Spectroscopic characterization of three fungicides using terahertz spectroscopy and DFT theory, combined with convolutional neural networks for quantitative analysis of tebuconazole.

Fungicides are essential agrochemicals for the prevention and control of plant diseases. Counterfeit products, often lacking enough active ingredients, can compromise disease management and pose risks to agricultural safety. Precise quantification of the chemical structure and concentration of active components enables reliable authentication of fungicide formulations, ensuring their efficacy in crop protection and supporting the quality and safety of agricultural production.

Graph-based feature learning methods for subject-dependent and subject-independent motor imagery EEG decoding.

Unlabelled: The significant intra-individual variability and inter-individual differences in scalp electroencephalogram (EEG) make it difficult to learn task-distinguishable features, posing a challenge for motor imagery brain-computer interfaces. Current feature learning methods often produce an incomplete feature space, struggling to accommodate these variations and differences. Additionally, the weak discriminative nature of this feature space results in diminished EEG classification performance.

Configuration analysis of mobile phone addiction among college students: findings from fsQCA method.

Purpose: Mobile phone addiction among college students is currently a common phenomenon and has many negative impacts on the students, such as decreased vision and low sleep quality. Since the existing research on this phenomenon has analyzed the net effects of causes on the result, this paper introduces the fuzzy set qualitative comparative analysis (fsQCA) method to explore the relationship between five antecedent conditions (self-control, self-esteem, loneliness, significant others, and school management) and college students' mobile phone addiction from the perspective of configuration.

Method: Taking college students as research subjects, and a questionnaire was used for data collection.

Hypothermia therapy enhances cognitive and motor recovery in patients with traumatic brain injury: a 6-month retrospective cohort study.

Objective: To evaluate the efficacy of therapeutic hypothermia in enhancing cognitive and motor outcomes six months post-TBI.

Methods: A retrospective cohort analysis was conducted on data from the Medical Information Mart for Intensive Care (MIMIC) database. A total of 259 TBI patients were divided into a Routine group (n = 135) and a Hypothermia Therapy group (n = 124).

Hybrid path planning algorithm for underactuated AUV based on RRT star and APF.

To address the kinematic constraints and real-time requirements of autonomous underwater vehicle (AUV), this paper proposes a novel path planning algorithm: Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*). The algorithm integrates four key techniques-Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and a Dynamic Artificial Potential Field-to improve sampling efficiency and path quality over conventional RRT-based methods. In addition, redundant node pruning and cubic non-uniform B-spline interpolation are employed to enhance trajectory smoothness and ensure kinematic feasibility.

Associations between body mass index and health-related physical fitness among Chinese university students: a cross-sectional study.

Obesity among university students is a growing concern with significant implications for physical and mental health. This study aims to examine the associations between body mass index (BMI) and health-related physical fitness among Chinese university students. Data were collected from 14,735 students (9,117 males and 5,618 females) aged 19-25 years.

The PLSR-ML fusion strategy for high-accuracy leaf potassium inversion in karst region of Southwest China.

Potassium is a critical macronutrient for plant growth, yet accurately and rapidly estimating its content in karst regions remains challenging due to complex terrestrial conditions. To address this, we collected leaf potassium content and reflectance data from 301 plant samples across nine karst regions in Guangxi Province. Our results showed that hybrid models combining Partial Least Squares Regression (PLSR) with three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP)-namely PLSR-RF, PLSR-XGBoost, and PLSR-MLP, demonstrated exceptional accuracy in estimating leaf potassium content.

Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.

Background: While the TNM staging system provides valuable insights into the extent of disease, predicting postoperative progression in early-stage non-small cell lung cancer (NSCLC) remains a significant challenge. An effective bioimaging prognostic marker for early-stage NSCLC, powered by artificial intelligence, could greatly assist clinicians in making informed treatment decisions.

Methods: A total of 926 patients from four centers (A, B, C, and D) with histologically confirmed stage I or II solid non-small cell lung cancer (NSCLC) who underwent surgical resection were retrospectively reviewed.

Risk classification of thymoma based on multi-feature fusion in dynamic enhanced CT.

Background: Accurate classification of high-risk and low-risk thymomas is critical for guiding treatment strategies and assessing prognosis. Thymoma is the most common primary tumor of the anterior mediastinum. However, previous studies have limitations in comprehensively utilizing imaging data, particularly in combining radiomics and deep learning (DL) features for preoperative classification.

Configuration analysis of negative silence in college classroom based on FSQCA method.

Currently, negative silence among students in college classroom is quite common. However, existing research on this phenomenon tends to analyze the net effects of antecedent conditions on outcome. In fact, negative silence in college classroom is the result of multiple factors working together.

Sparse group LASSO and nonlinear machine learning for frequency-feature optimization in noninvasive blood glucose monitoring via bioimpedance spectroscopy.

Diabetic patients need to test their blood glucose levels (BGL) frequently; however, traditional methods of blood collection and testing cause great pain to patients. In order to improve the quality of life of patients, this paper develops a noninvasive, portable, and continuous monitoring blood glucose detection system, which uses the latest bioimpedance integrated circuit to obtain the bioimpedance spectrum (BIS) of the inner forearm of the human body. The obtained BIS covers most of the frequencies up to 1 MHz.

Identification, Geographical Traceability, and Thermal Oxidation and Photodegradation Studies of Camellia Oil Based on Raman Spectroscopy.

Camellia oil, rich in monounsaturated fatty acids, squalene, tocopherols, and polyphenols, is highly valued for its nutritional benefits. However, its high market value and regional variations have led to frequent adulteration, highlighting the need for rapid, non-destructive methods for authentication, geographical traceability, and quality assessment. This study employed portable Raman spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to differentiate camellia oil from other edible oils and evaluate its thermal and photo-oxidative stability.

Corrigendum: Research on the coupling mechanism of the trade union and human resource management in China.

[This corrects the article DOI: 10.3389/fpsyg.2022.

Study on energy dissipation law of vibration disturbance damaged Zhanjiang Formation structural clay.

This study aims to investigate the energy dissipation laws and underlying mechanisms of damage in Zhanjiang Formation structural clay following vibrational disturbances. Based on the test results of the unconfined compressive strength of the disturbed Zhanjiang Formation structural clay, and in conjunction with theories related to energy dissipation, the analysis revealed that: (1) An increase in the disturbance degree leads to higher compaction strain and compaction dissipation energy, thereby enhancing the ductility of the sample; (2) Although disturbance reduces the strength of the sample, it increases its deformation capacity, indicating that the interplay between strength and deformation affects the level of energy dissipation; (3) The variation in the logarithmic value of the elastic energy dissipation ratio can be categorized into three phases: initial lgK > 0; lgK < 0 to (lgK)min; and an increase from (lgK)min, with (lgK)min marking the critical turning point for the transition from the elastic to the plastic phase of the soil. The study also profoundly analyzed the dynamic regulation mechanisms of vibrational disturbance energy from two perspectives: dynamic regulation and structural change, as well as energy dissipation and damage evolution.

Effects of traditional Chinese exercises on post-stroke depression: a meta-analysis of randomized controlled trials.

Post-stroke depression (PSD) is a common complication that worsens neurological recovery and increases mortality among stroke survivors, creating a significant burden on patients and families. While exercise interventions are known to alleviate depressive symptoms in mild stroke cases, the effects of traditional Chinese exercises on PSD have not been systematically reviewed. This study aimed to evaluate the clinical efficacy of traditional Chinese exercises in treating PSD.

Advanced multi-objective trajectory planning for robotic arms using a multi-strategy enhanced NSGA-II algorithm.

Facing the problems of large-scale rapid and disorderly loading, the robotic arm has the problems of large start-stop impact, easy to shake, and reduced production efficiency and service life, this paper proposes a robotic arm motion planning method based on the improved multi-objective algorithm called LNSGA-II. Firstly, the artificial potential field method is used to plan the shortest path without collision, extract the key motion sequences, and establish the multi-objective function to improve the operating efficiency of the robotic arm, the smoothness of the motion trajectory, and the reduction of energy consumption. Then to solve the nonlinear constraints in the multi-objective trajectory planning, the infeasibility degree is designed, and the NSGA-II is improved by using the mutation chaos strategy and the dynamic goal-oriented development strategy.

