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General General

Impact of leachate and landfill gas on the ecosystem and health: Research trends and the way forward towards sustainability.

In Journal of environmental management

Globally, a whopping increase in solid waste (SW) generation and the risks posed by climate change are major concerns. A wide spread practice for disposal of municipal solid waste (MSW) is landfill, which swells with population and urbanization. Waste, if treated properly, can be used to produce renewable energy. The recent global event COP 27 mainly stressed on production of renewable energy to achieve the Net Zero target. The MSW landfill is the most significant anthropogenic source of methane (CH4) emission. On one side, CH4 is a greenhouse gas (GHG), and on the other it is a main component of biogas. Wastewater that collects due to rainwater percolation in landfills creates landfill leachate. There is a need to understand global landfill management practices thoroughly for implementation of better practices and policies related to this threat. This study critically reviews recent publications on leachate and landfill gas. The review discusses leachate treatment and landfill gas emissions, focusing on the possible reduction technology of CH4 emission and its impact on the environment. Mixed leachate will benefit from the combinational therapy method because of its intricate combination. Implementation of circular material management, entrepreneurship ideas, blockchain, machine learning, LCA usage in waste management, and economic benefits from CH4 production have been emphasized. Bibliometric analysis of 908 articles from the last 37 years revealed that industrialized nations dominate this research domain, with the United States having the highest number of citations.

Ghosh Arpita, Kumar Sunil, Das Jit

2023-Mar-11

Climate change, Emission, Landfill, Leachate, Methane, Solid waste, Sustainability

General General

From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa.

In Journal of environmental management

Invasive plant species pose a direct threat to biodiversity and ecosystem services. Among these, Rosa rugosa has had a severe impact on Baltic coastal ecosystems in recent decades. Accurate mapping and monitoring tools are essential to quantify the location and spatial extent of invasive plant species to support eradication programs. In this paper we combined RGB images obtained using an Unoccupied Aerial Vehicle, with multispectral PlanetScope images to map the extent of R. rugosa at seven locations along the Estonian coastline. We used RGB-based vegetation indices and 3D canopy metrics in combination with a random forest algorithm to map R. rugosa thickets, obtaining high mapping accuracies (Sensitivity = 0.92, specificity = 0.96). We then used the R. rugosa presence/absence maps as a training dataset to predict the fractional cover based on multispectral vegetation indices derived from the PlanetScope constellation and an Extreme Gradient Boosting algorithm (XGBoost). The XGBoost algorithm yielded high fractional cover prediction accuracies (RMSE = 0.11, R2 = 0.70). An in-depth accuracy assessment based on site-specific validations revealed notable differences in accuracy between study sites (highest R2 = 0.74, lowest R2 = 0.03). We attribute these differences to the various stages of R. rugosa invasion and the density of thickets. In conclusion, the combination of RGB UAV images and multispectral PlanetScope images is a cost-effective method to map R. rugosa in highly heterogeneous coastal ecosystems. We propose this approach as a valuable tool to extend the highly local geographical scope of UAV assessments into wider areas and regional evaluations.

Bergamo Thaísa F, de Lima Raul Sampaio, Kull Tiiu, Ward Raymond D, Sepp Kalev, Villoslada Miguel

2023-Mar-11

Baltic, Coast, Estonia, Invasive species, Machine learning, Rosa rugosa, Satellite, Unoccupied aerial vehicles

General General

A robust cascaded deep neural network for image reconstruction of single plane wave ultrasound RF data.

In Ultrasonics

Reconstruction of ultrasound data from single plane wave Radio Frequency (RF) data is a challenging task. The traditional Delay and Sum (DAS) method produces an image with low resolution and contrast, if employed with RF data from only a single plane wave. A Coherent Compounding (CC) method that reconstructs the image by coherently summing the individual DAS images was proposed to enhance the image quality. However, CC relies on a large number of plane waves to accurately sum the individual DAS images, hence it produces high quality images but with low frame rate that may not be suitable for time-demanding applications. Therefore, there is a need for a method that can create a high quality image with higher frame rates. Furthermore, the method needs to be robust against the input transmission angle of the plane wave. To reduce the method's dependence on the input angle, we propose to unify the RF data at different angles by learning a linear data transformation from different angled data to a common, 0° data. We further propose a cascade of two independent neural networks to reconstruct an image, similar in quality to CC, by making use of a single plane wave. The first network, denoted as "PixelNet", is a fully Convolutional Neural Network (CNN) which takes in the transformed time-delayed RF data as input. PixelNet learns optimal pixel weights that get element-wise multiplied with the single angle DAS image. The second network is a conditional Generative Adversarial Network (cGAN) which is used to further enhance the image quality. Our networks were trained on the publicly available PICMUS and CPWC datasets and evaluated on a completely separate, CUBDL dataset obtained from different acquisition settings than the training dataset. The results thus obtained on the testing dataset, demonstrate the networks' ability to generalize well on unseen data, with frame rates better than the CC method. This paves the way for applications that require high-quality images reconstructed at higher frame rates.

