Theoretical Foundation Research Articles

Research on Option Pricing Method Based on the Black-Scholes Model

Option pricing is one of the core research topics in the field of financial engineering. As a classical option pricing method, the Black-Scholes model provides a significant foundation for both theory and practice in modern financial markets. This paper first elaborates on the theoretical foundation and mathematical derivation of the Black-Scholes model, analyzes its practical applications in option pricing, and explores its limitations, including the strict assumptions about market conditions and its applicability in environments with fluctuating volatility or sudden market jumps. To address these issues, this study improves the Black-Scholes model from two perspectives: stochastic volatility and jump-diffusion models. The improved models are validated through experimental designs, and their effectiveness is compared with the traditional model. The experimental results demonstrate that the improved models can provide more accurate pricing results in complex market environments. This research not only deepens the understanding of the Black-Scholes model but also offers new insights and approaches for pricing complex financial instruments.

Digital economy, international openness, and innovation capability: empirical evidence from Urban China

Abstract Using urban panel data from 2011 to 2019 in China, this study employs various econometric models to analyze the impact and mechanism of digital economy (DE) development on urban innovation capabilities. The empirical results show that DE development significantly bolsters urban innovation in China, a finding that remains consistent even after conducting rigorous robustness tests, such as using historical data as instrumental variables. Importantly, an analysis using the spatial Durbin model reveals a positive spatial spillover effect—DE development in one city enhancing innovation capabilities in neighboring cities. This effect not only diminishes the regional innovation gap but also stimulates regional coordinated innovation. The study also illustrates that a city’s degree of international openness enhances the positive impact of DE development on urban innovation capabilities. This highlights that promoting regional openness is an important mechanism to facilitate the beneficial effect of DE development on urban innovation capabilities. Providing both theoretical foundation and empirical evidence for the impact of DE development, this paper contributes valuable insights into promoting innovation-driven development via digital ecosystem empowerment, formulating differentiated DE strategies, and maximizing the spatial spillover effects of the DE.

The Matching of CSR Communication Strategies and CSR Models: A Content Analysis Based on Weibo

As social media becomes an essential tool for disseminating Corporate Social Responsibility (CSR) initiatives, there is a lack of academic discussion on how to effectively match different CSR models with communication strategies. This paper explores the theoretical foundations and practical applications of such matching on the Weibo platform based on established classifications of CSR models (Cultivating Goodwill, Compensating, and Corrective) and communication strategies (Broadcasting, Reacting, and Engaging). By analyzing CSR-related content published by Chinese companies on Weibo, the study finds that Corrective CSR matches best with engaging strategies, Compensating CSR aligns well with reacting strategies, and Cultivating Goodwill CSR tends to adopt broadcasting strategies. This research not only validates theoretical assumptions regarding CSR communication but also provides practical guidance for companies to optimize their social media communication strategies. Future research could further examine the impact of these matches on public sentiment and behavioral responses.

Application and Strategy Research of Data Analysis and Statistical Methods in Supply Chain Process and Supply Demand Optimization

This study digs into the importance and usefulness of data analysis and statistical methods in making supply chain processes better and balancing supply and demand. It looks at its usefulness in many real-life situations, like figuring out the best way to use resources, making the supply chain more open, managing suppliers better, making logistics more efficient, and lowering business risks. By stressing how important it is to combine data analysis and statistical methods with supply chain process optimization, real-life business examples make the usefulness and practicality of data analysis and statistical methods stand out. At the same time, the study deeply explores the challenges and opportunities encountered in the practical application of data analysis and statistical methods. Finally, we provide an in-depth summary of the research findings and offer suggestions for future research. The goal is to provide forward-looking insights and a solid theoretical foundation for the ongoing improvement and innovation of supply chain management, as well as help businesses better adapt to the tough international market competition environment.

Comparison of Image Enhancement Algorithms Based on Denoising and Edge Detection

Image enhancement technology is crucial in many fields, including noise reduction and edge enhancement. This paper compares median filtering and Gaussian filtering for image denoising by practicing. Median filtering removes salt-and-pepper noise well and preserves edges, suitable for discrete noise and detail-critical scenarios like remote sensing and medical imaging. Gaussian filtering smoothes Gaussian and random noise but blurs edges, applicable for background noise reduction. For edge enhancement, Sobel and Laplace operators are contrasted. Sobel operator highlights directional edges, useful in dynamic and noisy scenarios like surveillance. Laplace operator globally sharpens multi-directional edges, sensitive to details but noise, often used in medical image boundary extraction. Their combination improves edge detection. This research provides a solid theoretical foundation for optimizing the application of image enhancement techniques, offering valuable insights and guidance for selecting appropriate image processing algorithms in diverse fields such as medical image processing, autonomous driving, and other advanced technological domains. Future studies could focus on adaptive denoising algorithms to meet diverse image processing needs.

ESG Rating System in Sustainable Finance: Challenges and Suggestions for Improvement

As global attention to sustainable development intensifies, Environmental, Social, and Governance (ESG) criteria have become vital indicators for assessing corporate non-financial performance. Nonetheless, current ESG rating systems encounter several challenges, including lack of standardization, insufficient data transparency, and market dependence on rating outcomes, which impede their widespread application in investment decisions. This paper systematically reviews the construction logic and practical applications of existing ESG rating systems, providing an in-depth analysis of the disparities among major rating agencies concerning indicator selection, weight allocation, and data sources. By elucidating the impact of these disparities on rating accuracy and consistency, the paper proposes a series of improvement recommendations, such as the standardization of global rating criteria, enhancement of data transparency, and the utilization of emerging technologies to optimize the rating process. This research aims to offer theoretical foundations and practical insights for the enhancement of ESG rating systems, facilitating a more precise assessment of corporate sustainability performance by investors. The ultimate objective is to advance the global green economy transition and provide support to policymakers for further refinement of regulatory frameworks.

A Systematic Literature Review of Research on Data Finance in China

With the rapid development of the internet, the digital economy has made significant progress, and data has become a new type of production factor. Academic achievements related to data fiscal policy have also been increasingly enriched. This paper systematically reviews the major achievements in the field of data fiscal policy research in China. It focuses on the definition, significance, and value of data fiscal policy, as well as implementation measures. The paper summarizes the different definitions of the core concepts of data elements and data fiscal policy by scholars both at home and abroad, expounds on the theoretical foundations of data fiscal policy in terms of economic development, resource allocation, and fiscal revenue, and analyzes the important significance of data fiscal policy from the perspectives of economic transformation, fiscal revenue, and data element markets. The paper highlights its role in promoting high-quality development of the digital economy, enhancing government fiscal capacity, and optimizing resource allocation. Furthermore, it summarizes scholars specific suggestions regarding the implementation of data fiscal policy, including the design of data taxation systems, public data operation models, and lessons from international experiences. Based on the current state of research, this paper also analyzes and summarizes existing gaps in research and provides directions and recommendations for future studies on data fiscal policy.

The Path of Constructing the Supported Decision-Making System in the Interaction Between International and Domestic Law

This article focuses on the path of constructing the supported decision-making (SDM) system in the interaction between international and domestic law, exploring the role of Article 12 of the Convention on the Rights of Persons with Disabilities (CRPD) in promoting this system globally. The article reviews the theoretical foundation of the supported decision-making system and analyzes the legislative practices in countries such as Germany, Japan, and Canada. It summarizes the interactive model where international law, through soft norms and supervision mechanisms, promotes domestic legal reforms. Article 12 of the CRPD, centered on equal legal capacity and assistance and support, provides guidance for contracting states in system innovation, advancing legislation through concluding observations and compliance monitoring, and promoting the transformation from the traditional guardianship system to the supported decision-making system. This article argues that the promotion of the supported decision-making system not only reflects international laws guiding role in domestic law but also demonstrates how domestic practices feedback into and enrich the content of international law. This bidirectional interaction mechanism has laid a solid foundation for achieving the global goal of protecting the rights of persons with disabilities, providing both theoretical justification and practical support for the expansion of the supported decision-making system into other areas of rights protection.

Integrative mindfulness-based infant parenting program: theoretical foundations and a novel intervention protocol

Integrative mindfulness-based infant parenting program: theoretical foundations and a novel intervention protocol

Intergeneration Dynamics and Knowledge Sharing in Family Business Sustainability

Family business sustainability relies heavily on the interplay of intergenerational dynamics, where the effective transfer of knowledge, values, and relational resources is pivotal. This study explores the role of entrepreneurial knowledge sharing in enhancing intergenerational relationship quality and its subsequent impact on the sustainability of family businesses. Using the Resource-Based View (RBV) as a theoretical foundation, this research investigates how relational resources contribute to competitive advantage in the unique socio-cultural context of Central Java, Indonesia. The study employs Structural Equation Modeling (SEM) to analyze data collected from 200 family business owners through purposive sampling. The results demonstrate that entrepreneurial knowledge sharing significantly improves intergenerational relationship quality (β = 0.78, p = 0.012), which, in turn, has a strong positive effect on family business sustainability (β = 0.84, p = 0.007). Furthermore, intergenerational relationship quality acts as a partial mediator (β = 0.35, p = 0.009), amplifying the impact of knowledge sharing on sustainability. The findings highlight the dual role of entrepreneurial knowledge sharing in fostering both relational harmony and business performance. This research contributes to the literature by integrating cultural insights into the RBV framework and offering actionable strategies for family businesses to sustain their legacy. By institutionalizing trust, shared vision, and effective communication practices, family businesses can bridge generational divides and maintain competitiveness in dynamic market environments.

Research on soft errors induced by alpha particles and atmospheric neutrons based on 28 nm field programmable gate array

ABSTRACT The susceptibility of 28 nm static random access memory (SRAM)-based field programmable gate arrays (FPGAs) to soft errors was evaluated using alpha particles and atmospheric neutrons at the China Spallation Neutron Source (CSNS). A single event effect (SEE) test system was established to assess the sensitivity of various FPGA modules, including the configuration RAM (CRAM), block RAM (BRAM), configurable logic module (CLM), and arithmetic processing module (APM), utilizing both dynamic and static methods. The SEE error types for different sensitive modules were identified, and the SEE cross sections were calculated. The findings of this study can serve as a useful theoretical foundation for the design of radiation-hardened FPGA to enhance their reliability in radiation-prone environments.

Understanding and reducing delayed dental care for early childhood caries: a structural equation model approach

ObjectiveThis study aimed to examine the factors that affect treatment delay in early childhood caries (ECC), guided by a modern medical model. This study attempted to analyze the pathways influenced by these factors and provide a theoretical foundation for designing targeted intervention programs.MethodsData were collected from young children who visited the department of stomatology at a tertiary hospital from January to December 2023. Data were collected via a general information survey questionnaire, the Scale of Oral Health Outcomes for 5-year-old Children (SOHO-5), the Children’s Fear Survey Schedule-Dental Subscale-Chinese (CFSS-DS-C), the Parental Caregiver Perceptions Questionnaire-8 (P-CPQ-8), the Family Impact Scale (FIS-8), and the Perceived Barriers to Health Care-Seeking Decision-Chinese (PBHSD-C). The data in this study were analyzed using a variety of statistical tests, including the Kruskal-Wallis and Mann-Whitney tests, correlation analysis, multiple stepwise regression analysis, and structural equation modeling (SEM).ResultsThe treatment delay score of early childhood caries was 36.77 ± 10.11, indicating that the state of early childhood caries was currently at a moderate level of delay. The SOHO-5 score was 6.41 ± 1.78, the CFSS-DS-C score was 23.60 ± 6.91, the P-CPQ-8 score was 18.43 ± 4.33, and the FIS-8 score was 18.66 ± 4.28. Multi-factor analysis revealed key factors affecting treatment delay, including permanent residence, medical insurance type, oral health habits, reasons for visit, first symptoms, the time of first discovery of oral problems, brushing teeth before bedtime every day, a genetic history of dental caries and the staging of dental caries. A positive correlation existed between oral health, children’s dental fear and treatment delay, whereas social support was negatively correlated with treatment delay. The SEM, which is based on the modern medical model, revealed that children’s dental fear plays a mediating role in the relationships among social support, oral health, and treatment delay.ConclusionThe present study developed a novel model to study the ECC treatment delay, elucidated the causal links between the identified variables, and proposed potential intervention strategies to enhance oral health awareness, knowledge, and skills among young children and their parents. These strategies can help improve children’s dental visiting behavior and reduce treatment delay.

A pH stable fluoran-triphenylamine photosensitizer with efficient type I and type II ROS generation.

Photosensitizers (PSs) with robust pH stability and the ability to generate both type I and type II reactive oxygen species (ROS) have gained significant attention due to their versatility in various applications. In this study, we employed an electron donor-acceptor engineering strategy to design and synthesize a fluoran-triphenylamine photosensitizer (Fl-TPA), using an ester-protected ring-opened fluoran cation as the electron acceptor and triphenylamine (TPA) as the electron donor. Compared to fluoran with a spirolactone structure, Fl-TPA exhibits a significant redshift in absorption, with good light capture capabilities in the 300-600 nm range. In comparison with the reference compound Fl-H, which lacks the TPA group, Fl-TPA shows a substantial decrease in fluorescence intensity. Transient fluorescence measurements reveal biexponential decay characteristics for both compounds. Specifically, Fl-TPA shows τ1 = 0.21 ns (41%) and τ2 = 2.92 ns (59%), while Fl-H shows τ1 = 0.14 ns (93%) and τ2 = 2.23 ns (7%). The longer-lived component in Fl-TPA is more pronounced, suggesting the presence of additional non-radiative decay pathways, as further supported by the steady-state fluorescence analysis. Additionally, Fl-TPA exhibits a significant Stokes shift in solvents of varying polarity. Time-dependent density functional theory (TD-DFT) calculations reveal that the introduction of the strong electron-donating TPA group reduces the ΔES-T of Fl-TPA to 1.25 eV, which is significantly lower than that of Fl-H (1.46 eV), facilitating intersystem crossing (ISC). Thus, in the ROS generation experiment, it can be observed that Fl-H produces almost no ROS. In contrast, Fl-TPA not only exhibits high type I ROS generation capability, but also demonstrates excellent type II and total ROS generation capabilities, with performance far superior to the clinically approved near-infrared PS, indocyanine green (ICG). Moreover, Fl-TPA exhibits excellent pH stability compared to the non-esterified fluoran. The results of this study present a new photosensitizer with strong ROS generation capability and good stability across a wide pH range, providing a theoretical foundation for the design of PSs.

Mechanical behavior of synthetic fiber ropes for mooring floating offshore wind turbines.

The continuous development of floating wind turbine technology is pushing platforms further offshore into deeper seas. Consequently, the water depth required for mooring is increasing, demanding higher standards for the mooring system. This study explores the mechanical properties, including wear resistance and quasi-static stiffness, of nylon, polyester, and high-strength polyethylene mooring ropes through experimental design, aiming to address the challenges faced by floating offshore wind turbines (FOWT) in mooring line safety. In conducting yarn-on-yarn abrasion tests, materials, twist, marine lubricants, and dry and wet environments were chosen as research variables to analyse and compare the wear and tear frequency of sample ropes. The study found that the degree of wear on ropes composed of different materials is affected differently by twist and dry and wet environments, and the application of marine lubricants can significantly extend the friction fracture cycle of yarns. To study the tensile properties and static stiffness characteristics of polyester and nylon ropes, the cables were divided into three stages: preloading, initial installation, and ageing. The strain and reversible elongation of the cables at each stage were analysed, and an empirical formula for quasi-static stiffness considering the creep coefficient of the ropes was established. The study found that the ropes had reversible elongation after being left stationary, the more thorough the running-in, the smaller the inherent deformation, and the more stable the structure. The static stiffness of the cables increased with the loading time and force; however, it eventually approached a constant value, and the stiffness of polyester cables was found to be greater than that of nylon ropes. These results provide a solid theoretical foundation and practical guidance for the material selection of FOWT cables, which contributes to advancing the application and development of FOWT in renewable energy.

Molecular Mechanisms of Alfalfa Response to Abiotic Stresses

Alfalfa (Medicago sativa L.), a high-quality perennial legume forage, is pivotal in global animal husbandry and ecological systems. However, its growth and production are threatened by various abiotic stresses, including drought, salinity, low temperatures, and heavy metal toxicity. This review summarizes recent research on the molecular mechanisms underlying alfalfa’s responses to these environmental adversities. It provides a theoretical foundation for enhancing the stress resistance of alfalfa, offering a valuable reference for breeding high-quality, stress-resistant alfalfa varieties.

L-Theanine Metabolism in Tea Plants: Biological Functions and Stress Tolerance Mechanisms

L-theanine, a unique non-protein amino acid predominantly found in tea plants (Camellia sinensis), plays a pivotal role in plant responses to abiotic stress and significantly influences tea quality. In this review, the metabolism and transport mechanisms of L-theanine are comprehensively discussed, highlighting its spatial distribution in tea plants, where it is most abundant in young leaves and less so in roots, stems, and older leaves. The biosynthesis of L-theanine occurs through the enzymatic conversion of glutamate and ethylamine, catalyzed by theanine synthase, primarily in the roots, from where it is transported to aerial parts of the plant for further catabolism. Environmental factors such as temperature, light, drought, elevated CO2, nutrient unavailability, and heavy metals significantly affect theanine biosynthesis and hydrolysis, with plant hormones and transcription factors playing crucial regulatory roles. Furthermore, it has been demonstrated that applying L-theanine exogenously improves other crops’ resistance to a range of abiotic stresses, suggesting its potential utility in improving crop resilience amid climate change. This review aims to elucidate the physiological mechanisms and biological functions of L-theanine metabolism under stress conditions, providing a theoretical foundation for enhancing tea quality and stress resistance in tea cultivation.

Investigation and optimal design of band gap tunability in fractal phononic crystals

This study investigates the properties of band gaps of circular core filling fractal phononic crystals (CCFFPCs), specifically focusing on the impact of different filling positions on the frequency of band gaps. The research demonstrates that core filling at the central positions significantly influences the formation and widening of low-frequency band gaps, while filling at corner edges predominantly affects mid-frequency band gaps, and filling at edge centers effectively opens and broadens high-frequency band gaps. These results reveal the relationship between filling positions and band gap tuning, providing a theoretical foundation for precise band gap control across a full frequency range. Moreover, this study is the first to systematically clarify the impact of core filling positions on band gap frequencies, expanding the design strategies for band gaps in fractal phononic crystals. Furthermore, this study employs genetic algorithm optimization to achieve the maximum band gap width at different frequencies, enhancing the practical value of fractal phononic crystals in engineering applications. This research deepens theoretical understanding and provides valuable guidance for optimizing their use in broadband acoustic control and energy harvesting applications.

Comparative Genomic Analysis of Two Monokaryons of Auricularia heimuer Hei29

Auricularia heimuer is a valuable traditional Chinese fungus used as food and medicine. Hei29 is a strain derived from wild A. heimuer through systematic domestication and selection. It has been the primary A. heimuer variety in Northeast China for 30 years and offers the advantages of high yield, good commercial property, and stable traits. This study used protoplast nucleation on Hei29 to produce two amiable and paired monokaryons, Hei29-D1 and Hei29-D2. The genome of Hei29 was sequenced utilizing the Illumina PE150 and PacBio Sequel sequencing platforms. Hei29-D1 and Hei29-D2 had genomic sizes of 47.54 Mb and 47.49 Mb, GC contents of 56.95% and 56.99%, and an N50 of 2.37 Mb and 4.28 Mb, respectively. Hei29’s genome possessed two phytoene synthase (PSY) protein genes, one of which—PSY encoded by g894—has a transmembrane domain. The phylogenetic tree showed that Hei29 shared the closest evolutionary relationship with Auricularia subglabra TFB-10046 SS5. Collinearity analysis showed that the correlation between the two monokaryons was as high as 90.81%. Cluster analysis revealed that Hei29 contains 12,362 core genes, 223 unique genes in Hei29-D1, and 228 unique genes in Hei29-D2. This study is the first to sequence two related and paired monokaryons from A. heimuer, which is critical for fully understanding the genetic composition and information of the characteristic strain of A. heimuer in Northeast China. It establishes the data and theoretical foundation for gene mining, usage, and molecular breeding. It further promotes the genetic breeding and active substance utilization of A. heimuer.

Correlation of artificial neural network and multi-attribute transformation: a test case to predict the effective porosity of Cretaceous sandstones of the Sembar Formation, southeast Pakistan

Accurate prediction of reservoir properties through linear transformation and regression methods are successful in limited cases but are often geologically unrealistic and have no concrete theoretical foundation. Artificial Neural Network’s (ANN’s) have emerged as an effective tool for deriving nonlinear mathematical relationships between seismic attributes and well logs that are theoretically plausible and may prove geologically realistic. In this paper, we devise a methodology to integrate rock physics analysis, seismic inversion, multi-attribute transformation, and Feedforward Neural Network (FNN) modeling for accurate inter-well reservoir property predictions. We test this methodology on well logs and seismic data from the Cretaceous sandstones of the Sembar Formation, Southern Indus Basin, Pakistan. Viable productive gas zones are identified through rock physics and Model Based Inversion (MBI) analyses. Five volume-based seismic attributes are sequentially calculated through forward stepwise regression and cross-validated for inter-well porosity prediction. When a Probabilistic Neural Network (PNN) is trained in a non-linear mode integrated with multi-attribute transformation, correlation (r2) is improved from 72% to 88% between seismic attributes and porosity derived from logs. The PNN-derived porosity distribution is geologically more realistic than linear transformation and regression methods, supporting our model’s validity. We suggest that it is theoretically possible for the ANN to make predictions about any attribute of the reservoir via bridging target logs and seismic data within a short computation time.

Identified Candidate Genes of Semen Trait in Three Pig Breeds Through Weighted GWAS and Multi-Tissue Transcriptome Analysis

High-quality semen is an essential factor for the success of artificial insemination, and revealing the genetic structure of pig semen traits helps improve semen quality. This study aimed to identify candidate genes associated with semen traits in three pig breeds (Duroc, Landrace, and Yorkshire) through weighted GWAS and multi-tissue transcriptome analysis. In this study, to identify candidate genes associated with semen traits in Duroc, Landrace, and Yorkshire, we performed weighted GWAS in four traits (sperm motility, sperm progressive motility, sperm abnormality rate, and total sperm count) using 936 pigs and multi-tissue transcriptome analysis using 34 tissues RNA-seq data of 5457 pigs from FarmGTEx. It was found that 16, 9, and 12 significant SNPs associated with semen traits were identified in Duroc, Landrace, and Yorkshire, with corresponding 7, 5, and 7 candidate genes in these three breeds, respectively, which may be involved in mammal spermatogenesis, testicular function, and male fertility. Moreover, we not only found the same candidate gene DNAI2 as in previous studies but also found two new candidate genes PNLDC1 and RSPH3, which were identified simultaneously in both Landrace and Yorkshire. By integrating the GWAS and multi-tissue transcriptome analysis results, we found that candidate genes associated with semen traits of three pig breeds were highly expressed in the testis tissue. The three genotypes of rs320928244 had significant effects on the expression of the DYNLT1 gene in the testis tissue of Landrace. These results together showed that these candidate genes were mainly related to sperm motility defects. This study helps deepen the understanding of the genetic basis of semen traits and provides a theoretical foundation for improving the semen quality of Duroc, Landrace, and Yorkshire breeds.