Unified Method Research Articles

On simulations of 3D fractional WBBM model through mathematical and graphical analysis with the assists of fractionality and unrestricted parameters

This study retrieves some novel exact solutions to the family of 3D space–time fractional Wazwaz–Benjamin–Bona–Mahony (WBBM) equations in the context of diverse nonlinear physical phenomena resulting from water wave mechanics. The family of WBBM equations is transformed for this purpose using a space and time fractional transformation into an ordinary differential equation (ODE). The ODE then uses a strong method, namely the Unified Method. Consequently, lump solutions, dark-bright soliton, singular and multiple soliton solutions, and periodic solutions are investigated. The disparities between the current study's conclusions and previously acquired solutions via other approaches are examined. All wave solutions produced are determined to be novel in terms of fractionality, unrestricted parameters, and implemented technique sense. The impact of unrestricted parameters and fractionality on the obtained solutions are visually presented, along with physical explanations. It is observed that the wave portents are varied with the increase of unrestricted parameters as well as fractionality. We dynamically showed that the appropriate transformation and the applied Unified approach more proficient in the study of water wave dynamics and might be used in future researches to clarify the many physical phenomena. The novelty of this work validate that the proposed method seem simple and useful tools for obtaining the solutions in PDEs and it is expected to use in mathematical physics and optical engineering.

Deep Efficient Data Association for Multi-Object Tracking: Augmented with SSIM-Based Ambiguity Elimination.

Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data association. In the first step, objects of interest are detected in each frame of a video. The second step establishes the correspondence between these detected objects across different frames to track their trajectories. This paper proposes an efficient and unified data association method that utilizes a deep feature association network (deepFAN) to learn the associations. Additionally, the Structural Similarity Index Metric (SSIM) is employed to address uncertainties in the data association, complementing the deep feature association network. These combined association computations effectively link the current detections with the previous tracks, enhancing the overall tracking performance. To evaluate the efficiency of the proposed MOT framework, we conducted a comprehensive analysis of the popular MOT datasets, such as the MOT challenge and UA-DETRAC. The results showed that our technique performed substantially better than the current state-of-the-art methods in terms of standard MOT metrics.

Assessment of the impact of physical and chemical factors of the production environment on the morphological state of target organs in an animal experiment

The study of the morphological state of target organs of outbred (nonlinear) laboratory mice ICR (CD-1), which were under prolonged dynamic influence of physical and chemical factors of the production environment under model conditions, is of wide research interest, as it allows to assess the impact of these factors at the tissue and cellular level and to identify the presence of pathological changes in relation to the group control that was not exposed to the studied factors of the production environment during the 90-day experiment, which has clinical and practical significance. When assessing the 90-day dynamics (from 0 days – 6 months (background) to 30–60–90 days – 7–8–9 months) of the impact of environmental factors on the morphological state of tissues and organs of outbred (nonlinear) laboratory mice ICR (CD-1), generally accepted and unified methods were used. Animals (weighing 45–50 gr., sexually mature age – 6 months) were divided into 3 experimental groups (vibration exposure, noise exposure, chemical exposure) of 30 individuals each (15 females and 15 males) and 1 control group (15 females and 15 males), kept in standard comfortable conditions, and not exposed to the factors of the production environment. The experiment was conducted in accordance with the rules adopted by the European Convention for the Protection of Animals Used for Experimental Scientific Purposes (Strasbourg, 1986), after approval by the Ethical commission of the Novosibirsk Research Institute of Hygiene of Rospotrebnadzor. Statistical processing of the research materials was carried out using standard Statistica 10.0 application programs. The morphological analysis of the target organs of experimental animals revealed the presence of a complex of pathological changes in the 90-day dynamics of the impact of factors of the production environment, compared with the morphological state of the organs in the control group. The results obtained have clinical and practical significance, as they allow us to justify preventive measures aimed at preventing the negative impact of long-term production factors on the labor contingent. Further study of the morphological state of target organs of ICR laboratory mice exposed to prolonged dynamic exposure to physical and chemical factors of the production environment under model conditions will contribute to the establishment of mechanisms for the development of occupational diseases and the development of scientifically based medical and hygienic measures aimed at preserving the health of the labor contingent in the management of labor longevity.

