Effects of aging on surface properties and endogenous copper and zinc leachability of swine manure biochar and its composite with alkali-fused fly ash.

Biochar aging is a key factor leading to the decline of biochar stability and the release of endogenous pollutants. This study investigated the effects of five artificial and simulated aging processes on the surface properties and endogenous copper (Cu) and zinc (Zn) leachability of swine manure biochar and its composite with alkali-fused fly ash. Aging obviously reduced carbon (C) content on the surface of swine manure biochar and increased oxygen (O) content. Among all the aging treatments, high-temperature aging had the greatest effect on C content. Following the aging treatments, the C-C bond contents on the surfaces of swine manure biochar decreased significantly, whereas the C-O bonds increased significantly; however, there were less changes in the amounts of C-C and C-O bonds on the surfaces of modified biochar than on swine manure biochar. Aging significantly enhanced the leaching toxicity of Cu and Zn, and Zn availability and bioaccessibility in swine manure biochar and modified biochar. However, it minimized Cu availability and bioaccessibility, especially under high-temperature aging. Greater amounts of Zn than Cu were extracted from swine manure biochar and modified biochar. However, under all the aging treatments, the leaching toxicity, availability, and bioaccessibility of Cu and Zn in modified biochar were significantly lower than in swine manure biochar. This implies that modified biochar application poses lower environmental risks than swine manure biochar.

Distributed optical fiber hydrophone based on Φ-OTDR and its field test.

In this letter, a distributed optical fiber hydrophone (DOFH) based on Φ-OTDR is demonstrated and tested in the field. The specially designed sensitized optical cable with sensitivity up to -146 dB rad/µPa/m is introduced, and an array signal processing model for DOFH is constructed to analyze the equivalence and specificity of the distributed array of acoustic sensors. In the field test, a 104-meter-long optical cable and a Φ-OTDR system based on heterodyne coherent detection (Het Φ-OTDR) is utilized, and underwater acoustic signal spatial spectrum estimation, beamforming and motion trajectory tracking with high accuracy can be realized. As far as we know, this is the first report on the field trial of DOFH based on Φ-OTDR. The DOFH has the potential to achieve an array range of tens of kilometers, with elements spaced up to the meter level and flexible configuration, which has a broad application prospect for marine acoustic detection.

Temporal relationship between serial RT-PCR results and serial chest CT imaging, and serial CT changes in coronavirus 2019 (COVID-19) pneumonia: a descriptive study of 155 cases in China.

OBJECTIVE: To determine CT's role in the early detection of COVID-19 infection and serial CT changes in the disease course in patients with COVID-19 pneumonia. METHODS: From January 21 to February 18, 2020, all of the patients who were suspected of novel coronavirus infection and verified by RT-PCR tests were retrospectively enrolled in our study. All of the patients underwent serial RT-PCR tests and serial CT imaging. The temporal relationship between the serial RT-PCR results (negative conversion to positive, positive to negative) and serial CT imaging was investigated, and serial CT changes were evaluated. RESULTS: A total of 155 patients with confirmed COVID-19 pneumonia were evaluated. Chest CT detection time of COVID-19 pneumonia was 2.61 days earlier than RT-PCR test (p = 0.000). The lung CT improvement time was significantly shorter than that of RT-PCR conversion to negative (p = 0.000). Three stages were identified from the onset of the initial symptoms: stage 1 (0-3 days), stage 2 (4-7 days), and stage 3 (8-14 days and later). Ground glass opacity (GGO) was predominant in stage 1, then consolidation and crazy paving signs were dramatically increased in stage 2. In stage 3, fibrotic lesions were rapidly increased. There were significant differences in the main CT features (p = 0.000), number of lobes involved (p = 0.001), and lesion distribution (p = 0.000) among the different stages. CONCLUSION: Chest CT detected COVID-19 pneumonia earlier than the RT-PCR results and can be used to monitor disease course. Combining imaging features with epidemiology history and clinical information could facilitate the early diagnosis of COVID-19 pneumonia. KEY POINTS: • The chest CT detection time of COVID-19 pneumonia was 2.61 days earlier than that of an initial RT-PCR positive result (t = - 7.31, p = 0.000). • The lung CT improvement time was significantly shorter than that of RT-PCR conversion to negative (t = - 4.72, p = 0.000). • At the early stage (0-3 days), the CT features of COVID-19 were predominantly GGO and small-vessel thickening; at stage 2 (4-7 days), GGO evolved to consolidation and crazy paving signs. At stage 3 (8-14 days and later), fibrotic lesions significantly increased, accompanied by consolidation, GGO, and crazy paving signs.

Multi-source aliasing suppression for distributed fiber acoustic sensing with directionally coherent enhancement technology.

Distributed fiber acoustic sensing (DAS) can detect almost all disturbances along the sensing fiber and is widely applied. However, the signals from multiple adjacent disturbance sources are superimposed, according to the sensing principle. A directionally coherent enhancement technology is demonstrated for DAS to suppress multi-source aliasing in air. In preliminary works, two situations are considered for feasibility verification. The submerged weak target signal is effectively extracted from strong broadband noise, and two different same-frequency signals from two sources are separately rebuilt with the same detected signal. As far as we know, this is the first time that the directionally coherent enhancement is proposed for DAS and the multi-source aliasing is suppressed. This technique will help DAS find new important foreground in acoustic detection of large-scale plants with many similar noisy devices, including discharge detection in high voltage substations and acoustic emission flaw detection in mechanical factories.

Initial CT features and dynamic evolution of early-stage patients with COVID-19.

OBJECTIVE: To explore the initial CT features and dynamic evolution of early-stage patients with Coronavirus disease 2019 (COVID-19). METHODS: A total of 126 COVID-19 patients in the early stage were enrolled. The initial CT features and dynamic evolution characteristics of the progression and absorption process from the stage of admission to discharge were retrospectively analyzed in this study. RESULTS: The main initial CT features were as follows: bilateral distribution (112/126, 88.9%), diffuse distribution (106/126, 84.1%), multiple lesions (117/126, 92.9%), nodular shapes (84/126, 66.7%), patchy shapes (98/126, 77.8%), pure ground-glass opacities (GGO) (95/126, 75.4%), "vascular thickening sign" (98/126, 77.8%), "air bronchogram sign" (70/126, 55.6%), "crazy paving pattern" (93/126, 73.8%), and "pleura parallel sign" (72/126, 57.1%). The main dynamic evolution characteristics were as follows: ① Imaging findings of the progression process: the main CT changes were increased GGOs with consolidation (118/126, 93.7%), an increased "crazy paving pattern" (104/126, 82.5%), an increased "vascular thickening sign" (105/126, 83.3%), and an increased "air bronchogram sign" (95/126, 75.4%); ② Imaging findings of the absorption process: the main CT changes were the obvious absorption of consolidation displayed as inhomogeneous partial GGOs with fibrosis shadows, the occurrence of a "fishing net on trees sign" (45/126, 35.7%), an increased "fibrosis sign" (40/126, 31.7%), an increased "subpleural line sign" (35/126, 27.8%), a decreased "crazy paving pattern" (19.8%), and a decreased "vascular thickening sign" (23.8%); and ③ In the stage of discharge, the main CT manifestations were further absorption of GGOs, consolidation and fibrosis shadows in the lung, and no appearance of new lesions, with only a small amount of shadow with fibrotic streaks and reticulations remaining in some cases (16/126, 12.7%). CONCLUSION: The initial CT features and dynamic evolution of early-stage patients with COVID-19 have certain characteristics and regularity; CT of the chest is critical for early detection, evaluation of disease severity and follow-up of patients.

