Continuous Change Research Articles

Strategies for Enhancing Legal Application Skills among College Students

In modern society, improving legal application abilities is crucial to college students' personal development and career planning. With the progress of society and the continuous changes in the country's legal application environment, college students, as the main force leading social changes, need to improve their legal application capabilities to solve many legal problems that arise in social development and promote the country's legal environment and rule of law. Capacity building. Improving the legal application ability of college students is an urgent requirement for building a socialist society ruled by law and an inevitable requirement for implementing the basic strategy of governing the country according to law. The current actual situation is that college students have low legal application ability and there are some problems, mainly lack of legal knowledge, weak legal awareness, and lack of legal practical experience. In today's era, except for professional law students, the legal studies of ordinary college students are mainly concentrated in the political field, and they only learn some simple basic legal knowledge. In the learning process, they pay too much attention to theoretical knowledge, lack practical application capabilities, and lack of practical experience, which makes them Insufficient ability to understand and apply legal knowledge. This article mainly discusses the legal application ability of ordinary college students other than law students. By analyzing the reasons for the problems in college students' legal application ability, it proposes corresponding strategies: First, improve classroom teaching links and optimize legal education content. The second is to establish scientific training concepts and strengthen the cultivation of practical experience. The third is to create a three-dimensional training environment and enhance the cultivation of legal awareness. The fourth is to construct effective practice links and cultivate college students’ legal application abilities. Through the above-mentioned strategies, we can improve the legal application ability of college students, so that they will pay more and more attention to and become accustomed to resolving disputes through legitimate legal channels and protect their legitimate rights and interests. At the same time, they can also contribute to the progress of society and the construction of the rule of law in the country. Make a contribution.

Adaptive Online Continual Learning for In-Situ Quality Prediction in Manufacturing Processes

Abstract Manufacturing processes undergo continuous changes in order to meet various requirements, such as process/product changes and variations in tool/workpiece conditions, leading to mixed, heterogenous, or anomalous data. As a result, a quality prediction model trained from previous data may not perform well when new tasks emerge. In order to achieve in-time and accurate product quality prediction, it is crucial to develop a predictive method that adapt to variations in the manufacturing system, capable of learning from new tasks without forgetting previous ones and detecting unknown tasks. This study proposes a deep learning method integrated with continual learning for in-situ quality prediction that is capable of learning from new tasks without forgetting previous ones. To demonstrate this idea, deep CNNs are designed to analyze in-process sensor data, which consist of shared layers to capture the common underlying features across all tasks, and task-specific layers that capture specific characteristics of each individual task. In order to identify the task to which incoming product belongs, a task prediction approach based on task relevancy using filter subspace distance is proposed. When new data come in, the model first identifies the task, followed by predicting the quality of the current product. The proposed method is demonstrated on two case studies, including quality prediction of the workpiece using acoustic emissions during the laser-induced plasma micro-machining process, and quality prediction of the product through thermal images during the hot stamping process.

Analysis of the impact of key similarity criteria numbers of TBCC inlet during the mode transition

Abstract A simplified configuration was developed to facilitate the mode transition process within an over-under Turbine-Based Combined Cycle (TBCC) inlet. Leveraging dynamic mesh technology, an unsteady numerical simulation of the mode transition was conducted, emphasising the flow characteristics of the mode transition and the impact of key similarity criteria numbers. The findings indicate that at an incoming Mach number of 2.0, the mode transition is paired with a continuous alteration in the capture mass flow of the high-speed duct. This continual change instigates the inlet unstarting, with subsequent flow characteristics being contingent on the historical effect, exhibiting a degree of hysteresis characteristics. When the scale effect is considered, it is observed that a larger model scale results in higher Reynolds (Re) and Strouhal (St) numbers. This directly contributes to a notable delay in the unstart moment, a decrease in the unstart interval, and an enlargement of the hysteresis loop. An examination of control variables reveals that the Re number marginally influences mode transition characteristics, while the St number’s effect constitutes approximately 90% of the scale effect. This conclusively demonstrates that the St number is the predominant similarity criterion number in the mode transition process.

A Spatial Interpolation Method for Meteorological Data Based on a Hybrid Kriging and Machine Learning Approach

ABSTRACTConventional spatial interpolation methods for meteorological data are usually based on linear interpolation. However, with the improvements in the temporal and spatial resolution of observational data, local neighbouring stations are susceptible to the influence of underlying surface changes and high terrain gradients. Moreover, for interpolation at a single time point, the inability to extract continuous change information effectively from adjacent times limits the interpolation performance. In this paper, an improved hybrid deep learning‐kriging method is proposed that combines a graph neural networks (GNNs) prediction model with the kriging interpolation algorithm. The GNNs considers dynamic changes over time and combines spatial and temporal information to estimate (interpolate) meteorological data at target weather stations using reanalysis data as input. The experimental results show that the hybrid method exhibits good performance in interpolating station data in complex terrain areas and under uneven surface conditions. The interpolation effectiveness of this method is markedly improved compared to that of traditional kriging methods. Moreover, when applied to station‐to‐grid interpolation, the hybrid method still provides better interpolation results than those of kriging methods. Therefore, this research provides a new method and perspective for meteorological data interpolation.

Practical Evaluation of the Fetal Cardiac Function.

The fetal heart adapts dynamically to nutrient and oxygen needs from all fetal organs. These continuous changes make it difficult to define normal/abnormal cardiac function based only on the evaluation of a few cycles. Many signs of fetal cardiac dysfunction have been suggested; however, very few can stand as true manifestations of cardiac deterioration, and none has emerged as a single reliable marker of cardiac dysfunction. It is the combination of abnormal findings that provides a more accurate assessment of the status of the fetal heart function.

Oncomatrix: Molecular Composition and Biomechanical Properties of the Extracellular Matrix in Human Tumors

The extracellular matrix is an organized three-dimensional network of protein-based molecules and other macromolecules that provide structural and biochemical support to tissues. Depending on its biochemical and structural properties, the extracellular matrix influences cell adhesion and signal transduction and, in general, can influence cell differentiation and proliferation through specific mechanisms of chemical and mechanical sensing. The development of body tissues during ontogenesis is accompanied by changes not only in cells but also in the composition and properties of the extracellular matrix. Similarly, tumor development in carcinogenesis is accompanied by a continuous change in the properties of the extracellular matrix of tumor cells, called ‘oncomatrix’, as the tumor matures, from the development of the primary focus to the stage of metastasis. In this paper, the characteristics of the composition and properties of the extracellular matrix of tumor tissues are considered, as well as changes to the composition and properties of the matrix during the evolution of the tumor and metastasis. The extracellular matrix patterns of tumor tissues can be used as biomarkers of oncological diseases as well as potential targets for promising anti-tumor therapies.

MG-Net: A fetal brain tissue segmentation method based on multiscale feature fusion and graph convolution attention mechanisms

Background and Objective:Fetal brain tissue segmentation provides foundational support for comprehensively understanding the neurodevelopment of normal and congenital disease-affected fetuses. Manual labeling is very time-consuming, and automated segmentation methods can greatly improve the efficiency of doctors. At the same time, fetal brain tissue undergoes various changes throughout the pregnancy, leading to a continuous change in tissue contrast, which greatly increases the difficulty of training segmentation methods. This study aims to develop an automated segmentation model that can efficiently and accurately segment fetal brain tissue, improving the workflow for medical professionals. Methods:We propose a novel deep learning-based segmentation model that incorporates three innovative components: Firstly, a new Dual Dilated Attention Block (DDAB) is proposed in the encoder part to enhance the feature extraction of local spatial and structural contextual information. Secondly, a Multi-scale Deformable Transformer (MSDT) is integrated into the bottleneck to improve the feature extraction of global information on local spatial and structural contextual information. Thirdly, we use a novel block based on Graph Convolution Attention (GCAB) in the decoder, which effectively enhances the features at the decoder.The code is available at https://github.com/unicoco7/MG-Net/. Results:We trained and tested on the FeTA 2021 and FeTA 2022 datasets, and evaluated using seven popular metrics, including Dice, IoU, MAE, BoundaryF, PRE, SEN, and SPE. Compared to the current state-of-the-art 3D segmentation models such as nnFormer, SwinUNETR, and 3DUX-net, our proposed method has surpassed all of them in metrics like Dice, IoU, and MAE. Specifically, on the FeTA 2021 dataset, our model achieved a Dice of 0.8666, an IoU of 0.7646, and an MAE of 0.0027; on the FeTA 2022 dataset, it achieved a Dice of 0.8552, an IoU of 0.7470, and an MAE of 0.0005. Conclusion:In this paper, we propose a model for three-dimensional fetal brain tissue segmentation based on multi-scale feature fusion and graph convolution attention mechanism, and conduct experimental evaluation on the FeTA 2021 and FeTA 2022 datasets. Understanding the boundaries of fetal brain tissue is crucial for doctors’ diagnosis, so the proposed model is expected to improve the speed and accuracy of doctors’ diagnoses.

Different habitat condition proportions on farms affect the structure and diversity of dung beetle (Coleoptera, Scarabaeidae, Scarabaeinae) communities

The continuous changes and increase of land use into ranching and agriculture have caused shifts in the composition and structure of dung beetle communities due to the modification of vegetation structure. The impact of these changes can be approached using dung beetles as ecological indicators. Agricultural, ranching, and forestry are often integrated into the same farm in different proportions (landscape level), and their degree of integration with habitats changes over time and space. We assessed if different habitat proportions of crop, pasture, and forest land on three farms affect the structure and diversity of dung beetle (Coleoptera: Scarabaeinae) communities. Farms included from three to four habitat conditions each (plantations of timber species, banana-coffee plantation, living fences, pastures, secondary and riparian forest). Pitfall traps with dung and carrion as bait were placed across each habitat condition of each farm during three different sampling periods. Across all samplings, 1,198 dung beetle individuals belonging to 21 species were captured. Species diversity and composition vary according to the type of farm and the main factor that modifies this tendency is the proportion of pasture land composing the farm. Farms with a lower proportion of grazing land (11.96% for forestry farms and 32.19% for agricultural farms) and denser vegetation canopy, which cast more shadows, had greater beetle diversity. Conversely, farms having a greater proportion of grazing land had low diversity and a dominant tendency in the species abundance curve. Umbraphile species dominated the forest farm, indicating a strong affinity for shaded environments such as the one provided there, while species displaying no habitat preference exhibited higher abundance in the ranching farm. Additionally, a notable prevalence of small-sized species was observed in the ranching farm, contrasting with a relatively even distribution of sizes in the remaining landscapes. These tendencies suggest that shade positively influences biodiversity conservation. Riparian vegetation, living fences, and banana-coffee plantations are important connectivity elements in agricultural landscapes for shade-adapted dung beetle species.

Comparison of Mitochondrial Genome Expression Differences among Four Skink Species Distributed at Different Latitudes under Low-Temperature Stress.

Continual climate change strongly influences temperature conditions worldwide, making ectothermic animals as suitable species for studying the potential impact of climate change on global biodiversity. However, the study of how lizards distributed at different latitudes respond to climate change at the transcriptome level is still insufficient. According to the Climatic Variability Hypothesis (CVH), the range of climate fluctuations experienced by terrestrial animals throughout the year increases with latitude, so individuals at higher latitudes should exhibit greater thermal plasticity to cope with fluctuating environments. Mitochondria, as the energy center of vertebrate cells, may indicate species' plasticity through the sensitivity of gene expression. In this study, we focused on the changes in transcript levels of liver mitochondrial protein-coding genes (PCGs) in skinks from the genus Plestiodon (P. capito and P. elegans) and the genus Scincella (S. modesta and S. reevesii) under low-temperature conditions of 8 °C, compared to the control group at 25 °C. Species within the same genus of skinks exhibit different latitudinal distribution patterns. We found that the two Plestiodon species, P. elegans and P. capito, employ a metabolic depression strategy (decreased transcript levels) to cope with low temperatures. In contrast, the two Scincella species show markedly different patterns: S. modesta exhibits significant increases in the transcript levels of six genes (metabolic compensation), while in S. reevesii, only two mitochondrial genes are downregulated (metabolic depression) compared to the control group. We also found that P. capito and S. modesta, which live at mid-to-high latitudes, exhibit stronger adaptive responses and plasticity at the mitochondrial gene level compared to P. elegans and S. reevesii, which live at lower latitudes. We suggest that this enhanced adaptability corresponds to more significant changes in a greater number of genes (plasticity genes).

Estimation of travel time reduction for ice-breaking ships when considering ice information data

Abstract In light of the recent increase in polar shipping and potential future increase with continued climate change reliable routing in ice-covered waters becomes increasingly important for environmental, economic and safety concerns. Dependable route suggestions have the potential to reduce travel times through polar waters significantly. We apply the Anytime Repairing A* pathfinding algorithm to classified Copernicus Sentinel 1 radar images to estimate how much travel times can be reduced. For multiple example scenarios, it is quantified how much the travel time is reduced if a ship follows these suggestions compared to navigating without any ice information available exterior to the visual range (VR). It was found that having ice information available is most beneficial in complex ice situations, where it can reduce travel time by up to 34% for a VR of 2 km.

