SMCNet: State-Space Model for Enhanced Corruption Robustness in 3D Classification.

Accurate classification of three-dimensional (3D) point clouds in real-world environments is often impeded by sensor noise, occlusions, and incomplete data. To overcome these challenges, we propose SMCNet, a robust multimodal framework for 3D point cloud classification. SMCNet combines multi-view projection and neural radiance fields (NeRFs) to generate high-fidelity 2D representations with enhanced texture realism, addressing occlusions and lighting inconsistencies effectively.

Hierarchical Polyimide Nonwoven Fabric with Ultralow-Reflectivity Electromagnetic Interference Shielding and High-Temperature Resistant Infrared Stealth Performance.

Designing and fabricating a compatible low-reflectivity electromagnetic interference (EMI) shielding/high-temperature resistant infrared stealth material possesses a critical significance in the field of military. Hence, a hierarchical polyimide (PI) nonwoven fabric is fabricated by alkali treatment, in-situ growth of magnetic particles and "self-activated" electroless Ag plating process. Especially, the hierarchical impedance matching can be constructed by systematically assembling FeO/Ag-loaded PI nonwoven fabric (PFA) and pure Ag-coated PI nonwoven fabric (PA), endowing it with an ultralow-reflectivity EMI shielding performance.

Flight training and the anterior cingulate cortex.

Pilots are considered the final line of defense for aviation safety. Before becoming a pilot, an ab initio pilot must undergo systematic flight training. This study included 25 male flying cadets.

Solving UAV 3D Path Planning Based on the Improved Lemur Optimizer Algorithm.

This paper proposes an Improved Lemur Optimization algorithm (ILO), which combines the advantages of the Spider Monkey Optimization algorithm, Simulated Annealing algorithm, and Lemur Optimization algorithm. Through the use of an adaptive nonlinear decrement model, adaptive learning factors, and updated jump rates, the algorithm enhances its global exploration and local exploitation capabilities. A Gaussian function model is used to simulate the mountain environment, and a mathematical model for UAV flight is established based on constraints and objective functions.

DFT-guided synthesis of N, B dual-doped porous carbon from saccharina japonica for enhanced oxygen reduction catalysis.

Introduction: The oxygen reduction reaction (ORR) is a crucial determinant of the energy transformation capacity of fuel cells. This study investigates the performance of N and B dual-doped carbon in ORR.

Methods: Six models using density functional theory (DFT) are developed to compare the performance of different doping strategies.

N, P Co-Doped Hard Carbon Anodes for High-Performance Lithium-Ion Batteries with Enhanced Capacity Retention and Cycle Stability.

Compared to the traditional graphite anode, heteroatom-doped polymer carbon materials have high capacity retention due to their high porosity and porous structure. Therefore, they have great potential for application in lithium-ion battery (LIB) anodes. In this work, an N, P co-doped precursor polymer material (MBP), synthesized via a one-pot method using bisphenol-A (C-source), melamine (N-source), and 9,10-Dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (P-source).

Modeling the distribution of jet fuel price returns based on fat-tail stable Paretian distribution.

Jet fuel plays a crucial role as an essential energy source in aerospace and aviation operations. The recent increase in fuel prices has presented airlines with the new challenge of managing jet fuel costs to ensure consistent cash flow and minimize operational uncertainties. The conventional risk prediction models used by airlines often assume that risks are normally distributed according to the classical Central Limit Theorem, which can lead to under-hedging.

An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback Mechanism.

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning.

Research on the evaluation of the manual manipulation level of a student pilot in a typical scene.

The traditional evaluation method of student pilots ' manual manipulation level relies too much on teachers' subjective judgment. A quantitative evaluation method based on flight data is proposed to compensate for the shortcoming of the traditional method. According to the theory of the core competence of a pilot, the evaluation index system of manual manipulation level of student pilots in a typical scene is constructed.

Algebraic method for multisensor data fusion.

In this contribution, we use Gaussian posterior probability densities to characterize local estimates from distributed sensors, and assume that they all belong to the Riemannian manifold of Gaussian distributions. Our starting point is to introduce a proper Lie algebraic structure for the Gaussian submanifold with a fixed mean vector, and then the average dissimilarity between the fused density and local posterior densities can be measured by the norm of a Lie algebraic vector. Under Gaussian assumptions, a geodesic projection based algebraic fusion method is proposed to achieve the fused density by taking the norm as the loss.

Exploring the dynamics of sports records evolution through the gembris prediction model and network relevance analysis.

Background: Sports records hold valuable insights into human physiological limits. However, presently, there is a lack of integration and evolutionary patterns in the recorded information across various sports.

Methods: We selected sports records from 1992 to 2018, covering 24 events in men's track, field, and swimming.

A Flexible Wearable Strain Sensor Based on Nano-Silver-Modified Laser-Induced Graphene for Monitoring Hand Movements.

The advancement in performance in the domain of flexible wearable strain sensors has become increasingly significant due to extensive research on laser-induced graphene (LIG). An innovative doping modification technique is required owing to the limited progress achieved by adjusting the laser parameters to enhance the LIG's performance. By pre-treating with AgNO, we successfully manufactured LIG with a uniform dispersion of silver nanoparticles across its surface.

Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors.

Increased tensile strength induced by the precipitation of nanocrystals for welding joints of Zr-based amorphous alloys.

Zr-based amorphous alloys have attracted intensive attention for applications because of their excellent mechanical property. However, the welding process is inevitable for some special cases, such as the obtain of large size structure parts. It is significant to clarify the influence of introduced welding joints on mechanical properties in Zr-based amorphous alloys.

Enhancing UWB Indoor Positioning Accuracy through Improved Snake Search Algorithm for NLOS/LOS Signal Classification.

Non-line-of-sight (NLOS) errors significantly impact the accuracy of ultra-wideband (UWB) indoor positioning, posing a major barrier to its advancement. This study addresses the challenge of effectively distinguishing line-of-sight (LOS) from NLOS signals to enhance UWB positioning accuracy. Unlike existing research that focuses on optimizing deep learning network structures, our approach emphasizes the optimization of model parameters.

Research on the Collision Risk of Fusion Operation of Manned Aircraft and Unmanned Aircraft at Zigong Airport.

Low-altitude airspace is developing rapidly, but the utilization rate of airspace resources is low. Therefore, in order to solve the problem of the safe operation of the fusion of large UAVs and manned aircraft in the same airspace, this paper analyzes the theoretical calculation of the collision risk of the fusion operation of manned aircraft and UAVs at Feng Ming Airport in Zigong, verifying that while assessing the safety spacing of 10 km in the lateral direction, it further simulates the possibility of calculating the theoretical smaller safety spacing. The study will propose a new theory of error spacing safety margin and improve it according to the traditional Event collision risk model, combining the error spacing safety margin to establish an improved collision model more suitable for the fusion operation of manned and unmanned aircraft and reduce the redundancy of calculation.

Enhanced Indoor Positioning Using RSSI and Time-Distributed Auto Encoder-Gated Recurrent Unit Model.

This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data.

Nonflammable All-Fluorinated Electrolyte Enabling High-Voltage and High-Safety Lithium-Ion Cells.

In this study, a nonflammable all-fluorinated electrolyte for lithium-ion cells with a Li(NiMnCo)O cathode is investigated under high voltages. This electrolyte, named FT46, consists of fluoroethylene carbonate (FEC) and bis(2,2,2-trifluoroethyl) carbonate (TFEC) in a mass ratio of 4:6. Compared to a commercially available electrolyte and several other fluorinated electrolytes, cells containing FT46 demonstrate significantly better cycling performances under high voltage (3.

Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers' workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology's applications in the ATC communication system.

Correction: Research on online monitoring of aircraft skin laser paint removal thickness using standard curve method and PCA-SVR based on LIBS.

Correction for 'Research on online monitoring of aircraft skin laser paint removal thickness using standard curve method and PCA-SVR based on LIBS' by Wenfeng Yang , , 2024, https://doi.org/10.1039/D4AY00872C.

Urban drone stations siting optimization based on hybrid algorithm of MILP and machine learning.

Urban environments, characterized by high population density and intricate infrastructures, are susceptible to a range of emergencies such as fires and traffic accidents. Optimal placement and distribution of fire stations and ambulance centers are thus imperative for safeguarding both life and property. An investigation into the distribution inefficiencies of emergency service facilities in selected districts of Chengdu reveals that imbalanced distribution of these facilities results in suboptimal response times during critical incidents.