosophy, historical roots, purposes, and underpinning features of SL, contextual factors affecting the practice of SL in the partner institutions and countries, and future challenges and opportunities. Conclusions focus on SL growth and mainstreaming; SL as a core strategic pillar for developing deeper community-higher education partnerships and for strengthening civic engagement across higher education; the need to develop future research to explore these connections and the potential that SL can play in terms of increased civic, work-life and theoretical understandings and skills.

A Bilingual Child Diagnosed With Autism Spectrum Disorder (ASD)

Silvia and Antonio Juarez described their 4-year-old son Emanuel as often happy. Emanuel came from a bilingual home and spoke both Spanish and English. His favorite activities were reading and playing with his iPad over-and-over again. Reading was a favorite pasttime and something that the Juarez's felt he did well. While he was described as “loving to engage people,” it was not clear that his efforts were successful nor was it clear that they were reciprocated. Briefly observing Emanuel's interaction with his parents, it was noted Emanuel used echolalic phrases and engaged in repetitive and restricted behaviors, toe walking, and finger flicking. These behaviors were noted to be unusual and warranted further evaluation. The Juarez's primary concern was regarding Emanuel's “talking and being able to hold a conversation.” Through speech and language therapy, the family expected that Emanuel's communication skills would improve, and he would be able to participate in conversations and talk in sentences.

Application of Blockchain in E-Healthcare Systems

The e-healthcare system maintains sensitive and private information about patients. In any e-healthcare system, exchanging health information is often required, making privacy and security a primary concern for e-healthcare systems. Another major issue is that existing e-healthcare systems use centralized servers. These centralized servers require high infrastructure and maintenance costs for day-to-day services. Along with that, server failure may affect the working of e-healthcare systems drastically and may create life-threatening situations for patients. Blockchain technology is a very useful way to provide decentralized, secure storage for healthcare information. A blockchain is a time-stamped series of immutable records of data that is managed by a cluster of computers not owned by any single entity. These blocks create a chain of immutable, tamper-proof blocks in a ledger. This chapter will discuss the different aspects of blockchain and its application in different fields of the e-healthcare system.

DATA SECURITY — THE NEXT IMPERATIVE IN HEALTHCARE SYSTEMS

Wireless Sensor Networks (WSNs) have significantly impacted healthcare applications by giving the possibility of monitoring the patient’s physiological parameters using different sensors. The use of WSN and the wireless Body Area Networks (WBANs) offers possible solutions for monitoring the health parameters in remote areas. On the other hand, the use of wireless communication medium and information security is the primary concern in WBANs. Because WBANs use the different small sensors placed on the human body to collect the physiological data. They need resource and computational restrictions, thus, building the use of complex and advanced encryption algorithms infeasible. It is essential in the WBAN to monitor and transmit the data to provide reliable and secure communication. Wrong and incomplete information can create difficulties in patient health which can be sometimes more dangerous. This gives the motivation to make such security protocols or algorithms to achieve high security in WBANs. So, the research has been currently focused on reliable communication between the doctor and patient, routing algorithms, and the data’s security by using various new technologies. This paper discusses the different security threats and solutions for designing healthcare applications and routing and layer attacks. Furthermore, the paper has been focused on the Data Distribution Service Models for data security. The paper also includes artificial intelligence and machine learning algorithms in healthcare implemented by various companies.

Laboratory And Field Tests On A Prefabricated Steel-Bar Mesh-Panel System For Continuously-Reinforced-Concrete Pavement (CRCP)

The decline in the number of persons of working age is a social problem in Japan. This is a particularly serious concern for workers in the construction field; construction systems should be considered for productivity improvements. Prefabrication systems are an effective method for shortening construction cycles and times. In fact, various precast concrete members have been employed to realize more rapid construction and improvements in quality. Using precast concrete members is difficult because jointless roads are preferable for highway pavement. Continuously reinforced concrete pavement (CRCP), which has the advantages of concrete jointless construction and high ductility, is a suitable method for highway road construction. Typical Japanese highways built with CRCP reduce the amount of horizontal cracking by arranging transverse rebars at an angle of 60° to the main rebars. Note that rebar placement and bonding in conventional CRCP are troublesome and labor intensive owing to the long construction time required. We have developed prefabricated steel bar meshes for CRCP and can report some benefits relating to their practical application. To examine the fundamental properties of mesh panels, we conducted a laboratory experiment and a simulated field test. The primary concern of welded rebars are failures induced by cyclic loading. A flexural fatigue loading test using CRCP models was conducted. In addition, a comparative survey on conventional and prefabrication systems was performed in the simulated field test to quantify the constructability of CRCP and to observe the extent of cracking in concrete. This paper reports on our experimental investigation.