A Multimodal AI Framework for Automated Multiclass Lung Disease Diagnosis from Respiratory Sounds with Simulated Biomarker Fusion and Personalized Medication Recommendation.

Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine.

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification.

Background: Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline.

HETEROGENEOUS PERIDYNAMIC NEURAL OPERATORS: DISCOVER BIOTISSUE CONSTITUTIVE LAW AND MICROSTRUCTURE FROM DIGITAL IMAGE CORRELATION MEASUREMENTS.

Human tissues are highly organized structures with specific collagen fiber arrangements varying from point to point. The effects of such heterogeneity play an important role for tissue function, and hence it is of critical to discover and understand the distribution of such fiber orientations from experimental measurements, such as the digital image correlation data. To this end, we introduce the heterogeneous peridynamic neural operator (HeteroPNO) approach, for data-driven constitutive modeling of heterogeneous anisotropic materials.

Optimizing temperature distributions for training neural quantum states using parallel tempering.

Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas.

Insects in agricultural greenhouses: a metagenomic analysis of microbes in infesting tomato and cucumber crops.

Introduction: With the predicted 9-10 billion world population increase by 2050 and its accompanying need for sustainable food production, and with the harsh climate conditions challenging agriculture and food security in many countries world-wide, employing "horticultural protected cultivation practices" in farming for seasonal and off-seasonal crop production is on the rise, among which is the use of agricultural greenhouses. The importance of greenhouse farming has been, indeed, evident by the perceived increase in year-round crops production, curtail in production risks, upsurge in agricultural profits, outreaching food stability and security in many countries globally. Yet, and despite this acknowledged success of employing greenhouses in farming, many constraints, including the presence of insect pests, still chaperoned this practice over the years, significantly impacting crop quality and production.

Advanced Hybrid Closed-Loop Insulin Delivery Is Associated With Improved Glycemic Indicators and Normalization of Small Nerve Fibre Structure in Adults With Type 1 Diabetes.

Background: Hyperglycemia is a major driver of diabetic peripheral neuropathy (DPN) in type 1 diabetes mellitus (T1DM). Advanced hybrid closed-loop (AHCL) technologies improve glycemic control and reduce glycemic variability and may improve DPN.

Methods: Patients with T1DM treated for 9.

Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review.

Severe collisions can result from aggressive driving and poor road conditions, emphasizing the need for effective monitoring to ensure safety. Smartphones, with their array of built-in sensors, offer a practical and affordable solution for road-sensing. However, the lack of reliable, standardized datasets has hindered progress in assessing road conditions and driving patterns.

Applying the FAIR Principles to computational workflows.

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows.

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks.

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions-ReLU, leaky ReLU, GELU, and Mish-on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow.

Generalized representative structures for atomistic systems.

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g.

Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd.

Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints - non-negative weights and sparse coding of optic flow.

Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques.

We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.

X-ray Thomson scattering absolute intensity from the f-sum rule in the imaginary-time domain.

We present a formally exact and simulation-free approach for the normalization of X-ray Thomson scattering (XRTS) spectra based on the f-sum rule of the imaginary-time correlation function (ITCF). Our method works for any degree of collectivity, over a broad range of temperatures, and is applicable even in nonequilibrium situations. In addition to giving us model-free access to electronic correlations, this new approach opens up the intriguing possibility to extract a plethora of physical properties from the ITCF based on XRTS experiments.

Smoking induced salivary microbiome dysbiosis and is correlated with lipid biomarkers.

Background: The oral microbiome plays an essential role in maintaining oral homeostasis and health; smoking significantly affects it, leading to microbial dysbiosis. The study aims to investigate changes in the oral microbiome composition of smokers in the Qatari population and establish a correlation with lipid biomarkers.

Methods: The oral microbiota was profiled from saliva samples of 200 smokers and 100 non-smokers in the Qatari population, and 16s rRNA V3-V4 region were sequenced using the Illumina MiSeq platform.

A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks.

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication framework. In some scenarios, a large number of machine-type communication devices (MTCD) may simultaneously enter the communication coverage of a target base station. However, the current handover mechanism specified by the 3rd Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead within the access and core networks, which may have negative impacts on network efficiency.

Nonequilibrium Dynamics of Electron Emission from Cold and Hot Graphene under Proton Irradiation.

Characteristic properties of secondary electrons emitted from irradiated two-dimensional materials arise from multi-length and multi-time-scale relaxation processes that connect the initial nonequilibrium excited electron distribution with their eventual emission. To understand these processes, which are critical for using secondary electrons as high-resolution thermalization probes, we combine first-principles real-time electron dynamics with irradiation experiments. Our data for cold and hot proton-irradiated graphene show signatures of kinetic and potential emission and generally good agreement for electron yields between experiment and theory.

Understanding the black-box: towards interpretable and reliable deep learning models.

Deep learning (DL) has revolutionized the field of artificial intelligence by providing sophisticated models across a diverse range of applications, from image and speech recognition to natural language processing and autonomous driving. However, deep learning models are typically black-box models where the reason for predictions is unknown. Consequently, the reliability of the model becomes questionable in many circumstances.

Deposition products predicted from conceptual DFT: The hydrolysis reactions of MoF6, WF6, and UF6.

Metal hexafluorides hydrolyze at ambient temperature to deposit compounds having fluorine-to-oxygen ratios that depend upon the identity of the metal. Uranium-hexafluoride hydrolysis, for example, deposits uranyl fluoride (UO2F2), whereas molybdenum hexafluoride (MoF6) and tungsten hexafluoride deposit trioxides. Here, we pursue general strategies enabling the prediction of depositing compounds resulting from multi-step gas-phase reactions.

Rebound effects undermine carbon footprint reduction potential of autonomous electric vehicles.

Autonomous vehicles offer greater passenger convenience and improved fuel efficiency. However, they are likely to increase road transport activity and life cycle greenhouse emissions, due to several rebound effects. In this study, we investigate tradeoffs between improved fuel economy and rebound effects from a life-cycle perspective.

The brain's unique take on algorithms.

Perspectives for understanding the brain vary across disciplines and this has challenged our ability to describe the brain’s functions. In this comment, we discuss how emerging theoretical computing frameworks that bridge top-down algorithm and bottom-up physics approaches may be ideally suited for guiding the development of neural computing technologies such as neuromorphic hardware and artificial intelligence. Furthermore, we discuss how this balanced perspective may be necessary to incorporate the neurobiological details that are critical for describing the neural computational disruptions within mental health and neurological disorders.

Online dynamic ensemble deep random vector functional link neural network for forecasting.

This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL's representation ability. Each hidden layer's representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL's output.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia.