Analysis Of Complex Systems Research Articles

Reliability analysis of complex systems using subset simulations with Hamiltonian Neural Networks

We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampling of the Hamiltonian Monte Carlo method with computationally efficient gradient evaluations using Hamiltonian neural networks. This combination is especially advantageous because the neural network architecture conserves the Hamiltonian, which defines the acceptance criteria of the Hamiltonian Monte Carlo sampler. Hence, this strategy achieves high acceptance rates at low computational cost. Our approach estimates small failure probabilities using Subset Simulations. However, in low-probability sample regions, the gradient evaluation is particularly challenging. The remarkable accuracy of the proposed strategy is demonstrated on different reliability problems, and its efficiency is compared to the traditional Hamiltonian Monte Carlo method. We note that this approach can reach its limitations for gradient estimations in low-probability regions of complex and high-dimensional distributions. Thus, we propose techniques to improve gradient prediction in these particular situations and enable accurate estimations of the probability of failure. The highlight of this study is the reliability analysis of a system whose parameter distributions must be inferred with Bayesian inference problems. In such a case, the Hamiltonian Monte Carlo method requires a full model evaluation for each gradient evaluation and, therefore, comes at a very high cost. However, using Hamiltonian neural networks in this framework replaces the expensive model evaluation, resulting in tremendous improvements in computational efficiency.

Intelligent Engines: Revolutionizing Manufacturing and Supply Chains with AI

Artificial intelligence (AI) technologies are becoming a reality, with intelligent engines that can learn and simulate human thinking. These engines have three key features: micro-level intelligence with sensors, logic-based intelligence with software tools, and the ability to adapt and learn using algorithms. AI reduces the need for human intervention and cognitive thinking, finding more efficient solutions to complex problems in manufacturing and supply chain industries. AI simulates human cognition using software tools, allowing for the automation of tasks and analysis of complex systems. However, it raises questions about whether problems can be solved differently and the limitations of explicit algorithms.

THE ROLE OF BIG DATA IN SYSTEMS ENGINEERING: A REVIEW

This research review explores the transformative intersection of Big Data and Systems Engineering (SE), a convergence that leverages the vast capabilities of Big Data analytics to enhance the design, analysis, and management of complex systems. As the digital era ushers in unprecedented volumes of data, traditional systems engineering approaches are challenged to adapt, necessitating a paradigm shift towards integrating Big Data technologies. This review draws on recent scholarly contributions to highlight the implications, challenges, and innovative solutions emerging at this intersection. The integration of Big Data into Systems Engineering introduces significant opportunities for innovation. By processing and analyzing vast datasets, systems engineers can uncover hidden patterns and insights, leading to more efficient, reliable, and adaptable systems. However, this integration is not without its challenges. Information security emerges as a paramount concern, with the risk of insider attacks necessitating the development of new architectural solutions. Furthermore, the benchmarking and evaluation of Big Data systems present unique challenges due to the diversity of data and workloads involved. To address these challenges, this review examines several key contributions to the field. It discusses a formal framework for designing information systems that handle heterogeneous data, emphasizing the role of ontological models in separating system architecture from its implementation. The review also highlights the importance of security in Big Data systems, proposing a novel architecture for detecting insider attacks through data replication. Additionally, it explores the development of BigDataBench, a benchmark suite that facilitates the comprehensive evaluation of Big Data systems and architectures. Moreover, the review delves into the design of Big Data analytics architectures, focusing on goal-oriented modeling and the resolution of obstacles to quality goal achievement. It also introduces datar, a unified framework for Big Data Management Systems, showcasing a solution that manages Big Data in a pluggable, automatic, and intelligent manner. In conclusion, the intersection of Big Data and Systems Engineering heralds a new era of system design and management. By addressing the inherent challenges and leveraging innovative solutions, this convergence holds the potential to significantly enhance the capabilities of Systems Engineering, driving forward the development of complex, data-driven systems.
 Keywords: Big Data, Systems Engineering, Information Security, Data Management Systems, Benchmarking Big Data Systems.

Exploring the Reform of Piano Course Teaching based on AGIL Model

University music performance majors are strongly encouraged to take a piano fundamentals course as one of their necessary foundational topics. For college music achievement majors, it is very crucial to develop and raise students' piano playing proficiency and expertise. For the analysis and creation of complex systems, the AGIL model serves as a theoretical framework. This model may be used to create a complete methodology for piano instruction that emphasizes the growth of the student's flexibility, goal achievement, skill integration, and pause in action. Learning to play the piano may test these connections in the brain that control movement in students and people with poor motor abilities, and it can even improve coordination. Thus, we suggested the AGIL model for the reform of piano course teaching to further encourage the creative growth of piano instruction for music majors in colleges and universities in a diverse environment. The dataset used was made up of Chinese piano students. The performance measures used to assess suggested efficacy include ideal teaching, student performance, note-reading skill, teamwork ability, and student support rate. The evaluation's results indicate that the AGIL approach has provided 98.5% which seems to be useful for improving students' piano skills.

Research of the dynamic regulatory mechanism of Compound Danshen Dripping Pills on myocardial infarction based on metabolic trajectory analysis

BackgroundMyocardial infarction (MI) is a serious cardiovascular disease, which presents different pathophysiological changes with the prolongation of the disease. Compound danshen dripping pills (CDDP) has obvious advantages in MI treatment and widely used in the clinic. However, the current studies were mostly focused on the endpoint of CDDP intervention, lacking the dynamic attention to the disease process. It is of great value to establish a dynamic research strategy focused on the changes in pharmacodynamic substances for guiding clinical medication more precisely. PurposeIt is aimed to explore the dynamic regulating pattern of CDDP on MI based on metabolic trajectory analysis, and then clarify the variation characteristic biomarkers and pharmacodynamic substances in the intervention process. MethodsThe MI model was successfully prepared by coronary artery left anterior descending branch ligation, and then CDDP intervention was given for 28 days. Endogenous metabolites and the components of CDDP in serum were measured by LC/MS technique simultaneously to identify dynamic the metabolic trajectory and screen the characteristic pharmacodynamic substances at different points. Finally, network pharmacology and molecular docking techniques were used to simulate the core pharmacodynamic substances and core target binding, then validated at the genetic and protein level by Q-PCR and western blotting technology. ResultsCDDP performed typical dynamic regulation features on metabolite distribution, biological processes, and pharmacodynamic substances. During 1–7 days, it mainly regulated lipid metabolism and inflammation, the Phosphatidylcholine (PC(18:1(9Z/18:1(9Z)) and Sphingomyelin (SM(d18:1/23:1(9Z)), SM(d18:1/24:1(15Z)), SM(d18:0/16:1(9Z))) were the main characteristic biomarkers. Lipid metabolism was the mainly regulation pathway during 14–21 days, and the characteristic biomarkers were the Lysophosphatidylethanolamine (LysoPE(0:0/20:0), PE-NMe2(22:1(13Z)/15:0)) and Sphingomyelin (SM(d18:1/23:1(9Z))). At 28 days, in addition to inflammatory response and lipid metabolism, fatty acid metabolism also played the most important role. Correspondingly, Lysophosphatidylcholine (LysoPC(20:0/0:0)), Lysophosphatidylserine (LPS(18:0/0:0)) and Fatty acids (Linoelaidic acid) were the characteristic biomarkers. Based on the results of metabolite distribution and biological process, the characteristic pharmacodynamic substances during the intervention were further identified. The results showed that various kinds of Saponins and Tanshinones as the important active ingredients performed a long-range regulating effect on MI. And the other components, such as Tanshinol and Salvianolic acid B affected Phosphatidylcholine and Sphingomyelin through Relaxin Signaling pathway during the early intervention. Protocatechualdehyde and Rosmarinic acid affected Lysophosphatidylethanolamine and Sphingomyelin through EGFR Tyrosine kinase inhibitor resistance during the late intervention. Tanshinone IIB and Isocryptotanshinone via PPAR signaling pathway affected Lysophosphatidylcholine, Lysophosphatidylserine, and Fatty acids. ConclusionThe dynamic regulating pattern was taken as the entry point and constructs the dynamic network based on metabolic trajectory analysis, establishes the dynamic correlation between the drug-derived components and the endogenous metabolites, and elucidates the characteristic biomarkers affecting the changes of the pharmacodynamic indexes, systematically and deeply elucidate the pharmacodynamic substance and mechanism of CDDP on MI. It also enriched the understanding of CDDP and provided a methodological reference for the dynamic analysis of complex systems of TCM.

Artificial Intelligence in Mathematical Modeling of Complex Systems

This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.

