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Data, in deep learning (DL), are crucial to detect ships in synthetic aperture radar (SAR) images. However, SAR image annotation limitations hinder DL-based SAR ship detection. A novel data-selection method and teacher–student model are proposed in this paper to effectively leverage sparse labeled data and improve SAR ship detection performance, based on the semi-supervised oriented object-detection (SOOD) framework. More specifically, we firstly propose a SAR data-scoring method based on fuzzy comprehensive evaluation (FCE), and discuss the relationship between the score distribution of labeled data and detection performance. A refined data selector (RDS) is then designed to adaptively obtain reasonable data for model training without any labeling information. Lastly, a Gaussian Wasserstein distance (GWD) and an orientation-angle deviation weighting (ODW) loss are introduced to mitigate the impact of strong scattering points on bounding box regression and dynamically adjusting the consistency of pseudo-label prediction pairs during the model training process, respectively. The experiments results on four open datasets have demonstrated that our proposed method can achieve better SAR ship detection performances on low-proportion labeled datasets, compared to some existing methods. Therefore, our proposed method can effectively and efficiently reduce the burden of SAR ship data labeling and improve detection capacities as much as possible. [ABSTRACT FROM AUTHOR]

Underwater images suffer from low contrast and color distortion. In order to improve the quality of underwater images and reduce storage and computational resources, this paper proposes a lightweight model Rep-UWnet to enhance underwater images. The model consists of a fully connected convolutional network and three densely connected RepConv blocks in series, with the input images connected to the output of each block with a Skip connection. First, the original underwater image is subjected to feature extraction by the SimSPPF module and is processed through feature summation with the original one to be produced as the input image. Then, the first convolutional layer with a kernel size of 3 × 3, generates 64 feature maps, and the multi-scale hybrid convolutional attention module enhances the useful features by reweighting the features of different channels. Second, three RepConv blocks are connected to reduce the number of parameters in extracting features and increase the test speed. Finally, a convolutional layer with 3 kernels generates enhanced underwater images. Our method reduces the number of parameters from 2.7 M to 0.45 M (around 83% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our Rep-UWnet effectively improves high-level vision tasks like edge detection and single image depth estimation. This method not only surpasses the contrast method in objective quality, but also significantly improves the contrast, colorimetry, and clarity of underwater images in subjective quality. [ABSTRACT FROM AUTHOR]

The ecological environment of the Wuling Mountains region has been impacted by climate change and economic development, necessitating immediate reinforcement of ecological protection and restoration measures. The study utilized the normalized vegetation index (NDVI) as a proxy for ecological resilience. NDVI data from 2000 to 2020 were employed to compute the ecological resilience index of the Wuling Mountains area and to examine its spatial and temporal evolution as well as the factors influencing it. The findings indicate that: (1) The ecological resilience index increased in the Wuling Mountains area and Guizhou, Chongqing, and Hunan sub-areas but decreased in the Hubei sub-area. (2) The ecological resilience varies significantly in the Wuling Mountains area and the Guizhou, Hubei, and Hunan sub-regions, whereas it varies less in the Chongqing sub-region. (3) The primary elements influencing the ecological resilience capability of the Wuling Mountains area and its four sub-areas are climate conditions and socio-economic factors, respectively. The study can offer a scientific foundation for ecological conservation and restoration efforts in the Wuling Mountains area, as well as serve as a benchmark for measuring ecological resilience in other environmentally vulnerable regions. [ABSTRACT FROM AUTHOR]