Wasih Mohammad, Ahmad Sahil, Almekkawy Mohamed

2023-Mar-08

Coherent plane wave compounding, Convolutional neural networks, Deep learning, Delay and sum, Fast ultrasound plane wave beamforming, Ultrasound image reconstruction

General General

A meta-framework for multi-label active learning based on deep reinforcement learning.

In Neural networks : the official journal of the International Neural Network Society

Multi-label Active Learning (MLAL) is an effective method to improve the performance of the classifier on multi-label problems with less annotation effort by allowing the learning system to actively select high-quality examples (example-label pairs) for labeling. Existing MLAL algorithms mainly focus on designing reasonable algorithms to evaluate the potential values (as previously mentioned quality) of the unlabeled data. These manually designed methods may show totally different results on various types of datasets due to the defect of the methods or the particularity of the datasets. In this paper, instead of manually designing an evaluation method, we propose a deep reinforcement learning (DRL) model to explore a general evaluation method on several seen datasets and eventually apply it to unseen datasets based on a meta framework. In addition, a self-attention mechanism along with a reward function is integrated into the DRL structure to address the label correlation and data imbalanced problems in MLAL. Comprehensive experiments show that our proposed DRL-based MLAL method is able to produce comparable results as compared with other methods reported in the literature.

Chen Shuyue, Wang Ran, Lu Jian

2023-Mar-07

Deep reinforcement learning, Meta-learning, Multi-label active learning, Query strategy, Self-attention mechanism

General General

Impacts of respiratory fluctuations on cerebral circulation: a machine-learning-integrated 0-1D multiscale hemodynamic model.

In Physiological measurement ; h5-index 36.0

This study aims to accurately identify the effects of respiration on the hemodynamics of the human cardiovascular system (CVS), especially the cerebral circulation. 
Approach: We have developed a machine learning (ML)-integrated 0-1D multiscale hemodynamic model combining a lumped-parameter 0D model for the peripheral vascular bed and a one-dimensional (1D) hemodynamic model for the vascular network. In vivo measurement data of 21 patients were retrieved and partitioned into 8,000 data samples in which respiratory fluctuation (RF) of intrathoracic pressure (ITP) was fitted by the Fourier series. ML-based classification and regression algorithms were used to examine the influencing factors and variation trends of the key parameters in the ITP equations and the mean arterial pressure (MAP). These parameters were employed as the initial conditions of the 0-1D model to calculate the radial artery blood pressure (BP) and the vertebral artery blood flow volume (VAFV). 
Main results: During stable spontaneous respiration, the VAFV can be augmented at the inhalation endpoints by approximately 0.1 mL/s for infants and 0.5 mL/s for adolescents or adults, compared to those without RF effects. It is verified that deep respiration can further increase the ranges up to 0.25 mL/s and 1 mL/s, respectively. 
Significance: This study reveals that reasonable adjustment of respiratory patterns, i.e., in deep breathing, enhances the VAFV and promotes cerebral circulation.

Li Ruichen, Sughimoto Koichi, Zhang Xiancheng, Wang Sirui, Liu Hao

2023-Mar-13

0-1D multiscale hemodynamic model, cerebral circulation, machine learning, respiratory fluctuation, vertebral artery

Surgery Surgery

Multianalyte Serum Biomarker Panel for Early Detection of Pancreatic Adenocarcinoma.

In JCO clinical cancer informatics

PURPOSE : We determined whether a large, multianalyte panel of circulating biomarkers can improve detection of early-stage pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS : We defined a biologically relevant subspace of blood analytes on the basis of previous identification in premalignant lesions or early-stage PDAC and evaluated each in pilot studies. The 31 analytes that met minimum diagnostic accuracy were measured in serum of 837 subjects (461 healthy, 194 benign pancreatic disease, and 182 early-stage PDAC). We used machine learning to develop classification algorithms using the relationship between subjects on the basis of their changes across the predictors. Model performance was subsequently evaluated in an independent validation data set from 186 additional subjects.

RESULTS : A classification model was trained on 669 subjects (358 healthy, 159 benign, and 152 early-stage PDAC). Model evaluation on a hold-out test set of 168 subjects (103 healthy, 35 benign, and 30 early-stage PDAC) yielded an area under the receiver operating characteristic curve (AUC) of 0.920 for classification of PDAC from non-PDAC (benign and healthy controls) and an AUC of 0.944 for PDAC versus healthy controls. The algorithm was then validated in 146 subsequent cases presenting with pancreatic disease (73 benign pancreatic disease and 73 early- and late-stage PDAC cases) and 40 healthy control subjects. The validation set yielded an AUC of 0.919 for classification of PDAC from non-PDAC and an AUC of 0.925 for PDAC versus healthy controls.

CONCLUSION : Individually weak serum biomarkers can be combined into a strong classification algorithm to develop a blood test to identify patients who may benefit from further testing.

Firpo Matthew A, Boucher Kenneth M, Bleicher Josh, Khanderao Gayatri D, Rosati Alessandra, Poruk Katherine E, Kamal Sama, Marzullo Liberato, De Marco Margot, Falco Antonia, Genovese Armando, Adler Jessica M, De Laurenzi Vincenzo, Adler Douglas G, Affolter Kajsa E, Garrido-Laguna Ignacio, Scaife Courtney L, Turco M Caterina, Mulvihill Sean J

2023-Mar