Cascading failure dynamics on higher-order networks with load redistribution.

The phenomenon of load redistribution in complex networks has garnered extensive attention due to its profound impact and widespread occurrence. In recent years, higher-order structures have offered new insights into understanding the structures and dynamic processes of complex networks. However, the influence of these higher-order structures on the dynamics of load redistribution, cascade failures, and recovery processes remains to be fully explored.

Targeting the outer membrane of gram-negative foodborne pathogens for food safety: compositions, functions, and disruption strategies.

Foodborne pathogens are a major threat to both food safety and public health. The current trend toward fresh and less processed foods and the misuse of antibiotics in food production have made controlling these pathogens even more challenging. The outer membrane has been employed as a practical target to combat foodborne Gram-negative pathogens due to its accessibility and importance.

Research on terahertz image analysis of thin-shell seeds based on semantic segmentation.

Assessing crop seed phenotypic traits is essential for breeding innovations and germplasm enhancement. However, the tough outer layers of thin-shelled seeds present significant challenges for traditional methods aimed at the rapid assessment of their internal structures and quality attributes. This study explores the potential of combining terahertz (THz) time-domain spectroscopy and imaging with semantic segmentation models for the rapid and non-destructive examination of these traits.

Multi-Stage Audio-Visual Fusion for Dysarthric Speech Recognition With Pre-Trained Models.

Dysarthric speech recognition helps speakers with dysarthria to enjoy better communication. However, collecting dysarthric speech is difficult. The machine learning models cannot be trained sufficiently using dysarthric speech.

Improvement of Acoustic Models Fused with Lip Visual Information for Low-Resource Speech.

Endangered language generally has low-resource characteristics, as an immaterial cultural resource that cannot be renewed. Automatic speech recognition (ASR) is an effective means to protect this language. However, for low-resource language, native speakers are few and labeled corpora are insufficient.

Miniature Noninvasive Sensor Based on Impedance-Change Detection in Branches for Measuring Branch Ice Content in Overwintering Woody Plants.

Advancements in detection instruments have enabled the real-time acquisition of water information during plant growth; however, the real-time monitoring of freeze-thaw information during plant overwintering remains a challenge. Based on the relationship between the change in the water-ice ratio and branch impedance during freezing, a miniature noninvasive branch volume ice content (BVIC) sensor was developed for monitoring real-time changes in volumetric ice content and the ice freeze-thaw rate of woody plant branches during the overwintering period. The results of the performance analysis of the impedance measurement circuit show that the circuit has a lateral sensitivity range, measurement range, resolution, measurement accuracy, and power consumption of 0-35 mm, 0-100%, 0.

Smart Soil Water Sensor with Soil Impedance Detected via Edge Electromagnetic Field Induction.

To address the problems in the calibration of soil water content sensors, in this study, we designed a low-cost edge electromagnetic field induction (EEMFI) sensor for soil water content measurement and proposed a normalized calibration method to eliminate the errors caused by the measurement sensor's characteristics and improve the probe's consistency, replaceability, and calibration efficiency. The model calibration curve-fitting coefficients of the EEMFI sensors were above 0.98, which indicated a significant correlation.

Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China.

Objective: We aim to examine the adequacy of an innovation state-space modeling framework (called TBATS) in forecasting the long-term epidemic seasonality and trends of hemorrhagic fever with renal syndrome (HFRS).

Methods: The HFRS morbidity data from January 1995 to December 2020 were taken, and subsequently, the data were split into six different training and testing segments (including 12, 24, 36, 60, 84, and 108 holdout monthly data) to investigate its predictive ability of the TBATS method, and its forecasting performance was compared with the seasonal autoregressive integrated moving average (SARIMA).

Results: The TBATS (0.

Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model.

Objective: The high morbidity, complex seasonality, and recurring risk of hand-foot-and-mouth disease (HFMD) exert a major burden in China. Forecasting its epidemic trends is greatly instrumental in informing vaccine and targeted interventions. This study sets out to investigate the usefulness of an advanced exponential smoothing state space framework by combining Box-Cox transformations, Fourier representations with time-varying coefficients and autoregressive moving average (ARMA) error correction (TBATS) method to assess the temporal trends of HFMD in China.

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China.

Objective: The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet.

Methods: The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model.

Stable isotopes verify geographical origin of yak meat from Qinghai-Tibet plateau.

High-value yak meat from Qinghai-Tibet Plateau was investigated using stable isotopes (δC, δH, δO, δN and δS) to identify attributes which could verify and protect its geographical origin. Supervised PLS-DA was applied to the isotope data to discriminate four geographical locations. δC, δH, and δO values showed significant differences according to origin while δN and δS values did not show any change across the different regions.

Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models.

Drift is an important issue that impairs the reliability of sensors, especially in gas sensors. The conventional method usually adopts the reference gas to compensate for the drift. However, its classification accuracy is not high.

Large-Scale Person Re-Identification Based on Deep Hash Learning.

Person re-identification in the image processing domain has been a challenging research topic due to the influence of pedestrian posture, background, lighting, and other factors. In this paper, the method of harsh learning is applied in person re-identification, and we propose a person re-identification method based on deep hash learning. By improving the conventional method, the method proposed in this paper uses an easy-to-optimize shallow convolutional neural network to learn the inherent implicit relationship of the image and then extracts the deep features of the image.

A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification.

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations.