In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children. Full article

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research. Full article

Lyotropic liquid crystals represent an important class of anisotropic colloid systems. Their integration with optically active nanoparticles can provide us with responsive luminescent media that offer new fundamental and applied solutions for biomedicine. This paper analyzes the molecular-level behavior of such composites represented by tetraethylene glycol monododecyl ether and nanoscale carbon dots in microfluidic channels. Microfluidic confinement allows for simultaneously applying multiple factors, such as flow dynamics, wall effects, and temperature, for the precise control of the molecular arrangement in such composites and their resulting optical properties. The microfluidic behavior of composites was characterized by a set of analytical and modeling tools such as polarized and fluorescent microscopy, dynamic light scattering, and fluorescent spectroscopy, as well as image processing in Matlab. The composites were shown to form tunable anisotropic intermolecular structures in microchannels with several levels of molecular ordering. A predominant lamellar structure of the composites was found to undergo additional ordering with respect to the microchannel axis and walls. Such an alignment was controlled by applying shear and temperature factors to the microfluidic environment. The revealed molecular behavior of the composite may contribute to the synthesis of hybrid organized media capable of polarized luminescence for on-chip diagnostics and biomimetics. Full article

The global decline in the number of pollinators has elicited considerable public attention. To the general public, honeybees are considered to be the primary pollinators. Also, a decline in managed honeybee stocks is alarming and could lead to declining pollination services and reduced ecosystem biodiversity, although the Apis mellifera is the least likely pollinator species on the planet to be at risk of extinction. A less-than-complete understanding of honeybees and their ecology may hinder their conservation. Ascertaining the public’s level of knowledge about, and perception of, a problem can help in solving it. This research focused mainly on honeybees because people are unlikely to be able to recognize the different species of Apoidea. Schools are ideal places for understanding the basic knowledge and attitudes regarding this insect. We aimed to understand the perception and knowledge of 12–14-year-old children towards honeybees as well as to verify the existence of a correlation between knowledge level and positive perception. Secondary school students can play a key role in the conservation of biodiversity as they are carriers of knowledge in families and will be future citizens. To this end, 231 students were given a 26-item questionnaire related to their perception and knowledge of honeybees. Results indicate that the students have a good understanding of the role that bees play in nature but do not have a completely clear idea of this insect’s interactions with the environment. Results also show that the children feel a certain fear of honeybees, although they respect them. The average score of the ecological branch test exceeded the average score of the perceptual one, indicating that the subjects had a more positive education than perception. Full article

Liquid fermentation is an efficient culture for obtaining polysaccharides from edible mushrooms. In this study, the polysaccharide content and biomass were examined by introducing microorganisms into the Wolfiporia cocos fermentation system. Three edible mushroom co-fermentation systems were established, among which the Wolfiporia cocos-Ganoderma lucidum co-fermentation system significantly increased the mycelial biomass of the system by 57.71% compared to Wolfiporia cocos alone and 91.22% compared to Ganoderma lucidum alone, and the intracellular polysaccharide content was significantly increased. Physiological activities of polysaccharides showed that mycelial polysaccharides in the Wolfiporia cocos-Ganoderma lucidum system had stronger anti-tumor cell value-adding and anti-tumor cell migration activities compared with Wolfiporia cocos and Ganoderma lucidum fermentation alone. The transcriptomic study of Wolfiporia cocos mycelium induced by exogenous substances suggested that the exogenous substances could enhance the intracellular polysaccharide content of Wolfiporia cocos through the upregulation of the expression of α-glycosyltransferase encoded by ALG10 and the downregulation of α-glycosidases encoded by MAN1B in the glycolytic metabolism of Wolfiporia cocos. This study provides a new direction for the transformation of polysaccharides from Wolfiporia cocos and Ganoderma lucidum into functional foods and new product development, and provides an experimental basis. Full article

Vision starts in retinal photoreceptors when specialized proteins (opsins) sense photons via their covalently bonded vitamin A derivative 11cis retinaldehyde (11cis-RAL). The reaction of non-enzymatic aldehydes with amino groups lacks specificity, and the reaction products may trigger cell damage. However, the reduced synthesis of 11cis-RAL results in photoreceptor demise and suggests the need for careful control over 11cis-RAL handling by retinal cells. This perspective focuses on retinoid(s) synthesis, their control in the adult retina, and their role during retina development. It also explores the potential importance of 9cis vitamin A derivatives in regulating retinoid synthesis and their impact on photoreceptor development and survival. Additionally, recent advancements suggesting the pivotal nature of retinoid synthesis regulation for cone cell viability are discussed. Full article

Tunnel-boring machines (TBMs) are widely used in urban underground tunnel construction due to their fast and efficient features. However, shield-tunnel construction faces increasingly complex geological environments and may encounter geological hazards such as faults, fracture zones, water surges, and collapses, which can cause significant property damage and casualties. Existing geophysical methods are subject to many limitations in the shield-tunnel environment, where the detection space is extremely small, and a variety of advanced detection methods are unable to meet the required detection requirements. Therefore, it is crucial to accurately detect the geological conditions in front of the tunnel face in real time during the tunnel boring process of TBM tunnels. In this paper, a 3D-ERT advanced detection method using source-position electrode excitation is proposed. First, a source-position electrode array integrated into the TBM cutterhead is designed for the shield-tunnel construction environment, which provides data security for the inverse imaging of the anomalous bodies. Secondly, a 3D finite element tunnel model containing high- and low-resistance anomalous bodies is established, and the GREIT reconstruction algorithm is utilized to reconstruct 3D images of the anomalous body in front of the tunnel face. Finally, a physical simulation experiment platform is built, and the effectiveness of the method is verified by laboratory physical modeling experiments with two different anomalous bodies. The results show that the position and shape of the anomalous body in front of the tunnel face can be well reconstructed, and the method provides a new idea for the continuous detection of shield construction tunnels with boring. Full article

The US real estate market is a complex ecosystem influenced by multiple factors, making it critical for stakeholders to understand its dynamics. This study uses Zillow Econ (monthly) data from January 2018 to October 2023 across 100 major regions gathered through Metropolitan Statistical Area (MSA) and advanced machine learning techniques, including radial kernel Support Vector Machines (SVMs), used to predict the sale-to-list ratio, a key metric that indicates the market health and competitiveness of the US real estate. Recursive Feature Elimination (RFE) is used to identify influential variables that provide insight into market dynamics. Results show that SVM achieves approximately 85% accuracy, with temporal indicators such as Days to Pending and Days to Close, pricing dynamics such as Listing Price Cut and Share of Listings with Price Cut, and rental market conditions captured by the Zillow Observed Rent Index (ZORI) emerging as critical factors influencing the sale-to-list ratio. The comparison between SVM alphas and RFE highlights the importance of time, price, and rental market indicators in understanding market trends. This study underscores the interplay between these variables and provides actionable insights for stakeholders. By contextualizing the findings within the existing literature, this study emphasizes the importance of considering multiple factors in housing market analysis. Recommendations include using pricing dynamics and rental market conditions to inform pricing strategies and negotiation tactics. This study adds to the body of knowledge in real estate research and provides a foundation for informed decision-making in the ever-evolving real estate landscape. Full article

Data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) have been extensively used to assess Ocean Color radiometric products from various satellite sensors. This study, focusing on Ocean Color radiometric operational products from the Sentinel-3 Ocean and Land Colour Instrument (OLCI), aims at investigating where in the European seas the results of match-up analyses at the European marine AERONET-OC sites could be applicable. Data clustering is applied to OLCI remote sensing reflectance from the various sites to define different sets of optical classes, which are later used to identify class-based uncertainties. A set of fifteen classes grants medium-to-high classification levels to most European seas, with exceptions in the South-East Mediterranean Sea, the Atlantic Ocean, or the Gulf of Bothnia. In these areas, spectra are very often identified as novel with respect to the generated set of classes, suggesting their under-representation in AERONET-OC data. Uncertainties are finally mapped onto European seas according to class membership. The largest uncertainty values are obtained in the blue spectral region for almost all classes. In clear waters, larger values are obtained in the blue bands. Conversely, larger values are shown in the green and red bands in coastal and turbid waters. Full article

Biological invasions are of special conservation concern in the Iberian Peninsula and other regions with high levels of endemism. Environmental variability, such as the seasonal fluctuations of Mediterranean streams, is a key factor that affects the spread of aquatic species in novel habitats. Fish parasites have a great potential to reflect such changes in the habitat features of freshwater ecosystems. The aim of this study consisted of seasonally analysing the health status and parasitological traits of non-native fish in Iberian waters. In particular, a strongly invasive population of Languedoc minnow Phoxinus septimaniae (leuciscid species native to south-east France) was assessed in Tordera Stream (north-eastern Iberian Peninsula, Mediterranean conditions). Fish were sampled in April, July, and October 2023 by electrofishing. Health status (external/internal organs) was significantly better in autumn (HAI = 28.8) than spring (HAI = 35.6). Life-cycle complexity was higher in spring (LCI = 1.98), whereas parasite abundance and Shannon diversity were significantly lower in autumn (TA = 19.6 and H’ = 2.15, respectively). In October (more ‘benign’ environmental conditions in Iberian streams), minnows could display elevated foraging activity, with fish increasing their health condition and level of parasite resistance/tolerance. Overall results showed a particular seasonal profile of health and parasite infra-communities that allow this minnow species to thrive under highly fluctuating habitat conditions. This information could help environmental managers to control non-native fish in Mediterranean streams. Full article

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications. Full article

The Hispanic Society Museum & Library (HSML) holds a collection of nine viceregal barniz de Pasto objects, made by Indigenous artisans in the 17th and 18th centuries. Designed to imitate Asian lacquers and intended for European aesthetic tastes, barniz de Pasto is an example of Indigenous technique and knowledge that has survived to the present day. An in-depth analysis of five of these barniz de Pasto objects, dated to the first half and last quarter of the 17th century based on their iconography, was carried out through a combination of non-invasive and micro-invasive techniques, including portable X-ray fluorescence (pXRF) spectroscopy to investigate the possible presence of inorganic pigments, and fiber-optics reflectance spectroscopy (FORS) and reflectance imaging spectroscopy (RIS) to provide molecular information on colorants and their distributions across the objects. Dyes and pigments were also identified using Raman spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, and liquid chromatography/mass spectrometry (LC/MS). The nature of the resin was determined by FTIR and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), while the decoration stratigraphy and composition were analyzed by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). This paper confirms the use of mopa mopa, the resin used in the barniz de Pasto technique, in two objects not previously analyzed, and identifies indigo, insect-based red, calomel, lead white, and an unknown flavonol-based yellow dye, and challenges the use of calomel as a temporal marker for these works. Taken together, these results expand our understanding of the material use and explorations undertaken by artists during this time period to create such elaborate and enduring objects. Full article

Pork is one of the most widely produced and consumed meats in the world, and it is also an important source of animal protein. The continuous rise in feed prices has forced the pig industry to consider adding cost-effective alternative feed to pig diets. In this study, we aimed to explore the beneficial effects of tuna dark muscle as a nutritional supplement on the growth performance, serum lipids and antioxidant levels of Holland mini-piglets, as well as on the odor and volatile substances of pork and the gut microbiota. Two-month-old male mini-piglets (n = 24) were fed a control diet or supplemented with either 2% (LD) or 4% (HD) tuna dark muscle for 8 weeks. The use of tuna dark muscle at low and high dosages significantly increased the average daily weight gain, but it showed no significant effect on organ indices or blood lipids. In addition, dark muscle treatment significantly increased the antioxidant capacity, characterized by increased SOD and GSH-Px activities, and it decreased the content of MDA in serum. Moreover, tuna dark muscle feeding shifted the odor of rib muscle and tendon meat away from that of the control group, while similar odor patterns were observed in the longissimus dorsi muscle. Among these volatile substances, hexanal, nonanal, and heptanal increased in response to dietary tuna dark muscle and were regarded as indispensable contributors to the feeding. Furthermore, dietary tuna dark muscle modulated the gut microbiota of the piglets, increasing the abundance of beneficial bacteria such as butyric acid-producing bacteria, and reduced the abundance of harmful bacteria. The feeding strategy reported in this study not only reduces the production costs of pork but also utilizes tuna processing by-products in an environmentally friendly way. Full article

Reversible optical regulation has potential applications in optical anti-counterfeiting, storage, and catalysis. Compared to common power materials, the reverse opal structure has a larger specific surface area and an increased contact area for optical regulation, which is expected to achieve higher regulation rates. However, it is difficult to achieve reversible and repeatable regulation of the luminescent properties of photonic crystals, especially with the current research on the structural collapse of photonic crystals. In this work, WO₃: Yb³⁺, Er³⁺ inverse photonic crystals were prepared by the template approach, and reversible multi-mode optical modification was investigated. Upon heat treatment in a reducing atmosphere or air, the color of the photonic crystals can reversibly change from light yellow to dark green, accompanied by changes in absorption and upconversion of luminescence intensity. The stability and fatigue resistance of this reversible optical modification ability were explored through cyclic experiments, providing potential practical applications for photocatalysis, optical information storage, and electrochromism. Full article

The global demand for organic food products has rapidly increased over the last years, becoming an emerging niche market targeting the high-income segment. The higher retailing price for organic food products may increase the risk of fraudulent practices at the different stages of the food supply chain, and consequently, substantial control is needed. Currently, the authentication of organic food products, such as those of plant origin, remains a key challenge in analytical chemistry. While stable isotopes have emerged as a powerful tool for this purpose, most studies have focused on crops, missing the agricultural inputs used for fertilization that influence the isotopic values of the crops. In this study, we aimed to isotopically characterize commonly used fertilizers, soil conditioners, and micronutrient fertilizers in intensive organic agriculture in the largest organic production region in the world (Almería, Spain). Our goal was to clarify the limitations that nitrogen isotopic fingerprinting presents for the fertilizer input industry and to characterize the organic inputs. The conventional fertilizers analyzed in this study showed low δ¹⁵N values compared to their organic counterparts, except for some plant-based fertilizers, protein hydrolyzed fertilizers, and chelated nutrients. Both protein hydrolyzed fertilizers and micronutrient fertilizers presented a wide range of variability in their δ¹⁵N values, including some very low or even negative values, more similar to those of conventional fertilizers. The results of this study highlight the challenges of authenticating organic foods in agriculture when using nitrogen isotope analysis. Full article

Environmental impact assessment (EIA) performance has remained of interest, and over the past ten years, the evaluation technique has evolved. Thailand implemented an EIA with a health impact assessment (HIA) as an environmental health impact assessment (EHIA), which necessitated investigating and developing these instruments; however, its implementation performance has been questioned. The main goal of this study is to comparatively assess how well EIAs and EHIAs are performed in projects in an area in Thailand. Six projects in various sectors that were implemented in Eastern Thailand were studied. The 162 residents (nine local authorities and 153 villagers) closest to the project completed a survey and evaluated the performance according to three aspects (i.e., substantive, procedural, and transactive), using a rating scale and evaluation checklists. The results were presented as a percentage of the total scores and interpreted according to the five scales. The overall performance reached a satisfactory level, albeit not significantly different between cases; however, it was pointed out that the shortcomings of EHIAs and EIAs, particularly their dependability, lack of public involvement, and the need for more transparency, could be addressed through the establishment of an open access database, which would help to simplify the assessment of all stages of EIAs and EHIAs. Full article

