Molecular Mechanisms Underlying the Beneficial Effects of Exercise on Brain Function and Neurological Disorders

As life expectancy has increased, particularly in developed countries, due to medical advances and increased prosperity, age-related neurological diseases and mental health disorders have become more prevalent health issues, reducing the well-being and quality of life of sufferers and their families. In recent decades, due to reduced work-related levels of physical activity, and key research insights, prescribing adequate exercise has become an innovative strategy to prevent or delay the onset of these pathologies and has been demonstrated to have therapeutic benefits when used as a sole or combination treatment. Recent evidence suggests that the beneficial effects of exercise on the brain are related to several underlying mechanisms related to muscle–brain, liver–brain and gut–brain crosstalk. Therefore, this review aims to summarize the most relevant current knowledge of the impact of exercise on mood disorders and neurodegenerative diseases, and to highlight the established and potential underlying mechanisms involved in exercise–brain communication and their benefits for physiology and brain function.     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

A study of Q-learning considering negative rewards

In the reinforcement learning system, the agent obtains a positive reward, such as 1, when it achieves its goal. Positive rewards are propagated around the goal area, and the agent gradually succeeds in reaching its goal. If you want to avoid certain situations, such as dangerous places or poison, you might want to give a negative reward to the agent. However, in conventional Q-learning, negative rewards are not propagated in more than one state. In this article, we propose a new way to propagate negative rewards. This is a very simple and efficient technique for Q-learning. Finally, we show the results of computer simulations and the effectiveness of the proposed method.     

Parameter estimation of the electrothermal model of the ferromagnetic core

The paper presents a new form of the electrothermal model of the ferromagnetic core for SPICE and the way, in which the parameters of the model are determined. The manner of determining magnetic, geometric and thermal parameters of this model is described. The correctness of the proposed model and the manner of determining the values of the parameters are verified by comparing the calculated and measured characteristics of the selected ferromagnetic cores.     

2D operators on topographic and non‐topographic architectures—implementation,efficiency analysis,and architecture selection methodology

Topographic and non‐topographic image processing architectures and chips, developed within the CNN community recently, are analyzed and compared. It is achieved on a way that the 2D operators are collected to classes according to their implementation methods on the different architectures, and the main implementation parameters of the different operator classes are compared. Based on the results, an efficient architecture selection methodology is formalized. Copyright © 2010 John Wiley & Sons, Ltd.     

Facile synthesis of different morphologies of Te-doped ZnO nanostructures

Te-doped ZnO nanostructures were synthesized by an annealing (vapor–solid) process under ambient conditions, and characterized in terms of their morphological, structural, compositional and optical properties. The structural and morphological characterizations revealed that the synthesized nanostructures were well-defined multipods, needles and spherical particles, and possessed well-crystalline ZnO wurtzite hexagonal phase. Also, in the X-ray diffraction studies, the presence of a shift in the peak positions towards a lower angle, and a decrease in the intensity, with an increase in the Te concentration, as compared to the undoped ZnO, were observed. The chemical composition confirmed the presence of Te, in the case of multipod and needle morphologies. The effect of doping on the crystalline quality and optical properties was also investigated, by using photoluminescence (PL) and Raman spectrometers. The Raman results demonstrated that the doped ZnO nanostructures had a lower crystalline quality than the undoped ZnO. Moreover, the PL results showed a decrease in the band gap for the doped ZnO nanostructures, in comparison to the undoped ZnO. A possible growth mechanism was also proposed.     

An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks

Much of software developers' time is spent understanding unfamiliar code. To better understand how developers gain this understanding and how software development environments might be involved, a study was performed in which developers were given an unfamiliar program and asked to work on two debugging tasks and three enhancement tasks for 70 minutes. The study found that developers interleaved three activities. They began by searching for relevant code both manually and using search tools; however, they based their searches on limited and misrepresentative cues in the code, environment, and executing program, often leading to failed searches. When developers found relevant code, they followed its incoming and outgoing dependencies, often returning to it and navigating its other dependencies; while doing so, however, Eclipse's navigational tools caused significant overhead. Developers collected code and other information that they believed would be necessary to edit, duplicate, or otherwise refer to later by encoding it in the interactive state of Eclipse's package explorer, file tabs, and scroll bars. However, developers lost track of relevant code as these interfaces were used for other tasks, and developers were forced to find it again. These issues caused developers to spend, on average, 35 percent of their time performing the mechanics of navigation within and between source files. These observations suggest a new model of program understanding grounded in theories of information foraging and suggest ideas for tools that help developers seek, relate, and collect information in a more effective and explicit manner