Model Reference Gaussian Process Regression:Data-Driven State Feedback Controller

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system's state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model Reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller.</p

Model Reference Gaussian Process Regression:Data-Driven State Feedback Controller

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system's state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model Reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller.</p

Social Network Typologies and Digital Literacy Differences among Korean Older Adults: A Latent Class Analysis

Background This study categorized older Korean adults’ social networks and analyzed their characteristics and digital literacy differences based on type. Methods We analyzed data from 9,377 Korean older adult participants of the 2020 National Survey of Older Koreans, and performed latent class analysis (LCA) chi-square and Welch’s F analyses to understand the characteristics of each social network type. The Games–Howell post-hoc test was applied to determine the significance of differences between groups. Results The three social network types derived using LCA were “child-centered,” “child-friend,” and “friend-centered.” The digital literacy levels differed significantly according to social network type. Conclusion The results of this study can be used to propose intervention programs and services associated with older adults’ social networks by examining their social network types and the corresponding differences in digital literacy

Tailored growth of single-crystalline InP tetrapods

Despite the technological importance of colloidal covalent III-V nanocrystals with unique optoelectronic properties, their synthetic process still has challenges originating from the complex energy landscape of the reaction. Here, we present InP tetrapod nanocrystals as a crystalline late intermediate in the synthetic pathway that warrants controlled growth. We isolate tetrapod intermediate species with well-defined surfaces of (110) and ((1) over bar(1) over bar(1) over bar) via the suppression of further growth. An additional precursor supply at low temperature induces [(1) over bar(1) over bar(1) over bar]-specific growth, whereas the [110]-directional growth occurs over the activation barrier of 65.7 kJ/mol at a higher temperature, thus finalizes into the (111)-faceted tetrahedron nanocrystals. We address the use of late intermediates with well-defined facets at the sub-10 nm scale for the tailored growth of covalent III-V nanocrystals and highlight the potential for the directed approach of nanocrystal synthesis

Urban land planning: The role of a Master Plan in influencing local temperatures

Land use planning (LUP) is central for managing issues related to climatic variation in urban environments. However, Master Plans (MPs) usually do not include climatic aspects, and few studies have addressed climate change at the urban scale, especially in developing countries. This paper proposes a framework with ten categories for assessment of climatic variation in urban LUP. Each category comprises attributes that describe a complex of relationships in influencing local temperature variations. They are analyzed for the case of the Master Plan of Porto Alegre (MPPA), the Southernmost metropolis of Brazil. It is concluded that the MPPA is strongly grounded in climate-related land and zoning coordination, but exhibits weaknesses in building, cartographical and social aspects considered synergistically relevant for tackling problems related to urban climate variation. Furthermore, the MPPA does not contain provisions related to monitoring of local climate and greenhouse gases (GHG) emissions and it is ineffective for improving energy efficiency. Specific MPPA failures stemming from these weaknesses include: an increase of 21.79% in the city's urbanized area from 1986 to 2011 to accommodate a similar increase in population, with significant horizontal sprawl; average temperature rise of 0.392. °C from 1991-2000 to 2001-2010, with statistically significant increases in temperature found since 1931; significant vehicle traffic increases, especially since 2007. From these findings, it is possible to conclude that the MPPA does not offer answers to all the imbalances related to land use, and therefore gives insufficient support to tackle the issue of rising temperatures

Alcohol-Induced Blackout

For a long time, alcohol was thought to exert a general depressant effect on the central nervous system (CNS). However, currently the consensus is that specific regions of the brain are selectively vulnerable to the acute effects of alcohol. An alcohol-induced blackout is the classic example; the subject is temporarily unable to form new long-term memories while relatively maintaining other skills such as talking or even driving. A recent study showed that alcohol can cause retrograde memory impairment, that is, blackouts due to retrieval impairments as well as those due to deficits in encoding. Alcoholic blackouts may be complete (en bloc) or partial (fragmentary) depending on severity of memory impairment. In fragmentary blackouts, cueing often aids recall. Memory impairment during acute intoxication involves dysfunction of episodic memory, a type of memory encoded with spatial and social context. Recent studies have shown that there are multiple memory systems supported by discrete brain regions, and the acute effects of alcohol on learning and memory may result from alteration of the hippocampus and related structures on a cellular level. A rapid increase in blood alcohol concentration (BAC) is most consistently associated with the likelihood of a blackout. However, not all subjects experience blackouts, implying that genetic factors play a role in determining CNS vulnerability to the effects of alcohol. This factor may predispose an individual to alcoholism, as altered memory function during intoxication may affect an individual’s alcohol expectancy; one may perceive positive aspects of intoxication while unintentionally ignoring the negative aspects. Extensive research on memory and learning as well as findings related to the acute effects of alcohol on the brain may elucidate the mechanisms and impact associated with the alcohol-induced blackout

Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system&#x2019;s state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model Reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller