Specific Model Research Articles

Historiographical Models for the Study of Anne Conway’s Principia

Abstract In this article I analyze the different historiographical models that have contributed to the ‘interpretative heritage’ of Anne Conway. I argue that, due to the current state of scholarship on the history of women philosophers, the main mission of this scholarship is to increase the interpretative heritage of their works in general and of Anne Conway in particular. For this purpose, it is necessary to apply a pluralistic perspective regarding models. A pluralist perspective would argue that there are different historiographical models that apply to the study of the text and that each model, even the failed ones, increases the interpretative heritage. In this sense, I identify six main models used in the study of Conway’s Principia and propose that the sixth, the contextual/eclectic model, is more fundamental for the study of Anne Conway because it assists us in laying the foundations for other more restricted or specialized models.

Diagnostics of financial security management efficiency of agricultural enterprises

Subject. The article discusses ensuring the efficiency of financial security management of agricultural enterprises. Objectives. The study aims at the diagnostics of financial security management efficiency of agricultural enterprises. Methods. The study draws on general scientific and special methods, like analysis, synthesis, modeling, abstraction, systematization and generalization of obtained results. Results. The comprehensive assessment of the financial condition management process of enterprises in the Lugansk People's Republic reflects the level of achievement of management goals. My approach rests on the symbiosis of several areas of assessment: the level of financial security of the enterprise, determined by target parameters of financial condition; management performance in the field of financial security; the efficiency of using the resource potential of the enterprise. Conclusions. The financial security management of agricultural enterprises in the region is low, which is mainly due to a decrease in their financial condition and the land resource potential. The identified trends in the system of ensuring the financial security of enterprises should form the basis of a new concept for agriculture development in the region.

MizBERT: A Mizo BERT Model

This research investigates the utilization of pre-trained BERT transformers within the context of the Mizo language. BERT, an abbreviation for Bidirectional Encoder Representations from Transformers, symbolizes Google’s forefront neural network approach to Natural Language Processing (NLP), renowned for its remarkable performance across various NLP tasks. However, its efficacy in handling low-resource languages such as Mizo remains largely unexplored. In this study, we introduce MizBERT , a specialized Mizo language model. Through extensive pre-training on a corpus collected from diverse online platforms, MizBERT has been tailored to accommodate the nuances of the Mizo language. Evaluation of MizBERT’s capabilities is conducted using two primary metrics: masked language modeling and perplexity, yielding scores of 76.12% and 3.2565, respectively. Additionally, its performance in a text classification task is examined. Results indicate that MizBERT outperforms both the Multilingual BERT model and the Support Vector Machine algorithm, achieving an accuracy of 98.92%. This underscores MizBERT’s proficiency in understanding and processing the intricacies inherent in the Mizo language.

A two-stage approach for ship detection in restricted visibility based on dehazing and SE-YOLO algorithms

ABSTRACT The rapid and automatic detection of ships in restricted visibility is crucial for protecting the safety of maritime navigation. The frequent presence of fog in maritime environments causes restricted visibility, contributing to a higher occurrence of maritime accidents. Aiming to address ship target detection's low accuracy in restricted visibility weather, this paper proposes an improved dehazing model based on a two-stage ship detection algorithm. First, during the dehazing stage, a specialized model for maritime environments addresses the problem of unclear ship features in foggy images. The model is trained by a combination of synthetic images training and real images fine-tuning. Then, during the detection stage, a modified YOLO network for small ship detection called SE-YOLO is introduced to solve the issue of small ship targets' low detection accuracy. On the one hand, the modified Spatial Pyramid Pooling – Fast(SPPF) module is designed to reduce information loss during feature extraction; on the other hand, the attention mechanisms are integrated to enhance the network's sensitivity to the details of small ship targets and improve the model's overall detection performance for ships. Moreover, to simulate the real sea scene and test the effectiveness of our method, a maritime-haze dataset containing different concentrations of fog and various brightness is made for this research. Finally, The experimental results indicate that, compared to the traditional YOLOv5 method, our method performs better on detecting ships in restricted visibility environments, with the mean average precision (mAP) increased from 62.64% to 77.23%.

