A novel hybrid model for air quality prediction via dimension reduction and error correction techniques.

The monitoring of air pollution through the air quality index (AQI) is a fundamental tool in ensuring public health protection. Accurate prediction of air quality is necessary for the timely implementation of measures to control and manage air pollution, thereby mitigating its detrimental impact on human health. A novel hybrid prediction model is proposed, which is EMD-KMC-EC-SSA-VMD-LSTM.

Meniscus injury prediction model based on metric learning.

A meniscus injury is a prevalent condition affecting the knee joint. The construction of a subjective prediction model for meniscus injury represents a potentially invaluable diagnostic tool for physicians. Nevertheless, given the variability of pathological manifestations among individual patients, machine learning-based models may produce errors when attempting to predict specific medical records.

Selective laser melting of titanium matrix composites: An in-depth analysis of materials, microstructures, defects, and mechanical properties.

This paper provides an in-depth review of the advancements and challenges associated with Titanium Matrix Composites (TMCs) in Selective Laser Melting (SLM). Material selection, SLM processing parameters, and their influence on the microstructure and properties of TMCs are discussed. The relationship between processing parameters, material characteristics, and the development of defects such as balling, porosity, and cracking is examined.

Density Functional Theory Study on Screening and Key Metrics for Non-Metallic Oxygen Reduction Catalysts.

This study systematically investigates the oxygen reduction reaction (ORR) catalytic activity of graphene doped with various non-metallic impurities. The non-metal elements include boron (B), silicon (Si), nitrogen (N), phosphorus (P), arsenic (As), oxygen (O), sulfur (S), selenium (Se), tellurium (Te), fluorine (F), chlorine (Cl), bromine (Br), and iodine (I). We found that adsorbates tend to adsorb on positively charged impurity atoms.

Deconvolution of cell-type-associated markers predictive of response to neoadjuvant radiotherapy.

Tumor microenvironent contains prognostic molecular markers and therapeutic targets from different cellular sources, which are still not fully revealed in the resistance and recurrence after radiotherapy for rectal cancer. By integrating the scRNA-seq data, we deconvoluted the bulk transcriptomics of rectal cancer collected before preoperative neoadjuvant radiotherapy (nRT) into fractions and gene expression of the six cell types. The inferred cell-type-associated DEGs, abbreviated as caDEGs, of myeloid and stromal cells were enriched for overlapping yet unique biological processes including immunity, angiogenesis, and metabolism, respectively.

Theoretical screening of the metal-nonmetal pair anchored on N-doped graphene for the oxygen reduction reaction.

The oxygen reduction reaction (ORR) is a crucial process during hydrogen-based energy conversion at the cathode of proton-exchange membrane fuel cells, which causes a bottleneck owing to the high price and low efficiency of ORR catalysts. Single-atom catalysts (SACs) have garnered significant attention from researchers due to their exceptional activity and efficient atom utilization. To identify highly active SACs among numerous candidates, a three-step screening strategy was adopted to select the best ORR catalyst.

Calculation method and evolution rule of the strain energy density of sandstone under true triaxial compression.

Deep rock masses are typically in complex stress states, and research on the evolution of their strain energy density is of highly important for understanding their failure characteristics. In this work, a true triaxial stress‒balanced unloading test is designed to analyze the u and u evolution of sandstone under true triaxial compression conditions. The study results indicate that as σ increases, the elastic strain decreases in the σ and σ directions, whereas the residual strain progressively increases, and the magnitude of decrease in elastic strain exceeds the magnitude of increase in residual strain.

Mutation patterns in colorectal cancer and their relationship with prognosis.

Background: Colorectal cancer (CRC) is a prevalent malignancy and a leading cause of cancer-related mortality. Extensive research into the aetiology of CRC has revealed that somatic mutations in certain genes play a crucial role in CRC development.AIM: In this study, we utilized data from public databases to investigate prevalent mutation patterns in CRC and developed a prognostic predictive model for CRC patients based on mutant genetic characteristics and other relevant clinical features.

Climate change and Land Use/Land Cover Change (LUCC) leading to spatial shifts in net primary productivity in Anhui Province, China.

As an important part of terrestrial carbon cycle research, net primary productivity is an important parameter to evaluate the quality of terrestrial ecosystem and plays an important role in the analysis of global climate change and carbon balance. Anhui Province is in the Yangtze River Delta region in eastern China. Based on the theoretical basis of CASA model, this paper uses MODIS NDVI, vegetation type data, meteorological data, and LUCC to estimate the NPP of Anhui Province during 2001-2020 and analyzes its spatial-temporal pattern.

Dynamic weighted residual ensemble learning for hyperspectral image classification driven by features and samples.

Dynamic ensemble selection has emerged as a promising approach for hyperspectral image classification. However, selecting relevant features and informative samples remains a pressing challenge. To address this issue, we introduce two novel dynamic residual ensemble learning methods.

Data-knowledge co-driven feature based prediction model via photoplethysmography for evaluating blood pressure.

Background: Knowledge feature (KF) with clear physiological significance of photoplethysmography are widely used in predicting blood pressure. However, KF primarily focus on local information of photoplethysmography, which may struggle to capture the overall characteristics.

Methods: Firstly, functional data analysis (FDA) was introduced to extract two types of data feature (DF).

Advances in Laser Powder Bed Fusion of Tungsten, Tungsten Alloys, and Tungsten-Based Composites.

In high-tech areas such as nuclear fusion, aerospace, and high-performance tools, tungsten and its alloys are indispensable due to their high melting point, low thermal expansion, and excellent mechanical properties. The rise of Additive Manufacturing (AM) technologies, particularly Laser Powder Bed Fusion (L-PBF), has enabled the precise and rapid production of complex tungsten parts. However, cracking and densification remain major challenges in printing tungsten samples, and considerable efforts have been made to study how various processing conditions (such as laser power, scanning strategy, hatch spacing, scan speed, and substrate preheating) affect print quality.

Synergistic effect and microbial community structure of waste-activated sludge and kitchen waste solids residue mesophilic anaerobic co-digestion.

Anaerobic co-digestion was conducted on the solid residues after three-phase separation of kitchen waste (KWS) and waste-activated sludge (WAS), the synergistic effects and process performance were studied during co-digestion at different ratios of KWS to WAS. KWS and WAS mix ratios of 0:1, 1:4, 1:3, 1:2, 1:1, 2:1, 3:1, 4:1 and 1:0 (based on TS). The results showed that a ratio of KWS to WAS of 1:1 got a very high methane recovery with a methane yield of 310.

The measurement of China's provincial physical capital stock based on the improved method and indicators.

Physical capital stock is the basic indicator of macroeconomic empirical studies and also an important manifestation of macroeconomic activity capacity. When measuring China's provincial physical capital stock by the perpetual inventory method, the treatment for the depreciation rate and the initial physical capital stock in the existing literature has some defects in reflecting the economic performance. The purpose of this paper is to build the most reliable and reasonable statistical series of China's provincial physical capital stock.

Pyridinium-Based Fluorosulfonamide Reagents Enabled Photoredox-Catalyzed Radical Fluorosulfonamidation.

Sulfamoyl fluorides, as a crucial building block of SuFEx, have garnered extensive research interest due to their unique properties. However, the direct radical fluorosulfonamidation process for the synthesis of sulfamoyl fluorides has been overlooked. We herein disclosed a practical procedure for constructing a redox-active fluorosulfonamide radical reagent named fluorosulfonyl--pyridinium tetrafluoroborate (PNSF) from SOF.

Enterprise financial sharing and risk identification model combining recurrent neural networks with transformer model supported by blockchain.

The objective of this study is to investigate methodologies concerning enterprise financial sharing and risk identification to mitigate concerns associated with the sharing and safeguarding of financial data. Initially, the analysis examines security vulnerabilities inherent in conventional financial information sharing practices. Subsequently, blockchain technology is introduced to transition various entity nodes within centralized enterprise financial networks into a decentralized blockchain framework, culminating in the formulation of a blockchain-based model for enterprise financial data sharing.

Study on line-scan profile for a trapezoid line under varying sample temperatures through Monte-Carlo simulation.

This study investigates the influence of the sample inherent temperature on the line-scan profile for a silicon trapezoid line with different sidewall angles by Monte-Carlo simulation. This study demonstrates that the profile varies with temperature, particularly focusing on the 'shoulder', which becomes more pronounced with larger sidewall angles. The contrast of the secondary electron profile increases at low primary electron energy but decreases at relatively high PE energy as the temperature rises.

B-N Co-Doped Graphene: Stability and Catalytic Activity in Oxygen Reduction Reaction - A Theoretical Insight.

We systematically investigated the stable configurations and catalytic activity in the Oxygen Reduction Reaction (ORR) of graphene co-doped with boron and nitrogen (B-N) using first-principles methods. Compared to single B/N doping, co-doping with BN is energetically favored. We found that intermediate species of ORR process adsorb on boron atoms, which act as catalytic sites.

A simplified homogeneous approach for non-linear analysis of masonry infill panels under in-plane loads.

This paper aims to create a unified model that effectively combines continuous 2-dimensional elements and discrete components to capture the nonlinear characteristics and failure mechanisms of solid and perforated masonry infill panels. Given that masonry infill behavior is primarily influenced by shear deformations, an equivalent model is developed by using multiple small square panels arranged diagonally and interconnected by two-component springs, encompassing axial and shear behavior at their intersections. For the sake of simplicity, the divided panels are assumed to behave elastically, with plasticity concentrated only in the axial component of the connector springs.