Differential diagnosis of coronavirus disease 2019 from community-acquired-pneumonia by computed tomography scan and follow-up.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) is currently the most serious infectious disease in the world. An accurate diagnosis of this disease in the clinic is very important. This study aims to improve the differential ability of computed tomography (CT) to diagnose COVID-19 and other community-acquired pneumonias (CAPs) and evaluate the short-term prognosis of these patients. METHODS: The clinical and imaging data of 165 COVID-19 and 118 CAP patients diagnosed in seven hospitals in Anhui Province, China from January 21 to February 28, 2020 were retrospectively analysed. The CT manifestations of the two groups were recorded and compared. A correlation analysis was used to examine the relationship between COVID-19 and age, size of lung lesions, number of involved lobes, and CT findings of patients. The factors that were helpful in diagnosing the two groups of patients were identified based on specificity and sensitivity. RESULTS: The typical CT findings of COVID-19 are simple ground-glass opacities (GGO), GGO with consolidation or grid-like changes. The sensitivity and specificity of the combination of age, white blood cell count, and ground-glass opacity in the diagnosis of COVID-19 were 92.7 and 66.1%, respectively. Pulmonary consolidation, fibrous cords, and bronchial wall thickening were used as indicators to exclude COVID-19. The sensitivity and specificity of the combination of these findings were 78.0 and 63.6%, respectively. The follow-up results showed that 67.8% (112/165) of COVID-19 patients had abnormal changes in their lung parameters, and the severity of the pulmonary sequelae of patients over 60 years of age worsened with age. CONCLUSIONS: Age, white blood cell count and ground-glass opacity have high accuracy in the early diagnosis of COVID-19 and the differential diagnosis from CAP. Patients aged over 60 years with COVID-19 have a poor prognosis. This result provides certain significant guidance for the diagnosis and treatment of new coronavirus pneumonia.

ResNet-Locust-BN Network-Based Automatic Identification of East Asian Migratory Locust Species and Instars from RGB Images.

Locusts are agricultural pests found in many parts of the world. Developing efficient and accurate locust information acquisition techniques helps in understanding the relation between locust distribution density and structural changes in locust communities. It also helps in understanding the hydrothermal and vegetation growth conditions that affect locusts in their habitats in various parts of the world as well as in providing rapid and accurate warnings on locust plague outbreak. This study is a preliminary attempt to explore whether the batch normalization-based convolutional neural network (CNN) model can be applied used to perform automatic classification of East Asian migratory locust (AM locust), Oxya chinensis (rice locusts), and cotton locusts. In this paper, we present a way of applying the CNN technique to identify species and instars of locusts using the proposed ResNet-Locust-BN model. This model is based on the ResNet architecture and involves introduction of a BatchNorm function before each convolution layer to improve the network's stability, convergence speed, and classification accuracy. Subsequently, locust image data collected in the field were used as input to train the model. By performing comparison experiments of the activation function, initial learning rate, and batch size, we selected ReLU as the preferred activation function. The initial learning rate and batch size were set to 0.1 and 32, respectively. Experiments performed to evaluate the accuracy of the proposed ResNet-Locust-BN model show that the model can effectively distinguish AM locust from rice locusts (93.60% accuracy) and cotton locusts (97.80% accuracy). The model also performed well in identifying the growth status information of AM locusts (third-instar (77.20% accuracy), fifth-instar (88.40% accuracy), and adult (93.80% accuracy)) with an overall accuracy of 90.16%. This is higher than the accuracy scores obtained by using other typical models: AlexNet (73.68%), GoogLeNet (69.12%), ResNet 18 (67.60%), ResNet 50 (80.84%), and VggNet (81.70%). Further, the model has good robustness and fast convergence rate.

CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity.

PURPOSE: To report CT features of coronavirus disease-2019 (COVID-19) in patients with various disease severity. METHODS: The CT manifestations and clinical data of 73 patients with COVID-19 were retrospectively collected in 6 hospitals from Jan 21 to Feb 3, 2020. We analyzed the initial and follow-up CT features of patients with disease severity, according to the Guidelines for the Diagnosis and Treatment of New Coronavirus Pneumonia. RESULTS: Six patients (8%) were diagnosed as mild type pneumonia; these patients had no obvious abnormal CT findings or manifested mild changes of lung infection. All 43 patients (59 %) with common type presented unique or multiple ground-glass opacities (GGO) in the periphery of the lungs, with or without interlobular septal thickening. In the 21 patients (29 %) with severe type, extensive GGO and pulmonary consolidation were found in 16 cases (16/21, 76 %) and 5 cases (24 %), respectively. An extensive "white lung", with atelectasis and pleural effusion were found in critical type patients (3, 4%). On the resolutive phase of the disease, CT abnormalities showed complete resolution, or demonstrated residual linear opacities. CONCLUSIONS: Different CT features are seen according to disease severity, which can help COVID-19 stratification.