Degree Of Complexity Research Articles

Decision-focused neural adaptive search and diving for optimizing mining complexes

Optimizing industrial mining complexes, from extraction to end-product delivery, presents a significant challenge due to non-linear aspects and uncertainties inherent in mining operations. The two-stage stochastic integer program for optimizing mining complexes under joint supply and demand uncertainties leads to a formulation with tens of millions of variables and non-linear constraints, thereby challenging the computational limits of state-of-the-art solvers. To address this complexity, a novel solution methodology is proposed, integrating context-aware machine learning and optimization for decision-making under uncertainty. This methodology comprises three components: (i) a hyper-heuristic that optimizes the dynamics of mining complexes, modeled as a graph structure, (ii) a neural diving policy that efficiently performs dives into the primal heuristic selection tree, and (iii) a neural adaptive search policy that learns a block sampling function to guide low-level heuristics and restrict the search space. The proposed neural adaptive search policy introduces the first soft (heuristic) branching strategy in mining literature, adapting the learning-to-branch framework to an industrial context. Deployed in an online fashion, the proposed hybrid methodology is shown to optimize some of the most complex case studies, accounting for varying degrees of uncertainty modeling complexity. Theoretical analyses and computational experiments validate the components’ efficacy, adaptability, and robustness, showing substantial reductions in primal suboptimality and decreased execution times, with improved and more robust solutions that yield higher net present values of up to 40%. While primarily grounded in mining, the methodology shows potential for enabling smart, robust decision-making under uncertainty.

How clients choose procurement strategies and organizational control systems in the Swedish housing sector

Purpose This paper aims to examine how different contextual contingency factors and organizational goals influence construction clients’ decision-making when procuring contractors in the housing sector. More specifically, it investigates how clients’ choice of procurement strategies and organizational control systems is contingent upon various contextual factors and organizational goals. Design/methodology/approach It is based on an explorative interview study of clients and contractors in the Swedish housing sector underpinned by a review of organizational control literature. Findings The client's knowledge and resources, as well as project complexity and uncertainty, are the most important contextual contingency factors, while property management and sustainable development are the most important organizational goals that housing clients consider when designing procurement strategies. Research limitations/implications The paper contributes to the understanding of how construction clients choose procurement strategies, by providing new insights into effects of the mentioned contextual contingency factors and organizational goals on clients’ choice of control systems through their procurement strategies. Practical implications Property owners who continuously procure housing projects with sustainability requirements and high degrees of complexity and uncertainty should develop knowledge and resources related to their client role, to enable the design and implementation of appropriate procurement strategies. Originality/value Novel aspects of the paper are the demonstration of the value of a holistic approach, considering both contextual contingency factors and organizational goals, when selecting control systems and explicit discussion of how the client's knowledge and resources influence possibilities to implement different control systems.

Identification of microalgal particles using pixel feature analysis of Mueller matrix images

Fine sorting of aquatic particles is of great significance for water environment monitoring. Natural water contains aquatic particles that exhibit a high degree of diversity and complexity, and the identification of aquatic particles remains a persistent challenge in the field. In this article, we propose a new technique for identifying the target species of microalgal particles by using the pixel feature analysis of Mueller matrix images. This technique is independent of any prior knowledge or data about the existing particles in the environment, which is advantageous when applied to real-world situations. The pixel-level polarimetric features are fully leveraged to construct polarization feature templates, which can be used to characterize and filter specific microalgal particles in complex environments. This method could enable the accurate detection of harmful algal blooms species in natural water, which can facilitate early warning of algal blooms. The preliminary results show that the recall rate reached 97.2%, and the average accuracy is 98.9%, which demonstrate the effectiveness of this approach for identifying the target species of aquatic particles in natural water.

Effective removal of chromium, copper, and nickel heavy metal ions from industrial electroplating wastewater by in situ oxidative adsorption using sodium hypochlorite as oxidant and sodium trititanate nanorod as adsorbent

Sodium hypochlorite and synthesized sodium trititanate nanorods (Na2Ti3O7, 186 × 1270 nm) were used as the oxidant and adsorbents for in situ oxidative adsorption treatment of actual electroplating wastewaters containing Cr(VI) (2.6‒5.2 mg∙L‒1), Cu2+ (2.7‒5.4 mg∙L‒1), and Ni2+ (0.2705‒0.541 mg∙L‒1) ions at pH of 8.8‒9.1 and 20‒60 °C. The as-synthesized sodium trititanate nanorods were characterized by XRD, HRTEM, N2 adsorption/desorption, SEM, EDX, and Zeta potential techniques. The concentrations of heavy metal ions in wastewaters were analyzed by ICP technique. After in situ oxidative adsorption treatment under the concentrations of 25 g∙L‒1 for sodium hypochlorite and 125 mg∙L‒1 for sodium trititanate nanorods at 60 °C for 5 h, the heavy metal ion concentrations could be reduced from initial value of 2.6 to final value of 1.92 mg∙L‒1 for Cr(VI), 3.6 to 0.17 mg∙L‒1 for Cu2+, and from 0.2705 to 0.097 mg∙L‒1 for Ni2+, respectively. Cr(VI), Cu2+, and Ni2+ ions could be effectively removed by the in situ oxidative adsorption method. The in situ oxidative adsorption processes of Cr(VI), Cu2+, and Ni2+ ions are satisfactorily simulated by the pseudo-second order adsorption kinetics and Langmuir adsorption isotherm, respectively. Adsorption thermodynamics analyses reveal that the oxidative adsorption processes of Cr(VI), Cu2+, and Ni2+ ions are spontaneous and endothermic. The oxidation degree of metal-contained complexes influences the values of thermodynamics functions.

Achieving Occam's razor: Deep learning for optimal model reduction.

All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to apply Occam's razor to model parameters. Our method, FixFit, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model's degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.

Design and Analysis of a Novel Fractional-Order System with Hidden Dynamics, Hyperchaotic Behavior and Multi-Scroll Attractors

The design of chaotic systems with complex dynamic behaviors has always been a key aspect of chaos theory in engineering applications. This study introduces a novel fractional-order system characterized by hidden dynamics, hyperchaotic behavior, and multi-scroll attractors. By employing fractional calculus, the system’s order is extended beyond integer values, providing a richer dynamic behavior. The system’s hidden dynamics are revealed through detailed numerical simulations and theoretical analysis, demonstrating complex attractors and bifurcations. The hyperchaotic nature of the system is verified through Lyapunov exponents and phase portraits, showing multiple positive exponents that indicate a higher degree of unpredictability and complexity. Additionally, the system’s multi-scroll attractors are analyzed, showcasing their potential for secure communication and encryption applications. The fractional-order approach enhances the system’s flexibility and adaptability, making it suitable for a wide range of practical uses, including secure data transmission, image encryption, and complex signal processing. Finally, based on the proposed fractional-order system, we designed a simple and efficient medical image encryption scheme and analyzed its security performance. Experimental results validate the theoretical findings, confirming the system’s robustness and effectiveness in generating complex chaotic behaviors.

예비 초등교사들의 비구조화된 수학 문제 만들기 활동 분석

Objectives The purposes of this study were to analyze degree of structure and complexity of ill-structured mathematical problems made by prospective elementary teachers. Methods In order to do this, 109 prospective elementary teachers participated in ill-structured mathematical problem posing activities, as a result 24 group work papers were collected. The data was analyzed using framework which determines ill-structured problems and problem complexity. Results The results of this study showed that the degree of structure of the problems posed by prospective teachers varied. The prospective teachers posed problems with relatively high ill-structuredness in terms of authenticity, but with low ill-structuredness in terms of complexity and openness. The prospective teachers posed problems that were low of linguistic complexity but high of mathematical complexity. when prospective teachers asked students to solve problems they posed, most of them expected that difficulties would arise during the phase of carrying out the plan. Conclusions The results indicate that prospective elementary teachers need to develop their competence of posing ill-structured mathematical problems, in particular, there is a need to explore the meaning of openness of ill-structured problems. It also suggests that there is also a need to provide prospective teachers opportunities to anticipate and discuss students’ difficulties in ill-structured problem solving.

A novel socio-pragmatic framework for sentiment analysis in Dravidian–English code-switched texts

The advent of global digital communication has accentuated the complexity of multilingual code-switching, presenting formidable challenges for sentiment analysis by necessitating nuanced interpretation of linguistic and socio-pragmatic cues. This research introduces MultiSwitchNet, a pioneering sentiment analysis model tailored for the intricate dynamics of Dravidian languages mixed with English code-switched texts. MultiSwitchNet leverages the linguistic prowess of XLM-RoBERTa, combined with socio-pragmatic insights through a novel Socio-Pragmatic Embedding Layer (SPEL) utilizing DistilBERT. Evaluated on the Dravidian Code-Mixed Dataset, MultiSwitchNet achieved superior performance metrics, including an accuracy of 72.3%, precision of 72.9%, recall of 72.1%, and an F1-score of 72.5%. Its effectiveness extends across varying degrees of code-switching complexity and demonstrates remarkable consistency in sentiment prediction across semantically similar yet syntactically diverse sentences. Statistical analyses, including ANOVA and post hoc tests, underscore the model’s significant outperformance compared to both baseline and state-of-the-art models, highlighting the indispensability of integrating socio-pragmatic awareness for accurate sentiment analysis. MultiSwitchNet’s innovative approach sets a new benchmark for sentiment analysis in multilingual contexts and opens avenues for future research into models capable of discerning the rich socio-pragmatic fabric of global digital communication.

Pano2Geo: An efficient and robust building height estimation model using street-view panoramas

Building height serves as a crucial parameter in characterizing urban vertical structure, which has a profound impact on urban sustainable development. The emergence of street-view data offers the opportunity to observe urban 3D scenarios from the human perspective, benefiting the estimation of building height. In this paper, we propose an efficient and robust building height estimation model, which we call the Pano2Geo model, by precisely projecting street-view panorama (SVP) coordinates to geospatial coordinates. Firstly, an SVP refinement stratagem is designed, incorporating NENO rules for observation quality assessment from four aspects: number of buildings, extent of the buildings, number of nodes, and orthogonal observations, followed by the application of the art gallery theorem to further refine the SVPs. Secondly, the Pano2Geo model is constructed, which provides a pixel-level projection transformation from SVP coordinates to 3D geospatial coordinates for locating the height features of buildings in the SVP. Finally, the valid building height feature points in the SVP are extracted based on a slope mutation test, and the 3D geospatial coordinates of the building height feature points are projected using the Pano2Geo model, so as to obtain the building height. The proposed model was evaluated in the city of Wuhan in China, and the results indicate that the Pano2Geo model can accurately estimate building height, with an average error of 1.85 m. Furthermore, compared with three state-of-the-art methods, the Pano2Geo model shows superior performance, with only 10.2 % of buildings have absolute errors exceeding 2 m, compared to the Map-image-based (27.2 %), Corner-based (16.8 %), and Single-view-based (13.9 %) height estimation methods. The SVP refinement method achieves optimal observation quality with less than 50 % of existing SVPs, leading to highly efficient building height estimation, particularly in areas of a high building density. Moreover, the Pano2Geo model exhibits robustness in building height estimation, maintaining errors within 2 m even as building shape complexity and occlusion degree increase within the SVP. Our source dataset and code are available at https://github.com/Giser317/Pano2Geo.git.

Multitone Upconverted Phase-Modulated Signal RoF Link for 6G & Next-Generation Networks

The Next-Generation Networks offer a green environment with demanded data rates with ultra-low latency. Future efforts will be oriented in more degrees of freedom and system complexity with incorporating global optimization in terms of non-linearities, cost-effective, and reduced environmental impacts. The multitone upconverted radio over fibre (RoF) link with an optical modulator as phase modulator as desired alternatives with respect to optimization features and quantum photonic integration. In this paper, a framework has been developed to characterise the upconverted phase modulated signal model based on next-generation networks, in which an analytical model is investigated for single-tone upconverted, two-tone upconverted, and three-tone upconverted. Numerical simulations have been carried out to verify the novel outcomes using OptSIM software. The developed analytical models have a major contribution to the implementation of next-generation networks such as 5G, 6G, WiMAX, Quantum Communication, etc. The fundamentals of harmonics and the intermodulation product terms of third and fifth order via optical link are also evaluated for multitone Phase modulated RoF link with upconversion method. As per the finding outcomes, the proposed link is adaptable with frequency upconversion and long fibre distances.

Stereo-RIVO: Stereo-Robust Indirect Visual Odometry

Mobile robots and autonomous systems rely on advanced guidance modules which often incorporate cameras to enable key functionalities. These modules are equipped with visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) algorithms that work by analyzing changes between successive frames captured by cameras. VO/VSLAM-based systems are critical backbones for autonomous vehicles, virtual reality, structure from motion, and other robotic operations. VO/VSLAM systems encounter difficulties when implementing real-time applications in outdoor environments with restricted hardware and software platforms. While many VO systems target achieving high accuracy and speed, they often exhibit high degree of complexity and limited robustness. To overcome these challenges, this paper aims to propose a new VO system called Stereo-RIVO that balances accuracy, speed, and computational cost. Furthermore, this algorithm is based on a new data association module which consists of two primary components: a scene-matching process that achieves exceptional precision without feature extraction and a key-frame detection technique based on a model of scene movement. The performance of this proposed VO system has been tested extensively for all sequences of KITTI and UTIAS datasets for analyzing efficiency for outdoor dynamic and indoor static environments, respectively. The results of these tests indicate that the proposed Stereo-RIVO outperforms other state-of-the-art methods in terms of robustness, accuracy, and speed. Our implementation code of stereo-RIVO is available at: https://github.com/salehierfan/Stereo-RIVO.

An unsupervised learning-based guidewire shape registration for vascular intervention surgery robot

In a vascular interventional surgery robot(VISR), a high transparency master-slave system can aid physicians in the more precise manipulation of guidewires for navigation and operation within blood vessels. However, deformations arising from the movement of the guidewire can affect the accuracy of the registration, thus reducing the transparency of the master-slave system. In this study, the degree of the guidewire’s deformation is analyzed based on the Kirchhoff model. An unsupervised learning-based guidewire shape registration method(UL-GSR) is proposed to estimate geometric transformations by learning displacement field functions. It can effectively achieve precise registration of flexible bodies. This method not only demonstrates high registration accuracy but also performs robustly under different complexity degrees of guidewire shapes. The experiments have demonstrated that the UL-GSR method significantly improves the accuracy of shape point set registration between the master and slave sides, thus enhancing the transparency and operational reliability of the VISR system.

Vibrational and Dissipative Characteristic Measurement of Solid-state Wave Gyroscope Resonators: Algorithms Based on the Identification of Free Oscillation Equations

The article describes the methodological basis for constructing computational circuits of different complexity degrees and different purposes for solving a wide range of industrial and laboratory problems to measure angular irregularity of vibrational-dissipative resonatorcharacteristics of integrating solid-state wave gyroscopes. The main method to solve such problems is the identification of the coefficients of the differential equations of quartz hemispherical resonator state in the mode of its free oscillations. The resonator oscillation equations both in the fixed measuring axes and in the moving axes of standing waves were chosen as the equations of state. After reduction to slow variables, only slowly changing amplitudeequations are left for identification problems. In all the described circuits, it is assumed that the information signals for the solved identification tasks are formed in a measuring device similar to the standard gyroscope device. These algorithms make it possible to measure the vibrational and dissipative characteristics of resonators with different degrees of completeness and directionality for different standard initial requirements to themeasurement accuracy and experimental modes, which makes it possible to reduce the measurement time and labor intensity of the established set of production control operations. Among the tasks to be solved for the free run-down mode of the resonator wave pattern are the following: angular irregularity measurement of the gyroscope resonator dissipative properties under conditions of low and significant residual frequency difference; angular non-uniformity measurement of gyroscope resonator vibrational properties (its different frequency); simultaneous measurement of resonator oscillatory and dissipative characteristics for fixed and special rotating gyroscope modes. The detailed processing of the computational schemes for the implementation of these algorithms is focused on their practical application for various tasks of production operational control. At the same time, the choice of the most suitable computational algorithm from the considered options depends on the conditions, requirements and specifics of measurements.

Assessment of Blood Flow Velocity in Retinal Vasculitis Using the Retinal Function Imager-A Pilot Study.

Background: This pilot study aimed to evaluate the Retinal Function Imager (RFI) for visualizing retinal vasculature and assessment of blood flow characteristics in patients with retinal vasculitis. The RFI is a non-invasive imaging device measuring the blood flow velocity (BFV) in secondary and tertiary retinal vessels using hemoglobin as an intrinsic motion-contrast agent. Methods: To test the feasibility of the RFI for patients with retinal vasculitis, capillary perfusion maps (nCPMs) were generated from 15 eyes of eight patients (five females; mean age: 49 ± 12 years) with a mean uveitis duration of 74 ± 85 months. Five of these patients had birdshot chorioretinopathy, and three had primarily non-occlusive venous retinal vasculitis of unknown origin. To reflect that the BFV may be more reduced in patients with prolonged disease, patients were classified into a short-term (uveitis duration: 8-15 months) and a long-term uveitis group (uveitis duration: 60-264 months). Data were compared with healthy controls (16 eyes of 11 patients; mean age 45 ± 12 years; 8 females). Results: The mean BFV in the controls was 3.79 ± 0.50 mm/s in the retinal arteries and 2.35 ± 0.44 mm/s in the retinal veins, which was significantly higher compared to the retinal vasculitis group. Patients revealed an arterial BFV of 2.75 ± 0.74 mm/s (p < 0.001) and a venous BFV of 1.75 ± 0.51 mm/s (p = 0.016). In the short-term group, a trend towards a decreased venular and arteriolar BFV was seen, while a significant reduction was observed in the long-term group. The patients' microvasculature anatomy revealed by the nCPMs appeared unevenly distributed and a lower number of blood vessels were seen, along with a lower degree of complexity of their branching patterns, when compared with controls. Conclusions: This study demonstrated a reduction in venular and arteriolar BFVs in patients with retinal vasculitis. BFV alterations were already observed in early disease stages and became more pronounced in progressed disease. Additionally, we showed that retinal microvasculature changes may be observed by nCPMs. Retinal imaging with the RFI may serve as a diagnostic and quantifying tool in retinal vasculitis.

Controlled Reversible Visceral Arterial Ischemia, Venous Congestion and Combined Malperfusion via Midline Laparotomy in Rats.

Besides sepsis and malignancy, malperfusion is the third leading cause of tissue degradation and a major pathomechanism for various medical and surgical conditions. Despite significant developments such as bypass surgery, endovascular procedures, extracorporeal membrane oxygenation, and artificial blood substitutes, tissue malperfusion, especially of visceral organs, remains a pressing issue in patient care. The demand for further research on biomedical processes and possible interventions is high. Valid biological models are of utmost importance in enabling this kind of research. Due to the multifactorial aspects of tissue perfusion research, which include not only cell biology but also vascular microanatomy and rheology, an appropriate model requires a degree of biological complexity that only an animal model can provide, rendering rodents the obvious model of choice. Tissue malperfusion can be differentiated into three distinct conditions: (1) isolated arterial ischemia, (2) isolated venous congestion, and (3) combined malperfusion. This article presents a detailed step-by-step protocol for the controlled and reversible induction of these three types of visceral malperfusion via midline laparotomy and clamping of the abdominal aorta and caval vein in rats, underscoring the significance of precise surgical methodology to guarantee uniform and dependable results. Prime examples of possible applications of this model include the development and validation of innovative intraoperative imaging modalities, such as Hyperspectral Imaging (HSI), to objectively visualize and differentiate malperfusion of gastrointestinal, gynecological, and urological organs.

Integrated geological and geophysical workflow for structural modelling: a case study from the contractional foothills zone of the Colombian Eastern Cordillera

In fold-and-thrust belts where there is a high degree of structural complexity, artificial geometrical distortions are often present on seismic reflection profiles. These need to be minimized during modelling. We document a workflow in which depth mapping, velocity model building, well calibration and cross-section balancing are integrated into the seismic interpretation process to generate trustworthy structural models in complex zones. The proposed methodology is exemplified by a case study from the foothills zone of the Colombian Eastern Cordillera. In addition, sequential kinematic restoration of the modelled structure allowed an evaluation to be made of hydrocarbon migration routes during the period between the Oligocene and the middle Miocene. Following the previously mentioned workflow, we document a failed exploratory case study where all elements of the petroleum system are present except the trap. In this context, the documented case is a typical velocity pull-up. From this and published case studies, we conclude that in Andean settings and probably most on land contractional-foothill settings, the use of seismic images only does not provide enough evidence for the presence of traps and additional surface geological signatures must be documented. The proposed workflow therefore appears to be a useful tool for evaluating the exploration risk in structurally complex fold-and-thrust belt settings.

Mathematical modeling and seasonal solar radiation variability in Nigeria’s geo-political zones: A recurrence and multifractal analysis

This article delves into the seasonal variation of solar radiation patterns across Nigeria’s four geo-political zones, exploring their complex, scale-dependent behaviors. By employing chaotic quantifiers, the study characterizes irregular and self-similar patterns, enhancing our understanding of solar radiation variability and heterogeneity. The research uniquely fits meteorological parameters onto a global solar radiation model and focuses on the underexplored nonlinear aspects within tropical regions, specifically in the Nigerian context. Utilizing daily data from ERA INTERIM satellite archives for representative stations, the study employs nonlinear time series analysis methods like recurrence plots, recurrence qualitative analysis, and multifractal spectral analysis to comprehensively explore the unpredictable behaviors observed. The quantifier spectrums play a key role in revealing intricate scaling behaviors and correlation structures among environmental variables, shedding light on patterns often concealed by conventional statistical methods. For instance, we found that solar radiation variability in the northern region increases more significantly during the dry season compared to the wet season, unlike in the southern region. Additionally, the multifractal spectral analysis revealed a higher degree of complexity in solar radiation patterns during transition periods between seasons. The findings reveal a low recurrence quantitative analysis, long right tail, and truncations at both ends of the spectrum. This suggests instability in solar radiation across different seasons and locations. Nonetheless, the results also demonstrate that solar radiation is consistently available throughout the year, which is typical of tropical regions.

Beyond Bronchopulmonary Dysplasia: A Comprehensive Review of Chronic Lung Diseases in Neonates.

In neonates, pulmonary diseases such as bronchopulmonary dysplasia and other chronic lung diseases (CLDs) pose significant challenges due to their complexity and high degree of morbidity and mortality. This review discusses the etiology, pathophysiology, clinical presentation, and diagnostic criteria for these conditions, as well as current management strategies. The review also highlights recent advancements in understanding the pathophysiology of these diseases and evolving strategies for their management, including gene therapy and stem cell treatments. We emphasize how supportive care is useful in managing these diseases and underscore the importance of a multidisciplinary approach. Notably, we discuss the emerging role of personalized medicine, enabled by advances in genomics and precision therapeutics, in tailoring therapy according to an individual's genetic, biochemical, and lifestyle factors. We conclude with a discussion on future directions in research and treatment, emphasizing the importance of furthering our understanding of these conditions, improving diagnostic criteria, and exploring targeted treatment modalities. The review underscores the need for multicentric and longitudinal studies to improve preventative strategies and better understand long-term outcomes. Ultimately, a comprehensive, innovative, and patient-centered approach can enhance the quality of care and outcomes for neonates with CLDs.

Shoreline morphological change prognostic model based on spatiotemporal framework imagery data on the northern coast of Java, Indonesia

This study focuses on identifying the degree of shoreline changes on the north coast of Java Island (Indonesia), particularly the Batang Regency segment, using multiple data sources: Landsat 9 and 8, Landsat 5 TM, and PlanetScope spanning from 2000 to 2023. The degree of change was obtained by statistically calculating net shoreline movement (NSM), linear regression rate (LRR), and end-point rate (EPR) using the digital shoreline analysis system (DSAS) software. The three rate-of-change statistics were used to calculate the multidecadal shoreline change prediction or projection for the next 10 and 20 years using a Kalman filter-based model with a simple data assimilation technique. Based on these results, NSM indicated that the shoreline experienced a total accretion and erosion of 361.04 and 111.58 m, respectively, from 2000 to 2023. Over these years, the highest accretion rate reached 16.44 m/year (according to LRR) or 15.83 m/year (EPR), while the maximum erosion rate was up to 11.34 m/year (LRR) or 9.43 m/year (EPR). The use of two data sources with different spatial resolutions produces varying statistical values, which depend on the degree of detail and complexity of the data derived from the source imagery. The coastline prognosis model produced with the Kalman filter shows that the entire coastline in Batang Regency will most likely experience an average accretion and erosion of 18.03 and 21.42 m, respectively, in 10 years, while that in the next 20 years will be 26.38 and 33.3 m, respectively.

Економічна динаміка: сучасний зміст, інноваційний базис та настанови для політики розвитку

The article substantiates the relevance of updating the categorical thesaurus of the theory of economic dynamics on the basis of modern methodology, which combines the cognitive potentials and tools of global evolutionism, the general theory of systems and synergy. In the context of the evolutionary approach, the authors provide the content and characteristics of the complex forms of economic system’ movement (self-organization, development / degradation, evolution / involution) and the accompanying qualitative changes (modifications, transformations, metamorphoses, architectural shifts) of economic reality. There are revealed the hierarchy of dynamic processes according to the degree of complexity and their interrelationships and principles of mutual transitions. Attention is focused on innovations and innovative changes, which, depending on the level (depth) of the implemented novelty, and the speed and scale of its diffusion, determine the trajectory of the economic system’ movement over time. Taking into account the time criterion, the authors systematize the signs of progressive and regressive economic dynamics. Noted the significant role of organizational and regulatory influences, primarily that of the innovation policy, for designing and constructing the expected future. A view is proposed to consider innovation policy as a system of measures aimed at the strategic management of economic genetics. The content of these measures consists in selective changes in inherited production and technological structures and reproductive cycles, as well as inherited models of the behavior of economic actors. The direction of selection, which represents a choice of priorities for innovative development is determined by the imperatives of self-preservation, effective self-realization and self-development of the economic system in a highly competitive environment.