Leadership in small business environments: from the heroic model to distributed approach

Purpose This study explores the process of interactions experienced by leaders during the evolution of the small firm. Three key propositions are conceptualized into a model with the main aim of supporting leaders of small organizations in understanding better the leadership phenomena. Design/methodology/approach This qualitative research project investigates the dynamics of leadership in small businesses through an inductive and abductive approach, inherent to grounded theory. This enabled the development of propositions directly from data. Field research involved responsive interviews with 12 established small enterprise leaders. Selective and theoretical sampling coupled with ongoing data comparison and analysis were applied to uncover and refine emerging leadership concepts. Findings Intellectual assets of leaders in small firms propel the recognition and sensitivity to the process of organizational evolution which requires a continuous adaptation of the leadership paradigm. Research limitations/implications The researchers’ philosophical stance to construct reality based on the participants own experiences, cannot produce generalized findings on the subject area. Practical implications Enhanced Decision-Making and Strategic Planning: Small business leaders can leverage their academic and industry backgrounds to make more informed and strategic decisions. Facilitating Organizational Change: By understanding the necessity of continuous change, small business leaders can implement frameworks that encourage flexibility and adaptability within their organizations. Adopting Distributed Leadership: Encouraging small business owners to move away from centralized control towards a more distributed leadership model can lead to better organizational outcomes. Originality/value A considerable amount of research has been devoted to leadership in the past decades. There is very little evidence on behavioral patterns of leaders, grounded in data within the context of the Small Firm aimed at the practitioner. This work is contribution towards this shortfall.

Development of Piophila megastigmata (Diptera: Piophilida) at seven constant temperatures

In forensic entomology, the time-related growth and development of carrion insects allows for the estimation of the minimum postmortem interval (PMImin). Piophila megastigmata (Diptera: Piophilidae) is of great significance in estimating PMImin in the late stage of corpse decay. In this paper, the development of P. megastigmata was investigated under the seven constant temperatures of 16–34 °C. The total development time at each temperature was 970.38, 824.50, 593.13, 498.25, 392.00, 385.63 and 405.87 h, respectively. The isomorphen diagram shows the trend of development during different developmental stages at different temperatures. By using a revised linear regression model, the estimated lower lethal developmental thresholds (TL) and thermal summation constant (K) were found to be 10.57 °C and 6936.78 degree hours, respectively. According to a nonlinear model, the TL, upper lethal developmental thresholds (TH), and intrinsic optimum temperature (TΦ) were determined to be 7.53, 34.68, and20.43 °C, respectively. Also, logistic function and an isomegalen diagram were constructed according to the continuous changes in larval body length, and illustrates the time required to develop to a certain length at different temperatures. The obtained results offer crucial fundamental developmental information regarding P. megastigmata, which can be applied in PMImin estimation.

DEVELOPING THE CREATIVE ECONOMY AS A FACTOR OF INNOVATION

In this article, it is emphasized that various aspects of creativity as an innovative resource of economic development, the emergence of creativity as a defining element of economic life are the basis of the process of continuous changes.

Evaluating water balance components in a tropical ecological zone under land use changes

Water availability for domestic, institutional, and agricultural production is threatened by the continuous change in land use land cover (LULC). These changes in LULC features affect the dynamics of the hydrological processes such as surface runoff, infiltration, and evapotranspiration, resulting in variations in water availability especially, for human consumption and crop yield. Therefore, the Soil and Water Assessment Tool (SWAT) hydrological model was deployed to simulate the Pra River Basin (PRB)’s water balance response to changing LULC from 1986 to 2020. Open and closed forests reduced from an area of 11,071 km2 to 5902 km2 and 5125 km2 to 4990 km2 while settlement and cropland increased from 1100 km2 to 2392 km2 and 5303 km2 to 9320 km2 representing an average annual expansion rate of +24.7 km2/yr and +118 km2/yr respectively over the three decades. The growth in settlement and cropland resulted in an upsurge of more than 33 % and 20 % in surface runoff and water yield respectively, but decreases of over 4 % and 17 % in evapotranspiration and baseflow respectively. These changes have resulted in a seasonal increase in surface runoff, baseflow, and evapotranspiration (ET) during the wet season but a decrease during the dry season. The PRB has experienced a dramatic decline in forest areas, chiefly in areas where settlement and farmlands have been expanded. The areas where surface runoff and water yield showed significant upsurge and reduction in ET are the northwestern and southern enclaves of the basin. These findings can aid in effectively planning and managing land use to control the gradually depleting water resources in the basin.

Transseptal Transition Patterns DuringLeft Bundle Branch Area LeadImplantation.

Continuous deep septal pacing and signal recording during implantation of left bundle branch pacing (LBBP) lead enables to monitor beat-to-beat changes of electrocardiogram (ECG) and myocardial current of injury (COI) as the lead crosses the septum. This study aimed to characterize patterns of continuous QRS, ST-T, and COI change for monitoring of the lead depth and instantaneous determination of the obtained capture type (LBBP vs left ventricular septal pacing [LVSP]). The ECG and COI during lead implantation were scrutinized for sudden changes of V6 R-wave peak time, V1initial and terminal R-wave amplitude, V3-V6 R-wave amplitude, repolarization pattern and S-wave amplitude in I, V5-V6, and COI drop. The sudden and gradual transition patterns were diagnosed depending on the presence or absence of the above beat-to-beat ECG phenomena, respectively. A total of 212 pacemaker recipients were analyzed; LBBP and LVSP were obtained in 77.4% and 22.6%, respectively. There were 4.7 ± 2.1 and 0.2 ± 0.6 beat-to-beat phenomena in LBBP and LVSP patients, respectively. The sudden transition pattern, recognized in 80.7%, had sensitivity and specificity for LBBP diagnosis of 98.8% and 81.2%, respectively. A sudden drop of COI (29.4 ± 8.5mV to 12.8 ± 4.9mV) was observed in 53.9% patients (LBBP was simultaneously obtained in 92.7%). Capture of left bundle branch during lead penetration is a beat-to-beat phenomenon. Two transseptal transition patterns were identified: 1) sudden, which is typical for obtaining LBBP; and 2) gradual, which is typical for obtaining LVSP. A sudden COI drop, a very observable phenomenon, also identified reaching the left subendocardial area.

Quantitative analysis of human blood glucose by dynamic optical rotation angle of multiple wavelengths based on the polarization camera

Measurement of blood glucose concentration is important in the prevention and treatment of diabetes. To improve the measurement signal-to-noise ratio and accuracy, using a polarization camera as the sensor, this paper proposes a multi-wavelength dynamic optical rotation angle method to quantitatively analyze human blood glucose. First, this paper utilizes a split-time approach to drive nine different wavelengths of light sources to work separately to achieve the collection of multiple different wavelengths of data and increase the amount of information. Second, to capture images with an array sensor such as a camera, using space-for-accuracy, this paper uses the cumulative value of the gray values of all pixel points in the image at each angle as the light intensity value to improve the amplitude (light intensity) resolution. Third, the extraction method of the dynamic optical rotation angle in this paper is to extract the difference between the maximum and minimum values of the optical rotation angle from each cycle of the signal of the continuous cyclic change of the length of the optical path, and the accumulated value of the difference of multiple cycles is recorded as the dynamic optical rotation angle, which not only reduces the errors caused by the individual differences, the external environment and the measuring instrument, but also has the effect of averaging and improves the signal-to-noise ratio of the optical rotation angle. To prove the effectiveness of the proposed methods, human finger real and simulation experiments are carried out, respectively. Firstly, the polarization camera is used to collect the image data of elliptically polarized light of nine different wavelengths of linearly polarized light after passing through the human body’s finger, and obtains the optical rotation angle under each wavelength that changes with the photoplethysmography (PPG) through data processing, and extracts the dynamic optical rotation angles of the nine wavelengths by the extraction method, and the human body’s blood glucose can be quantitatively analyzed. Secondly, this paper builds a simulation model to simulate the blood volume conversion of the human finger, and uses the simulation experimental system to collect the dynamic optical rotation angles of nine wavelengths of a total of 137 human serum samples, and uses partial least squares regression to establish a model between the dynamic optical rotation angles and blood glucose concentration. In the results of predicting all samples using the model, the correlation coefficient and root mean square error were 0.7960 and 2.16 mmol/L, respectively, both of which improved by 137.26 % and decreased by 35.33 %, respectively, compared to the existing method. The experimental results show that the method in this paper has high accuracy in detecting the blood glucose concentration in human beings, which is of great significance in realizing the noninvasive quantitative analysis of human blood glucose components by the polarized light method.

Research on the Relationship between the School-Running Characteristics of Private Colleges and Universities and the Development Objectives of the Government

The development objectives of the Chinese government have a guiding effect on the school-running characteristics of private colleges and universities. This paper explores the relationship between the characteristics of private colleges and universities and the development objectives of the provincial government, using Jiangsu Province as a case study. All the schools have the consciousness of fully considering the development objectives of Jiangsu Province when formulating the school-running characteristics. Their characteristics all contain some training methods, training courses, or institutional construction aiming at the development objectives. Most universities can support the government’s objectives on employment and innovation. Only a third of the schools provide government support for science research. Private colleges and universities should make full use of the free and flexible advantages of private education, combine them with the continuous changes of policies and markets, and create clear, scientific, and dynamic school-running characteristics, in order to achieve long-term development.

Vertical farming goes dynamic: optimizing resource use efficiency, product quality, and energy costs

Vertical farming is considered to be a key enabler for transforming agrifood systems, especially in or nearby urbanized areas. Vertical farming systems (VFS) are advanced indoor cropping systems that allow for highly intensified and standardized plant production. The close control of environmental parameters makes crop production stable and repeatable, ensuring year-round uniform product quality and quantity irrespective of location. However, due to continuous changes in plant physiology and development, as well as frequent changes in electricity prices, the optimum conditions for crop production and its associated costs can change within days or even minutes. This makes it beneficial to dynamically adjust setpoints for light (intensity, spectrum, pattern, and daylength), CO2, temperature, humidity, air flow, and water and nutrient availability. In this review, we highlight the beneficial effects that dynamic growth conditions can have on key plant processes, including improvements in photosynthetic gas exchange, transpiration, organ growth, development, light interception, flowering, and product quality. Our novel findings based on modeling and experimentation demonstrate that a dynamic daily light intensity pattern that responds to frequent changes in electricity prices can save costs without reducing biomass. Further, we argue that a smart, dynamic VFS climate management requires feedback mechanisms: several mobile and immobile sensors could work in combination to continuously monitor the crop, generating data that feeds into crop growth models, which, in turn, generate climate setpoints. In addition, we posit that breeding for the VFS environment is at a very early stage and highlight traits for breeding for this specialized environment. We envision a continuous feedback loop between dynamic crop management, crop monitoring, and trait selection for genotypes that are specialized for these conditions.

Exploring Inclusion, Diversity, Equity, and Accessibility in the Built Environment: A Case Study

Continuous changes in society and the need for sustainable development demand updates in designing better built environments to respond to the variety of user needs. Notwithstanding the growing interest of research and the introduction of guidelines and standards on inclusion, diversity, equity, and accessibility, there are still several limitations in effectively and efficiently embedding such principles for the design of buildings and neighborhoods. Previous research demonstrated the critical need for innovative tools and methods to support professionals in designing responsive, inclusive spaces for an extended range of users. This article reports the results of a pilot study using the new IDEA Audit Tool for assessing how inclusion, diversity, equity, and accessibility are perceived by building occupants in a specific facility. The analysis of significant data provided by this study shows the challenges and highlights the benefits of the tool, including fostering an evidence-based decision-making process, speeding up the prioritization of critical design improvements, demonstrated through a six-month trial with a London-based inclusive design firm. The research-driven outcomes showcase the huge potential that the tool offers to improve the company strategy while evolving towards more inclusive, accessible spaces that foster diversity and equity, and has the potential to be replicated in several fields of action to raise awareness and improve the application of IDEA principles in all phases of the design process.

Automatic modeling and optimization of tapered laminates with ply drops

Ply-drop (PD) is the termination of specific plies for laminated composite structures to obtain continuous thickness changes. It brings flexibility to the design of tapered composite laminates. However, as a structural defect, ply drops could have an impact on performance. Considering the impact of ply drop during stacking sequence design can provide more accurate performance analysis, but this will bring challenges in modeling and optimization. To consider the PD impact and achieve convenience in optimization, this paper proposes a high-fidelity finite element automatic modelling method of tapered laminates and corresponding optimization framework. By parameterizing the PD information and defining the basic elements and nodes of start stacking surface of the structure, the entire finite element model is layer-wisely constructed and controllable. Subsequently, based on the genetic algorithm framework, a repair strategy and its genetic operations are proposed to ensure that the design variables satisfy the ply-drop design guidelines. And a detailed optimization process is provided. Finally, the strength and deflection performance optimization problem of a tapered laminate with PD from 28 layers to 16 layers under three-point bending test is introduced for illustration of the proposed automatic modeling and optimization method. Comparisons between simulation results and experimental data of the obtained optimization solution verify the effectiveness of the proposed modeling and optimization method.