State-of-the-art

In monocrystalline Si (c-Si) solar cells, identification and mitigation of bulk defects are crucial to achieving a high photoconversion efficiency. To spectroscopically detect defects in the c-Si bulk, it is desirable to passivate the surface defects. Passivation of the c-Si surface with dielectrics such as Al2 O3 and SiN x requires deposition at elevated temperatures, which can influence defects in the bulk. Herein, we report on the passivation of different Czochralski (Cz) Si wafer surfaces by an organic copolymer, Nafion...

Accurate atom mapping, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. In this paper, we present a novel end-to-end approach that formulates the atom mapping problem as a deep graph matching task. Our proposed model, AMNet (Atom Matching Network), utilizes molecular graph representations and employs various atom and bond features using graph neural networks to capture the intricate structural characteristics of molecules, ensuring precise atom correspondence predictions...

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin...

This study provides a comparative analysis of two state-of-the-art automatic mosquito pupae sex sorters currently available: the ORINNO and the WOLBAKI Biotech pupae sex separation systems, which both exploit the sexual size dimorphism of pupae. In Aedes aegypti, the WOLBAKI sex sorter and the ORINNO with a sieve mesh size of 1.050 mm achieved sex separation with female contamination rates below 1%, low pupae mortality rates and high male flight capacity. However, in Ae. albopictus, there was more variability, with female contamination rates above the 1% threshold and pupae mortality reaching 27% when using the ORINNO sorter...

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree...

Phosphorus to an optimum extent is an essential nutrient for all living organisms and its scarcity may cause food security, and environmental preservation issues vis-à-vis agroeconomic hurdles. Undesirably excess phosphorus intensifies the eutrophication problem in non-marine water bodies and disrupts the natural nutrient balance of the ecosystem. To overcome such dichotomy, biodegradable polymer-based adsorbents have emerged as a cost-effective and implementable approach in striking a "desired optimum-undesired excess" balance pertaining to phosphate in a sustainable manner...

Structural and photometric anomalies in the brain magnetic resonance images (MRIs) affect the segmentation performance. Moreover, a sudden change in intensity between two boundaries of the brain tissues makes it prone to data uncertainty, resulting in the misclassification of the pixels lying near the cluster boundaries. The discrete cosine transform (DCT) domain-based filtering is an effective way to deal with structural and photometric anomalies, while the intuitionistic fuzzy C-means (IFCM) clustering can handle the uncertainty using the intuitionistic fuzzy set (IFS) theory...

Alkaline polymer membrane electrochemical energy conversion devices offer the prospect of using non-platinum group catalysts. However, their cationic functionalities are currently not sufficiently stable for vapor-phase applications, such as fuel cells. Herein, we report 1,6-diazabicyclo[4.4.4]tetradecan-1,6-ium (in-DBD), a cationic proton cage, that is orders of magnitude more resistant to hydroxide-promoted degradation than state-of-the-art organic cations under ultra-dry conditions and elevated temperature, and the first organic cation-hydroxide to persist at critically low hydration levels ( < 10% RH at 80 °C)...

BACKGROUND: The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses. FINDINGS: We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2-dimensional convolutional neural network combining trinucleotide pair relative distance and frequency...

The advent of Nanohybrid (NH) fertilizers represents a groundbreaking advancement in the pursuit of precision and sustainable agriculture. This review abstract encapsulates the transformative potential of these innovative formulations in addressing key challenges faced by modern farming practices. By incorporating nanotechnology into traditional fertilizer matrices, nanohybrid formulations enable precise control over nutrient release, facilitating optimal nutrient uptake by crops. This enhanced precision not only fosters improved crop yields but also mitigates issues of over-fertilization, aligning with the principles of sustainable agriculture...

Objective. We present the first fully two-dimensional attenuation imaging technique developed for pulse-echo ultrasound systems. Unlike state-of-the-art techniques, which use line-by-line acquisitions, our method uses steered emissions to constrain attenuation values at each location with multiple crossing wave paths, essential to resolve the spatial variations of this tissue property. Approach. At every location, we compute normalized cross-correlations between the beamformed images that are obtained from emissions at different steering angles...

Training deep learning models for image registration or segmentation of dynamic contrast enhanced (DCE)-MRI data is challenging. This is mainly due to the wide variations in contrast enhancement within and between patients. To train a model effectively, a large dataset is needed, but acquiring it is expensive and time consuming. Instead, style transfer can be used to generate new images from existing images.&#xD; &#xD;In this study, our objective is to develop a style transfer method that incorporates spatio-temporal information to either add or remove contrast enhancement from an existing image...

BACKGROUND: Liver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentation of the hepatic and portal veins (PVs) on computed tomography (CT) images is of great importance for preoperative planning. However, manually labeling the mask of vessels is laborious and time-consuming, and the labeling results of different clinicians are prone to inconsistencies...

Accurate and robust medical image segmentation is crucial for assisting disease diagnosis, making treatment plan, and monitoring disease progression. Adaptive to different scale variations and regions of interest is essential for high accuracy in automatic segmentation methods. Existing methods based on the U-shaped architecture respectively tackling intra- and inter-scale problem with a hierarchical encoder, however, are restricted by the scope of multi-scale modeling. In addition, global attention and scaling attention in regions of interest have not been appropriately adopted, especially for the salient features...

Currently prevalent multi-modal 3D detection methods rely on dense detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not scalable for long-range detection. Recently, LiDAR-only fully sparse architecture has been gaining attention for its high efficiency in long-range perception. In this paper, we study how to develop a multi-modal fully sparse detector. Specifically, our proposed detector integrates the well-studied 2D instance segmentation into the LiDAR side, which is parallel to the 3D instance segmentation part in the LiDAR-only baseline...

Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions from point clouds, which are limited to reconstructing closed surfaces. Some other methods tried to represent open surfaces using unsigned distance functions (UDF) which are learned from ground truth distances. However, the learned UDF is hard to provide smooth distance fields due to the discontinuous character of point clouds. In this paper, we propose CAP-UDF, a novel method to learn consistency-aware UDF from raw point clouds...

Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary topology while not relying on a 3D Euclidean grid, resulting in a learnable parameterization that is unlimited in resolution. Unfortunately, these methods are often unsuitable for applications that require an explicit mesh-based surface representation because converting an implicit field to such a representation relies on the Marching Cubes algorithm, which cannot be differentiated with respect to the underlying implicit field...

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most temporal graph learning methods model current interactions by incorporating historical neighborhood. However, such methods only consider first-order temporal information while disregarding crucial high-order structural information, resulting in suboptimal performance...

Federated recommender systems (FRSs), with their improved privacy-preserving advantages to jointly train recommendation models from numerous devices while keeping user data distributed, have been widely explored in modern recommender systems (RSs). However, conventional FRSs require transmitting the entire model between the server and clients, which brings a huge carbon footprint for cost-conscious cross-device learning tasks. While several efforts have been dedicated to improving the efficiency of FRSs, it's suboptimal to treat the whole model as the objective of compact design...

PURPOSE: The Posterior Fossa Society, an international multidisciplinary group, hosted its first global meeting designed to share the current state of the evidence across the multidisciplinary elements of pediatric post-operative cerebellar mutism syndrome (pCMS). The agenda included keynote talks from world-leading speakers, compelling abstract presentations and engaging discussions led by members of the PFS special interest groups. METHODS: This paper is a synopsis of the first global meeting, a 3-day program held in Liverpool, England, UK, in September 2022...