Video elicited physiological signal dataset considering indoor temperature factors

IntroductionHuman emotions vary with temperature factors. However, most studies on emotion recognition based on physiological signals overlook the influence of temperature factors. This article proposes a video induced physiological signal dataset (VEPT) that considers indoor temperature factors to explore the impact of different indoor temperature factors on emotions.MethodsThis database contains skin current response (GSR) data obtained from 25 subjects at three different indoor temperatures. We selected 25 video clips and 3 temperatures (hot, comfortable, and cold) as motivational materials. Using SVM, LSTM, and ACRNN classification methods, sentiment classification is performed on data under three indoor temperatures to analyze the impact of different temperatures on sentiment.ResultsThe recognition rate of emotion classification under three different indoor temperatures showed that anger and fear had the best recognition effect among the five emotions under hot temperatures, while joy had the worst recognition effect. At a comfortable temperature, joy and calmness have the best recognition effect among the five emotions, while fear and sadness have the worst recognition effect. In cold temperatures, sadness and fear have the best recognition effect among the five emotions, while anger and joy have the worst recognition effect.DiscussionThis article uses classification to recognize emotions from physiological signals under the three temperatures mentioned above. By comparing the recognition rates of different emotions at three different temperatures, it was found that positive emotions are enhanced at comfortable temperatures, while negative emotions are enhanced at hot and cold temperatures. The experimental results indicate that there is a certain correlation between indoor temperature and physiological emotions

Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

BackgroundMachine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance.MethodsThe PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared.ResultsOut of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias.ConclusionThe current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction.Study registrationThis protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b)

A comparative analysis of morphology, microstructure, and volatile metabolomics of leaves at varied developmental stages in Ainaxiang (Blumea balsamifera (Linn.) DC.)

IntroductionAinaxiang (Blumea balsamifera (Linn.) DC.) is cultivated for the extraction of (-)-borneol and other pharmaceutical raw materials due to its abundant volatile oil. However, there is limited knowledge regarding the structural basis and composition of volatile oil accumulation in fresh B. balsamifera leaves.MethodsTo address this problem, we compare the fresh leaves’ morphology, microstructure, and volatile metabonomic at different development stages, orderly defined from the recently unfolded young stage (S1) to the senescent stage (S4).Results and discussionDistinct differences were observed in the macro-appearance and microstructure at each stage, particularly in the B. balsamifera glandular trichomes (BbGTs) distribution. This specialized structure may be responsible for the accumulation of volatile matter. 213 metabolites were identified through metabolomic analysis, which exhibited spatiotemporal accumulation patterns among different stages. Notably, (-)-borneol was enriched at S1, while 10 key odor metabolites associated with the characteristic balsamic, borneol, fresh, and camphor aromas of B. balsamifera were enriched in S1 and S2. Ultra-microstructural examination revealed the involvement of chloroplasts, mitochondria, endoplasmic reticulum, and vacuoles in the synthesizing, transporting, and storing essential oils. These findings confirm that BbGTs serve as the secretory structures in B. balsamifera, with the population and morphology of BbGTs potentially serving as biomarkers for (-)-borneol accumulation. Overall, young B. balsamifera leaves with dense BbGTs represent a rich (-)-borneol source, while mesophyll cells contribute to volatile oil accumulation. These findings reveal the essential oil accumulation characteristics in B. balsamifera, providing a foundation for further understanding

YOLO-Submarine Cable: An Improved YOLO-V3 Network for Object Detection on Submarine Cable Images

Due to the strain on land resources, marine energy development is expanding, in which the submarine cable occupies an important position. Therefore, periodic inspections of submarine cables are required. Submarine cable inspection is typically performed using underwater vehicles equipped with cameras. However, the motion of the underwater vehicle body, the dim light underwater, and the property of light propagation in water lead to problems such as the blurring of submarine cable images, the lack of information on the position and characteristics of the submarine cable, and the blue–green color of the images. Furthermore, the submarine cable occupies a significant portion of the image as a linear entity. In this paper, we propose an improved YOLO-SC (YOLO-Submarine Cable) detection method based on the YOLO-V3 algorithm, build a testing environment for submarine cables, and create a submarine cable image dataset. The YOLO-SC network adds skip connections to feature extraction to make the position information of submarine cables more accurate, a top-down downsampling structure in multi-scale special fusion to reduce the network computation and broaden the network perceptual field, and lightweight processing in the prediction network to accelerate the network detection. Under laboratory conditions, we illustrate the effectiveness of these modifications through ablation studies. Compared to other algorithms, the average detection accuracy of the YOLO-SC model is increased by up to 4.2%, and the average detection speed is decreased by up to 1.616 s. The experiments demonstrate that the YOLO-SC model proposed in this paper has a positive impact on the detection of submarine cables

Printing on Particles: Combining Two-Photon Nanolithography and Capillary Assembly to Fabricate Multimaterial Microstructures

Additive manufacturing at the micro- and nanoscale has seen a recent upsurge to suit an increasing demand for more elaborate structures. However, the integration of multiple distinct materials at small scales remains challenging. To this end, capillarity-assisted particle assembly (CAPA) and two-photon polymerization direct laser writing (2PP-DLW) are combined to realize a new class of multimaterial microstructures. 2PP-DLW and CAPA both are used to fabricate 3D templates to guide the CAPA of soft- and hard colloids, and to link well-defined arrangements of functional microparticle arrays produced by CAPA, a process that is termed "printing on particles." The printing process uses automated particle recognition algorithms to connect colloids into 1D, 2D, and 3D tailored structures, via rigid, soft, or responsive polymer links. Once printed and developed, the structures can be easily re-dispersed in water. Particle clusters and lattices of varying symmetry and composition are reported, together with thermoresponsive microactuators, and magnetically driven "micromachines", which can efficiently move, capture, and release DNA-coated particles in solution. The flexibility of this method allows the combination of a wide range of functional materials into complex structures, which will boost the realization of new systems and devices for numerous fields, including microrobotics, micromanipulation, and metamaterials.ISSN:0935-9648ISSN:1521-409

Modular assembly of microswimmers with liquid compartments

Artificial microswimmers, i.e. colloidal scale objects capable of self-propulsion, have garnered significant attention due to their central role as models for out of equilibrium systems. Moreover, their potential applications in diverse fields such as biomedicine, environmental remediation, and materials science have long been hypothesized, often in conjunction with their ability to deliver cargoes to overcome mass transport limitations. A very efficient way to load molecular cargoes is to disperse them in a liquid compartment, however, fabricating microswimmers with multiple liquid compartments remains a significant challenge. To address this challenge, we present a modular fabrication platform that combines microfluidic synthesis and sequential capillarity-assisted particle assembly (sCAPA) for microswimmers with various liquid compartments. We demonstrate the synthesis of monodisperse, small polymer-based microcapsules (Ø = 3–6 μm) with different liquid cargoes using a flow-focusing microfluidic device. By employing the sCAPA technique, we assemble multiple microcapsules into microswimmers with high precision, resulting in versatile microswimmers with multiple liquid compartments and programmable functionalities. Our work provides a flexible approach for the fabrication of modular microswimmers, which could potentially actively transport cargoes and release them on demand in the future.ISSN:0953-8984ISSN:1361-648

Enhanced thermal performance of internal Y-shaped bifurcation microchannel heat sinks with metal foams

Internal Y-shaped bifurcation has been proved to be an advantageous way on improvingthermal performance of microchannel heat sinks according to the previous research. Metal foams are known due to their predominate performance such as low-density, largesurface area, and high thermal conductivity. In this paper, different parameters of metalfoams in Y-shaped bifurcation microchannel heat sinks are designed and investigatednumerically. The effects of Reynolds number, porosity of metal foam, and the pore density(PPI) of the metal foam on the microchannel heat sinks are analyzed in detail. It is foundthat the internal Y-shaped bifurcation microchannel heat sinks with metal foam exhibitbetter heat transfer enhancement and overall thermal performance. This researchprovides broad application prospects for heat sinks with metal foam in the thermalmanagement of high power density electronic devices

Implicit trust between the Uyghur and the Han in Xinjiang, China.

Trust is a vital lubricant that increases the sense of security in social interactions. In this study, we investigated the intergroup trust between the Uyghur and the Han, the two largest ethnic groups in Xinjiang, China, with a Go/No-Go Association Task. Specifically, we instructed Uyghur and Han participants to respond to ethnic faces (Uyghur vs. Han) and trust/distrust words and measured the strength of the automatic associations between the faces and words for both in-group and out-group pairs. As expected, both ethnic groups showed implicit in-group trust and out-group distrust, but the Han group demonstrated stronger in-group trust and out-group distrust toward the Uyghur than the Uyghur group toward the Han. However, the magnitude of distrust of the Han toward the Uyghur was small to medium as compared with that reported by other intergroup relationship research. In addition, participant geographic location was associated with out-group distrust. These findings offer implications for developing effective strategies to encourage trust between conflicting groups