A literature review of bioactive substances for the treatment of periodontitis: , and clinical studies.

Periodontitis is a common chronic inflammatory disease of the supporting tissues of the tooth that involves a complex interaction of microorganisms and various cell lines around the infected site. To prevent and treat this disease, several options are available, such as scaling, root planning, antibiotic treatment, and dental surgeries, depending on the stage of the disease. However, these treatments can have various side effects, including additional inflammatory responses, chronic wounds, and the need for secondary surgery.

Research on improved black widow algorithm for medical image denoising.

Improving the quality of medical images is crucial for accurate clinical diagnosis; however, medical images are often disrupted by various types of noise, posing challenges to the reliability and diagnostic accuracy of the images. This study aims to enhance the Black Widow optimization algorithm and apply it to the task of denoising medical images to improve both the quality of medical images and the accuracy of diagnostic results. By introducing Tent mapping, we refined the Black Widow optimization algorithm to better adapt to the complex features of medical images.

Antimicrobial, antibiofilm, and antivirulence properties of Eisenia bicyclis-extracts and Eisenia bicyclis-gold nanoparticles towards microbial pathogens.

Nanomaterials derived from seaweed have developed as an alternative option for fighting infections caused by biofilm-forming microbial pathogens. This research aimed to discover potential seaweed-derived nanomaterials with antimicrobial and antibiofilm action against bacterial and fungal pathogens. Among seven algal species, the extract from Eisenia bicyclis inhibited biofilms of Klebsiella pneumoniae, Staphylococcus aureus, and Listeria monocytogenes most effectively at sub-MIC levels.

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer.

Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper.

Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images.

Introduction: The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce.

Methods: We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform.

Antioxidant Effects and Probiotic Properties of MS103 Isolated from Sweet Pickled Garlic.

Fermented vegetable-based foods, renowned for their unique flavors and human health benefits, contain probiotic organisms with reported in vitro antioxidative effects. This study investigates the probiotic properties of MS103 ( MS103) and its antioxidant activities using an in vitro oxidative stress model based on the hydrogen peroxide (HO)-induced oxidative damage of RAW 264.7 cells.

Unlocking Mutual Gains-An Experimental Study on Collaborative Autonomous Driving in Urban Environment.

Convoy driving, a specialized form of collaborative autonomous driving, offers a promising solution to the multifaceted challenges that transportation systems face, including traffic congestion, pollutant emissions, and the coexistence of connected autonomous vehicles (CAVs) and human-driven vehicles on the road, resulting in mixed traffic flow. While extensive research has focused on the collective societal benefits of convoy driving, such as safety and comfort, one critical aspect that has been overlooked is the willingness of individual vehicles to participate in convoy formations. While the collective benefits are evident, individual vehicles may not readily embrace this paradigm shift without explicit tangible benefits and incentives to motivate them.

An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image.

Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths.

Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction.

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to be able to monitor and predict the incidence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction.

Impacts of aging on circadian rhythm and related sleep disorders.

Circadian rhythm is an essential component of biology that organizes the internal synchrony of the organism in response to the environment. Aging significantly impacts circadian rhythm and is also associated with specific sleep complaints in mammals, including earlier awakening and decreased sleep consolidation at the end of the night. However, the regulation mechanism of aging on the circadian rhythm is far from clear.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization.

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China.

Extraction of eutrophic and green ponds from segmentation of high-resolution imagery based on the EAF-Unet algorithm.

Inland ponds exhibit remarkable ubiquity across the globe, playing a vital role in the sustainability of global continental freshwater resources and contributing significantly to their biodiversity. Numerous ponds are eutrophic and experience recurrent seasonal or year-round algal blooms or persistent duckweed cover, conferring a characteristic green hue. Here, we denote these eutrophic and green ponds as EGPs.

Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.

Preterm birth (PTB) presents a complex challenge in pregnancy, often leading to significant perinatal and long-term morbidities. "While machine learning (ML) algorithms have shown promise in PTB prediction, the lack of interpretability in existing models hinders their clinical utility. This study aimed to predict PTB in a pregnant population using ML models, identify the key risk factors associated with PTB through the SHapley Additive exPlanations (SHAP) algorithm, and provide comprehensive explanations for these predictions to assist clinicians in providing appropriate care.

DyLFG: A Dynamic Network Learning Framework Based on Geometry.

