Decoding potential lncRNA and disease associations through graph representation learning and gradient boosting with histogram.

Long noncoding RNAs (lncRNAs) are important regulators and promising targets for complex diseases. They have manifested dense relationships with various diseases. Although laboratory techniques have validated many lncRNA-disease associations (LDAs), they are costly, laborious, and time-consuming.

Hybrid optimization-based deep learning for energy efficiency resource allocation in MIMO-enabled wireless networks.

Resource allocation in multiple-input multiple-output (MIMO)-enabled wireless networks is designated for multiple users, which aims to optimize the distribution of network resources. This network's main intent is to maximize system performance by improving energy efficiency. However, the users of MIMO need many resources for effective operation.

Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets.

The proliferation of Internet of Things (IoT) devices has created unprecedented cybersecurity vulnerabilities, with botnets emerging as a critical threat to network infrastructure. This study focuses on traditional machine learning and deep learning approaches, proposes a novel ensemble framework to address these issues, integrating Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), Random Forest (RF), and Logistic Regression (LR) via a weighted soft-voting mechanism. Our approach introduces a Quantile Uniform transformation to reduce feature skewness, a multi-layered feature selection method to enhance discriminative power, an individual performance of deep learning-traditional machine learning and a hybrid models (ensemble models) for robust detection.

Data asset valuation model based on generative artificial intelligence.

In the digital economy era, the significance of data assets has increasingly become evident, particularly against the backdrop of the rapid development of Generative Artificial Intelligence. This paper constructed a data asset valuation model based on Generative AI, aimed at dynamically assessing the commercial value of data assets. The model integrates data feature extraction, value generation algorithms, and market adaptability evaluations to address the shortcomings of traditional valuation methods in dynamic market environments.

Optimization and performance study of large aspect ratio SiC microgrooves by waterjet assisted laser machining.

Silicon carbide (SiC) ceramics hold significant application value in high-end fields such as semiconductors and aerospace due to their exceptional mechanical properties and thermal stability. Large-aspect-ratio (LAR) microgrooves in ceramics are crucial for enabling advanced functionalities in various applications, including microfluidic devices, microelectromechanical systems (MEMS), and thermal management systems, where precise control over fluid flow, structural integrity, and heat dissipation is required. However, their extreme hardness poses challenges for traditional mechanical machining, including low efficiency and severe tool wear, while laser machining is prone to defects such as heat-affected zones and recast layers.

A distinctive human fatty acid metabolism profile associated with osteoporotic patients.

Evidence indicates a relationship between osteoporosis and nutritional factors. Nevertheless, the correlation between fatty acids and bone health remains not fully elucidated. This research sought to explore the potential associations between fatty acids (FAs) and osteoporosis.

High-Temperature Performance Enhancement of Asphalt Binders Modified with Single-Use Masks: A Rheological Analysis with Predictive Modeling.

Due to high temperatures and repeated load, asphalt pavements commonly experience rutting distress, a challenge that can be considerably reduced by modifying the binder components. This research focused on evaluating the performance of asphalt binders with single-use masks (SUMs) when subjected to high temperatures. For this purpose, dynamic shear rheometer (DSR)-based frequency sweep, temperature sweep, and multiple stress creep recovery (MSCR) experiments were performed on various asphalt binders, including both unmodified and SUM-modified (SUMM) samples.

Resource management for multi-drone communications in next-generation NOMA-enabled wireless networks.

The communication network of Unmanned Aerial Drones (UAD) is expected to become a vital element in the development of next-generation wireless networks, offering flexible infrastructure that extends network coverage to remote or disaster-stricken locations while enhancing capacity during critical events and large-scale emergencies. As UAD technology evolves, its role in ensuring consistent, widespread connectivity becomes more essential, though it faces challenges such as high latency, low spectral efficiency, and fairness issues across multiple drones. This research presents an optimization framework designed for multi-UAD communication networks based on Non-Orthogonal Multiple Access (NOMA) to address these difficulties.

KronNet a lightweight Kronecker enhanced feed forward neural network for efficient IoT intrusion detection.

The rapid expansion of Internet of Things (IoT) networks necessitates efficient intrusion detection systems (IDS) capable of operating within the stringent resource constraints of IoT devices. This study introduces KronNet, a lightweight feed-forward neural network enhanced with Kronecker product operations, designed for real-time IoT intrusion detection. KronNet leverages Gaussian Mixture Model (GMM)-based oversampling and a hybrid loss function combining Focal Loss and Cross-Entropy with adaptive class weighting to address class imbalance, ensuring robust detection across diverse attack types.

Recent Design and Application Advances in Micro-Electro-Mechanical System (MEMS) Electromagnetic Actuators.

Micro-electro-mechanical system (MEMS) electromagnetic actuators have rapidly evolved into critical components of various microscale applications, offering significant advantages including precision, controllability, high force density, and rapid responsiveness. Recent advancements in actuator design, fabrication methodologies, smart control integration, and emerging application domains have significantly broadened their capabilities and practical applications. This comprehensive review systematically analyzes the recent developments in MEMS electromagnetic actuators, highlighting core operating principles such as Lorentz force and magnetic attraction/repulsion mechanisms and examining state-of-the-art fabrication technologies, such as advanced microfabrication techniques, additive manufacturing, and innovative material applications.

Adaptive fixed-time fault-tolerant trajectory tracking control for disturbed robotic manipulator.

