Toxic Comment Classification Based on Bidirectional Gated Recurrent Unit and Convolutional Neural Network

For English toxic comment classification, this paper presents the model that combines Bi-GRU and CNN optimized by global average pooling (BG-GCNN) based on the bidirectional gated recurrent unit (Bi-GRU) and global pooling optimized convolution neural network (CNN) . The model treats each type of toxic comment as a binary classification. First, Bi-GRU is used to extract the time-series features of the comment and then the dimensionality is reduced through global pooling optimized convolution neural network. Finally, the classification result is output by Sigmoid function. Comparative experiments show the BG-GCNN model has a better classification effect than Text-CNN, LSTM, Bi-GRU, and other models. The Macro-F1 value of the toxic comment dataset on the Kaggle competition platform is 0.62. The F1 values of the three toxic label classification results (toxic, obscene, and insult label) are 0.81, 0.84, and 0.74, respectively, which are the highest values in the comparative experiment.

Noninvasive estimation of local speed of sound by pulse-echo ultrasound in a rat model of nonalcoholic fatty liver

Objective: Speed of sound has previously been demonstrated to correlate with fat concentration in the liver. However, estimating speed of sound in the liver noninvasively can be biased by the speed of sound of the tissue layers overlying the liver. Here, we demonstrate a noninvasive local speed of sound estimator, which is based on a layered media assumption, that can accurately capture the speed of sound in the liver. We validate the estimator using an obese Zucker rat model of non-alcoholic fatty liver disease and correlate the local speed of sound with liver steatosis. Approach: We estimated the local and global average speed of sound noninvasively in 4 lean Zucker rats fed a normal diet and 16 obese Zucker rats fed a high fat diet for up to 8 weeks. The ground truth speed of sound and fat concentration were measured from the excised liver using established techniques. Main Results: The noninvasive, local speed of sound estimates of the livers were similar in value to their corresponding "ground truth'' measurements, having a slope ± standard error of the regression of 0.82 ± 0.15 (R2 = 0.74 and p < 0.001). Measurement of the noninvasive global average speed of sound did not reliably capture the ``ground truth'' speed of sound in the liver, having a slope of 0.35 ± 0.07 (R2 = 0.74 and p < 0.001). Decreasing local speed of sound was observed with increasing hepatic fat accumulation (approximately -1.7 m/s per 1% increase in hepatic fat) and histopathology steatosis grading (approximately -10 to -13 m/s per unit increase in steatosis grade). Local speed of sound estimates were highly correlated with steatosis grade, having Pearson and Spearman correlation coefficients both ranging from -0.87 to -0.78. In addition, a lobe-dependent speed of sound in the liver was observed by the ex vivo measurements, with speed of sound differences of up to 25 m/s (p < 0.003) observed between lobes in the liver of the same animal. Significance: The findings of this study suggest that local speed of sound estimation has the potential to be used to predict or assist in the measurement of hepatic fat concentration and that the global average speed of sound should be avoided in hepatic fat estimation due to significant bias in the speed of sound estimate.

Medical Image Classification Based on Information Interaction Perception Mechanism

Colorectal cancer originates from adenomatous polyps. Adenomatous polyps start out as benign, but over time they can become malignant and even lead to complications and death which will spread to adherent and surrounding organs over time, such as lymph nodes, liver, or lungs, eventually leading to complications and death. Factors such as operator’s experience shortage and visual fatigue will directly affect the diagnostic accuracy of colonoscopy. To relieve the pressure on medical imaging personnel, this paper proposed a network model for colonic polyp detection using colonoscopy images. Considering the unnoticeable surface texture of colonic polyps, this paper designed a channel information interaction perception (CIIP) module. Based on this module, an information interaction perception network (IIP-Net) is proposed. In order to improve the accuracy of classification and reduce the cost of calculation, the network used three classifiers for classification: fully connected (FC) structure, global average pooling fully connected (GAP-FC) structure, and convolution global average pooling (C-GAP) structure. We evaluated the performance of IIP-Net by randomly selecting colonoscopy images from a gastroscopy database. The experimental results showed that the overall accuracy of IIP-NET54-GAP-FC module is 99.59%, and the accuracy of colonic polyp is 99.40%. By contrast, our IIP-NET54-GAP-FC performed extremely well.

