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The climate of a terrestrial exoplanet is controlled by the type of host star, the orbital configuration, and the characteristics of the atmosphere and the surface. Many rocky exoplanets have higher eccentricities than those in the Solar system, and about 18 per cent of planets with masses <10 M⊕ have e > 0.1. Underexplored are the implications of such high eccentricities on the atmosphere, climate, and potential habitability on such planets. We use WACCM6, a state-of-the-art fully coupled Earth-system model, to simulate the climates of two Earth-like planets: one in a circular orbit (e  = 0), and one in an eccentric orbit (e  = 0.4) with the same mean insolation. We quantify the effects of eccentricity on the atmospheric water abundance and loss given the importance of liquid water for habitability. The asymmetric temperature response in the eccentric orbit results in a water vapour mixing ratio in the stratosphere (>20 ppmv) that is approximately five times greater than that for circular orbit (∼4 ppmv). This leads to at most ∼3 times increases in both the atmospheric hydrogen loss rate and the ocean loss rate compared with the circular case. Using the Planetary Spectrum Generator, we simulate the idealized transmission spectra for both cases. We find that the water absorption features are stronger at all wavelengths for the e  = 0.4 spectrum than for the circular case. Hence, highly eccentric Earth-like exoplanets may be prime targets for future transmission spectroscopy observations to confirm, or otherwise, the presence of atmospheric water vapour. [ABSTRACT FROM AUTHOR]

Abstract: A synergetic classification algorithm for lymphocyte discrimination is developed. By improving the ISODATA algorithm, it is possible to cluster the cell types in the training set with respect to the cluster center samples which plays a role as the cell prototype set types. Based on synergetic theory, a two-step synergetic competition mechanism was established: the order parameter competition was introduced between the test cell sample and the prototypes of each prototype set, by setting the prototype of the maximum order parameter as the best prototype of each set; similar matching competition was performed between the test cell sample and the best prototypes. After that the classification is achieved by selecting the most similar result as the final result. This classification method was applied on four cell groups, i.e. mantle cells, follicular centrocytes, follicular centroblastic cells and centroblastic lymphoma cells. The results show that the averaged lymphocyte classification accuracy is up to 94.1% [Copyright &y& Elsevier]

Simple Summary: The utilization of computer vision technology and automation for monitoring and collecting pigeon eggs is of significant importance for improving labor productivity and the breeding of egg-producing pigeons. Currently, research both domestically and internationally has predominantly focused on the detection of eggs from poultry such as chickens, ducks, and geese, leaving pigeon egg recognition largely unexplored. This study proposes an effective and lightweight network model, YOLOv8-PG, based on YOLOv8n, which maintains high detection accuracy while reducing the model's parameter count and computational load. This approach facilitates cost reduction in deployment and enhances feasibility for implementation on mobile robotic platforms. In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model's robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model's parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms. [ABSTRACT FROM AUTHOR]

The energy use analysis of coal-fired power plant units is of significance for energy conservation and consumption reduction. One of the most serious problems attributed to Chinese coal-fired power plants is coal waste. Several units in one plant may experience a practical rated output situation at the same time, which may increase the coal consumption of the power plant. Here, we propose a new hybrid methodology for plant-level load optimization to minimize coal consumption for coal-fired power plants. The proposed methodology includes two parts. One part determines the reference value of the controllable operating parameters of net coal consumption under typical load conditions, based on an improved K-means algorithm and the Hadoop platform. The other part utilizes a support vector machine to determine the sensitivity coefficients of various operating parameters for the net coal consumption under different load conditions. Additionally, the fuzzy rough set attribute reduction method was employed to obtain the minimalist properties reduction method parameters to reduce the complexity of the dataset. This work is based on continuously-measured information system data from a 600 MW coal-fired power plant in China. The results show that the proposed strategy achieves high energy conservation performance. Taking the 600 MW load optimization value as an example, the optimized power supply coal consumption is 307.95 g/(kW·h) compared to the actual operating value of 313.45 g/(kW·h). It is important for coal-fired power plants to reduce their coal consumption. [ABSTRACT FROM AUTHOR]

Cancer-associated fibroblasts (CAFs), enriched in the tumor stroma, have received increasing attention because of their multifaceted effects on tumorigenesis, development, metastasis, and treatment resistance in malignancies. CAFs contributed to suppressive microenvironment via different mechanisms, while CAFs also exerted some antitumor effects. Therefore, CAFs have been considered promising therapeutic targets for their remarkable roles in malignant tumors. However, patients with malignancies failed to benefit from current CAFs-targeted drugs in many clinical trials, which suggests that further in-depth investigation into CAFs is necessary. Here, we summarize and outline the heterogeneity and plasticity of CAFs mainly by exploring their origin and activation, highlighting the regulation of CAFs in the tumor microenvironment during tumor evolution, as well as the critical roles performed by CAFs in tumor immunity. In addition, we summarize the current immunotherapies targeting CAFs, and conclude with a brief overview of some prospects for the future of CAFs research in the end. 6QEMmoi1AqqA4eegeaUWcQ Video Abstract [ABSTRACT FROM AUTHOR]

To investigate the dynamic changes in the health locus of control (HLC) and subjective well-being (SWB) of older women with breast cancer, to explore the relationship between the HLC and SWB of these patients at different time points. The research method adopted was a longitudinal study. Convenience sampling was used to select older women with breast cancer and their HLC and SWB at different times were evaluated. The ages of the patients were ≥60 years. The survey was conducted starting from the day of admission and at 1 month, 3 months, and 6 months after surgery. HLC and SWB The HLC and SWB of older women with breast cancer changed dynamically (P < 0.05), and the patients were in poor condition at T1. At each time point, the internal HLC (IHLC) for these patients was positively correlated with the total SWB score. Additionally, powerful others HLC (PHLC), chance HLC (CHLC), and SWB were negatively correlated (P < 0.05). The HLC and SWB of older women with breast cancer change dynamically. Medical staff should pay more attention to the HLC and SWB of patients 1 month after surgery. SWB can be improved by improving the patients' IHLC and reducing their PHLC and CHLC. [ABSTRACT FROM AUTHOR]

Video panoramic image stitching is extremely time-consuming among other challenges. We present a new algorithm: (i) Improved, self-adaptive selection of Harris corners. The successful stitching relies heavily on the accuracy of corner selection. We fragment each image into numerous regions and select corners within each region according to the normalized variance of region grayscales. Such a selection is self-adaptive and guarantees that corners are distributed proportional to region texture information. The possible clustering of corners is also avoided. (ii) Multiple-constraint corner matching. The traditional Random Sample Consensus (RANSAC) algorithm is inefficient, especially when handling a large number of images with similar features. We filter out many inappropriate corners according to their position information, and then generate candidate matching pairs based on grayscales of adjacent regions around corners. Finally we apply multiple constraints on every two pairs to remove incorrectly matched pairs. By a significantly reduced number of iterations needed in RANSAC, the stitching can be performed in a much more efficient manner. Experiments demonstrate that (i) our corner matching is four times faster than normalized cross-correlation function (NCC) rough match in RANSAC and (ii) generated panoramas feature a smooth transition in overlapping image areas and satisfy real-time human visual requirements. [ABSTRACT FROM AUTHOR]