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

Rationale And Objectives: The aim of this study is to develop a deep learning-based multimodal feature interaction-guided fusion (DL-MFIF) framework that integrates macroscopic information from computed tomography (CT) images with microscopic information from whole-slide images (WSIs) to predict the epidermal growth factor receptor (EGFR) mutations of primary lung adenocarcinoma in patients with advanced-stage disease.

Materials And Methods: Data from 396 patients with lung adenocarcinoma across two medical institutions were analyzed. The data from 243 cases were divided into a training set (n=145) and an internal validation set (n=98) in a 6:4 ratio, and data from an additional 153 cases from another medical institution were included as an external validation set.

Reducing torque ripple for switched reluctance motors by current reshaping neural network.

The high torque ripple poses a limitation on the application of switched reluctance motors (SRMs). In this paper, a current reshaping neural network (CRNN) is proposed to mitigate the torque ripple. The principle of how current affects electromagnetic torque is analyzed bas the indirect torque control method.

Constructing a prediction model for acute pancreatitis severity based on liquid neural network.

Acute pancreatitis (AP) is a common disease, and severe acute pancreatitis (SAP) has a high morbidity and mortality rate. Early recognition of SAP is crucial for prognosis. This study aimed to develop a novel liquid neural network (LNN) model for predicting SAP.

Properties of 3D-printed continuous silk fiber-reinforced poly(caprolactone).

In this study, poly(caprolactone)/continuous mulberry silk (PCL/MS) composite filaments were prepared the pre-impregnation method and were employed for FDM technology. The MS was treated with alkali. The tensile properties and morphology of PCL/MS composite filaments were investigated using a universal mechanical testing machine and a scanning electron microscope.

Composite ZIF-8 with CsBiI to Enhance the Photodegradation Ability on Methylene Blue.

The development of green, efficient, and reusable photocatalysts is important for pollution degradation. In recent years, CsBiI has been shown to be an effective photocatalyst. However, the rapid recombination of electrons and holes weakens the photocatalytic activity.

Condensation of Data and Knowledge for Network Traffic Classification: Techniques, Applications, and Open Issues.

The accurate and efficient classification of network traffic, including malicious traffic, is essential for effective network management, cybersecurity, and resource optimization. However, traffic classification methods in modern, complex, and dynamic networks face significant challenges, particularly at the network edge, where resources are limited and issues such as privacy concerns and concept drift arise. Condensation techniques offer a solution by reducing the data size, simplifying complex models, and transferring knowledge from traffic data.

Effect of Timing of Branched-Chain Amino Acid Supplementation on Muscle Recovery after Resistance Training in Healthy Males.

This study aimed to investigate the effects of different timing of branched-chain amino acid (BCAA) supplementation (pre-/post-exercise) on the recovery of delayed-onset muscle soreness (DOMS) and associated inflammatory factors after resistance training. A double-blind randomized controlled experimental design was used in this study. Twenty-four untrained male college students volunteered to receive BCAA supplementation and completed resistance training.

Research on the Resourceful and Efficient Recycling Technology of Waste Thermal Insulation Pipe Material.

This study was conducted to explore a new method for degrading and recycling waste chemical pipe insulation and cooling materials. A chemical degradation method was employed, utilizing alkali metal catalysts and a two-component alcoholysis system consisting of specific ratios of diethylene glycol and butylene glycol. The system effectively degraded the polyisocyanurate (PIR) insulation with the aim of reusing the waste material.

Calculation of Robot Multi-Fingered Grasping Force and Displacement Based on the Newton-Subgradient Non-Smooth Greedy Randomized Kaczmarz Method for Solving Linear Complementarity Problem.

The calculation of grasping force and displacement is important for multi-fingered stable grasping and research on slipping damage. By linearizing the friction cone, the robot multi-fingered grasping problem can be represented as a linear complementarity problem (LCP) with a saddle-point coefficient matrix. Because the solution methods for LCP proposed in the field of numerical computation cannot be applied to this problem and the Pivot method can only be used for solving specific grasping problems, the LCP is converted into a non-smooth system of equations for solving it.