Оптимизация разнообразия агентов: размышления, гипотезы, прогнозы

The problem of compressing the diversity of assessments and actions of agents by modern versions of arti-ficial intelligence is recognized as potentially very dangerous by practical managers. In previous studies, using the quasi-Fisher representation of our probabilistic model of hybrid economic systems, the authors showed that these concerns are justified at least for agents symmetrical in deviations, such as producers of goods "worse than the market", "better than the market" in terms of cost, since they correspond to the Fisher theory of evolution. How-ever, examples, in particular from agriculture, show that asymmetric situations are not exotic and need special modeling, the results of which are presented in this paper. The first simulation results show that for this obviously asymmetric situation, it is advisable to control the compression of diversity in order to achieve optimal values synchronized with the variability of the environment. Within the framework of the general concept of studying the problem - the compression of AI diversity – this means the expediency of a differentiated attitude by industry.

The Brownian motion, Wiener process, and Pu migration as examples of random walk

The theory of random walks holds significance across various domains. To this end, this paper discusses the application of random walk theory in the modeling and analysis of complex systems, including the Brownian motion, Wiener process, and Pu migration. The first part mainly explains the concepts of Brownian motion and random walks, as well as their relationship. The core characteristic is the independence and normal distribution of increments, which makes it a special model of random walks. The next part is the Wiener process, which is a continuous time stochastic process. It can describe random phenomena and solve complex problems in finance, encompassing areas such as stock price prediction, option pricing, and risk management. The last part is about Pu migration, with the clear introduction of Pu migration in biology. The authors discuss why the random walk theory can be regarded as the better approach to analysis this problem. Thus, this paper highlights the importance of the random walk in solving many scientific issues.

Multi-Paragraph Machine Reading Comprehension with Hybrid Reader over Tables and Text

ABSTRACT In machine reading comprehension, answers to questions may be found in either text or tables. Previous studies have shown that table-specific pre-trained language models perform well when applied to tables; however, applying such models to input data that consists of both tables and text can be challenging. To address this issue, we introduce the hybrid reader model that can manage both tables and text using a modified K-Adapter architecture for effectively encoding the structured information of tables. The training process infuses knowledge for tabular data into a pre-trained model while retaining its original weights from pre-training. Hence, the pre-trained model is able to learn and utilize table information without sacrificing its previous training. Our proposed hybrid reader model achieved comparable or superior performance to that of a specialized model on the Korean MRC dataset, KorQuAD 2.0, using the provided adapters. Furthermore, we conducted experiments on an additional English MRC dataset and confirmed that our proposed model achieves performance comparable to that of the existing model. Our study indicates that employing a single hybrid model instead of two separate models can require fewer computing resources and less time while achieving comparable or superior performance, especially for techniques that apply projection and adapter.

Securing Breakneck Pace of 5G Networks Air Interfaces Through Proactive AI Monitoring

The promise of 5G networks enabling emerging technologies comes with formidable new security challenges. This paper proposes an AI-driven real-time security monitoring and incident response framework tailored to 5G infrastructure. A multi-layered architecture is presented using specialized deep learning models for radio access, edge, core, and network slice threat detection. Models including CNNs, RNNs, and transformers perform traffic analysis, signal classification, and log anomaly detection. A centralized controller aggregates model outputs into an integrated threat intelligence engine that deduces attack context and recommends mitigations. Further, a conversational bot interacts with security analysts in natural language to explain threats, suggest responses, and answer queries. The intelligent assistant is designed using dialog trees and transformer networks trained on security datasets. Evaluation on real-world 5G trial networks demonstrates 95% accuracy in classifying radio signal spoofing attacks and 98% precision in identifying malware infections. Analyst surveys confirm improved productivity and faster incident response with the AI assistant. As 5G matures, robust analytics and AI collaboration will grow increasingly critical for secure network operations. This research aims to provide both a conceptual framework and proven techniques as key enablers.

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Deep learning for quadratic hedging in incomplete jump market

We propose a deep learning approach to study the minimal variance pricing and hedging problem in an incomplete jump diffusion market. It is based on a rigorous stochastic calculus derivation of the optimal hedging portfolio, optimal option price, and the corresponding equivalent martingale measure through the means of the Stackelberg game approach. A deep learning algorithm based on the combination of the feed-forward and LSTM neural networks is tested on three different market models, two of which are incomplete. In contrast, the complete market Black–Scholes model serves as a benchmark for the algorithm’s performance. The results that indicate the algorithm’s good performance are presented and discussed. In particular, we apply our results to the special incomplete market model studied by Merton and give a detailed comparison between our results based on the minimal variance principle and the results obtained by Merton based on a different pricing principle. Using deep learning, we find that the minimal variance principle leads to typically higher option prices than those deduced from the Merton principle. On the other hand, the minimal variance principle leads to lower losses than the Merton principle.

