Photoselective isotope fractionation dynamics of N with cosmo and atmospheric chemistry perspectives.

Stable isotope ratio measurements provide valuable insights into a broad range of natural processes, from planetary atmospheres and climate to interstellar chemistry. Nitrogen, which has two stable isotopes, exhibits varying isotope ratios across the solar system. To model these observations, the isotope fraction as a function of energy is essential.

Chromium catalyzed acceptorless dehydrogenative (cross)coupling of primary amines to secondary imines.

We present here acceptorless dehydrogenative coupling of primary amines to form secondary aldimines catalyzed by a complex of Earth-abundant chromium. The reaction is promoted by Cr(DAFO)(CO) (DAFO = 4,5-diazafluorene-9-one) without using any additives, base or oxidant generating NH and H as sole by-products. Dehydrogenative cross-coupling of primary amines with aniline derivatives to unsymmetrical secondary imines was also achieved with good to excellent yields.

Modeling the time to dropout under phase-wise variable stress fixed cohort setup.

The event of a student dropping out from an academic program depends on several factors namely course content, change in interest, financial problems among many others. These factors vary interdependently with different phases of the academic program. We assume that the factors put different amount of academic stresses on a student in different phases of the program.

An explainable Bi-LSTM model for winter wheat yield prediction.

Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. Although other sequential DL methods like 1D Convolutional Neural Networks (1D-CNN) and Bidirectional long short-term memory (Bi-LSTM) have shown high accuracy for various tasks, including crop yield prediction, their application in regional scale crop yield prediction remains largely unexplored.

Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.

Objective: In this paper, we explore the correlation between performance reporting and the development of inclusive AI solutions for biomedical problems. Our study examines the critical aspects of bias and noise in the context of medical decision support, aiming to provide actionable solutions. Contributions: A key contribution of our work is the recognition that measurement processes introduce noise and bias arising from human data interpretation and selection.

Terpenylation of Ketones and a Secondary Alcohol under Hydrogen-Borrowing Manganese Catalysis.

An Earth-abundant Mn-PNP pincer complex-catalyzed terpenylation of cyclic and acyclic ketones and secondary alcohol 1-phenylethanol using isoprenoid derivatives prenol, nerol, phytol, solanesol, and E-farnesol as allyl surrogates is reported. The C-C coupling reactions are green and atom-economic, proceeding via dehydrogenation of alcohols following a hydrogen autotransfer methodology aided by metal-ligand cooperation.

Chromium-catalyzed sustainable C-C and C-N bond formation: -alkylation and Friedländer quinoline synthesis using alcohols.

The synthesis of a novel phosphine-based pincer chromium(II) complex CrCl(PONN) (Cr-1) is reported in this study. The complex exhibited promising catalytic performance in C-C and C-N bond formation using the borrowing hydrogen methodology. Cr-1 catalyzed the α-alkylation of ketones using primary alcohols as alkyl surrogates in the presence of catalytic amount of a base.

Improved Video-Based Point Cloud Compression via Segmentation.

A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume.

Molybdenum-catalyzed hydrogenation of carbon dioxide, bicarbonate, and inorganic carbonates to formates.

Herein, we report the hydrogenation of carbon dioxide to sodium formate catalyzed by low-valent molybdenum phosphine complexes. The 1,3-bis(diphenylphosphino)propane (DPPP)-based Mo complex was found to be an efficient catalyst in the presence of NaOH affording formate with a TON of 975 at 130 °C in THF/HO after 24 h utilizing 40 bar (CO : H = 10 : 30) pressure. The complex was also active in the hydrogenation of sodium bicarbonate and inorganic carbonates to the corresponding formates.

Editorial for the Special Issue "Machine Learning in Computer Vision and Image Sensing: Theory and Applications".

Machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [...

Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.

Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older.

NNN manganese complex-catalyzed α-alkylation of methyl ketones using alcohols: an experimental and computational study.

We present here a phosphine-free, quinoline-based pincer Mn catalyst for α-alkylation of methyl ketones using primary alcohols as alkyl surrogates. The C-C bond formation reaction proceeds a hydrogen auto-transfer methodology. The sole by-product formed is water, rendering the protocol atom efficient.

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines.

Advances in Group VI Metal-Catalyzed Homogeneous Hydrogenation and Dehydrogenation Reactions.

Transition metal-catalyzed homogeneous hydrogenation and dehydrogenation reactions for attaining plethora of organic scaffolds have evolved as a key domain of research in academia and industry. These protocols are atom-economic, greener, in line with the goal of sustainability, eventually pave the way for numerous novel environmentally benign methodologies. Appealing progress has been achieved in the realm of homogeneous catalysis utilizing noble metals.

LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals.

Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature.

PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI.

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely.

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays.

L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets.

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems.

A review of automated sleep disorder detection.

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection.

Interventions Risk Evaluation and Management in Aseptic Manufacturing.

Interventions performed by personnel during an aseptic process can be a key source of microbiological contamination of sterile biopharmaceutical products, irrespective of the type of manufacturing system used. Understanding the relative risk of this source of contamination provides valuable information to help make decisions for the design, qualification, validation, operation, monitoring, and evaluation of the aseptic process. These decisions can be used to improve the aseptic process and provide assurance of the sterility of the products.

Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.

Background: Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples.

Heart rate variability for medical decision support systems: A review.

Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection.