Related consistent lures increase the judgment of multiplication facts: Evidence using event-related potential technique

Simple multiplication errors are primarily shown in whether the lures are related to the operands (relatedness, such as 3 × 4 = 15 vs. 17) or whether the same decades are shared with the correct answers (consistency, such as 3 × 4 = 16 vs. 21). This study used a delayed verification paradigm and event-related potential technique to investigate the effects of relatedness and consistency in simple multiplication mental arithmetic for 30 college students in an experiment of presenting probes in auditory channels. We found that, compared to the related inconsistent lures, the related consistent lures showed significantly faster reaction time and induced significantly large amplitudes of N400 and late positive component. The findings suggest that related consistent lures are less affected by the activation diffusion of the arithmetic problem, and the credibility of being perceived as the correct answer is less; the lures related to operands and sharing the same decades with the accurate results can promote the judgment of multiplication mental arithmetic, and the results support the Interacting Neighbors Model

Palmitoleate Induces Hepatic Steatosis but Suppresses Liver Inflammatory Response in Mice

The interaction between fat deposition and inflammation during obesity contributes to the development of non-alcoholic fatty liver disease (NAFLD). The present study examined the effects of palmitoleate, a monounsaturated fatty acid (16∶1n7), on liver metabolic and inflammatory responses, and investigated the mechanisms by which palmitoleate increases hepatocyte fatty acid synthase (FAS) expression. Male wild-type C57BL/6J mice were supplemented with palmitoleate and subjected to the assays to analyze hepatic steatosis and liver inflammatory response. Additionally, mouse primary hepatocytes were treated with palmitoleate and used to analyze fat deposition, the inflammatory response, and sterol regulatory element-binding protein 1c (SREBP1c) activation. Compared with controls, palmitoleate supplementation increased the circulating levels of palmitoleate and improved systemic insulin sensitivity. Locally, hepatic fat deposition and SREBP1c and FAS expression were significantly increased in palmitoleate-supplemented mice. These pro-lipogenic events were accompanied by improvement of liver insulin signaling. In addition, palmitoleate supplementation reduced the numbers of macrophages/Kupffer cells in livers of the treated mice. Consistently, supplementation of palmitoleate decreased the phosphorylation of nuclear factor kappa B (NF-κB, p65) and the expression of proinflammatory cytokines. These results were recapitulated in primary mouse hepatocytes. In terms of regulating FAS expression, treatment of palmitoleate increased the transcription activity of SREBP1c and enhanced the binding of SREBP1c to FAS promoter. Palmitoleate also decreased the phosphorylation of NF-κB p65 and the expression of proinflammatory cytokines in cultured macrophages. Together, these results suggest that palmitoleate acts through dissociating liver inflammatory response from hepatic steatosis to play a unique role in NAFLD

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Systematic Review of Sensing Technology in Human-Building Interaction Research

Human-building interaction is an emerging field of study that investigates the interactions and reciprocal impacts of humans and building systems. In this discipline, sensing technology is critical for data collection. The application of sensing technology is divided into six categories based on the research topics: (1) occupancy status, (2) occupant physiological indicators, (3) building components, (4) building environment, (5) building consumption, and (6) fusion of multi-sensing system. By evaluating 127 relevant research articles, this study attempts to provide a systematic review of the implementation of sensing technologies in each HBI research topic. Four significant sensing technologies were investigated for the occupancy status study: camera-based sensing, infrared-based sensing, radial frequency signal-based sensing, and ultrasonic sensor. Methodologies for biosensing brain activity, muscle and skin function, and cardiac function were examined as occupant physiological indicator measurements. The magnetic reed and vibration sensors were discussed for sensing changes in building components. The air property sensor, sound sensor, and illuminance sensor were introduced to monitor the building environment. The smart meter and smart plug were examined for sensing building consumption, and the application of multi-sensor fusion was also included in this article. Furthermore, this systematic study discussed three aspects of contemporary sensing technology deployment: data concealment, sensor cost tradeoffs, and privacy concerns

