General Algorithm Research Articles

Automation of the Oil Extraction Process Performed by Means of A Screw Press

Abstract The continuous development of the oil-manufacturing industries causes the necessity of improving extraction technologies. In this case, the specific interest is focused on the control systems of screw presses. Among a great variety of such machines, the small household presses are in significant demand among consumers. Various seeds and kernels require different technological conditions to be provided in order to maximize the qualitative and quantitative characteristics of the extracted oil. Therefore, the main objective of this research is developing and testing the control system allowing for automation of the oil extraction process. Particularly, the temperature parameters of the pressing chamber, extracted oil, and electric motor are to be monitored and limited. In addition, the consumer should be able to predefine the mass of the oil to be extracted. Considering the small household screw press LiangTai LTP200, the general algorithm (block diagram) of the control system operation is proposed and the corresponding experimental prototype is developed. The latter is based on the Arduino Mega microcontroller and is equipped with three temperature sensors, two coolers (fans), one heater, and one mass sensor. The proposed control system allows for continuous monitoring and limiting of the pressing chamber, oil, and electric motor temperatures, as well as the mass of the extracted oil. The experimental data show that the pressing chamber preheating process lasts for about 3 min (170…190 s) and its maximal temperature does not exceed 44°C. The temperature of the extracted oil does not rise over 61°C. The motor temperature changes within the range of 69...71°C. The oil extraction productivity is as follows: 1.2 kg/h (sunflower seeds), 1.06 kg/h (walnut kernels), 0.9 kg/h (almond kernels), and 0.78 kg/h (peanut kernels). The obtained results can used in further investigations focused on analyzing the influence of these parameters on the quantitative and qualitative characteristics of the extracted oil.

Improved General Relativity Search Algorithm (IGRSA) for Designing Power System Stabilizers

This paper proposes a novel optimization algorithm for designing power system stabilizers (PSSs). The prospect of attaining higher stability motivated the authors to creat a new optimization algorithm for this study. A novel algorithm named the Improved General. Relativity Search Algorithm (IGRSA) was also developed by cloud theory to improve the performance of GRSA. The supremacy of the proposed approach is tested by comparing it with an introduced objective function in a medium multi-machine power system. The nonlinear simulation results and eigenvalues analysis has demonstrated that the proposed approach in this study is highly effective in enhancing the dynamic stability of the power system.

Principle and Application for Rumination Computing Algorithms

To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

A general Bayesian algorithm for the autonomous alignment of beamlines.

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

Does a Fractional-Order Recurrent Neural Network Improve the Identification of Chaotic Dynamics?

This paper presents a quantitative study of the effects of using arbitrary-order operators in Neural Networks. It is based on a Recurrent Wavelet First-Order Neural Network (RWFONN), which can accurately identify several chaotic systems (measured by the mean square error and the coefficient of determination, also known as R-Squared, r2) under a fixed parameter scheme in the neural algorithm. Using fractional operators, we analyze whether the identification capabilities of the RWFONN are improved, and whether it can identify signals from fractional-order chaotic systems. The results presented in this paper show that using a fractional-order Neural Network does not bring significant advantages in the identification process, compared to an integer-order RWFONN. Nevertheless, the neural algorithm (modeled with an integer-order derivative) proved capable of identifying fractional-order dynamical systems, whose behavior ranges from periodic and multi-stable to chaotic oscillations. That is, the performances of the Neural Network model with an integer-order derivative and the fractional-order network are practically identical, making the use of fractional-order RWFONN-type networks meaningless. The results deepen the work previously published by the authors, and contribute to developing structures based on robust and generic neural algorithms to identify more than one chaotic oscillator without retraining the Neural Network.