This article introduces a fixed-time trajectory control method for robotic manipulators, aimed at improving trajectory precision despite external disturbances, actuator faults, and uncertainties. Initially, a fast fixed-time nonsingular terminal sliding surface (FFNTSS) is utilized, featuring a bounded convergence time that remains unaffected by the initial conditions. This sliding surface not only prevents the occurrence of singularity but also guarantees fast convergence.

Enhanced medical image segmentation using novel level set evolution and efficient optimization.

Accurate and efficient medical image segmentation is a critical yet challenging task due to issues like intensity inhomogeneity, poor contrast, noise, and blur. In this paper, we introduce a novel framework that addresses these challenges by leveraging adaptive level set evolution, enhanced with a unique edge indication function. Unlike prior edge-based algorithms, which frequently fail with noisy images and have large computing costs, our method incorporates an improved edge indicator term into the level set architecture, considerably improving performance on degraded images.

Nonlinear optical dynamics and complex wave structures in nonlinear dispersive media.

The research focuses on optical solitons and employs the generalized auxiliary equation technique to obtain soliton resolutions for the nonlinear Kairat-X equation. This equation considers wave number groups influenced by time and velocity dispersion in non-linear mediums. Because of their stability and numerous uses in signal processing, telecommunications, and quantum physics, optical solitons are appreciated.

Customizable pattern synthesis: a deep generative approach for lantern designs.

Pattern design is essential in various domains, especially in traditional lantern production, where patterns convey cultural history and artistic values. Our research presents an innovative generative model that produces customizable lantern patterns, integrating classical aesthetics with modern design features a generative adversarial network (GAN)-based framework. The model was trained on an extensive dataset of over 17,000 pattern images over ten various categories.

Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network.

The cross-border intelligent marketing algorithm based on traditional linear models is relatively single in information feature extraction, making it difficult to effectively handle complex scenarios containing a large amount of implicit information in users and markets, resulting in poor personalized marketing effectiveness. To address this issue, this article proposes a cross-border intelligent marketing model that integrates rating information and user labels using a multi-layer perceptron grey wolf optimization and convolutional neural network (MLP-GWO-CNN). This model extracts implicit high-order information through nonlinear methods and can handle complex and sparse marketing data.

In-Situ Diagnostics of Multi-Thermal Fluid Injection Parameters Using Stray Radiation Suppression and Circular Cell-Enhanced Laser Absorption Spectroscopy.

Multithermal fluid (MTF) component ratios and injection parameters are critical inputs in offshore heavy oil development, such as injection adjustment and monitoring, productivity prediction, and generator combustion process optimization. We implement simultaneous in situ diagnostics of two emblematic injection parameters, the gas-water ratio (GWR) and noncondensable gases proportion (NCGP), in a pilot-scale environment. A system-level integration of a novel laser absorption spectroscopy multigas sensor system based on integrating stray radiation suppression and a circular cell-enhanced strategy is proposed.

Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net.

Objective: Segmenting and reconstructing 3D models of bone tumors from 2D image data is of great significance for assisting disease diagnosis and treatment. However, due to the low distinguishability of tumors and surrounding tissues in images, existing methods lack accuracy and stability. This study proposes a U-Net model based on double dimensionality reduction and channel attention gating mechanism, namely the DCU-Net model for oncological image segmentation.

HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization.

Whole slide imaging (WSI) has transformed diagnostic medicine, particularly in the field of cancer diagnosis and treatment. The use of deep learning algorithms for predicting WSIs has opened up new avenues for advanced medical diagnostics. Additionally, stain normalization can reduce the color and intensity variations present in WSI from different hospitals.

Facile synthesis of CoTe-FeC for efficient overall water-splitting in an alkaline medium.

The large amounts of attention directed towards the commercialization of renewable energy systems have motivated extensive research to develop non-precious-metal-based catalysts for promoting the electrochemical production of H and O from water. Here, we report promising technology, , electrochemical water splitting for OER and HER. This work used a simple hydrothermal method to synthesize a novel CoTe-FeC nanocomposite directly on a stainless-steel substrate.

A study on the impact of enterprise digital transformation on informed trading.

Informed trading, driven by information asymmetry and market imperfections, varies in presence across markets. This form of trading not only distorts market transaction prices and hinders resource allocation but also initiates adverse selection transactions, increasing liquidity risks and potentially precipitating market crashes, thereby impeding the market's healthy development. Utilizing information asymmetry theory and principal-agent theory, this paper analyzes data from A-share listed companies from 2011 to 2022.

Correction: Exploring the properties of ZrCO/GaS van der Waals heterostructures for optoelectronic applications.

Correction for 'Exploring the properties of ZrCO/GaS van der Waals heterostructures for optoelectronic applications' by Altaf Ur Rahman , , 2024, https://doi.org/10.1039/D4CP02370F.

Three-Dimensional Reconstruction and Visualization of Underwater Bridge Piers Using Sonar Imaging.

The quality of underwater bridge piers significantly impacts bridge safety and long-term usability. To address limitations in conventional inspection methods, this paper presents a sonar-based technique for the three-dimensional (3D) reconstruction and visualization of underwater bridge piers. Advanced MS1000 scanning sonar is employed to detect and image bridge piers.

Exploring the properties of ZrCO/GaS van der Waals heterostructures for optoelectronic applications.

We investigate the structural, electronic, and optical properties of eight possible ZrCO/GaS van der Waals (vdW) heterostructures using first-principles calculations based on a hybrid functional. These structures display favorable stability, indicated by matching crystal structures and negative formation energies. In all considered configurations, these heterostructures act as indirect band gap semiconductors with a type-II band alignment, allowing efficient electron-hole separation.