(1) Background: In arid and semi-arid reservoirs, water surface evaporation is the main method of water dissipation in order to inhibit the evaporation of water and enhance economic efficiency. The evaporation inhibition rate of water-piled PV at different times of the year is derived from the anti-evaporation test of water-piled PV, and a new idea is proposed for water conservation in plains reservoirs in arid areas. (2) Methods: The test was conducted by dividing the area into groups A and B, with and without PV panel shading. In situ observation and numerical calculation were used to measure the atmosphere's temperatures, test group, and PV module. The saturated water vapor pressure difference was then calculated according to Dalton's principle to analyze the economic benefits of water saving. (3) Results: Based on the test results, it was found that the shading of PV panels had a cooling effect on the water body, the PV module, and the atmosphere. Group A showed a 44.2% decrease in the saturation water vapor pressure difference compared to Group B. The maximum evaporation suppression rate of 40.2% was observed in July, while the minimum rate of 12.2% was observed in January. The average evaporation suppression rate for the entire year was 29.2%. By utilizing the annual water savings for agricultural irrigation, it is possible to cover 38 hm2 of land and generate a revenue of 39,000 CNY. (4) Conclusions: The photovoltaic water cover can effectively reduce water evaporation and generate economic benefits. [ABSTRACT FROM AUTHOR]

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home. [ABSTRACT FROM AUTHOR]

The transition of the energy model dominated by centralized fossil energy use and the emergence of the Energy Internet and the Integrated Community Energy System (ICES) has gained attention. ICES involved the connection of electricity, heat, gas, and other kinds of energy, and was a significant form of the targeted transformation of conventional single energy networks. Within this system, the traditional demand response (DR) was transformed into an integrated demand response (IDR) in which all energy consumers could participate. The purpose of this study is to discuss the important technologies and models along with assessment and optimization strategies for the implementation of ICES and IDR, based on an extensive literature review. The analysis results show the "IDR + ICES" ecosystem proved to hold great potential for achieving renewable energy penetration, energy efficiency, and climate change control goals, while there are still many limitations in the coordination and reliability of the model and the design of the market mechanism. To conclude, the challenges and opportunities that ICES and IDR face were summarized, and future avenues for research are outlined. [ABSTRACT FROM AUTHOR]

Building-energy consumption is the primary aim of urban energy consumption, which can aid in optimization of building operation and management techniques, creating sustainable building and built environments. However, modellers' understanding of the relationship between building-energy modelling (BEM) accuracy and computational load is still qualitative and deprived of accurate quantitative study. Based on a bottom-up engineering methodology, this study aims to quantitatively explore the effects of building-model input data with different resolution accuracies on energy simulation results, including evaluation of computational load. According to the actual parameters of the case-study building, 108 models with varying input resolution levels were developed to estimate hourly energy usage and annual mean ambient temperature. The results demonstrated that with input parameters at low resolution levels, geometric parameters such as exterior windows, interior windows, and shading exhibited significantly lower computational loads, resulting in reduced errors in the final simulation performance, whereas the occupancy schedule, thermal zoning, and HVAC configuration parameters exhibited significant declines in simulation performance and accuracy. This study presents a methodology applicable to the majority of low-rise, rectangular office structures. Future work would concentrate on carrying out comparison tests for different building forms and types while gradually improving the automation of the process to enable use of the appropriate accuracy level in assessing the crucial issue of energy-modelling input. [ABSTRACT FROM AUTHOR]

In this paper, a compact cat swarm optimization algorithm based on a Small Sample Probability Model (SSPCCSO) is proposed. In the same way as with previous algorithms, there is a tracking mode and a searching mode in the processing of searching for optimal solutions, but besides these, a novel differential operator is introduced in the searching mode, and it is proved that this could greatly enhance the search ability for the potential global best solution. Another highlight of this algorithm is that the gradient descent method is adopted to increase the convergence velocity and reduce the computation cost. More importantly, a small sample probability model is designed to represent the population of samples instead of the normal probability distribution. This representation method could run with low computing power of the equipment, and the whole algorithm only uses a cat with no historical position and velocity. Therefore, it is suitable for solving optimization problems with limited hardware. In the experiment, SSPCCSO is superior to other compact evolutionary algorithms in most benchmark functions and can also perform well compared to some population-based evolutionary algorithms. It provides a new means of solving small sample optimization problems. [ABSTRACT FROM AUTHOR]