Adapting Cache Line Size to Application Behavior

A cache line size has a significant effect on miss rate and memory traffic. Today's computers use a fixed line size, typically 32B, which may not be optimal for a given application. Optimal size may also change during application execution. This paper describes a cache in which the line (fetch) size is continuously adjusted by hardware based on observed application accesses to the line. The approach can improve the miss rate, even over the optimal for the fixed line size, as well as significantly reduce the memory traffic

Exploring the Physical-World Adversarial Robustness of Vehicle Detection

Adversarial attacks can compromise the robustness of real-world detection models. However, evaluating these models under real-world conditions poses challenges due to resource-intensive experiments. Virtual simulations offer an alternative, but the absence of standardized benchmarks hampers progress. Addressing this, we propose an innovative instant-level data generation pipeline using the CARLA simulator. Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks. Our findings highlight diverse model performances under adversarial conditions. YOLO v6 demonstrates remarkable resilience, exhibiting just a marginal 6.59% average drop in average precision (AP). In contrast, the ASA attack yields a substantial 14.51% average AP reduction, twice the effect of other algorithms. We also note that static scenes yield higher recognition AP values, and outcomes remain relatively consistent across varying weather conditions. Intriguingly, our study suggests that advancements in adversarial attack algorithms may be approaching its “limitation”. In summary, our work underscores the significance of adversarial attacks in real-world contexts and introduces the DCI dataset as a versatile benchmark. Our findings provide valuable insights for enhancing the robustness of detection models and offer guidance for future research endeavors in the realm of adversarial attacks

WPPG Net: A Non-contact Video Based Heart Rate Extraction Network Framework with Compatible Training Capability

Our facial skin presents subtle color change known as remote Photoplethysmography (rPPG) signal, from which we could extract the heart rate of the subject. Recently many deep learning methods and related datasets on rPPG signal extraction are proposed. However, because of the time consumption blood flowing through our body and other factors, label waves such as BVP signals have uncertain delays with real rPPG signals in some datasets, which results in the difficulty on training of networks which output predicted rPPG waves directly. In this paper, by analyzing the common characteristics on rhythm and periodicity of rPPG signals and label waves, we propose a whole set of training methodology which wraps these networks so that they could remain efficient when be trained at the presence of frequent uncertain delay in datasets and gain more precise and robust heart rate prediction results than other delay-free rPPG extraction methods

Effects of Grazing on Ecosystem CO2 Exchange in a MeadowGrassland on the Tibetan Plateau During the Growing Season

Effects of human activity on ecosystem carbon fluxes (e.g., net ecosystem exchange (NEE), ecosystem respiration (R eco), and gross ecosystem exchange (GEE)) are crucial for projecting future uptake of CO2 in terrestrial ecosystems. However, how ecosystem that carbon fluxes respond to grazing exclusion is still under debate. In this study, a field experiment was conducted to study the effects of grazing exclusion on R eco, NEE, and GEE with three treatments (free-range grazing (FG) and grazing exclusion for 3 and 5&nbsp;years (GE3 and GE5, respectively)) in a meadow grassland on the Tibetan Plateau. Our results show that grazing exclusion significantly increased NEE by 47.37 and 15.84&nbsp;%, and R eco by 33.14 and 4.29&nbsp;% under GE3 and GE5 plots, respectively, although carbon sinks occurred in all plots during the growing season, with values of 192.11, 283.12, and 222.54&nbsp;g&nbsp;C&nbsp;m&minus;2 for FG, GE3, and GE5, respectively. Interestingly, grazing exclusion increased temperature sensitivity (Q 10) of R eco with larger increases at the beginning and end of growing season (i.e., May and October, respectively). Soil temperature and soil moisture were key factors on controlling the diurnal and seasonal variations of R eco, NEE, and GEE, with soil temperature having a stronger influence. Therefore, the combined effects of grazing and temperature suggest that grazing should be taken into consideration in assessing global warming effects on grassland ecosystem CO2 exchange.</p