A Novel Artificial General Intelligence Security Evaluation Scheme Based on an Analytic Hierarchy Process Model with a Generic Algorithm

The rapid development of Artificial General Intelligence (AGI) in recent years has provided many new opportunities and challenges for human social production. However, recent evaluation methods have some problems with regard to consistency, subjectivity and comprehensiveness. In order to solve the above problems, in this paper, we propose an Artificial General Intelligence Security Evaluation scheme (AGISE), which is based on analytic hierarchy process (AHP) technology with a genetic algorithm, to comprehensively evaluate the AGI security based on multiple security risk styles and complex indicators. Firstly, our AGISE combines AHP technology with a genetic algorithm to realize reliable, consistent and objective evaluation for AGI security. Secondly, in our AGISE, we propose implementing more effective AGI security evaluation classification and indicator settings. Finally, we demonstrate the effectiveness of our AGISE through experiments.

Immune Checkpoint Inhibitors in the Emergency Department

Immune-checkpoint inhibitors (ICIs) have transformed the landscape for cancer treatment by enhancing the endogenous immune system’s ability to eliminate cancer cells. Tumor cells evade the body’s immune response by blocking T cell activation; ICIs function by blocking this inhibition, thereby boosting the immune system’s ability to kill tumor cells. Despite producing astonishing improvements in prognosis and remission-free time frames for cancer patients, ICIs carry risks for well documented treatment-related complications and immune-related adverse events (irAEs) that can be life-threatening. IrAEs can occur in almost any organ system, and they usually occur early in treatment, typically within the first three months. Some common complications involve the cutaneous, gastrointestinal tract, pulmonary, and endocrine organ systems. Guidelines outlining the general treatment algorithms for irAEs have been published by the Society for Immunotherapy of Cancer (SITC) and the National Comprehensive Cancer Network (NCCN). When patients present to the Emergency Department with symptoms consistent with irAEs, a rigorous and thorough infectious work-up is required, and treatment usually begins. This includes immunosuppression with corticosteroids, symptomatic treatment, and/or discontinuation of the ICI depending on the grade and severity of presentation. Early recognition and management of irAEs among ED providers, in conjunction with treating oncologists, are imperative to improve patient outcomes in the Emergency Department.

Modeling High Energy Molecules and Screening to Find Novel High Energy Candidates.

High energy materials (HEMs) play pivotal roles in diverse military and civil-commercial sectors, leveraging their substantial energy generation. Integrating machine learning (ML) into HEM research can expedite the discovery of high-energy compounds, complementing or replacing traditional experimental approaches. This manuscript presents an application of our in-house Iterative Stochastic Elimination (ISE) algorithm to identify HEMs. ISE is a generic algorithm that produces reasonable solutions for highly complex combinatorial problems. In molecular discovery, ISE focuses on physicochemical properties to distinguish between different classes of molecules. Due to its long track record in discovering novel, highly active biomolecules, we decided to apply ISE to another type of molecular discovery: High-energy materials. Two distinct ISE models, Model A (92 HEMs) and Model B (169 HEMs), integrated non-HEMs for comprehensive analysis. The results showcase significant achievements for both Models A and B. Model A identified 69% of active molecules in Model B, of which 62% had the highest score. Model B identified 80% of active molecules in Model A, with 61% having the highest score among those 80%. Subsequently, Model C was developed, merging all active molecules (261) from Models A and B. Statistical data indicate that Model C is a high-quality model. It was used to screen and score nearly 2 million molecules from the Enamine database. We find 66 molecules with the highest score of 0.89, plus 8 with that score which are active molecules included in the learning set of Model C. From the 66 molecules, 21 (32%) contain at least one nitro group. In conclusion, this study positions the ISE algorithm as a potential tool for discovering novel HEM candidates, offering a promising pathway for efficient and sustainable advancements in high-energy materials research.

Combined and General Methodologies of Key Space Partition for the Cryptanalysis of Block Ciphers

This paper proposes two new methods of key space partitioning for the cryptanalysis of block ciphers. The first one is called combined methodology of key space partition (CoMeKSPar), which allows us to simultaneously set some of the first and last consecutive bits of the key. In this way, the search is performed using the remaining middle bits. CoMeKSPar is a combination of two methods already proposed in the scientific literature, the Borges, Borges, Monier (BBM) and the Tito, Borges, Borges (TBB). The second method is called the general algorithm of key space reduction (GAKSRed), which makes it possible to perform a genetic algorithm search in the space formed by the unknown bits of the key, regardless of their distribution in the binary block. Furthermore, a method of attacking block ciphers is presented for the case where some key bits are known; the basic idea is to deduce some of the remaining bits of the block. An advantage of these methods is that they allow parallel computing, which allows simultaneous searches in different sub-blocks of key bits, thereby increasing the probability of success. The experiments are performed with the KLEIN (Small) lightweight block cipher using the genetic algorithm.

Using open online courses on programming in organizing independent work of students

The article highlights the need to use online courses on programming, including automatic code checking, in organizing independent work of high school students. An overview of open online courses designed to study the Python programming language, implemented on different platforms, is provided. The capabilities of each online course for solving educational problems, such as general training of students in programming, preparation for state exams, developing skills in solving Olympiad problems, organizing project activities, and career guidance for schoolchildren are discussed. The technical capabilities of the platforms on which the courses are presented are analyzed from the point of view of their didactic potential. Forms of organizing independent work of students using online courses on programming are proposed. The general algorithm for solving programming problems is specified taking into account the specifics of using the automatic system for checking and evaluating the solution implemented in online courses.

Stacked Generalization with Sequential-Model Based Optimization for estimating Used Car Valuation in Indonesia

In Indonesia, the purchase and sale of used vehicles is a common practice. The valuation of a used vehicle is influenced by several factors, making it challenging to determine an appropriate selling price. To address this issue, this study employs a stacked generalization (stacking) algorithm to integrate Machine Learning (ML) techniques that have demonstrated efficacy in prior research on used car valuations. The Sequential Model-Based Optimization (SMBO) algorithm is employed to achieve high accuracy while ensuring an efficient hyperparameter optimization process. The initial price of a vehicle is undoubtedly a significant determinant of its resale value. However, this fact is frequently overlooked in previous studies on developing car price estimation models. This study makes a contribution to the field by addressing this issue. The use of the initial price as an input for the model enables two distinct types of analysis: one for the assessment of used car prices and the other for the measurement of the degree of residual valuation of used cars in relation to their initial costs. The results demonstrated that the optimized stacking model exhibited superior predictive ability compared to the other algorithms in both analyses. Feature analysis substantiated the considerable influence of the initial price on the used car's price. This study also corroborates the assertion that accurately predicting the valuation of a used car cannot be achieved by solely considering the usage of the previous owner, such as the car's age and mileage. It is crucial to take into account the car's original attributes, particularly its initial price.

ІНКРЕМЕНТНИЙ ПІДХІД ДО ПЛАНУВАННЯ РОБОТИ СТУДЕНТІВ НАД СПІЛЬНИМ ПРОГРАМНИМ ПРОЄКТОМ

As of today, well-known product and outsourcing IT companies use Agile models for the software development life cycle (SDLC), the advantages of which include rapid adaptation to changes, continuous feedback, improved product quality, increased team engagement, focus on customer value, transparency and control, as well as the reduction of internal and external risks. All Agile models are iterative, meaning the development process is divided into separate, similar iterations, which have different names in different models. Familiarization with and practical application of modern Agile models by students at the first (bachelor’s) level of higher education in the field of information technology is an important task for future IT specialists. To achieve this task, the most popular Agile model, Scrum, was chosen, which is also incremental, meaning that each iteration delivers additional functionality that is ready for use. The main roles of participants and their responsibilities, artifacts, and ceremonies of the Scrum process are reviewed. The article proposes applying the Scrum model for iterative and incremental software development that will solve a multi-criteria optimization problem using a game-theoretic approach. The formulation and general algorithm for solving the multi-criteria optimization problem using a game-theoretic approach are presented, as well as an example of formulating the Product Backlog, which contains twelve functional requirements to be implemented by the student Scrum team over twelve iterations (sprints). The recommended process for dividing students into Scrum teams and assigning roles to participants is described. An example of incremental implementation based on the Scrum model of the SDLC for solving the multi-criteria optimization problem using a game-theoretic approach is also provided.

Semantics Lifting for Syntactic Sugar

Syntactic sugar plays a crucial role in engineering programming languages. It offers convenient syntax and higher-level of abstractions, as witnessed by its pervasive use in both general-purpose and domain-specific contexts. Unfortunately, the traditional approach of translating programs containing syntactic sugars into the host language can lead to abstraction leakage, breaking the promise of convenience and hindering program comprehension. To address this challenge, we introduce the idea of semantics lifting that aims to statically derive self-contained evaluation rules for syntactic sugars. More specifically, we propose a semantics-lifting framework that consists of (i) a general algorithm for deriving host-independent semantics of syntactic sugars from the semantics of the host language and the desugaring rules, (ii) a formulation of the correctness and abstraction properties for a lifted semantics, and (iii) a systematic investigation of sufficient conditions that ensure a lifted semantics is provably correct and abstract. To evaluate our semantics-lifting framework, we have implemented a system named Osazone and conducted several case studies, demonstrating that our approach is flexible, effective, and practical for implementing domain-specific languages.

Exercising of integrated approach for the specialized meat products development

The principles of the specialized food products development differ from the traditional technologies, which require the integrated approach to their creation, taking into consideration the specified properties, purpose and type of the food product. This article describes the general algorithm for developing the specialized dietary therapeutic and prophylactic nutrition meat products, and demonstrates the implementation of the individual stages of the algorithm on the example of developing the food products for people who suffer from the most socially significant diseases like diabetes and CVD. The algorithm of methodology is a sequence of stages executed during the product development and a description of their content and practical implementation. The modern approaches to creation of healthy food products for people with socially significant diseases, the recommendations of the World Health Organization based on the analysis of scientific literature posted in open sources and publicly available databases were used as the material of the research, as well as the results of our own studies in the field of technologies for dietary therapeutic and prophylactic nutrition meat products. The specialized meat products for therapeutic and prophylactic nutrition, information about their ingredient composition, nutritional value, results of preclinical and clinical trials were used as the objects of the study. The stages of product creation include medical and biological substantiation of the composition, designing of a virtual model of the food product, technology development, evaluation of the safety and efficiency of the resulting product taking into consideration the technological impact, preclinical and clinical evaluation. The study showed the difference in the approaches to the dietary meat products development depending on the purpose — whether its therapeutic nutrition or prophylactic nutrition. The developed methodology can be used as a tool that provides for scientifically justified development of the specialized meat products and substantiation of their efficiency.

Mathematical model and general algorithm for solving the problem of processing messages taking into account their value and aging in aircraft systems

Mathematical model and general algorithm for solving the problem of processing messages taking into account their value and aging in aircraft systems

Exploring iterative and non-iterative Fourier series-based methods of control optimization in application to a discontinuous capsule drive model

AbstractThe paper explains iterative and non-iterative approaches to control optimization with use of the Fourier series-based method. Both variants of the presented algorithm are used to numerically approximate optimal control of a discontinuous pendulum capsule drive. Firstly, the general algorithm and its two realizations (iterative and non-iterative) are presented. It is shown that the iterative variant assures non-decreasing quality of solutions in subsequent repetitions of the procedure and the background of such guarantees is explained. A numerical example follows: control of a self-propelled capsule drive is optimized using both approaches. Results are compared and discussed. It is expected that the presented methods can be useful in optimal control estimation for complex systems, particularly discontinuous ones.

Privately Generated Key Pairs for Post Quantum Cryptography in a Distributed Network

In the proposed protocol, a trusted entity interacts with the terminal device of each user to verify the legitimacy of the public keys without having access to the private keys that are generated and kept totally secret by the user. The protocol introduces challenge–response–pair mechanisms enabling the generation, distribution, and verification of cryptographic public–private key pairs in a distributed network with multi-factor authentication, tokens, and template-less biometry. While protocols using generic digital signature algorithms are proposed, the focus of the experimental work was to implement a solution based on Crystals-Dilithium, a post-quantum cryptographic algorithm under standardization. Crystals-Dilithium generates public keys consisting of two interrelated parts, a matrix generating seed, and a vector computed from the matrix and two randomly picked vectors forming the secret key. We show how such a split of the public keys lends itself to a two-way authentication of both the trusted entity and the users.

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

AbstractEnsemble‐based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process‐based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite‐dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet‐based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling‐Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics‐informed, seq‐to‐seq streamflow forecasting, which further reduces biases in the pre‐trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

General secure encryption algorithm for separable reversible data hiding in encrypted domain

The separable reversible data hiding in encrypted domain (RDH-ED) algorithm leaves out the embedding space for the information before or after encryption and makes the operation of extracting the information and restoring the image not interfere with each other. The encryption method employed not only affects the embedding space of the information and separability, but is more crucial for ensuring security. However, the commonly used XOR, scram-bling or combination methods fall short in security, especially against known plaintext attack (KPA). Therefore, in or-der to improve the security of RDH-ED and be widely applicable, this paper proposes a high-security RDH-ED encryption algorithm that can be used to reserve space before encryption (RSBE) and free space after encryption (FSAE). During encryption, the image undergoes block XOR, global intra-block bit-plane scrambling (GIBS) and inter-block scrambling sequentially. The GIBS key is created through chaotic mapping transformation. Subsequently, two RDH-ED algorithms based on this encryption are proposed. Experimental results indicate that the algorithm outlined in this paper maintains consistent key communication traffic post key conversion. Additionally, its computational complexity remains at a constant level, satisfying separability criteria, and is suitable for both RSBE and FSAE methods. Simultaneously, while satisfying the security of a single encryption technique, we have expanded the key space to 28Np×Np!×8!Np, enabling resilience against various existing attack methods. Notably, particularly in KPA testing scenarios, the average decryption success rate is a mere 0.0067% and 0.0045%, highlighting its exceptional security. Overall, this virtually unbreakable system significantly enhances image security while preserving an appropriate embedding capacity.

Measuring technological development and sovereignty

The article discusses the issue of measuring technological sovereignty, which is essential for developing a scientific and technical strategy to enhance Russia’s technological independence. Index methods do not provide an accurate representation of the level of technological sovereignty. Solving this problem requires assessing the efficiency of economic technologies. The aim of the study is to explain the direct (non-indexed) method of measuring technology sovereignty, taking into account qualitative features of technologies. The study employs the theory of technological change and empirical regression analysis and uses statistical data from the Russian economy as its information base. A general algorithm for measuring technological independence is proposed. It is applied to each technological area separately, considering the qualitative characteristics of the technologies used. An indicator for assessing the level of producibility is calculated based on the ratio of innovative to non-innovative products and services. The impact of investments in new and old technologies on this indicator is assessed, allowing to obtain a structural picture of Russia’s technological development and renewal. The general conclusion is that investments in new technologies have not been a determining factor in increasing the level of producibility for the Russian economy. The measurement of technological sovereignty requires changes to the federal statistical system for tracking technological development. This would not only allow for a more accurate quantification of the superiority of domestically produced technologies over imported ones but would also reflect the qualitative aspects of potential dependence on foreign technologies in a more comprehensive way. It is important to identify the balance between new and obsolete technologies, as well as between domestic and foreign-produced technologie