Simple Summary: Toxoplasmosis is one of the most common parasitic zoonoses in the world. It not only threatens human health and safety but also causes considerable losses to the global animal husbandry industry. Swine, as an intermediate host of Toxoplasma gondii, has important economic and public health significance, and the global prevalence of T. gondii in swine varies between 10% and 60%. Therefore, it is important to establish simple, effective, sensitive and specific diagnostic methods for the detection and prevention of T. gondii infection in swine. In the present study, we developed a colloidal gold immunochromatographic strip based on rTgROP14, recombinant protein A and monoclonal antibody TgROP14-5D5 for serological detection of T. gondii in swine populations. This new ICT achieved good specificity and sensitivity and has potential for routine serological detection of T. gondii in swine. Toxoplasma gondii, a worldwide distributed apicomplexan protozoan, can infect almost all warm-blooded animals and may cause toxoplasmosis. In order to provide a point-of-care detection method for T. gondii infection, an immunochromatographic test (ICT) was established. The proposed test uses recombinant T. gondii rhoptry protein 14 (ROP14) conjugated with 20 nm gold particles, recombinant protein A as the detection line and monoclonal antibody TgROP14-5D5 as the control line. The specificity, sensitivity, positive predictive value, negative predictive value and stability of this new ICT were evaluated. rTgROP14 was specifically recognized by positive serum of T. gondii but not negative serum. mAb TgROP14-5D5 showed higher specific recognition of T. gondii antigens and was therefore selected for subsequent colloidal gold strip construction. The new ICT based on TgROP14 exhibited good diagnostic performance with high specificity (86.9%) and sensitivity (90.9%) using IHA as a "reference standard". Among 436 field porcine sera, ICT and IHA detected 134 (30.7%) and 99 (22.7%) positive samples, respectively. The relative agreement was 87.8%. These data indicate that this new ICT based on TgROP14 is a suitable candidate for routine testing of T. gondii in the field. [ABSTRACT FROM AUTHOR]

In the single pruning algorithm, channel pruning or filter pruning is used to compress the deep convolution neural network, and there are still many redundant parameters in the compressed model. Directly pruning the filter will largely cause the loss of key information and affect the accuracy of model classification. To solve these problems, a parallel pruning algorithm combined with image enhancement is proposed. Firstly, in order to improve the generalization ability of the model, a data enhancement method of random erasure is introduced. Secondly, according to the trained batch normalization layer scaling factor, the channels with small contribution are cut off, the model is initially thinned, and then the filters are pruned. By calculating the geometric median of the filters, redundant filters similar to them are found and pruned, and their similarity is measured by calculating the distance between filters. Pruning was done using VGG19 and DenseNet40 on cifar10 and cifar100 data sets. The experimental results show that this algorithm can improve the accuracy of the model, and at the same time, it can compress the calculation and parameters of the model to a certain extent. Finally, this method is applied in practice, and combined with transfer learning, traffic objects are classified and detected on the mobile phone. [ABSTRACT FROM AUTHOR]

Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) ≤ 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD ≤ 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h. [ABSTRACT FROM AUTHOR]

In this report, we present a new method for visual detection of Pb2+. Gold nanoparticles (Au-NPs) were synthesized in one step at room temperature, using gallic acid (GA) as reducer and stabilizer. Pb2+ is added during the gold nanoparticle formation. Analysis of Pb2+ is conducted by a dual strategy, namely, colorimetry and spectrometry. During Au-NPs synthesis, addition of Pb2+ would lead to formation of Pb-GA complex, which can induce the aggregation of newly-formed small unstable gold nanoclusters. Consequently, colorimetric detection of trace Pb2+ can be realized. As the Pb2+ concentration increases, the color turns from red-wine to purple, and finally blue. This method offers a sensitive linear correlation between the shift of the absorption band (Δλ) and logarithm of Pb2+ concentration ranging from 5.0×10(-8) to 1.0×10(-6) M with a linear fit coefficient of 0.998, and a high selectivity for Pb2+ detection with a low detection limit down to 2.5×10(-8) M.