Planar Lorentz invariant velocities with a wave equation application

In this paper we determine the functional form of those planar velocity fields for which the associated system of two ordinary differential equations are automatically invariant under a Lorentz transformation. For planar motion we determine first order partial differential equations for the velocity components u(x,y,t) and w(x,y,t) in the x− and y−directions respectively and their general solutions in terms of two arbitrary functions. These partial differential equations and the associated partial differential relations connecting energy and momentum are fully compatible with the Lorentz invariant energy–momentum relations and appear not to have been given previously in the literature. For a particular special relativistic model, one example is given involving similarity solutions of the wave equation. An interesting special case gives rise to a family of particle paths which are characterized by a single arbitrary function, and for which the magnitude of their velocities is the speed of light. This is indicative of the abundant possibilities existing in the “fast-lane”.

Fine Mapping of QTLs for Alkaline Tolerance in Crucian Carp (Carassius auratus) Using Genome-Wide SNP Markers.

Crucian carp (Carassius auratus) is widely distributed in the world and has become an economically freshwater fish. The population in Lake Dali Nur can tolerate the extreme alkaline environment with alkalinity over 50 mmol/L (pH 9.6), thus providing a special model for exploring alkali-tolerant molecular markers in an extremely alkaline environment. In this study, we constructed a high-density and high-resolution linkage map with 16,224 SNP markers based on genotyping-by-sequencing (GBS) consisting of 152 progenies and conducted QTL studies for alkali-tolerant traits. The total length of the linkage map was 3918.893 cM, with an average distance of 0.241 cM. Two QTLs for the ammonia-N-tolerant trait were detected on LG27 and LG45. A QTL for the urea-N-tolerant trait was detected on LG27. Interestingly, mapping the two QTLs on LG27 revealed that the mapped genes were both located in the intron of CDC42. GO functional annotation and KEGG enrichment analysis results indicated that the biological functions might be involved in the cell cycle, cellular senescence, MAPK, and Ras signaling pathways. These findings suggest that CDC42 may play an important role in the process of dealing with extremely alkaline environments.

Impulsive coupled systems with regular and singular ϕ-Laplacians and generalized jump conditions

This work contains sufficient conditions for the solvability of a third-order coupled system with two differential equations involving different Laplacians, fully discontinuous nonlinearities, two-point boundary conditions, and two sets of impulsive effects. The first existing result is obtained from Schauder’s fixed point theorem, and the second one provides also the localization of a solution via the lower and upper solutions technique.We point out that it is the first time that impulsive coupled systems with strongly nonlinear fully differential equations and generalized impulse effects are considered simultaneously. Moreover, the singular case is applied to a special relativity model in classical electrodynamics.

3D finite element modeling of fiber-reinforced pervious concrete with novel artificial intelligence supported image processing method

Pervious concrete is still being developed for stormwater management and flood risk reduction through experimental research and numerical modeling techniques. Due to its unique void structure, standard finite element solid modeling techniques need to be improved to represent this type of concrete, which requires special modeling techniques. This study presents a novel artificial intelligence-supported image processing method to model pervious concrete with varying fiber types and proportions realistically. The artificial intelligence-supported system initially reads the image sections obtained from the pervious concrete samples and processes these images. Afterward, many realistic three-dimensional (3D) finite element (FE) models were obtained, considering the void structure of the pervious concrete and the fiber types it contains with the developed coding. Laboratory tests were performed on specimens to evaluate the compressive and flexural strengths and to verify model adequacy. In addition, a constant-level permeability test was conducted to determine the permeability of the concrete. The accuracy of the models was confirmed by comparing the experimental results with 3D finite element analyses. The validation of this novel realistic modeling technique with laboratory experiments demonstrated that successful outcomes can be obtained using this method, particularly for other types of concrete with void or fiber-reinforced concretes that are complex to model.

On the sieve M-estimation for a special bilinear time series model with time-functional variance noises

. The bilinear model has been frequently used in fields like control theory and economics to model seismic data. In this article, time-functional variance (TFV) noises are embedded into a specific bilinear model. We propose a generalized autoregressive conditional heteroskedasticity-type maximum likelihood estimator (GMLE) with a sieve method and then provide various inferential techniques based on this GMLE. It is shown that under the finite fourth moment of errors, the GMLE is consistent and asymptotically normally distributed. A simulation study and analysis of real data are additionally carried out to evaluate GMLE’s finite sample performance.

Mayfly Algorithm for Modelling a Horizontal Flexible Plate Structure

Flexible plates are widely used in engineering and the industry, primarily due to the lightweight nature compared to rigid counterparts. These structures offer benefits such as cost savings, lower energy consumptions and improved operational safety. However, a notable drawback is that flexible structures are vulnerable to unwanted vibrations, which can cause structural damages. Hence, the development of specialized models are essential to effectively addressing this challenge. Researchers have devised various approaches to suppress unwanted vibrations, with contemporary studies often employing system identification techniques utilizing swarm intelligence algorithms to construct dynamic models of flexible structures. Therefore, this research employs the potent mayfly algorithm (MA), known for its effectiveness in optimization tasks. The developed models using MA were then compared with traditional approach known as recursive least square (RLS) through a comparative analysis. The outcome reveals that RLS exhibited the lowest mean square error (MSE) at , while MA had an MSE of Yet, MA adeptly depicted the characteristics of the system, outperforming the RLS in these validation by indicating a 95% confidence level in the correlation test and exhibiting robust stability in the pole-zero diagram. Consequently, MA serves as a fitting algorithm to accurately depict the real behaviour of the flexible plate structure.

P‐266: Late‐News Poster: Quality Assessment Towards Reflective Pattern Based on Diffraction Appearance

While polarizer‐free (Pol‐less) OLED displays increase the efficiency of the panels with lowering the thickness, there comes an issue of the screen quality drop due to diffraction. We have investigated phenomena and invented a novel model for diffraction, since the color‐difference‐based evaluation alone has limitations in responding to customer needs in various environments and reflecting situations in which actual users embrace the screen quality. The main spatial features of the diffraction appearance were classified into the size and complexity of the pattern, and each factor was quantified through a specialized model. Diffraction features can be automatically calculated as numerical values from the invented model reflecting perceptual levels, which acts as a ‘ruler’ to achieve a good 'grade' of image quality in the product development stage. In this paper, we demonstrate what plays vital roles in diffraction appearance on Pol‐less displays and how to evaluate the diffraction in representative values.

Statistical approaches enabling technology-specific assay interference prediction from large screening data sets

High throughput screening (HTS) technologies allow the biological testing of hundreds of thousands of compounds per day. Typically, a substantial proportion of the initial hits obtained by HTS are artifacts caused by assay interference. Therefore, global and technology-specific in silico models for identifying and predicting compounds interfering with biological assays have been developed. The global models benefit from training on large screening data sets, while the specialized models benefit from training on assay technology-specific experimental data. In this work, we develop and explore strategies for generating better predictors of technology-specific assay interference by utilizing the large bioactivity data matrices global models are trained on and employing partially new compound labeling approaches to maintain the assay technology awareness of specialized models. We demonstrate the utility of the statistically derived interference labels in machine learning using fluorescence-based assay interference as a representative example. Our random forest and multi-layer perceptron classifiers showed improved performance compared to existing models, achieving Matthews correlation coefficients (MCCs) of up to 0.47 on holdout data and up to 0.45 on an external test set. These results demonstrate that accurate assay-specific interference labels can be derived from large bioactivity data matrices, enabling the development of new machine-learning models without the need for further experimental data.

Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)

The advent of foundation models signals a new era in artificial intelligence. The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. The increasing requirement for annotated eye-image datasets presents a significant opportunity for SAM to redefine the landscape of data annotation in gaze estimation. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks. Our results are consistent with studies in other domains, demonstrating that SAM's segmentation effectiveness can be on-par with specialized models depending on the feature, with prompts improving its performance, evidenced by an IoU of 93.34% for pupil segmentation in one dataset. Foundation models like SAM could revolutionize gaze estimation by enabling quick and easy image segmentation, reducing reliance on specialized models and extensive manual annotation.

Interoperability Structure of Smart Water Conservancy Based on Internet of Things

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO’s interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO.