Theoretical Foundation Research Articles

PROTEÇÃO DIGITAL: A (IN)EFICÁCIA DA LEGISLAÇÃO BRASILEIRA NO COMBATE AO CRIME DE ESTELIONATO VIRTUAL

With technological advances in the globalized world, the practice of cybercrimes has intensified, among which the so-called virtual fraud stands out, the topic of which will be discussed in this article. Given this context, the present problem arises: to analyze what are the challenges and strategies to combat the crime of virtual fraud and what are its effects on the legal world to protect users on the internet? Thus, this article aims to understand the relevance of the discussion on cyber fraud, with regard to its effectiveness in Brazilian legislation in the virtual environment. The general objective will be to analyze the new classification brought by Law No. 14,155, of 2021, which inserted the crime of electronic fraud, within which the crime of cyber fraud fits. The specific objectives focus on the analysis of the differences between the crimes of fraud and virtual fraud (cyber fraud). Regarding the methodology, using the deductive and descriptive method, based on theoretical foundations found in bibliographic research, such as public documents, books, scientific articles and internet data. The research results indicated that current laws are not sufficient to address and curb the problem of cyber fraud.

Characterization of the regulatory network and pathways in duodenum affecting chicken abdominal fat deposition

The excessive accumulation of abdominal fat in chickens has resulted in a reduction in both the feed conversion efficiency and the slaughter yield. To elucidate the regulatory mechanisms and metabolic pathways affecting abdominal fat deposition in the context of broiler breeding, a cohort of 400 Qingyuan partridge chickens with varying abdominal fat deposition was established. Whole transcriptome sequencing analyses were conducted on the duodenum of 20 representative chickens to ascertain the regulatory networks at this vital digestive and absorptive organ. Consequently, 116 differentially expressed genes were identified, exhibiting a trend of increasing or decreasing expression in correlation with the accumulation of abdominal fat. A total of 36 DEmRNAs, 170 DElncRNAs, 92 DEcircRNAs and 88 DEmiRNAs were identified as differentially expressed between chickens with extremely high and low abdominal fat deposition. The functional enrichment analyses demonstrated that the differentially expressed RNA in the duodenum were involved in the regulation of chicken abdominal fat deposition by mediating a series of metabolic pathways, including the Wnt signaling pathway, the PPAR signaling pathway, the Hippo signaling pathway, the FoxO signaling pathway, the MAPK signaling pathway and other signaling pathways that are involved in fatty acid metabolism and degradation. The construction of putative interaction pairs led to the suggestion of two lncRNA-miRNA-mRNA ceRNA networks comprising two mRNAs, two miRNAs, and 29 lncRNAs, as well as two circRNA-lncRNA-miRNA-mRNA ceRNA networks comprising 26 mRNAs, 12 miRNAs, 17 lncRNAs, and nine circRNAs, as core regulatory networks in the duodenum affecting chicken abdominal fat deposition. The aforementioned genes including TMEM150C, REXO1, PIK3C2G, ppp1cb, PARP12, SERPINE2, LRAT, CYP1A1, INSR and APOA4, were proposed as candidate genes, while the miRNAs, including miR-107-y, miR-22-y, miR-25-y, miR-2404-x and miR-16-x, as well as lncRNAs such as ENSGALT00000100291, TCONS_00063508, TCONS_00061201 and TCONS_00079402 were the candidate regulators associated with chicken abdominal fat deposition. The findings of this study provide a theoretical foundation for the molecular mechanisms of mRNAs and non-coding RNAs in duodenal tissues on abdominal fat deposition in chickens.

Study on the Parameters of Collaborative Fire Prevention by Plugging Air Leakage with Isolation Wall and Injecting Nitrogen in the Goaf of Re-Mining Working Face

ABSTRACT This study focuses on preventing coal spontaneous combustion in re-mining working face, using Yunhe Coal Mine as a case study. The investigation of spontaneous combustion characteristics in both new and re-mined coal samples revealed that the re-mined coal exhibits a higher oxygen consumption rate and greater heat release intensity compared to new mining coal. Through field observations and numerical simulations, the distribution range of oxidation zones in goaf within different zones of the re-mining working face was determined. Based on these findings, a collaborative fire prevention measure is proposed, involving plugging air leakage with isolation wall and injecting nitrogen into the goaf. The implementation of these fire prevention measures results in a reduction in the size of the oxidation zone, an increase in the size of the asphyxiation zone, and a decrease in the maximum temperature. To further enhance fire prevention efforts, it is recommended that distinct cooperative fire prevention parameters be established for both new mining zones and re-mining zones. These research findings provide a theoretical foundation for more efficient fire prevention measures in re-mining working face.

Optimizing Irrigation and Nitrogen Application to Enhance Millet Yield, Improve Water and Nitrogen Use Efficiency and Reduce Inorganic Nitrogen Accumulation in Northeast China

Enhancing irrigation and nitrogen fertilizer application has become a vital strategy for ensuring food security in the face of population growth and resource scarcity. A 2-year experiment was conducted to determine to investigate the effects of different irrigation lower limits and nitrogen fertilizer application amounts on millet growth, yield, water use efficiency (WUE), N utilization, and inorganic nitrogen accumulation in the soil in 2021 and 2022. The experiment was designed with four irrigation lower limits, corresponding to 50%, 60%, 70%, and 80% of the field capacity (FC), referred to as I50, I60, I70, and I80. Four nitrogen fertilizer application were also included: 0, 50, 100, and 150 kg·hm−2 (designated as F00, F50, F100, and F150), resulting in a total of 16 treatments. Binary quadratic regression equations were established to optimize the irrigation and nitrogen application. The results demonstrated that the plant height, stem diameter, leaf area index, aboveground biomass, yield, spike diameter, spike length, spike weight, WUE, and nitrogen agronomic efficiency for millet initially increased before subsequently decreasing as the irrigation lower limit and nitrogen fertilizer application increased. Their maximum values were observed in the I70F100. However, the nitrogen partial factor productivity (PFPN) exhibited a gradual decline with increasing nitrogen application, reaching its peak at F50. Additionally, PFPN displayed a pattern of initial increase followed by a decrease with rising irrigation lower limits. The accumulation of NO3−-N and NH4+-N in the 0~60 cm soil layer increased with the increase of nitrogen fertilizer application in both years, while they tended to decrease as the irrigation lower limit increased. An optimal irrigation lower limit of 64% FC to 74% FC and nitrogen fertilizer application of 80 to 100 kg ha−1 was recommended for millet based on the regression equation. The findings of this study offer a theoretical foundation and technical guidance for developing a drip irrigation and fertilizer application for millet cultivation in Northeast China.

Grundlagen einer 'integralen' Grammatik

Within the framework of an integral grammar, syntax, morphology and lexicology represent three types of consideration of the same reality, which is the linguistic construction of meaning. The distinction between these three linguistic disciplines may be useful in some cases, but in other cases it may hinder the explanation of linguistic phenomena. Integral linguistics is capable of unifying the disciplines - integrating them. It can, however, use modular methods where appropriate. It could therefore be seen as a kind of construction grammar with the option of modularising its descriptions and explanations. This is not to say that this approach advocates methodical indifference; on the contrary, it presupposes a particularly rigorous deduction of its concepts and descriptive methods. The foundations for such a grammar have already been laid in Saussure's Cours de linguistique générale, which – with striking foresight – anticipates the ideas of nowadays' construction grammars and provides them with a hitherto unprecedented theoretical foundation. This is the starting point for the present article, which places Saussure's reflections within the framework of a theory of language centred on the speech event. It defines the functional levels of the sentence on the ground of the dimensions of utterance meaning, deduces the notions of syntactic function and syntactic constituency from the synthetic and analytical mechanisms of the syntagmatic dimension of language, and those of form and category from the abstraction principles of the paradigmatic dimension of language. Finally, it will be shown how the basic ideas of constructional grammars, in particular that of the syntax lexicon continuum, can be convincingly and exhaustively explained by the universal principles of grammar postulated by Saussure: the syntagmatic and paradigmatic structuration of language.