Identification of distinct genotypes in circulating RSV A strains based on variants in the virus replication-associated genes.

Respiratory syncytial virus (RSV) is a common cause of respiratory infection that often leads to hospitalization of infected younger children and older adults. RSV is classified into two strains, A and B, each with several subgroups or genotypes. One issue with the definition of these subgroups is the lack of a unified method of identification or genotyping. We propose that genotyping strategies based on the genes coding for replication-associated proteins could provide critical information on the replication capacity of the distinct subgroups, while clearly distinguishing genotypes. Here, we analyzed the virus replication-associated genes N, P, M2, and L from de novo assembled RSV A sequences obtained from 31 newly sequenced samples from hospitalized patients in Philadelphia and 78 additional publicly available sequences from different geographic locations within the United States. In-depth analysis and annotation of variants in the replication-associated proteins identified the polymerase protein L as a robust target for genotyping RSV subgroups. Importantly, our analysis revealed non-synonymous variations in L that were consistently accompanied by conserved changes in its co-factor P or the M2-2 protein, suggesting associations and interactions between specific domains of these proteins. Similar associations were seen among sequences of the related human metapneumovirus. These results highlight L as an alternative to other RSV genotyping targets and demonstrate the value of in-depth analyses and annotations of RSV sequences as it can serve as a foundation for subsequent in vitro and clinical studies on the efficiency of the polymerase and fitness of different virus isolates.IMPORTANCEGiven the historical heterogeneity of respiratory syncytial virus (RSV) and the disease it causes, there is a need to understand the properties of the circulating RSV strains each season. This information would benefit from an informative and consensus method of genotyping the virus. Here, we carried out a variant analysis that shows a pattern of specific variations among the replication-associated genes of RSV A across different seasons. Interestingly, these variation patterns, which were also seen in human metapneumovirus sequences, point to previously defined interactions of domains within these genes, suggesting co-variation in the replication-associated genes. Our results also suggest a genotyping strategy that can prove to be particularly important in understanding the genotype-phenotype correlation in the era of RSV vaccination, where selective pressure on the virus to evolve is anticipated. More importantly, the categorization of pneumoviruses based on these patterns may be of prognostic value.

A multidirectional pairwise coupling approach with spectral features unmixing to quantify total phosphorus, total nitrogen, and chlorophyll-a in urban rivers

Comprehensive and effective water quality monitoring is vital to water environment management and prevention of water quality from degradation. Unmanned aerial vehicle (UAV) remote sensing techniques have gradually matured and prevailed in monitoring water quality of urban rivers, posing great opportunity for more effective and flexible quantitative estimation of water quality parameter (WQP) than satellite remote sensing techniques. However, current UAV remote sensing methods often entail large quantities of cost-prohibitive in-situ collected training samples with corresponding chemical analysis in different monitoring watersheds, laying time and fiscal pressure on local environmental protection department. They suffer relatively low calculation accuracy and stability and their applicability in various watersheds is constrained. This study developed a unified two-stage method, multidirectional pairwise coupling (MDPC) with information sharing and delivery of different modeling stages to efficiently predict concentrations of WQPs including total phosphorus (TP), total nitrogen (TN), and chlorophyll-a (Chl-a) from hyperspectral data. MDPC incorporates exterior and interior feature interaction and gravity model variant to improve prediction accuracy and stability with consideration of mutual effect in the proximity. The structure design and workflow of MDPC ensure high robustness and application prospect due to achievement of good performance with less training samples, improving applicability and feasibility. The experiments show that MDPC has achieved good performance on retrieval of WQPs concentrations including TP, TN, and Chl-a, the results mean absolute percent error (MAPE) and coefficient of determination (R2) ranging from 6.34 % to 11.94 % and from 0.74 to 0.93. This study provides a systematic and scientific reference to formulate a feasible and efficient water environment management scheme.

Biplane Reduction: A Novel Technique for Restoring Fibula Length in Maisonneuve Fracture.

Fibular length is important for ankle stability. Failure to adequately restore and stabilize fibula length may result in malunion, hindering postoperative functional exercises and recovery of ankle function. However, there is no unified and effective reduction method for fibular shortening in Maisonneuve fractures. In this study, we introduce the biplane reduction as an effective surgical technique for restoring the length of the fibula in Maisonneuve fractures. This retrospective study enrolled 12 patients preoperatively diagnosed with Maisonneuve fractures between June 2019 and June 2022. All patients underwent our biplane reduction technique to restore the length of their fibula. Operation time, hospital stay, complications, FTA (the angle between the fibular tip and the superior articular surface of the talus), visual analog pain score, and the American Orthopaedic Foot and Ankle Society ankle-hindfoot score were recorded. An independent sample t-test was used for component comparisons, and a paired sample t-test or one-way analysis of variance was used for intra-group comparisons. No patient was lost to follow-up and no obvious postoperative complications were observed. After operation, FTA recovered from 37.52 ± 0.37 ° to 40.42 ± 0.43 °, which was significantly improved compared with that before operation (p < 0.01). At the last follow-up, both the VAS and AOFAS scores showed significant improvement compared to pre-surgery scores. The reduction technique proposed in this study to restore the length of the fibula in Maisonneuve fractures is simple and effective, does not require special equipment, and has the advantage of directly examining the reduction condition.

Unified method for predicting the fatigue life of pipe–sphere joints in grid structures

Pipe–sphere joints (PSJs), which are commonly used in grid structures, are susceptible to fatigue failure under cyclic loading caused by suspended cranes. This paper presents a unified method for predicting the fatigue life of PSJs, including the weld toes on both the pipe and sphere surfaces, based on equivalent structural stress. A decomposition and recombination fitting (DRF) method was proposed to determine the optimal functional form of stress concentration factors (SCFs). A nonlinear regression analysis was conducted on the calculated results of 61 common PSJs to obtain a semi-analytical expression for the structural stress of the weld toes. Using this unified method, the fatigue life of the weld toes on both the pipe and sphere surfaces was estimated. The results indicated that the logarithmic ratios between the predicted fatigue life and experimental results were typically 0.93–1.08 for weld toes on pipe surfaces and 0.97–1.13 for weld toes on spherical surfaces, confirming the accuracy of the method. This unified method is applicable to predict the fatigue life of PSJs of various sizes and involves concise mathematical calculations independent of finite element analysis, thereby facilitating engineering applications. The DRF method can be used as a reference for fitting SCFs to specific structures. Furthermore, this prediction method enables the identification of the failure modes in PSJs. As the size of the steel pipe increased, the fatigue failure gradually shifted from the pipe surface to the sphere surface.

ТЕОРЕТИКО-МЕТОДОЛОГІЧНИЙ (КОНТЕКСТНИЙ) АНАЛІЗ ЗМІШАНОГОНАВЧАННЯ ТА ПЕРСПЕКТИВИ ЙОГО ВПРОВАДЖЕННЯ УСУЧАСНІЙ ОСВІТНІЙ ПРАКТИЦІ