Green mold, caused by Penicillium digitatum, is the major cause of citrus postharvest decay. Currently, the application of sterol demethylation inhibitor (DMI) fungicide is one of the main control measures to prevent green mold. However, the fungicide-resistance problem in the pathogen P. digitatum is growing. The regulatory mechanism of DMI fungicide resistance in P. digitatum is poorly understood. Here, we first performed transcriptomic analysis of the P. digitatum strain Pdw03 treated with imazalil (IMZ) for 2 and 12 h. A total of 1338 genes were up-regulated and 1635 were down-regulated under IMZ treatment for 2 h compared to control while 1700 were up-regulated and 1661 down-regulated under IMZ treatment for 12 h. The expression of about half of the genes in the ergosterol biosynthesis pathway was affected during IMZ stress. Further analysis identified that 84 of 320 transcription factors (TFs) were differentially expressed at both conditions, making them potential regulators in DMI resistance. To confirm their roles, three differentially expressed TFs were selected to generate disruption mutants using the CRISPR/Cas9 technology. The results showed that two of them had no response to IMZ stress while ∆PdflbC was more sensitive compared with the wild type. However, disruption of PdflbC did not affect the ergosterol content. The defect in IMZ sensitivity of ∆PdflbC was restored by genetic complementation of the mutant with a functional copy of PdflbC. Taken together, our results offer a rich source of information to identify novel regulators in DMI resistance. Full article

The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas. Full article

The dilemma of weak participation and non-participation of rural communities is a universal topic of global development. The rural public space is an important field for local residents to interact, communicate, and engage with each other, and is an important place for the sustainable development of rural areas. However, previous studies have neglected to understand the intrinsic connection between rural public space and community participation from the perspective of community communication ecology. Based on the concept of age-friendly communities, this study’s fieldwork in rural Shanghai, China, using the methodology of grounded theory, found that physical, social, and psychological factors all have an impact on community engagement among rural residents. Specifically, environmental quality, facility support, community networks, social participation, call to action, place attachment, spatial perception, and self-transformation are identified as the core elements that significantly influence community engagement among rural residents. This study further reveals that the multiple factors influencing community engagement among rural residents are complex and interdependent rather than operating independently. Spatial support, communicative triggers, and symbolic identification, respectively, operate at the technological level (physical–social factors), social level (social–psychological factors), and discursive level (psychological–physical factors) to promote community engagement among older adults in Chinese rural areas. Through this research, we hope to further the realization of rural civic engagement and the sustainability of local communities and to provide scholarly insights into the promotion of more equitable community life. Full article

The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, no diagnostic procedures exist for this disease. However, several computational approaches have proven successful in detecting PD at early stages, overcoming the disadvantages of traditional methods of diagnosis. In this study, a machine learning (ML) detection system based on the voice signals of PD patients is proposed. The AdaBoost classifier has been utilized to construct the model and trained on a dataset obtained from the machine learning repository of the University of California, Irvine (UCI). This dataset includes voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrated promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Furthermore, the robustness of the proposed model is rigorously assessed through cross-validation, revealing consistent performance across all iterations. The overarching objective of this study is to contribute to the scientific community by furnishing a robust system for the detection of PD. Full article

Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS’s performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS’s positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites’ signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC’s performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy. Full article

This paper introduces stochastic disturbances into a semi-parametric SEIR model with infectivity in an incubation period. The model combines the randomness of disease transmission and the nonlinearity of transmission rate, providing a flexible framework for more accurate description of the process of infectious disease transmission. On the basis of the discussion of the deterministic model, the stochastic semi-parametric SEIR model is studied. Firstly, we use Lyapunov analysis to prove the existence and uniqueness of global positive solutions for the model. Secondly, the conditions for disease extinction are established, and appropriate stochastic Lyapunov functions are constructed to discuss the asymptotic behavior of the model’s solution at the disease-free equilibrium point of the deterministic model. Finally, the specific transmission functions are enumerated, and the accuracy of the results are demonstrated through numerical simulations. Full article