Gábor Kőrösi, Oliver Czimbalmos, Gabriella Kekesi
et al.
Abstract We present a high-throughput behavioral dataset acquired with Ambitus, an automated reward-based corridor system that records locomotor and exploratory activities and cognitive functions after minimal handling. The collection contains 91 raw and derived variables, each measured across four consecutive trials, for 1,342 Long-Evans rats, including a triple-hit schizophrenia-like substrain (Lisket) bred through 16 generations. All data files, detailed metadata and analysis scripts are openly available on Zenodo. This resource enables longitudinal and multivariate studies of behavioral phenotypes, trans-generational effects, and strain differences, and it provides a benchmark for machine-learning-based marker discovery in rodent models.
Divya Nimma, Okram Ricky Devi, Bibek Laishram
et al.
Global warming is a phenomenon whereby the planet's exposure to the sun's radiation worsens from the high emission of gasses believed to trap heat within the atmosphere. Carbon dioxide (CO2) is the leading greenhouse gas majorly responsible for global warming and other related issues and is a danger to global society. This one has a particular role in portraying the key importance of the shifting climate that invariably influences water supply and agricultural production. Global warming presents complex challenges to aquatic organisms and stocks and other natural aquatic life resources. This study examines how freshwater and marine species are affected by climate change in aquatic habitats. Aquatic species' metabolism, growth, reproduction, and dispersal are all impacted by rising temperatures and altered water chemistry brought on by increased greenhouse gas emissions, especially CO2. The goal is to pinpoint the ecosystems and vulnerable species that are most impacted by these changes and suggest flexible management techniques. The suggested remedies center on creating sustainable conservation strategies that lessen the effects of climate change on aquatic biodiversity and increase these ecosystems' resilience. The socio-economic interdependencies between water and climate change impact agricultural and water resources, and the pressures exerted on water bodies and water supply landscapes. Another area is related to alterations in the physical and chemical properties of the water, such as the temperature, which is a well-known effect of climate change: 'This causes abnormalities in the metabolism and physiology of aquatic species.' These alterations flow through the chain and regime of growth, reproduction, feeding habits and distribution, migration, and mass of fish and other creatures in the water system. However, the long-term effect of climate variation and climate change on freshwater ecosystems requires much scientific investigation to address challenges in aquatic ecosystem conservation and sustainability. This being the case, adaptive management solutions that address the interrelated impacts of climate change have to be applied and implemented to reduce vulnerability in aquatic ecosystems worldwide.
Abstract With the rapid development of science and technology, augmented reality technology provides intelligent and application services. The research is based on imaging techniques using augmented reality technology and camera image capture. Then, it uses screen error algorithms and scale-invariant feature transformation operators to test the quality of scene spatial models. The experimental results demonstrated that the camera significantly improved the frame rate of scene model rendering and could steadily enhance rendering efficiency. For image quality and its influencing factors, binary robust invariant scalable keypoints and scale-invariant feature transformation algorithms in viewpoint changes had the highest recall of 92%. The map drawing module, Hessian matrix, and scale-invariant feature transformation algorithm in the image blurring test achieved the highest recall rate of 98%. This demonstrates the advantage of using a scale-invariant feature transformation operator to capture scene space influence and provide a more accurate spatial model reference for augmented reality technology. This enhances the functional design of the guide system.
Computational linguistics. Natural language processing, Electronic computers. Computer science
Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. In the social sciences, research over the past decades has developed comprehensive models to describe media bias and effective, yet often manual and thus cumbersome, methods for analysis. In contrast, in computer science fast, automated, and scalable methods are available, but few approaches systematically analyze media bias. The models used to analyze media bias in computer science tend to be simpler compared to models established in the social sciences, and do not necessarily address the most pressing substantial questions, despite technically superior approaches. Computer science research on media bias thus stands to profit from a closer integration of models for the study of media bias developed in the social sciences with automated methods from computer science. This article first establishes a shared conceptual understanding by mapping the state of the art from the social sciences to a framework, which can be targeted by approaches from computer science. Next, we investigate different forms of media bias and review how each form is analyzed in the social sciences. For each form, we then discuss methods from computer science suitable to (semi-)automate the corresponding analysis. Our review suggests that suitable, automated methods from computer science, primarily in the realm of natural language processing, are already available for each of the discussed forms of media bias, opening multiple directions for promising further research in computer science in this area.
ACT-R is a hybrid cognitive architecture. It is comprised of a set of programmable information processing mechanisms that can be used to predict and explain human behavior including cognition and interaction with the environment. We start by reviewing its history, which shapes its current form, contrasts and relates it to other architectures, and helps readers to anticipate where it is going. Based on this history, we then describe it as a theory of cognition that is realized as a computer program. After this, we briefly discuss tools for working with ACT-R, and also note several major accomplishments that have been gained by working with ACT-R in both basic and applied science, including summarizing some of the insights about human behavior. We conclude by discussing its future, which we believe will include adding emotions and physiology, increasing usability, and the use of nongenerative models. This article is categorized under: Computer Science > Artificial Intelligence Psychology > Reasoning and Decision Making Psychology > Theory and Methods.
Feifan Liu, Torsten Neubert, Olivier Chanrion
et al.
Abstract Blue corona discharges are often generated in thunderclouds penetrating into the stratosphere and are the optical manifestation of narrow bipolar events (NBEs) observed in radio signals. While their production appears to depend on convection, the cause and nature of such discharges are not well known. Here we show the observations by a lightning detection array of unusual amounts of 982 NBEs during a tropical storm on the coastline of China. NBEs of negative polarity are predominantly observed at the cloud top reaching the stratosphere, and positive NBEs are primarily at lower altitudes. We find that the dominant polarity changes with the typical time of development of thunderstorm cells, suggesting that the polarity depends on the phase of the storm cells. Furthermore, we find that the lightning jump of negative NBEs is associated with above-anvil cirrus plumes of ice crystals and water vapor in the lower stratosphere. We propose that variations in updrafts induce changes in the altitude and charge concentrations of the cloud layers, which lead to the polarity transition. Our results have implications for studies of the chemical perturbations of greenhouse gas concentrations by corona discharges at the tropopause.
Antoine Verreault, Paul-Vahe Cicek, Alexandre Robichaud
Oversampling analog-to-digital converters (ADC) serve as the backbone of high-performance, high-precision data interfaces, owing to their remarkable ability to filter out quantization noise. This attribute makes them the preferred choice for applications requiring high signal-to-noise ratio (SNR) and moderate bandwidth, with great design flexibility. This paper provides an extensive survey of the latest advancements in oversampling ADC tailored for such applications as documented in recent literature. Specifically focusing on design techniques employed within the last five years, the survey encompasses various oversampling ADC architectures, including discrete-time and continuous-time <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>, noise-shaping SAR, zoom, incremental, and time-domain modulators. A thorough performance comparison between these different topologies is presented, highlighting designs that achieve the best figures-of-merit. Furthermore, the paper explores circuit-level design trends commonly shared among these architectures, with particular attention given to amplifier designs for loop filters. Conclusions drawn highlight the limitations of much of the research works in the context of implementing ADC within complete systems, while also providing insight into the expected future trends that will shape the field moving forward.