Dynamic network representation learning has recently attracted increasing attention because real-world networks evolve over time, that is nodes and edges join or leave the networks over time. Different from static networks, the representation learning of dynamic networks should not only consider how to capture the structural information of network snapshots, but also consider how to capture the temporal dynamic information of network structure evolution from the network snapshot sequence. From the existing work on dynamic network representation, there are two main problems: (1) A significant number of methods target dynamic networks, which only allow nodes to increase over time, not decrease, which reduces the applicability of such methods to real-world networks.

Research on weed identification in soybean fields based on the lightweight segmentation model DCSAnet.

Weeds can compete with crops for sunlight, water, space and various nutrients, which can affect the growth of crops.In recent years, people have started to use self-driving agricultural equipment, robots, etc. for weeding work and use of drones for weed identification and spraying of weeds with herbicides, and the effectiveness of these mobile weeding devices is largely limited by the superiority of weed detection capability.

A neural network model of mathematics anxiety: The role of attention.

Anxiety about performing numerical calculations is becoming an increasingly important issue. Termed mathematics anxiety, this condition negatively impacts performance in numerical tasks which can affect education outcomes and future employment. The disruption account proposes poor performance is due to anxiety disrupting limited attentional and inhibitory resources leaving fewer cognitive resources for the current task.

Dynamic modeling and analysis of the rotor-stator coupling system of a coaxial contra-rotating gearbox.

In this paper, a main gearbox using an encased differential gear train to achieve coaxial contra-rotating is considered, and a dynamic modeling method of the rotor-stator coupling system of the gearbox based on box model updating is introduced. The transverse torsional dynamic model of the gear transmission subsystem is established based on the lumped parameter method. The finite element model of the box is updated according to the modal test data, and the reduced dynamic parameters of the box are obtained.

A remaining useful life estimation method based on long short-term memory and federated learning for electric vehicles in smart cities.

In modern society, environmental sustainability is a top priority as one of the most promising entities in the new energy sector. Electric vehicles (EVs) are rapidly gaining popularity due to their promise of better performance and comfort. Above all, they can help address the problem of urban air pollution.

An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases.

Objective: Chronic lung-related diseases, with asthma being the most prominent example, characterized by diverse symptoms and triggers, present significant challenges in disease management and prediction of exacerbations across patients. This research aimed to devise a practical solution by introducing a personalized alert system tailored to individual lung function and environmental conditions, offering a holistic approach for the management of a range of chronic respiratory conditions.

Methods: In response to these challenges, we developed a personalized alert system based on individual lung function tests conducted in diverse environmental conditions, as determined by air-quality sensors.

Surface defect detection of steel based on improved YOLOv5 algorithm.

To address the challenge of achieving a balance between efficiency and performance in steel surface defect detection, this paper presents a novel algorithm that enhances the YOLOv5 defect detection model. The enhancement process begins by employing the K-means++ algorithm to fine-tune the location of the prior anchor boxes, improving the matching process. Subsequently, the loss function is transitioned from generalized intersection over union (GIOU) to efficient intersection over union (EIOU) to mitigate the former's degeneration issues.

A trading matching model for aquatic products based on blockchain and credit mechanisms.

Current online transactions of aquatic products are often plagued by problems such as low efficiency, high platform supervision cost, insufficient trust and leakage of transaction data. Blockchain has been widely used in many different fields due to its decentralization, non-tampering and distributed data management. In order to resolve the existing problems, a blockchain-based aquatic product trading matching model integrated with credit mechanisms is proposed in this study to improve the efficiency, quality, security and satisfaction of online transactions for aquatic products.

An image encryption algorithm based on the double time-delay Lorenz system.

The traditional image encryption technology has the disadvantages of low encryption efficiency and low security. According to the characteristics of image information, an image encryption algorithm based on double time-delay chaos is proposed by combining the delay chaotic system with traditional encryption technology. Because of the infinite dimension and complex dynamic behavior of the delayed chaotic system, it is difficult to be simulated by AI technology.

Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images.

Breast cancer poses a significant risk to women's health, and it is essential to provide proper diagnostic support. Medical image processing technology is a key component of all supporting diagnostic techniques, with Image Segmentation (IS) being one of its primary steps. Among various methods, Multilevel Image Segmentation (MIS) is considered one of the most effective and straightforward approaches.

Feature fusion network based on few-shot fine-grained classification.

The objective of few-shot fine-grained learning is to identify subclasses within a primary class using a limited number of labeled samples. However, many current methodologies rely on the metric of singular feature, which is either global or local. In fine-grained image classification tasks, where the inter-class distance is small and the intra-class distance is big, relying on a singular similarity measurement can lead to the omission of either inter-class or intra-class information.

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction.

The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator.