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On-axis structured beams generation via moiré and Mie resonant metallo-dielectric moiré gratings
Abstract Structured beams carrying orbital angular momentum have been generated conventionally using spiral phase plates, fork gratings, and metasurfaces. Spiral phase plates are non-planar, fork gratings do not produce structured beams on the axis, and metasurfaces need subwavelength unit cell level design. In this work, we show a method to generate on-axis structured beams, at the zeroth order of a diffraction grating with experimentally relevant efficiency using moiré patterned binary gratings that are compatible with planar fabrication, do not need subwavelength unit cell level design, and can be used with a spatial light modulator. By logically superposing two binary forked gratings, we create a moiré pattern that enables on-axis structured beam generation at the zeroth order of the diffraction grating. We demonstrate, using experiments and simulations, the generation of on-axis zeroth order structured beams using spatial light modulator based reconfigurable moiré gratings and Mie resonant metallo-dielectric standalone moiré gratings, showcasing the versatility of this approach in different configurations. Simulations and experiments demonstrate that the on-axis structured beam is generated by the moiré pattern within the gratings, and its shape is determined by the topological charges of the overlapping binary forked gratings. Additionally, we demonstrate color-selective on-axis structured beam generation at the zeroth order of the grating, where the color-selectivity of the on-axis structured beam depends on the grating period and arises due to Mie resonance in standalone nanofabricated metallo-dielectric moiré gratings. The on-axis structured beam generation at the zeroth order of the grating using the proposed method may have several applications, including sensing and optical trapping.
Exploring the efficacy of combination of point-of-use filters and peracetic acid disinfection in reducing total viable counts in final rinse water of endoscopes
Descriptive norms nudge meaningful engagement for the third age: two experimental studies
A novel approach for synthesizing silver nanoparticles with antibacterial and cytotoxic activities using the leaf extract of hydroponically grown Moringa oleifera
Geometrically aware transformer for point cloud analysis
Abstract With the increasing use of 3D point cloud data in autonomous driving, robotic perception, and remote sensing, efficient and accurate point cloud analysis remains a critical challenge. This study presents PointGA, a lightweight Transformer-based model that enhances geometric perception for improved feature extraction and representation. First, PointGA expands the original 3D coordinates into various geometric information, introducing more prior knowledge into the network. Second, a trigonometric position encoding suitable for point clouds is designed, which effectively enhances the expressive capability of positional information and performs preliminary feature extraction through pooling layers, significantly improving the model’s robustness across various tasks. Finally, a positional differential self-attention (PDA) mechanism with linear complexity is developed to optimize feature representation and achieve efficient computation. Experimental results demonstrate that PointGA achieves 87.6% overall accuracy on the ScanObjectNN dataset for classification and 66.2% mean intersection over union(mIoU) on the S3DIS Area 5 dataset for segmentation, outperforming existing methods. These findings highlight the model’s capability to balance efficiency and accuracy, offering a promising solution for point cloud analysis tasks.
A three-subtype prognostic classification based on base excision repair and oxidative stress genes in lung adenocarcinoma and its relationship with tumor microenvironment
Development and validation of a cancer-associated fibroblast gene signature-based model for predicting immunotherapy response in colon cancer
Detection of sugar beet seed coating defects via deep learning
Accurate, scalable, and fully automated inference of species trees from raw genome assemblies using ROADIES
Current genome sequencing initiatives across a wide range of life forms offer significant potential to enhance our understanding of evolutionary relationships and support transformative biological and medical applications. Species trees play a central role in many of these applications; however, despite the widespread availability of genome assemblies, accurate inference of species trees remains challenging due to the limited automation, substantial domain expertise, and computational resources required by conventional methods. To address this limitation, we present ROADIES, a fully automated pipeline to infer species trees starting from raw genome assemblies. In contrast to the prominent approach, ROADIES incorporates a unique strategy of randomly sampling segments of the input genomes to generate gene trees. This eliminates the need for predefining a set of loci, limiting the analyses to a fixed number of genes, and performing the cumbersome gene annotation and/or whole genome alignment steps. ROADIES also eliminates the need to infer orthology by leveraging existing discordance-aware methods that allow multicopy genes. Using the genomic datasets from large-scale sequencing efforts across four diverse life forms (placental mammals, pomace flies, birds, and budding yeasts), we show that ROADIES infers species trees that are comparable in quality to the state-of-the-art studies but in a fraction of the time and effort, including on challenging datasets with rampant gene tree discordance and complex polyploidy. With its speed, accuracy, and automation, ROADIES has the potential to vastly simplify species tree inference, making it accessible to a broader range of scientists and applications.
Genetic diversity and natural selection of Plasmodium falciparum Pf41 vaccine candidate in clinical isolates from Senegal
Spatial and temporal evolution and influencing factors of human settlement environment quality in Xinjiang, China
Why does AI hinder democratization?
This paper examines the relationship between democratization and the development of AI and information and communication technology (ICT). Our empirical evidence shows that in the past 10 y, the advancement of AI/ICT has hindered the development of democracy in many countries around the world. Given that both the state rulers and civil society groups can use AI/ICT, the key that determines which side would benefit more from the advancement of these technologies hinges upon “technology complementarity.” In general, AI/ICT would be more complementary to the government rulers because they are more likely than civil society groups to access various administrative big data. Empirically, we propose three hypotheses and use statistical tests to verify our argument. Theoretically, we prove a proposition, showing that when the above-mentioned complementarity assumption is true, the AI/ICT advancements would enable rulers in authoritarian and fragile democratic countries to achieve better control over civil society forces, which leads to the erosion of democracy. Our analysis explains the recent ominous development in some fragile-democracy countries.
Integrating ontogenetic and behavioral analysis in fossil and extant Lynx pardinus (Temminck, 1827)
Effects of ulinastatin on therapeutic outcomes and inflammatory markers in pediatric septic shock patients
Dual level dengue diagnosis using lightweight multilayer perceptron with XAI in fog computing environment and rule based inference
Abstract Over the last fifty years, arboviral infections have made an unparalleled contribution to worldwide disability and morbidity. Globalization, population growth, and unplanned urbanization are the main causes. Dengue is regarded as the most significant arboviral illness among them due to its prior dominance in growth. The dengue virus is mostly transmitted to humans by Aedes mosquitoes. The human body infected with dengue virus (DenV) will experience certain adverse impacts. To keep the disease under control, some of the preventative measures implemented by different countries need to be updated. Manual diagnosis is typically employed, and the accuracy of the diagnosis is assessed based on the experience of the healthcare professionals. Because there are so many patients during an outbreak, incompetence also happens. Remote monitoring and massive data storage are required. Though cloud computing is one of the solutions, it has a significant latency, despite its potential for remote monitoring and storage. Also, the diagnosis should be made as quickly as possible. The aforementioned issue has been resolved with fog computing, which significantly lowers latency and facilitates remote diagnosis. This study especially focuses on incorporating machine learning and deep learning techniques in the fog computing environment to leverage the overall diagnostic efficiency of dengue by promoting remote diagnosis and speedy treatment. A dual-level dengue diagnosis framework has been proposed in this study. Level-1 diagnosis is based on the symptoms of the patients, which are sent from the edge layer to the fog. Level-1 diagnosis is done in the fog to manage the storage and computation issues. An optimized and normalized lightweight MLP has been proposed along with preprocessing and feature reduction techniques in this study for the Level-1 Diagnosis in the fog computing environment. Pearson Correlation coefficient has been calculated between independent and target features to aid in feature reduction. Techniques like K-fold cross-validation, batch normalization, and grid search optimization have been used for increasing the efficiency. A variety of metrics have been computed to assess the effectiveness of the model. Since the suggested model is a “black box,” explainable artificial intelligence (XAI) tools such as SHAP and LIME have been used to help explain its predictions. An exceptional accuracy of 92% is attained with the small dataset using the proposed model. The fog layer sends the list of probable cases to the edge layer. Also, a precision of 100% and an F1 score of 90% have been attained using the proposed model. The list of probable cases is sent from the fog layer to the edge layer, where Level-2 Diagnosis is carried out. Level-2 diagnosis is based on the serological test report of the suspected patients of the Level-1 diagnosis. Level-2 diagnosis is done at the edge using the rule-based inference method. This study incorporates dual-level diagnosis, which is not seen in recent studies. The majority of investigations end at Level 1. However, this study minimizes incorrect treatment and fatality rates by using dual-level diagnosis and assisting in confirmation of the disease.