Infrared Extinction Coefficients of Artificial Aerosol

The artificial aerosol is widely used in the modern battlefields to protect the potentialtargets and to conceal the movement of personnel and materials. In this paper, the double-bandinfrared extinction coefficients of the artificial aerosol have been calculated and compared withthe experimental data. The particulates were assumed to be small spheres and Mie's theory wasemployed with the grain size distribution function being lognormal. The numerical and theexperimental results show that the size distribution and the materials of the particles are decisiveof their infrared extinction capability

SQ-Swin: a Pretrained Siamese Quadratic Swin Transformer for Lettuce Browning Prediction

Packaged fresh-cut lettuce is widely consumed as a major component of vegetable salad owing to its high nutrition, freshness, and convenience. However, enzymatic browning discoloration on lettuce cut edges significantly reduces product quality and shelf life. While there are many research and breeding efforts underway to minimize browning, the progress is hindered by the lack of a rapid and reliable methodology to evaluate browning. Current methods to identify and quantify browning are either too subjective, labor intensive, or inaccurate. In this paper, we report a deep learning model for lettuce browning prediction. To the best of our knowledge, it is the first-of-its-kind on deep learning for lettuce browning prediction using a pretrained Siamese Quadratic Swin (SQ-Swin) transformer with several highlights. First, our model includes quadratic features in the transformer model which is more powerful to incorporate real-world representations than the linear transformer. Second, a multi-scale training strategy is proposed to augment the data and explore more of the inherent self-similarity of the lettuce images. Third, the proposed model uses a siamese architecture which learns the inter-relations among the limited training samples. Fourth, the model is pretrained on the ImageNet and then trained with the reptile meta-learning algorithm to learn higher-order gradients than a regular one. Experiment results on the fresh-cut lettuce datasets show that the proposed SQ-Swin outperforms the traditional methods and other deep learning-based backbones

Connected and Autonomous Vehicles (CAVs) Challenges with Nonmotorized Amenities Environments

With the deployment of Connected and Automated Vehicles in the coming decades, road transportation will experience a significant upheaval. CAVs (Connected and Autonomous Vehicles) have been a main emphasis of Transportation and the automotive sector, and the future of transportation system analysis is widely anticipated. The examination and future development of CAVs technology has been the subject of numerous researches. However, as three essential kinds of road users, pedestrians, bicyclists, and motorcyclists have experienced little to no handling. We explored the influence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment

Investigating the Effectiveness of Road-related Mitigation Measures under Semi-controlled Conditions: A Case Study on Asian Amphibians

Road traffic is the main factor causing the decline in amphibian populations worldwide. The proper design of an amphibian tunnel is one of the most efficient measures to mitigate the negative impacts of road traffic on amphibians. However, no study has investigated the effectiveness of amphibian tunnels under semi-controlled conditions in Asian amphibians. Here, we selected two representative amphibian species, the Chinese brown frog, Rana chensinensis, and the Asiatic toad, Bufo gargarizans, which suffer the most severe road mortality along the roads in Northeast China. We placed experimental arrays of culverts of various sizes (diameters of 1.5, 1, and 0.5 m for circular culverts; side lengths of 1.5, 1, and 0.5 m for box culverts), and substrate type (soil, concrete, and metal) to examine the preferences of both species during the migratory season between May and September in 2016 and 2017. The results revealed that the Chinese brown frog preferred mid- and large-sized culverts as well as soil culverts. We concluded that culverts with a side length ≥ 1 m, lined with soil, and accompanied by a ≥ 0.4 m high guide drift fence and ≤ 45° gradient on the roadside ditch wall would best facilitate road crossings for both species and likely for other amphibian species in Northeast China

LoMAE: Low-level Vision Masked Autoencoders for Low-dose CT Denoising

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE's robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer's denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels

Microbial profiling identifies potential key drivers in gastric cancer patients

Gastric cancer (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related death in the world. Microbiota is believed to be associated with GC. Growing evidences showed Helicobacter pylori played a key role in GC development. However, little was known about the microbiota in gastric juices and tissues in GC patients, and thus it was difficult to understand other potential microbial causation for GC. Here, we collected the gastric juice and surgically removed gastric tissues from GC patients to give insight into GC microbiota. Most microbes identified in the gastric samples were opportunistic pathogens or resident flora of the human microbiota. Further network analyses identified five opportunistic pathogens as keystone species. H. pylori is the direct cause of GC, but other opportunistic microbes might also function in GC development. The microbiota in the gastric juice and gastric tissue of the GC patients were complex, and some dominant opportunistic pathogens contributed to the GC development. This study introduces microbiota in gastric juice, gastric normal tissue and gastric cancer tissue of GC patients, and highlights the potential keystone microbes functioned during GC development

Private-Library-Oriented Code Generation with Large Language Models

Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. This paper will delve into utilizing LLMs for code generation in private libraries, as they are widely employed in everyday programming. Despite their remarkable capabilities, generating such private APIs poses a formidable conundrum for LLMs, as they inherently lack exposure to these private libraries during pre-training. To address this challenge, we propose a novel framework that emulates the process of programmers writing private code. This framework comprises two modules: APIFinder first retrieves potentially useful APIs from API documentation; and APICoder then leverages these retrieved APIs to generate private code. Specifically, APIFinder employs vector retrieval techniques and allows user involvement in the retrieval process. For APICoder, it can directly utilize off-the-shelf code generation models. To further cultivate explicit proficiency in invoking APIs from prompts, we continuously pre-train a reinforced version of APICoder, named CodeGenAPI. Our goal is to train the above two modules on vast public libraries, enabling generalization to private ones. Meanwhile, we create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval, and meticulously handcraft test cases for each benchmark to support comprehensive evaluations. Numerous experiments on the four benchmarks consistently affirm the effectiveness of our approach. Furthermore, deeper analysis is also conducted to glean additional insights

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment

High-throughput Sequencing to Analyze Changes in the Structural Diversity of the Flora of Cheddar Cheese during Processing

In order to clarify the microflora structure in Cheddar cheese processing, MiSeq high-throughput sequencing technology was used to analyze the community structure of Cheddar cheese at three stages of processing (post-pasteurization, curdling, and ripening 0, 30, 60 and 90 d) in this study. The results showed that the community structure varies widely of cheddar cheese during processing. The highest microbial community diversity and abundance were found after pasteurization (Chao1 index and Shannon index mean values were 6.09 and 1415.78, respectively). The dominant microflora in the pasteurization stage at the genus level was Stenotrophomonas (21.04%). The community structure was relatively similar in the curd and ripening stages, Lactococcus were the dominant flora in both stages, with abundance averaging more than 85%. During the ripening period, the relative abundance of Lactococcus increased first and then decreased. The community structure in the pasteurized cheeses was different compared to the other groups, and there was less change in the community structure of the groups during the ripening period. This study provides a basis for clarifying the community structure of Cheddar cheese, and has a certain reference value for the expansion of Cheddar cheese microbiome information