The article is devoted to the problem of blended learning, which is increasingly attracting the attention of both domestic and foreign researchers and practicing educators. Taking into account the challenges of globalization, which have directly affected the education system, the problem of applying pedagogical practices that enable the combination of different formats of education has become extremely relevant. In particular, it is about the combination of face-to-face learning with various formats of electronic learning (asynchronous and/or synchronous), which is the essence of blended learning. Currently, the support and further implementation of blended learning in the educational process of higher education are quite complicated. Among other problems, the authors of the article single out the lack of a clear definition of the essence of blended learning and the incomplete readiness (methodological, technological and technical) of the pedagogical staff for the implementation of the blended learning model in practice. The article is devoted to the theoretical-methodological (contextual) analysis of the concept of "blended learning", the definition of its polysemic essence based on the study of domestic and foreign scientific sources and the identification of prospects for implementation in modern domestic and foreign educational practice. It was determined that currently blended learning is usually interpreted as a model, technology, method or a special process of acquiring knowledge. Essential features were analyzed; the main components of combined training are defined. The most typical definitions of blended learning are systematized, the essence of which is represented by the first words of the definition: unifying method, model, technology, methodology, teaching method, a type of hybrid methodology, purposeful process of acquiring knowledge, skills and abilities, educational concept, training, teaching system It is substantiated that, despite the indisputable advantages, there are certain challenges - both objective and subjective - that stand in the way of the implementation of blended learning.

Deep learning for derivatives pricing: a comparative study of asymptotic and quasi-process corrections

AbstractIn this study, we compare two methods for using neural networks to efficiently learn the price of derivatives. The first method, proposed by Funahashi (Quant Financ 21(4):575–592, 2021a), involves training the neural networks to learn the difference between the derivative price and its asymptotic expansion, rather than learning the derivative price directly. The target derivative price is then obtained by adding the approximate solution with the predicted value of neural networks. This method reduces the required amount of learning data, often by a factor of one hundred to one thousand, compared to the case where the derivative price is directly learned through neural networks. In the second method, established in this paper, the neural networks learn the difference between the derivative price written on the underlying asset price that follows the target complex stochastic process and the derivative price written on the underlying asset price that has a relatively simple stochastic process that has a closed-form solution for the target derivative prices. This method provides an alternative valuation method when no efficient approximate solution for the derivative value is observed and if one can arbitrarily determine the model parameters of the quasi-process that approximates the original process. We also propose a unified method to determine the model parameters of quasi-processes from underlying asset processes. These methods prove valuable in cases where general analytic solutions are absent, as seen in widely used financial models such as the stochastic volatility models. These cases involve time-consuming numerical calculations to generate learning data, highlighting the value of the two methods, which significantly compress calculation times.

Unified metagenomic method for rapid detection of microorganisms in clinical samples

BackgroundClinical metagenomics involves the genomic sequencing of all microorganisms in clinical samples ideally after depletion of human DNA to increase sensitivity and reduce turnaround times. Current human DNA depletion methods preferentially preserve either DNA or RNA containing microbes, but not both simultaneously. Here we describe and present data using a practical and rapid mechanical host-depletion method allowing simultaneous detection of RNA and DNA microorganisms linked with nanopore sequencing.MethodsThe human cells from respiratory samples are lysed mechanically using 1.4 mm zirconium-silicate spheres and the human DNA is depleted using a nonspecific endonuclease. The RNA is converted to dsDNA to allow the simultaneous sequencing of DNA and RNA.ResultsThe method decreases human DNA concentration by a median of eight Ct values while detecting a broad range of RNA & DNA viruses, bacteria, including atypical pathogens (Legionella, Chlamydia, Mycoplasma) and fungi (Candida, Pneumocystis, Aspergillus). The first automated reports are generated after 30 min sequencing from a 7 h end-to-end workflow. Sensitivity and specificity for bacterial detection are 90% and 100%, respectively, and viral detection are 92% and 100% after 2 h of sequencing. Prospective validation on 33 consecutive lower respiratory tract samples from ventilated patients with suspected pneumonia shows 60% concordance with routine testing, detection of additional pathogens in 21% of samples and pathogen genomic assembly achieve for 42% of viruses and 33% of bacteria.ConclusionsAlthough further workflow refinement and validation on samples containing a broader range of pathogens is required, it holds promise as a clinically deployable workflow suitable for evaluation in routine microbiology laboratories.

Method of Diagnosing Sarcopenia in Morbidly Obese Patients

Introduction. Sarcopenia refers to a clinical syndrome characterized by progressive skeletal muscle disease, loss of muscle mass and strength resulting in impaired physical performance. The prevalence of sarcopenia in the population ranges from 5% to 13% in people aged 43.0 ± 11.1 years and reaches 50% in those over 70. Various methods have been used to diagnose sarcopenia, which, however, have their limitations. Currently, CT scanning is considered the most accurate method for the detection of sarcopenia. Therefore, the development of new methods for early diagnosis of sarcopenia appears relevant. Aim. To investigate different methods of diagnosing sarcopenia in morbidly obese patients. Materials and methods. The study presents an analysis of various methods for diagnostics of sarcopenia in morbidly obese patients, including a patented method based on the research conducted in the BSMU Clinic (Utility Patent No. 2815157 dated 11.03.2024). Results and discussion. Doctors used the suggested method of diagnosis, defined the value of sarcopenia index equal or less than 15, and diagnosed sarcopenia. Conclusion. The prevalence and severity of sarcopenia vary depending on the classification criteria and thresholds used. Since a unified diagnostic method is yet to be developed, it is necessary to continue research into this issue, and introduce sarcopenia detection, prevention, and correction techniques into widespread clinical practice. Indices of sarcopenia and obesity, obtained during an additional preoperative study, provide significant prognostic information superior to body mass index, thereby enabling the direction and scope of surgical interventions to be determined and survival outcomes to be improved.

Формування вихідних даних для вирішення задач маршового розрахунку надзвукового обтікання ракет різного компонування

This paper gives a general overview of components and layouts used in supersonic rockets of different purposes. The rocket layout is specified as a structure and a set of components (a wing, a rudder, a stabilizer, a destabilizer, and a superstructure) arranged along the rocket structure. The goal of this work is to develop a unified approach to specifying the shape parameters of rocket layouts regardless of the rocket type. For complex-shape rocket layouts, the paper proposes an approach in which the shapes of the rocket structure and the additional components installed thereon are specified independently. The additional components of the rocket layout are bound to the rocket structure using operation parameters. The use of the operation parameters binds each additional component of the rocket layout to the rocket structure, thus offering a unified method for specifying the geometrical parameters of variously shaped rocket layouts. This approach is developed towards more complex shapes of rocket layout elements arbitrarily placed on rocket structures. The outside shape of each rocket component is specified in a Cartesian system of coordinates rigidly bound thereto. A unified approach to specifying the outside shape of various rocket components is presented. According to the general scheme of specifying the geometrical parameters of rocket layout components, they are specified by three methods: analytically, by plan shape, and by plan shape with board and end chord profiles. To describe the outside shape of a component, the specification method and the number and the values of its key parameters are specified. To specify rocket layout input data, one has to fix the number of additional components to be installed on the rocket structure. For each layout component, the parameters that define its shape, location on the rocket structure, and deflection angle are specified. To each layout component there corresponds an input data set of its own. The set consists of parameters that define the shape of the component and parameters of its operation as a part of the layout. A standard input data file for specifying rocket layout shapes is configured.

A new classification for emergency critically ill patients and analysis of their adverse events during intrahospital transport: A cluster analysis.

Critical patients may experience various adverse events during transportation within hospitals. Therefore, quickly evaluating and classifying patients before transporting them from the emergency department and focusing on managing high-risk patients are critical. At present, no unified classification method exists; all the current approaches are subjective. To ensure transportation safety, we conducted a cluster analysis of critically ill patients transferred from the emergency department to the intensive care unit. Single-centre cohort study. This study was conducted at a comprehensive first-class teaching hospital in Beijing. Convenience sampling and continuous enrolment were employed. We collected data from 1 January 2019, to 31 December 2021. All patients were transferred from the emergency department to the intensive care unit, and cluster analysis was conducted using five variables. A total of 584 patients were grouped into three clusters. Cluster 1 (high systolic blood pressure group) included 208 (35.6%) patients. Cluster 2 (high heart rate and low blood oxygen group) included 55 (9.4%) patients. Cluster 3 (normal group) included the remaining 321 (55%) patients. The oxygen saturation levels of all the patients were lower after transport, and the proportion of adverse events (61.8%) was the highest in Cluster 2 (p < .05). This study utilized data on five important vital signs from a cluster analysis to explore possible patient classifications and provide a reference for ensuring transportation safety. Before transferring patients, we should classify them and implement targeted care. Changes in blood oxygen levels in all patients should be considered, with a focus on the occurrence of adverse events during transportation among patients with high heart rates and low blood oxygen levels.

Assessment of Methods to Derive Tensile Properties of Ultra-High-Performance Fiber-Reinforced Cementitious Composites.

There is no unified method for deriving the tensile properties of fiber-reinforced ultra-high-performance cementitious composites (UHPCC). This study compares the most common material tests based on a large series of laboratory tests performed on a self-developed UHPCC mixture. The cementitious matrix, with a compressive strength of over 150 MPa and a matrix tensile strength of 8-10 MPa, was reinforced with 2% by volume of 15 mm long and 0.2 mm diameter straight high-strength steel microfibers. Over 100 uniaxial tensile tests were performed on three test configurations using cylindrical cores drilled out from larger prismatic specimens in three perpendicular directions. In addition to uniaxial tests, flexural tests on prismatic elements and flexural tests on thin plates were conducted, and the tensile properties were derived through digital image correlation (DIC) measurements and inverse analysis. Furthermore, splitting tensile tests on cylindrical specimens were employed to ascertain the tensile properties of the matrix. The outcomes of the diverse laboratory tests are presented and discussed in detail. The relationships between crack width and deflection in the context of flexural tests were developed and presented. In conjunction with compression tests and modulus of elasticity tests, the constitutive law is presented for the investigated materials.

Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance

This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting years that summarize multiple accident year loss metrics. By integrating spatial clustering techniques, the study not only improves the credibility of predictive models but also introduces a strategic dimension reduction method that concurrently enhances the interpretability of predictive models used. The evolving nature of spatial patterns over time poses a significant barrier to a better understanding of complex insurance systems as these patterns transform due to various factors. While spatial clustering effectively identifies regions with similar loss data characteristics, maintaining up-to-date clusters is an ongoing challenge. This research underscores the importance of studying spatial patterns of auto insurance claim data across major insurance coverage types, including Accident Benefits (AB), Collision (CL), and Third-Party Liability (TPL). The research offers regulators valuable insights into distinct risk profiles associated with different coverage categories and territories. By leveraging spatial loss data from pre-pandemic and pandemic periods, this study also aims to uncover the impact of the COVID-19 pandemic on auto insurance claims of major coverage types. From this perspective, we observe a statistically significant increase in insurance premiums for CL coverage after the pandemic. The proposed unified spatial clustering method incorporates a relabeling strategy to standardize comparisons across different accident years, contributing to a more robust understanding of the pandemic effects on auto insurance claims. This innovative approach has the potential to significantly influence data visualization and pattern recognition, thereby improving the reliability and interpretability of clustering methods.

MODCEL-MHUS: a comprehensive multilayer hydrodynamic unified simulation for stormwater, sanitary sewer systems, and urban surface.

Numerous countries and regions have embraced implementing a separate sewer system, segregating sanitary and storm sewers into distinct systems. However, the functionality of these systems often needs to improve due to irregular interconnections, resulting in a mixed and malfunctioning system. Sewage collection is crucial for residential sanitation, but untreated collection significantly contributes to environmental degradation. Analyzing the simultaneous operation of both systems becomes vital for effective management. Using mathematical tools for precise and unified diagnosis and prognosis becomes imperative. However, municipal professionals and companies need more tools specifically designed to evaluate these systems in a unified way, mapping all the hydraulic connections observed in practice. This study proposes a unified simulation method for stormwater and sanitary sewer urban systems, addressing real-world scenarios and potential interferences. The primary goal is to develop a simulation method for both systems, considering system interconnections and urban layouts, involving hydrodynamic and water quality simulations. The practical application of this method, the Multilayer Hydrodynamic Simulation Method (MODCEL-MHUS), successfully identifies issues in urban water networks and suggests solutions, making it a valuable tool for urban water management and environmental engineering professionals.

The Solutions of the Space-Time Fractional Cubic Nonlinear Schrödinger Equation by Using the Unified Method

The Solutions of the Space-Time Fractional Cubic Nonlinear Schrödinger Equation by Using the Unified Method

A dataset of 1km-grid comprehensive indexes of land use degree density in China (2020)

The comprehensive index of land use degree quantitatively reflects the development and use degree of the ecological environment by human activities from the aspect of land use. The dataset is derived from the 30-meter land use raster data produced based on the 2020 national land change survey data. We used the land use comprehensive index model to quantitatively analyze the national land use degree evaluation by dividing the land use into four degrees: unused land, the land used for forestry, meadow and waters, agricultural land, and urban settlement land. We comprehensively calculated the degree of human intervention in regional land use. In order to ensure data correctness and accuracy, we adopted unified and standardized data processing methods, sample collection rules, and quality control systems to control the quality of the data results. This dataset can be used for a valuable reference for the research on territorial spatial planning, regional development and development evaluation, main body functional area evaluation, and coupling analysis of human-land relations.

Real-Time Simulation System for Small Scale Regional Integrated Energy Systems

Regional Integrated Energy Systems (RIESs) integrate wide spectrum of energy sources and storage with optimized energy management and further pollution reduction. This paper presents a real-time simulation system for RIESs powered by multiple digital signal processors (DSPs) with different means of data exchange. The RIES encompasses the DC microgrid (DMG), the district heat network (DHN), and the natural gas network (NGN). To realize multi-energy flow simulation, averaged switch models are investigated for different types of device-level units in the DMG, and the unified energy path method is used to build circuit-dual models of the DHN and NGN. A hierarchical island strategy (HIS) and a multi-energy dispatch strategy (MEDS) are proposed to enhance the energy flow control and operating efficiency. The two-layer HIS can adjust the operating status of device-level units in real time to achieve bus voltage stability in the DMG; MEDS uses energy conversion devices to decouple multi-energy flows and adopts the decomposed flow method to calculate the flow results for each network. The real-time simulation hardware platform is built, and both electricity-led and thermal-led experiments are carried out to verify the accuracy of models and the effectiveness of the proposed strategy. The proposed system with an energy management strategy aims to provide substantial theoretical and practical contributions to the control and simulation of RIESs, thus supporting the advancement of integrated energy systems.

Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks

This article provides a unified analysis of the multistability of fraction-order multidimensional-valued memristive neural networks (FOMVMNNs) with unbounded time-varying delays. Firstly, based on the knowledge of fractional differentiation and memristors, a unified model is established. This model is a unified form of real-valued, complex-valued, and quaternion-valued systems. Then, based on a unified method, the number of equilibrium points for FOMVMNNs is discussed. The sufficient conditions for determining the number of equilibrium points have been obtained. By using 1-norm to construct Lyapunov functions, the unified criteria for multistability of FOMVMNNs are obtained, these criteria are less conservative and easier to verify. Moreover, the attraction basins of the stable equilibrium points are estimated. Finally, two numerical simulation examples are provided to verify the correctness of the results.