Patterns of development of religious culture in modern society: problems of systematization and philosophical understanding

Introduction. Religious culture is becoming one of the most important subjects of philosophical science today. The philosophical methodology makes it possible to systematize the available knowledge not only of the philosophical, but also of the general scientific, private scientific level. An analytical review of scientific publications devoted to the identification of laws, patterns and principles of the development of religious culture at the present stage has shown that there are many of them, but they are scattered and very diverse. Therefore, the problem of philosophical understanding of the phenomenon of religious culture at the present stage of development of society is an extremely urgent task.The purpose of the study is to study domestic and foreign publications devoted to the study of the patterns of development of religious culture at the present stage and to identify the possibility of their systematization based on philosophical methodology.Materials and Methods. The main research methods used were methods of analytical review, analysis, generalization and comparison, as well as the dialectical method.Results. In the course of the study, the authors came to the conclusion that the application of philosophical methodology in the study of laws, patterns and principles of the development of religious culture in modern society allows them to build a multidimensional typology, including several levels: civilizational, social, anthropological. At each level, it is possible to study dialectical relations both within religious culture and between it and other phenomena, which will allow us to determine the laws and patterns of its development at each level, as well as between them, and will allow us to conduct a predictive analysis of possible further trajectories of the development of this phenomenon, its impact on human destiny and civilization.Discussion and Conclusions. This complex system analysis will be the subject of research not only by the authors of the article, but also by scientific schools that exist on the basis of Mininsky University. The results of this research will form the basis for the study of religious culture in such areas of scientific activity of the university as philosophy of culture, religious studies, philosophy of religion, social philosophy, history of philosophy, philosophical anthropology, theology, pedagogical and psychological sciences.

Malaria Surveillance Information System using an Android Mobile Phone: A Qualitative Study in Lahat District, South Sumatra Province, Indonesia

Introduction: Delays in case-based surveillance are a significant obstacle to eliminating malaria. This study aims to optimise the current electronic malaria information system (eMIS), which uses an eMIS android-based mobile. Method: Qualitative research was conducted in the Lahat District, South Sumatra Province. We select informants directly involved in, or playing a role in, malaria control activities, such as program managers at community health centres (Puskemas) and health services. The system is built using the Framework for the Application of Systems Techniques (FAST), a structured method for analysing complex systems. It helps professionals break down problems, uncover hidden relationships, and make better decisions. FAST is widely used in engineering, project management, and business process optimisation to tackle complex challenges and improve problem-solving. Results: The paper revealed suggestions for designing and optimising the current eMIS. One recommended method for overcoming the obstacles is to optimise an eMIS Android-based mobile for malaria reporting. Eight key informants participated in the interviews. The main obstacles by key informants are the free time available for giving deep information and annoyance from outside during interviews due to their other activities. Hence, the researchers made appointments with informants and interviewed in a particular room, not the informant’s office. Conclusion: Our study demonstrates that eMIS android-based mobile is a better refinement of the current eMIS based on PC to support malaria surveillance for malaria elimination programs in an endemic area. Data entry with an eMIS android-based mobile is faster, more accurate, and allows real-time collaboration, outperforming sheet applications.

Spectralomics – Towards a holistic adaptation of label free spectroscopy

Vibrational spectroscopy, largely based on infrared absorption and Raman scattering techniques, is much vaunted as a label free approach, delivering a high content, holistic characterisation of a sample, with demonstrable applications in a broad range of fields, from process analytical technologies and preclinical drug screening, to disease diagnostics, therapeutics, prognostics and personalised medicine. However, in the analysis of such complex systems, a trend has emerged in which spectral analysis is reduced to the identification of individual peaks, based on reference tables of assignments derived from literature, which are then interpreted as biomarkers. More sophisticated analysis attempts to unmix the spectrum of the complex mixture into constituent components, which are then used to characterise the biochemistry of a sample and changes to it, in terms of its constituent components. Data mining the spectra, and in particular change due to kinetic processes, remains a challenge, and it is proposed that the rate of temporal evolution of the combination spectrum can be used in itself as a label by which to guide the spectral analysis. Ultimately, it is argued that the true potential of label free spectroscopy is best harnessed in a truly “spectralomic” approach, by which the spectral signature of an “event”, such as drug intercalation in the DNA of the nucleus of a cell, or a key stage of a cellular pathway such as oxidative stress, is presented. It is envisioned that, in the future, such Spectralomics pathway analysis will be fully integrated with similar omics approaches, potentially ultimately through deep learning algorithms, and underpinned by systems biology kinetic models, to provide a living human cell atlas, describing the function and dysfunction of organism at a cellular level, as the basis for improved healthcare.

Decision Support System Driven by Thermo-Complexity: Scenario Analysis and Data Visualization

The present modelling aims to construct a computational information representation system useful for decision support system (DSS) solutions in the realization of intelligent systems or complex systems analysis solutions. Starting from an n-dimensional space (with n ≥ 7) represented by problem variables (referred to as CSF—Critical Success Factors), a dimensional embedding procedure is used to transition to a two-dimensional space. In the two-dimensional space, thanks to new lattice motion algorithms, the decision support system can determine the optimal solution with a lower computational cost based on the decision-maker’s preferences. Finally, thanks to an algorithm that takes into account the hierarchical order of importance of the seven CSFs as per the expert’s liking or according to his optimization logics, a return is made to the n-dimensional space and the final solution in the original space. As we will see, the starting and ending states in the n-dimensional space (referred to as micro-states) when projected into the two-dimensional space generate states (referred to as macro-states) which are degenerate. In other words, the correspondence between micro-states and macro-states is not one-to-one, as multiple micro-states correspond to one macro-state. Therefore, in relation to the decision-maker’s preferences, it will be the responsibility of the decision support system to provide the decision-maker with the micro-state of interest in the n-dimensional space (dimensional emergence procedure), starting from the obtained optimal macro-state. This result can be achieved starting from a flat chain of sensors capable of measuring/emulating certain specific parameters of interest. As we will see, it emerges that by considering random–exhaustive rolling value paths in order to track and potentially intervene to rebalance a dynamic system representing the state of stress/sensing of a system of interest, we are using the most general and, therefore, complex hypotheses of ergodic theory. In this work, we will focus on the representation of information in n-dimensional and two-dimensional spaces, as well as construct evaluation scenarios. We will also show the results of the decision support system in some cases of specific interest, thanks to a specific lattice motion algorithm of the realized decision-making environment.

Direct lingam and visibility graphs for analyzing brain connectivity in BCI.

The brain-computer interface (BCI) is a direct pathway of communication between the electrical activity of the brain and an external device. The present paper was aimed to investigate directed connectivity between different areas of the brain during motor imagery (MI)-based BCI. For this purpose, two methods were implemented including, Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam. The visibility graph (VG) is a robust algorithm for analyzing complex systems such as the brain. Direct Lingam uses a non-Gaussian model to extract causal links which is appropriate for analyzing large-scale connectivity. First, LPHVG map MI-EEG (electroencephalogram) signals into networks. After extracting the topological features of the networks, a support vector machine classifier was applied to categorize multi-classes MI. The network of all classes was found to be different from one another, and the kappa value of classification was 0.68. The degree sequence of LPHVG was calculated for each channel in order to obtain the direction of brain information flow. Transfer entropy (TE) is used to compute the relations of the channel degree sequence. Therefore, the directed graph between channels was formed. This method is called LPHVG_TE directed graph. The Bayesian network, also known as the Direct LiNGAM model, was implemented for the second method. Finally, images of the LPHVG and Direct Lingam were classified by convolutional neural network (CNN). In this study, Data sets 2a of BCI competition IV was used. The outcomes reveal that the brain network developed by LPHVG (92.7%) might be more effective to distinguish 4 classes of MI than the Direct Lingam (90.6%) and it was shown that graph theory has the potential to get better efficiency of BCI.

Review of multi‐scale mechanical behavior research on composite solid propellants based on data‐driven approach

AbstractComposite solid propellant is a kind of viscoelastic composite with high filling ratio and multi‐scale composition characteristics, and its macroscopic mechanical properties strongly depend on the microstructure of the propellant materia. However, with the increasing complexity of composition, structure and properties of composite solid propellants, the traditional research paradigm based on experimental observation, theoretical modeling and numerical simulation has encountered new scientific challenges and technical bottlenecks in the mechanical behavior analysis, charge design and manufacturing of composite solid propellants. Among them, the problems such as insufficient experimental observation, lack of theoretical model, limited numerical analysis and difficult verification of results restrict the development of composite solid propellants in future‐oriented engineering applications to a certain extent. The data‐driven computational mechanics method can directly establish complex relationships between variables from high‐dimensional and high‐throughput data, which can capture trends that are difficult to be found by traditional mechanics research methods, and has inherent advantages in the analysis, prediction and optimization of complex systems. This paper mainly reviews and evaluates the research of neural network based modeling, model‐free data‐driven calculation and data‐driven multi‐scale calculation, which provides the correct direction for the subsequent research of multi‐scale mechanical behavior of composite solid propellants based on data‐driven.

Optimization of reverse NOE pumping experiments in the analysis of complex systems with densely crowded NMR spectra.

We present an optimization of Reverse NOE-pumping (RNP) in order to observe the 1H signals of ligands bound to proteins. Although various ligand-based NMR screening methods have been proposed, the most frequently used method has been Saturation-Transfer Difference (STD), owing to the relatively easy setup of experiments. Yet the critical point of STD is the selective irradiation of protein without irradiating ligand, and thus the STD technique is unable to observe 1H ligand signals, which resonate across the entire 1H spectral width. In the present study, the RNP experiment has been improved to develop an effective NMR-based screening technique. The optimized RNP spectra reveal less subtraction artifacts and phase distortion than the original RNP spectra, indicating its applicability to any type of ligand molecules.

Tool‐tip vibration prediction based on a novel convolutional enhanced transformer

AbstractSuperior surface finish remains a fundamental criterion in precision machining operations, and tool‐tip vibration is an important factor that significantly influences the quality of the machined surface. Physics‐based models heavily rely on assumptions for model simplification when applied to complex high‐end systems. However, these assumptions may come at the cost of compromising the model's accuracy. In contrast, data‐driven techniques have emerged as an attractive alternative for tasks such as prediction and complex system analysis. To exploit the advantages of data‐driven models, this study introduces a novel convolutional enhanced transformer model for tool‐tip vibration prediction, referred to as CeT‐TV. The effectiveness of this model is demonstrated through its successful application in ultra‐precision fly‐cutting (UPFC) operations. Two distinct variants of the model, namely, guided and nonguided CeT‐TV, were developed and rigorously tested on a data set custom‐tailored for UPFC applications. The results reveal that the guided CeT‐TV model exhibits outstanding performance, characterized by the lowest mean absolute error and root mean square error values. Additionally, the model demonstrates excellent agreement between the predicted values and the actual measurements, thus underlining its efficiency and potential for predicting the tool‐tip vibration in the context of UPFC.

Operational safety hazard identification methodology for automated driving systems fleets

The safety of Automated Driving Systems (ADS) operating as Mobility as a Service (MaaS) depends on multiple factors in addition to the vehicle’s functionality, reliability, and performance. Currently, no comprehensive approach has been formally developed to identify operational safety hazards and define the operational safety responsibilities of the key agents involved in Level 4 (L4) ADS MaaS operations. This work develops and applies a structured hazard identification methodology for this operation. The methodology leverages and complements the strengths of various hazard identification and modeling methods, including Event Sequence Diagram (ESD), Concurrent Task Analysis (CoTA), System-Theoretic Process Analysis (STPA), and Fault Tree Analysis (FTA). The methodology is applied to analyze the operation of a fleet of L4 ADS vehicle fleets without a safety driver, monitored and supervised by remote operators. The results highlight the fleet operator’s role in ensuring the correct vehicle operation and preventing and mitigating incidents. The analysis demonstrates the developed methodology’s strengths and suitability for operational safety analysis of complex systems’ operations, considering the inherent complexity of the interactions between multiple human and machine agents.

Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation.

The global incidence of type 2 diabetes (T2D) persists at epidemic proportions. Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding of pathophysiology and on-going therapeutic development, effectiveness and access are persistent limitations. Among the greatest challenges, the extensive research efforts have not promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging fields of multi-omics combined with machine learning (ML) and augmented intelligence (AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine. This paper explores the current challenges associated with the identification of predictive biomarkers for T2D and discusses potential actionable solutions for biomarker identification and validation. The articles included were collected from PubMed queries. The selected topics of inquiry represented a wide range of themes in diabetes biomarker prediction and prognosis. The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad of contributing factors in terms of early detection. There is an opportunity to leverage AI and ML to significantly enhance the understanding of the underlying mechanisms of the disease and identify prognostic biomarkers. The innovative technologies being developed by GATC are expected to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive and in-depth analysis of complex biological systems. GATC is an emerging leader guiding the establishment of a systems approach towards research and predictive, personalized medicine. The integration of these technologies with clinical data can contribute to a more comprehensive understanding of T2D, paving the way for precision medicine approaches and improved patient outcomes.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry.

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Fragment molecular orbital-based variational quantum eigensolver for quantum chemistry in the age of quantum computing

Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQE) was proposed to address these issues, its scalability remains limited. Many efforts, including new ansätze and Hamiltonian modifications, have been made to overcome these challenges. In this work, we introduced the novel Fragment Molecular Orbital/Variational Quantum Eigensolver (FMO/VQE) algorithm. This method combines the fragment molecular orbital (FMO) approach with VQE and efficiently utilizes qubits for quantum chemistry simulations. Employing the UCCSD ansatz, the FMO/VQE achieved an absolute error of just 0.053 mHa with 8 qubits in a H24\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{H}}}_{24}$$\\end{document} system using the STO-3G basis set, and an error of 1.376 mHa with 16 qubits in a H20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{H}}}_{20}$$\\end{document} system with the 6-31G basis set. These results indicated a significant advancement in scalability over conventional VQE, maintaining accuracy with fewer qubits. Therefore, our FMO/VQE method exemplifies how integrating fragment-based quantum chemistry with quantum algorithms can enhance scalability, facilitating more complex molecular simulations and aligning with quantum computing advancements.

$\mathbb{T}-$Relative Fuzzy Linear Programming for $\mathbb{T}-$Relative Fuzzy Target Coverage Problems

Optimal set covering problems are commonplace in communication, remote sensing, logistics, image processing, and network fields [3]. Thus, studies on determining optimal covering sets (sensors) of points (targets) in a region have emerged recently. One characteristic of these studies is the consideration of cases where a target is considered fully covered when it falls within a coverage area ("Boolean" coverage). Consequently, optimality solutions/methods/algorithms founded on this coverage scheme are usually too restrictive and (or) precise and so are not suitable for many complex and real life situations, which are most times plagued with ambiguity, vagueness, imprecision and approximate membership of points and (or) covering sets. Fuzzy structures have proven to be suitable for the representation and analysis of such complex systems with many successful applications. Although fuzzy sets generalizes a set, a more recent generalization for both and its related concepts is the Relative fuzzy set [1] which gives a dynamic fuzzy representation to sets. $\mathbb{T}-$Relative Fuzzy fixed points results of $\mathbb{T}-$Relative fuzzy maps were studied in [5] and recently, the concept of $\mathbb{T}-$Relative fuzzy linear programming [6] was introduced as a generalization of fuzzy linear programming. The results were applied to generalize the Boolean set based covering problems in literature to a $\mathbb{T}-$Relative fuzzy Boolean coverage one. Although, Shan et al. in [15] and others [16] - [21] have given a probabilistic coverage consideration but this lacks subjectivity in representing vagueness and imprecision inherent in most systems. In this present article the Linear Programming (LP) formulation of “A Computational Physics-based Algorithm for Target Coverage Problems" by Jordan Barry and Christopher Thron is generalized by considering a fuzzy and relative fuzzy target coverage instead of the crisp set Boolean coverage. Also we introduce the Fuzzy Linear Programming (FLP) and the $\mathbb{T}-$Relative Fuzzy Linear Programming (RFLP) for the set coverage problem which allows for ascertaining dynamic optimality with aspiration levels.

Calibrating the Conatus in Morphogenetic Régulation: Towards a Problématique of Perseverance

AbstractThe intersection between Critical realism, complex system thinking and Luhmannian autopoiesis has been subject to various debates. By showing how a complex system necessitates a trans‐immanent philosophical foundation, Knio proposed in a previous article a problématique of calibration which seeks to bring back to the fore the importance of considering a complex causality generated by environments onto boundaries and systems in an iterative, recursive, and emergentist way. The next step is to understand the motivation behind the actions of a trans‐immanent system. This paper contributes to this discussion by operationalizing the motivation behind action in terms of the Spinozian conatus. In so doing, this research shows how trans‐immanent systems such as people and society not only objectify (socially construct) but objectivate (create) objects behind desire. Finally, the forgoing shows how systemic persistence is not a simple matter of inertia or imitation but it is a matter of empowering reflexivity or, perseverance. This is shown through a thorough overview of the different interpretations of the conatus, followed by their application to several case studies within pre‐existing and prominent theories of institutional change within capitalism. As a result, the conatus as based on a trans‐immanent system offers great potential in institutional analysis; exemplified in the Critical Realist model of social change: Morphogenetic Régulation. This research contributes not only to political, economic, social, and cultural analyses of institutional change but analyses of complex and open systems as a whole, and thus understandings of human empowerment.