Deep Learning for Solving Partial Differential Equations: A Review of Literature

Partial Differential Equations (PDEs) are fundamental in modeling various phenomena in physics, engineering, and finance. Traditional numerical methods for solving PDEs, such as finite element and finite difference methods, often face limitations when applied to high-dimensional and complex systems. In recent years, deep learning has emerged as a promising alternative for approximating solutions to PDEs, offering potential improvements in both efficiency and scalability. This paper provides a comprehensive review of the literature on deep learning-based methods for solving PDEs, focusing on key approaches such as Physics-Informed Neural Networks (PINNs), deep Galerkin methods, and neural operators. These methods leverage the expressiveness of neural networks to capture underlying physics while avoiding the curse of dimensionality associated with classical techniques. We explore the theoretical foundations, advantages, and limitations of these deep learning models, along with their applications in diverse fields like fluid dynamics, quantum mechanics, and financial modeling. Additionally, this review examines recent advancements in hybrid models that combine traditional numerical methods with deep learning approaches to enhance accuracy and stability. Through this review, we highlight key trends and open challenges in the field, paving the way for future research at the intersection of deep learning and computational mathematics.

Pore Engineering in Biomass-Derived Carbon Materials for Enhanced Energy, Catalysis, and Environmental Applications

Biomass-derived carbon materials (BDCs) are highly regarded for their renewability, environmental friendliness, and broad potential for application. A significant advantage of these materials lies in the high degree of customization of their physical and chemical properties, especially in terms of pore structure. Pore engineering is a key strategy to enhance the performance of BDCs in critical areas, such as energy storage, catalysis, and environmental remediation. This review focuses on pore engineering, exploring the definition, classification, and adjustment techniques of pore structures, as well as how these factors affect the application performance of BDCs in energy, catalysis, and environmental remediation. Our aim is to provide a solid theoretical foundation and practical guidance for the pore engineering of BDCs to facilitate the rapid transition of these materials from the laboratory to industrial applications.

Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.

Combined administration of catalpol, puerarin, gastrodin, and borneol modulates the Tlr4/Myd88/NF-κB signaling pathway and alleviates microglia inflammation in Alzheimer’s disease

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder affecting millions of people worldwide, with no effective treatment currently available. In recent decades, various traditional Chinese medicines (TCMs) and their active ingredients have shown the potential to attenuate the pathogenesis of AD in cellular and animal models. However, the effects of TCM formulas, which are typically administered in practice, have been less studied. This study aims to investigate the therapeutic effects of several formulas consisting of 4 components herbal components: catalpol, puerarin, gastrodin, and borneol, on streptozotocin (STZ)-induced AD models in cells and rats. The new object recognition (NOR), elevated plus maze (EMP), and Morris water maze (MWM) tests were used to evaluate the cognitive functions of rats. Golgi staining, Haematoxylin and Eosin (HE) staining, and Nissl staining analyses were employed assess the physiology of hippocampal tissues. Gene expression profiles were analyzed used transcriptomics and reverse transcription quantitative polymerase chain reaction analysis, while protein expression levels were determined using immunoblotting, immunohistochemical, and immunofluorescence. The production of cytokines was evaluated with enzyme-linked immunosorbent assay. The results demonstrated that the combined administration of these 4 components (CPGB) had superior mitigating effects on AD cell model, as evidenced by the reduced pro-inflammatory cytokine production and decreased deposition of Aβ protein. Further in vivo and in vitro experiments confirmed that varying doses of CPGB formula effectively ameliorated STZ-induced cognitive deficits, as shown by NOR, MWM, and EMP tests, as well as pathological changes in hippocampal tissues and a 3-dimensional brain neurovascular unit (3D-NVU) model, including decreased deposition of Aβ protein and formation of plaques. Transcriptome sequencing and analysis identified 35 genes with significantly altered expression levels due to STZ and CPGB treatment in hippocampal tissues, which were enriched in the Tlr4/Myd88/NF-κB signaling pathway. Interference with this pathway significantly influenced the therapeutic effects of CPGB in the 3D-NVU model. Collectively, these findings suggest that the combined administration of catalpol, puerarin, gastrodin, and borneol offers superior therapeutic effects on AD by modulating the Tlr4/Myd88/NF-κB signaling pathway. This study strengthens the theoretical foundation for using TCMs to treat AD, proving new insights and references for alleviating and treating AD.

A SUBSTITUIÇÃO DA PRISÃO PREVENTIVA POR PRISÃO DOMICILIAR DA MULHER GESTANTE: UMA ANÁLISE DOS JULGADOS DO STJ NO PERÍODO ENTRE 2022-2023

The subject of this article will be to discuss art. 318-A, added to the Code of Criminal Procedure as amended by Law 13,769/18, which establishes that preventive detention imposed on pregnant women or women who are mothers or guardians of children or people with disabilities will be replaced by house arrest. Thus, the aim is to analyze the replacement of preventive detention by house arrest. To this end, the problem will be to analyze whether the judging body can replace preventive detention by house arrest whenever the agent preventively arrested is pregnant. The general objective of this article will be to analyze the Rulings of the STJ that judged the requests to replace prison with house arrest of pregnant women, in the period 2022-2023, identifying whether the replacement of preventive detention by house arrest is carried out in all cases provided for in the Code of Criminal Procedure. As for the specific objectives, they focus on verifying whether the mere fact that the agent is pregnant already expresses the granting of house arrest, in the cases provided for in the CPP. Regarding the methodology, basic research will be used, with a bibliographic review, with a descriptive approach, using the deductive and qualitative method, based on theoretical foundations found in digital books, periodicals, theses and jurisprudential examination of STJ and STF judgments.

Translation of a Bipolar Fuzzy Set in Hilbert Algebras

This study explores the application of bipolar fuzzy set theory within Hilbert algebras, introducing and examining the concept of bipolar fuzzy (β, α)-translations of a bipolar fuzzy set φ =(φ+, φ−) in two distinct forms: Type I and Type II. Fundamental properties of these bipolar fuzzy translations are investigated in depth, alongside the introduction of bipolar fuzzy extensions andintensities, broadening the utility and flexibility of bipolar fuzzy sets in capturing nuanced bipolar information. Moreover, this work addresses the intricate relationships between the complement of a bipolar fuzzy subalgebra, bipolar fuzzy ideal, and bipolar fuzzy deductive system with respect to their level cuts. The findings significantly contribute to the broader theoretical foundation and potential applications of bipolar fuzzy logic in Hilbert algebras, offering valuable insights for managing complex bipolar information in uncertain environments.

Theoretical Foundations and Ethical Considerations in Quantum Law

This article explores the emerging field of quantum law, examining its theoretical foundations and the ethical considerations that arise from the intersection of quantum mechanics and legal theory. As quantum technologies advance, legal systems face unprecedented challenges in adapting to a reality where traditional concepts of causality, locality, and determinism are called into question. This research synthesizes insights from quantum physics, legal philosophy, and ethics to construct a framework for understanding and addressing the implications of quantum phenomena in legal contexts. Through a comprehensive review of existing literature and theoretical analysis, we identify key areas of impact, including privacy, intellectual property, and criminal justice. The study concludes by proposing guidelines for the development of quantum-aware legal systems and highlighting the need for interdisciplinary collaboration in shaping the future of quantum law.

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

Theoretical and Methodological Foundations of Uncertainty Modeling in Real Estate Markets

Motivation: The need to improve the accuracy and reliability of market valuation and risk assessment in real estate markets, especially under conditions of uncertainty.Aim: To integrate theoretical foundations and methodological approaches for modeling aleatoric and epistemic uncertainties in real estate markets using credal networks and confidence boxes (c-boxes).Approach: This paper presents a comprehensive theoretical and methodological framework for uncertainty modeling in real estate markets, focusing on the application of credal networks and confidence boxes. It does not include empirical validation or practical case studies, instead providing a detailed conceptual and methodological discussion.Results: The proposed method demonstrates significant improvements in uncertainty quantification and market analysis accuracy in theoretical terms, offering valuable insights for investors, urban planners, and policymakers. However, empirical validation is suggested for future research to confirm practical applicability.

Strategic Imperatives of Muslim Leadership in Post-War Sri Lanka: Citizenship, Rights and Reconciliation

This study examines how a few selected mainstream Sri Lankan Muslim civil organisations have been responding to the Sinhala Buddhist ideological paradigm in post-war Sri Lanka. The ideology perceives that Sinhala Buddhists are true citizens of Sri Lanka while minorities are “others” or “guests”. Hence, the ideology plays a structural role in generating anti-Muslim sentiments in post-war Sri Lanka. Contrary to prevailing wisdom which argues that minorities attempt to deconstruct the majoritarian ideological foundation in their struggle for equality, dominant and mainstream Muslim civil society organisations in Sri Lanka have chosen to reconcile with it while resisting only its practical implications upon the community. Even though this strategy brings self-contradictory elements into play on theoretical grounds, Muslim civil society organisations think that it is a practically reasonable strategy given the developing socio-political context of the state. Based on primary and secondary data and the thematic qualitative analysis, this study builds an argument by analysing discourses of the selected mainstream Muslim organisations that minorities pick strategies for their struggle against majoritarian state and ideology taking their political and other demographical realities into account. Hence, their choices are not static but rather dynamic.

Neurorhetoric: Rhetoric Intersection with Neuroscience

Abstract: Neurorhetoric examines the cognitive and neurological underpinnings of successful persuasive communication. This article seeks to examine the convergence of rhetoric and neuroscience by looking at the rhetorical framework established by Aristotle, which includes ethos, pathos, and logos. The assumption is that the integration of rhetoric with neuroscience has the potential to greatly enhance the practical relevance of rhetoric approaches in fields such as education, politics, marketing, healthcare, etc. The study employs methods of desk research and critical review of foundational and contemporary scientific publications in the fields of rhetoric, neurorhetoric, and psychology. Future studies should prioritize the advancement of the theoretical and empirical foundations of neurorhetoric. Collaboration between experts in rhetoric, linguistics, and neuroscience among others, will facilitate the achievement of this goal. Collective efforts like this will enhance our comprehension of the complex interaction between the brain, lan¬guage, and persuasion. Exploring the neural mechanisms of persuasion is a rela¬tively new field of research, even though the art of persuasion and argumentation, known as rhetoric, has been practiced since ancient times. Neurorhetoric explores the intersection between different rhetorical canons, such as ethos, pathos, and logos, and specific brain functions. This investigation focuses on both real-world and virtual communication contexts. Keywords: rhetoric, persuasion, cognitive processes, neuroscience, neu¬rorhetoric.

Isolation and Identification of Pear Ring Rot Fungus and Resistance Evaluation of Different Pear Varieties

Botryosphaeria dothidea is a significant plant pathogen responsible for causing ulcers, wilt, and fruit decay across a wide range of host plants. One notable fungal disease attributed to B. dothidea is pear tree ring rot, which currently ranks among the most severe diseases affecting pear trees in China. This pathogen primarily targets branches and fruits, occasionally impacting leaves as well, leading to tree weakening, fruit rot, and leaf drop. The annual repercussions of this disease severely affect both the yield and quality of pear fruits, thereby impeding the healthy development of the pear industry. Recent studies have indicated that other species within the B. dothidea complex can also induce pear ring rot; however, specific physiological strains of B. dothidea remain unreported. Consequently, this study collected tissues from pear trees infected with ring rot from orchards located in Liaoning, Hebei, Shandong, and other regions throughout China. Through morphological characterization combined with pathogenicity assessments and DNA sequence comparisons involving partial internal transcribed spacer (ITS), translation elongation factor (TEF), and β-tubulin (TUB) genes, 21 strains belonging to the Botryosphaeria spp. were identified. These 21 strains served as research subjects for inoculating dormant annual branches from 30 germplasm resources of pear trees in vitro. The results demonstrated that all tested strains could induce lesions on the branches which were characterized by dark brown spots. Furthermore, inoculation experiments involving these 21 strains were conducted to evaluate the resistance levels of various pear varieties against ring rot disease. The resistance was assessed by inoculating different isolates onto distinct pear varieties; this approach established the criteria for evaluating resistance while minimizing identification errors stemming from the variable responses exhibited by certain varieties towards individual strains. Ultimately, this study aims to provide a theoretical foundation for effective prevention and treatment strategies against pear ring rot.

Integrating management and sustainability literature: Comment on the paper by Gonçalves et al.

AbstractThis commentary explores the integration of management and sustainability literature, using the work of Gonçalves et al. (2024) as a starting point. I situate their work within the broader context of literature that connects the management and sustainability fields, tracing historical developments and diverse perspectives. Key areas for further exploration include the fields' different levels of analysis, the commensurability of theoretical foundations, the balance between theoretical and applied research and the importance of interdisciplinary, transdisciplinary and systems approaches in advancing sustainability research and practice. The commentary serves as a starting point for further research aimed at supporting the transformative changes needed for a more sustainable society.

Low-Frequency Electrical Heating for In Situ Conversion of Shale Oil: Modeling Thermal Dynamics and Decomposition

In situ conversion presents a viable strategy for exploiting low to moderate maturity shale oil. Traditional methods, however, require dense well patterns and substantial energy, which are major hurdles. This study introduces a novel approach employing low-frequency electrical heating via production wells to enhance heat transfer without necessitating additional heating wells. Utilizing a self-developed simulator, we developed a numerical model to evaluate the efficacy of this method in augmenting reservoir temperature and facilitating substance decomposition. Findings indicate that low-frequency electrical heating significantly elevates reservoir temperatures, accelerates hydrocarbon cracking, and boosts fluid production. A sensitivity analysis on various heating strategies and reservoir characteristics showed that elevated heating power can further pyrolyze the heavy oil in the product to light oil, while higher porosity formations favor increased oil and gas output. The study also explores the effect of thermal conductivity on heating efficiency, suggesting that while better conductivity improves heat distribution, it may increase the proportion of heavy oils in the output. Overall, this investigation offers a theoretical foundation for refining in situ conversion technologies in shale oil extraction, enhancing both energy efficiency and production quality.

Calculation and Comparative Analysis of Common Fault Scores for Secure Computers Based on Embedded Neural Networks

With the rapid development of computer networks and information technology, government departments, financial institutions, and enterprises are increasingly dependent on software, and software security issues have become a focus of attention. In this context, how to effectively evaluate the security of software has become an important issue for research institutions both domestically and internationally. On the basis of exploring software definition, this paper not only analyzes the security and potential threats of software in computer networks, but also introduces Embedded Neural Networks (ENNs) as an evaluation tool, and combines Fuzzy Analytic Hierarchy Process (FAHP) to deeply explore a new method for software security risk assessment. By utilizing the powerful pattern recognition capability of ENNs, software logs, system call sequences, and other data can be classified and analyzed to distinguish between normal and abnormal behavior. This ability is crucial for identifying security incidents such as malicious software and unauthorized access. ENNs are designed with resource constraints in mind, which can reduce energy consumption while ensuring performance. For software systems that require long-term operation, this means higher security and stability. Practice has proven that combining ENNs with FAHP can more scientifically and effectively evaluate software security. This method not only improves the accuracy and efficiency of evaluation, but also provides a more solid theoretical foundation and technical support for software security protection.