Early Warning Research Articles

[Analysis of the Spatiotemporal Distribution of Algal Blooms and Its Driving Factors in Chaohu Lake Based on Multi-source Datasets].

Eutrophication and harmful algae blooms are one of the common ecological and environmental problems faced by freshwater lakes all over the world. As a typical inland freshwater lake, Chaohu Lake exhibits a high level of eutrophication and algae blooms year-round and shows a spatiotemporal difference in different regions of the lake. In order to understand the basic regularity of the development and outbreak of algal blooms in Chaohu Lake, the data from the comprehensive water observation platform and remote sensing were integrated to obtain the spatiotemporal distribution of algal blooms from 2015 to 2020. Then, an evaluation model based on Boosted Regression Trees (BRT) was constructed to quantitatively assess the importance and interactions of various environmental factors on algal blooms at different stages. The results indicated that:① The occurrence of algal blooms in Chaohu Lake exhibited significant seasonal variations, with the cyanobacteria beginning to recover in spring and bring about a light degree of algal blooms in the western and coastal areas of Chaohu Lake. The density of cyanobacteria reached its maximum in summer and autumn, accompanied by moderate and severe degrees of algal bloom outbreaks. ② During the non-outbreak period, the variation in the cyanobacteria density was greatly affected by physical and chemical factors, which explained 80.3% of the variance in the change in cyanobacteria density. The high concentrations of dissolved oxygen content in the water column and the weak alkalinity (7.2-7.6) and appropriate water temperature (about 3℃) provided a favorable environmental condition for the breeding and growth of cyanobacteria. In addition, the onset of algal blooms was closely related to the air temperature steadily passing through the threshold. According to the statistics, the date of first outbreak of algal blooms in Chaohu Lake was 11 days or so after the air temperature steadily remained above 7℃. ③ During the outbreak period, the occurrence of algal blooms was influenced by the combination of cyanobacterial biomass and meteorological conditions such as temperature, wind speed, and sunshine duration. The cumulative contribution ratio of the four factors was as high as 95%, and each factor had an optimal interval conductive to the outbreak of algal blooms. Furthermore, the results of multi-factor interaction analysis indicated a larger probability of the outbreak of algal blooms in Chaohu Lake under the combined effect of high cyanobacteria density, suitable temperature, and the breeze. This study analyzed and revealed the spatiotemporal characteristics and the dominant influencing factors of algal blooms in Chaohu Lake at different stages, which could provide the scientific basis for the prediction, early warning, and disposal of algal blooms under the context of climate change.

Functional trait-based phytoplankton biomass and assemblage analyses in the pre-growing season for comprehensive algal bloom risk assessment

Algal bloom (AB) risk assessment is critical for maintaining ecosystem health and human sustainability. Previous AB risk assessments have focused on the potential occurrence of ABs and related factors in the growing season, whereas their hazards, especially in the pre-growing season, have attracted less attention. Here, we performed a comprehensive AB risk assessment, including water trophic levels, phytoplankton biomass, functional trait-based assemblages, and related environmental factors, in the pre-growing season in Dongting Lake, China. Although mesotrophic water and low phytoplankton biomass suggested low AB potential, toxic taxa, which constituted 13.28% of the phytoplankton biomass, indicated non-negligible AB hazards. NH4+ and water temperature were key factors affecting phytoplankton motility and toxicity. Our study establishes a new paradigm for quantitative AB risk assessment, including both potential AB occurrence and hazards. We emphasize the importance of phytoplankton functional traits for early AB warning and NH4+ reduction for AB control in the pre-growing season.

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

Abstract This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the Structural Health Monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the Earthquake Early Warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in real-time mode was investigated in the frequency and time domains, accepting the displacements from the Linear Variable Differential Transformer (LVDT) sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in real-time with the SF-VA technique.

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

The Effect of Implementation of the National Early Warning Scoring System 2 on the Outcomes of COVID-19 Hospitalized Patients

Abstract Introduction It is crucial to have tools to predict the clinical deterioration of coronavirus disease 2019 (COVID-19) patients. We aimed to study the efficacy of the National Early Warning Scoring System (NEWS2) application in predicting the risk of clinical relapse and outcomes in hospitalized COVID-19 patients at Palestinian specialized centers for COVID-19. Patients and Methods A quasi-experimental design was applied. A sample of 384 adult patients was divided into two groups. For the pre-NEWS phase (control group) and the post-NEWS phase (study group), all study participants were observed until death or hospital discharge. Results Comparing the pre-NEWS phase with the post-NEWS phase, a significant decrease was found in the mean length of hospital stay from 8.1 ± 5.5 to 6.4 ± 5.3 days (p = 0.002) and a reduction in the mortality rate from 19.8% during the pre-NEWS phase to 12.5% during the post-NEWS phase (p = 0.071). The predictive value of NEWS was an excellent predictor of admission to the intensive care unit (ICU), as indicated by an area under the receiver operating characteristic curve of 0.91 (95% confidence interval: 0.87–0.96, p < 0.001). Also, a significant difference in the frequency of monitoring patients' vital signs was observed between the control group (pre-NEWS phase) and the study group (post-NEWS phase) following clinical deterioration (10.1 ± 7.8 [pre-NEWS phase] vs. 23.4 ± 0.7 [post-NEWS phase], p < 0.001). Conclusion Implementation of NEWS2 showed a significant improvement in hospitalized COVID-19 patient outcomes (length of stay, predicted ICU admissions, mortality rate, and frequency of vital signs measurements), which indirectly raised the follow-up of those patients by the medical team and attributed to a significant prediction of their deterioration.

Study on Early Identification of Rainfall-Induced Accumulation Landslide Hazards in the Three Gorges Reservoir Area

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is “first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions”. This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall.

GRAPES: Earthquake Early Warning by Passing Seismic Vectors Through the Grapevine

AbstractEstimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield‐based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake.

Early warning signals of the termination of the African Humid Period(s)

The transition from a humid green Sahara to today’s hyperarid conditions in northern Africa ~5.5 thousand years ago shows the dramatic environmental change to which human societies were exposed and had to adapt to. In this work, we show that in the 620,000-year environmental record from the Chew Bahir basin in the southern Ethiopian Rift, with its decadal resolution, this one thousand year long transition is particularly well documented, along with 20–80 year long droughts, recurring every ~160 years, as possible early warnings. Together with events of extreme wetness at the end of the transition, these droughts form a pronounced climate “flickering”, which can be simulated in climate models and is also present in earlier climate transitions in the Chew Bahir environmental record, indicating that transitions with flickering are characteristic of this region.

On-Site Earthquake Early Warning Model for Selected Records in the NGA-West2 Dataset Using S- and P-Wave Spectral Ratios

ABSTRACT On-site earthquake early warning (EEW) requires the best estimates of earthquake magnitude and distance parameters within a few seconds after the P-wave arrival for estimating the subsequent S-wave parameters. The errors in the estimates of the earthquake P-wave parameters will propagate into the estimates of the S-wave parameters. To solve this problem, we used the methodology by Zhao and Zhao (2019), which uses the spectral ratio R3P between the response spectral values of the first 3 s of the S wave and that of the first 3 s of the P wave, referred to as the R3P model. The modeling presented here was based on strong-motion records from the Next Generation Attenuation (NGA)-West2 dataset. We also used the spectral ratio RFP between the response spectral values from the full records from the S-wave arrival time to the end of the record and that of the first 3 s P wave to develop a second EEW model (the RFP model). An advantage of these two models is that the magnitude and hypocentral distance are not required in considerable magnitude and distance ranges. This means that the errors in the estimated source and path parameters from the first 3 s of the P wave will not affect the model predictions. A theoretical justification for these results is that the magnitude and the distance scaling rates for the first 3 s of the P wave are similar to those of the first 3 s of the S wave. This may also apply to the full S-wave window within useful EEW magnitude and distance ranges. We also found that when the estimated magnitude and distance for a record are necessary, the effect of the corresponding errors would be smaller than using a ground-motion prediction equation (GMPE), because the magnitude and distance scaling rates from this study are smaller than those of many GMPEs.

Water Resource Utilization Assessment in China Based on the Dynamic Relationship between Economic Growth and Water Use

Water scarcity has significantly hampered China’s economic, social, and environmental development. Ensuring sustainable water utilization is crucial given the mounting water stress accompanying continuous economic growth. A quantitative water resource forewarning model was constructed using the vector autoregressive (VAR) model. By analyzing the key indicators related to water systems and GDP data from 2001 to 2022, the VAR model revealed the long-term dynamic correlation between water consumption and economic growth using generalized impulse response, co-integration, and predictive variance decomposition analyses. The results revealed the presence of a long-term equilibrium between water consumption and economic growth, with a stable co-integration relationship and an optimal lag period of one year. The positive impact of water consumption on economic development increased during the 2001–2022 period, indicating a rising dependence of GDP on water resources. Water usage rose with economic development, while the water resource carrying capacity remained high and continued to grow. Based on the generalized impulse response, co-integration, and predictive variance decomposition analyses, this study predicted water-use-related indicators, providing vital early warnings for China’s water environment carrying capacity from 2023 to 2050. This enabled informed decision-making and fostered sustainable water management practices for the future.

Extracting Pole Characteristics of Complex Radar Targets for the Aircraft in Resonance Region Using RMSPSO_ARMA

The extraction algorithm of target characteristics resonance set at the high-frequency band plays a significant role in defense applications like early warning. The paper proposes a new method to extract the characteristic resonance set of radar targets in the resonance frequency region using an autoregressive moving average model optimized by root mean square propagation particle swarm optimization. Considering that the total scattering response in the resonant region consists of early-time and late-time responses, the autoregressive moving average model approximate the total scattering responses to avoid errors caused by intercepting the late-time response. Further, the paper investigated the impact of the swarm optimization algorithm on the accuracy of moving average model parameters when obtaining the resonance set at different target aspects. The extracted characteristic resonance results within a range of azimuth directions through an aircraft target paradigm indicate that the new method is more convenient and precise than the matrix beam prediction method.

Detection and analysis of serum bile acid profile in patients with colonic polyps

BACKGROUND Analyzing the variations in serum bile acid (BA) profile can provide a certain biological basis for early warning and prevention of various diseases. There is currently no comprehensive study on the relationship between the serum BA profile and colonic polyps. AIM To study the serum BA profile detection results of patients with colonic polyps, and analyze the correlation between BA and colonic polyps. METHODS From January 1, 2022, to June 1, 2023, 204 patients with colonic polyps who were diagnosed and treated at Zhongda Hospital Southeast University were chosen as the study subjects, and 135 non-polyp people who underwent physical examination were chosen as the control group. Gathering all patients' clinical information, typical biochemical indicators, and BA profile. RESULTS Compared with the control group, the serum levels of taurocholic acid, glycocholic acid, glycochenodeoxycholic acid, and taurochenodeoxycholic acid in the colonic polyp group were significantly higher than those in the control group, while the content of deoxycholic acid (DCA) was lower than that in the control group (P < 0.05). When colonic polyps were analyzed as subgroups, it was shown that there was a strong correlation between changes in the BA profile and polyp diameter, location, morphology, pathological kind, etc. CONCLUSION The serum BA profile showed significant changes in patients with colonic polyps, with a significant increase in primary conjugated BA content and a decrease in secondary free bile acid DCA content. There is a certain correlation between primary free BA and pathological parameters of polyps.

DSTN: Dynamic Spatio-Temporal Network for Early Fault Warning in Chemical Processes

Multivariate time series prediction, especially in early fault warning for chemical processes, poses significant challenges. The advent of graph neural network (GNN) method has made breakthroughs in this domain by enabling the processing of topological data. However, the traditional methods suffer from the issue of over-smoothing and inability to capture intricate multi-scale spatio-temporal dependencies. Additionally, the existing graph structures fall short in describing the complex spatial relationships among multi-stage sensors, impeding their adaptability to dynamically evolving chain reaction scenarios. To alleviate these limitations, a novel Dynamic spatio-Temporal Network for early fault warning in chemical processes, named DSTN for short, is proposed in this paper. We extract the spatial and temporal features of the time series by the designed dynamic GNN and the improved Transformer network. Then, we integrate the spatio-temporal features through the residual network. DSTN has the following advantages: (1) A one-dimensional convolutional neural network is seamlessly incorporated into the Transformer architecture for bolstering its capacity to discern both global and local features within time series. (2) The continuous sliding window and mutual information methods are employed to construct a dynamic topology graph, and a K-order adjacency matrix is designed to rectify the inefficiencies in learning weights associated with convolution kernel parameters. (3) Multiple spatio-temporal modules interconnected via residual connection to adaptively fuse multi-scale features. Experimental results demonstrate that our proposed DSTN method outperforms existing methods in terms of both performance and interpretability in early fault warning of chemical processes.

Planning For Resilience: Examining Gaps in Cyclone Preparedness and Impacts on Marginalized Communities in India's East coast cities

Cyclones and associated storm surges pose a major threat to coastal communities in India, especially along the Bay of Bengal where their intensity is higher. This paper examines cyclone resilience planning in four coastal cities - Visakhapatnam, Puri, Rameshwaram, and Velankanni. A multi-dimensional analysis is undertaken to focus on disaster preparedness, tourism economy contributions, socio-economic inclusion, and sustainability principles. The key objective is to study the integration of cyclone resilience with equitable, tourism-based development to strengthen vulnerable communities through conceptual modeling. The research methodology employs a mixed methods approach combining literature review, secondary data analysis of resilience infrastructure and tourism statistics, and conceptual model development. Findings highlight continued infrastructure and preparedness gaps alongside persisting socio-economic disparities limiting resilience access for marginalized groups. While progress is visible on early warnings, shelters, and evacuations, constraints exist regarding storm-proofing of amenities, customized enterprise support, secure housing, and risk financing tools. The study concludes that resilient, sustainable outcomes require planning processes that engage tourism-dependent communities through bottom-up participation. Coupled with hazard-resilient land use standards, customized livelihood protection schemes, resilient affordable housing programs, and coherent governance; tourism-centric coastal cities can enhance capacities to absorb cyclone impacts and enable inclusive rebuilding. Key Words: Cyclone resilience, Coastal city planning, Socio-economic vulnerabilities, Tourism economies

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.

Flood early warning system at Jakarta dam using internet of things (IoT)-based real-time fishbone method to support industrial revolution 4.0

This research aims to develop an effective flood early warning system to provide timely information to the public and support the government in disaster management. The Raspberry Pi mini-computer functions as the central control, collecting data from the Water Level Sensor to measure water height, the Ultrasonic Sensor for further monitoring, the DHT11 Sensor to monitor temperature and humidity, and a Buzzer as an audible warning device. The research method involves review of the literature and data acquisition from related journals. These data are utilized to design an Internet of Things (IoT)-based flood detection tool with the Raspberry Pi minicomputer as the main controller. The system can be implemented in vulnerable locations such as reservoirs, sluice gates, and rivers, as part of the Smart City and Smart Environment concepts. The test results indicate that the developed early warning system, integrating the Raspberry Pi minicomputer, the Water Level Sensor, the the Ultrasonic Sensor DHT11 Sensor, and Buzzer, approaches perfection. Real-time information is transmitted through the Twitter social media platform, which is shown to be more effective than manual notifications. The system can provide accurate early warnings, reduce flood-related damages, and positively contribute to flood prevention and disaster management efforts. This research is expected to make a significant contribution to improving the community and government preparedness for future flood disasters.

Predicting river phytoplankton blooms and community succession using ecological niche modeling

AbstractExcessive phytoplankton concentrations in rivers can result in the loss of plant and invertebrate communities, and threaten drinking water supplies. Whilst the physicochemical controls on algal blooms have been identified previously, how these factors combine to control the initiation, size, and cessation of blooms in rivers is not well understood. We applied flow cytometry to quantify diatom, chlorophyte, and cyanobacterial group abundances in the River Thames (UK) at weekly intervals from 2011 to 2022, alongside physicochemical data. A niche modeling approach was used to identify thresholds in water temperature, flow, solar radiation, and soluble reactive phosphorus (SRP) concentrations required to produce periods of phytoplankton growth, with blooms only occurring when all thresholds were met. The thresholds derived from the 2011 to 2018 dataset were applied to a test data set (2019–2022), which predicted the timing and duration of blooms at accuracies of > 80%. Diatoms and nano‐chlorophyte blooms were initiated by flow and water temperature, and usually terminated due to temperature and flow going out of the threshold range, or SRP and Si becoming limiting. Cyanobacterial bloom dynamics were primarily controlled by water temperature and solar radiation. This simple methodology provides a key understanding of phytoplankton community succession and inter‐annual variation and can be applied to any river with similar water quality and phytoplankton data. It provides early warnings of algal and cyanobacterial bloom timings, which support future catchment management decisions to safeguard water resources, and provides a basis for modeling changing phytoplankton bloom risk due to future climate change.

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

Ensemble multiclassification model for predicting developmental toxicity in zebrafish

In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.

Write your paper on the motherland?

ABSTRACT ‘Write your paper on the motherland’ is an influential Chinese slogan encouraging researchers to focus on domestic issues and prioritize local applications of their work, though interpretations differ. The 2024 ‘International Journal Early Warning List’ update sparked renewed debate over the slogan’s meaning. This letter argues that misinterpreting this slogan as merely promoting domestic journal submissions could lead to a more conservative submission behavior and a more closed academic system. This reflects a common challenge among non-English-speaking countries to balance international reach with local contributions in publications.