Germany’s Agricultural Land Footprint and the Impact of Import Pattern Allocation

Footprints are powerful indicators for evaluating the impact of the bioeconomy of a country on environmental goods, domestically and abroad. In this study, we apply a hybrid approach combining a Multi-Regional Input-Output model and land use modelling to compute the agricultural land footprint (aLF). Furthermore, we added information on land-use change to the analysis and allocated land conversion to specific commodities. The German case study shows that the aLF abroad is larger by a factor of 2.5 to 3 than the aLF in Germany. In 2005 and 2010, conversion of natural and semi-natural land-cover types abroad allocated to Germany due to import increases was 2.5 times higher than the global average. Import increases to Germany slowed down in 2015 and 2020, reducing land conversion attributed to the German bioeconomy to the global average. The case study shows that the applied land footprint provides clear and meaningful information for policymakers and other stakeholders. The presented methodological approach can be applied to other countries and regions covered in the underlying database EXIOBASE. It can be adapted, also for an assessment of other ecosystem functions, such as water or soil fertility.

Corn Disease Classification Using Transfer Learning and Convolutional Neural Network

Indonesia is an agricultural country with abundant agricultural products. One of the crops used as a staple food for Indonesians is corn. This corn plant must be protected from diseases so that the quality of corn harvest can be optimal. Early detection of disease in corn plants is needed so that farmers can provide treatment quickly and precisely. Previous research used machine learning techniques to solve this problem. The results of the previous research were not optimal because the amount of data used was slightly and less varied. Therefore, we propose a technique that can process lots and varied data, hoping that the resulting system is more accurate than the previous research. This research uses transfer learning techniques as feature extraction combined with Convolutional Neural Network as a classification. We analysed the combination of DenseNet201 with a Flatten or Global Average Pooling layer. The experimental results show that the accuracy produced by the combination of DenseNet201 with the Global Average Pooling layer is better than DenseNet201 with Flatten layer. The accuracy obtained is 93% which proves the proposed system is more accurate than previous studies.

1. The Arctic world

‘The Arctic world’ begins with the definition of the Arctic, which is understood as the land, sea, and ice lying north of the Arctic Circle set at a latitude of approximately 66.5° N. The Arctic tree line is a robust indicator of Arctic-ness as everything to the north is a landscape characterized by shrubs, dwarf trees, and lichen. Arctic warming occurs at least twice as rapidly as the global average, which is a phenomenon known as Arctic amplification. Since 1980, the warming trajectory in the Arctic has been much steeper than that of the rest of the planet.

Multisource Data Fusion Diagnosis Method of Rolling Bearings Based on Improved Multiscale CNN

Intelligent diagnosis applies deep learning algorithms to mechanical fault diagnosis, which can classify the fault forms of machines or parts efficiently. At present, the intelligent diagnosis of rolling bearings mostly adopts a single-sensor signal, and multisensor information can provide more comprehensive fault features for the deep learning model to improve the generalization ability. In order to apply multisensor information more effectively, this paper proposes a multiscale convolutional neural network model based on global average pooling. The diagnostic model introduces a multiscale convolution kernel in the feature extraction process, which improves the robustness of the model. Meanwhile, its parallel structure also makes up for the shortcomings of the multichannel input fusion method. In the multiscale fusion process, the global average pooling method is used to replace the way to reshape the feature maps into a one-dimensional feature vector in the traditional convolutional neural network, which effectively retains the spatial structure of the feature maps. The model proposed in this paper has been verified by the bearing fault data collected by the experimental platform. The experimental results show that the algorithm proposed in this paper can fuse multisensor data effectively. Compared with other data fusion algorithms, the multiscale convolutional neural network model based on global average pooling has shorter training epochs and better fault diagnosis results.

1223. In Vitro Activity of Ceftazidime-Avibactam and Comparator Agents Against Enterobacterales from ICU and Non-ICU Wards Collected in Latin America and Globally as part of the ATLAS Surveillance Program 2017-2019

Background Ceftazidime-avibactam (CAZ-AVI) is a β-lactam/non-β-lactam β-lactamase inhibitor combination with activity against Enterobacterales producing class A, C and some class D β-lactamases. Resistance caused by these β-lactamases is especially high in ICUs. This study evaluated the in vitro activity of CAZ-AVI and comparators against Enterobacterales isolates from patients in ICU and non-ICU wards. Methods Non-duplicate clinical isolates were collected in 2017-2019 from patients in Asia/Pacific, Europe, Latin America, and Middle East/Africa. Susceptibility testing was performed using CLSI broth microdilution and interpreted using CLSI 2021 and FDA (tigecycline) breakpoints. PCR and sequencing were used to determine the β-lactamase genes present in all isolates with meropenem (MEM) MIC &gt;1 µg/ml, and Escherichia coli, Klebsiella spp. and Proteus mirabilis with aztreonam or ceftazidime MIC &gt;1 µg/ml. Results The activity of CAZ-AVI and comparators is shown in the table. Susceptibility rates among global Enterobacterales were generally lower for isolates from patients in ICU than non-ICU wards, but this difference was small for CAZ-AVI, which inhibited &gt;96% of isolates from both ward types. Among MEM-nonsusceptible (NS) isolates, CAZ-AVI was active against 62.3% and 65.6% of ICU and non-ICU isolates, respectively, and 36.3% and 33.2%, respectively, carried metallo-β-lactamases (MBLs). CAZ-AVI inhibited &gt;97% of MEM-NS MBL-negative isolates collected globally. Antimicrobial activity against all Enterobacterales from both ICU and non-ICU wards in Latin America (LA) was generally similar to the global average. Among MEM-NS isolates, antimicrobial activity of CAZ-AVI was &gt;10 percentage points higher in LA than the global average among isolates from both ward types, at least partly because of a lower proportion of MBL-positive isolates in this subset (24.4% and 22.0% in ICU and non-ICUs, respectively). CAZ-AVI inhibited &gt;98% of MEM-NS MBL-negative isolates from LA. Results Table Conclusion CAZ-AVI provides a valuable treatment option for infections caused by Enterobacterales that do not carry MBLs, including those from patients in ICU wards, where antimicrobial resistance is typically higher. Disclosures Sibylle Lob, PhD, IHMA (Employee)Pfizer, Inc. (Independent Contractor) Meredith Hackel, PhD MPH, IHMA (Employee)Pfizer, Inc. (Independent Contractor) Gregory Stone, PhD, AztraZeneca (Shareholder, Former Employee)Pfizer, Inc. (Employee) Daniel F. Sahm, PhD, IHMA (Employee)Pfizer, Inc. (Independent Contractor)

The Problem

This chapter frames the problem of climate change. It opens with a brief overview of Earth’s energy balance and the greenhouse effect and then outlines the root causes of the problem along with key controls in the climate system that determine its responses. This is followed by an introduction of spatial variability and fluctuations through time in the expressions of climate change, which are key to understanding regional impacts. Such geographic and temporal variations do not invalidate the existence of the global average temperature increase, but merely cause fluctuations around the global average. Finally, the chapter shows that achieving the Paris Agreement’s 1.5°C warming limit will require the removal of 260–1030 billion tons of atmospheric carbon dioxide. The low-end estimate applies to best-case scenarios and the high-end estimate to scenarios where business-as-usual (as in the past two decades) emissions are allowed until 2030 or beyond.