Self-organizing Feature Research Articles

Surplus value quantification of overdue medical devices based on Kohonen network algorithm.

With the continuous updating and progress of medical equipment, the overdue medical device has problems such as management difficulties, resource waste, and potential security risks. Therefore, this paper used the Kohonen network algorithm to quantitatively evaluate and analyze the surplus value of overdue medical devices. In this paper, the Kohonen network algorithm was used to build a quantitative model of the surplus value of the overdue medical device, and the self-organization characteristics and data-driven learning ability of the Kohonen network were used to predict the surplus value of the equipment more accurately. Support vector machine was used to quantitatively evaluate and predict the surplus value of overdue medical devices, and further optimize the model performance, to provide more accurate and reliable decision support for medical equipment management. The Kohonen network algorithm used in this paper evaluated the correlation between the service life and maintenance cost of eight types of overdue medical devices and quantitatively predicted the surplus value of overdue medical devices with the random forest algorithm. According to the comparison of prediction bias, the maximum deviation between the expected surplus value and the actual surplus value is only 1, and the deviation value by the random forest algorithm is as low as 6, the Kohonen network algorithm in this paper has better prediction performance than the random forest algorithm. In the experiment of comparative analysis and verification by introducing the decision tree algorithm, the average error rate of the Kohonen network algorithm in this paper was only 20.57%, which was far lower than 46.34% of the random forest algorithm and 65.31% of decision tree algorithm. The Kohonen network algorithm used in this paper can effectively quantitatively evaluate and predict the surplus value of overdue medical devices, thus improving the efficiency of medical equipment management, reducing costs, and ensuring patient safety.

Near-Field-Regulated Ultrafast Laser Supra-Wavelength Structuring Directly on Ultrahard Metallic Glasses.

The ultrafast-laser-matter interactions enable "top-down" laser surface structuring, especially for materials difficult to process, with "bottom-up" self-organizing features. The subwavelength scenarios of laser-induced structuring are improved in defects and long-range order by applying positive/negative feedbacks. It is still hardly reported for supra-wavelength laser structuring more associated with complicated thermo/hydro-dynamics. For the first time to the knowledge, the near-field-regulated ultrafast-laser lithography of self-arrayed supra-wavelength micro/nano-pores directly on ultra-hard metallic glass is developed here. The plasmonic hot spots on pre-structures, as the positive feedback, clamped the lateral geometries (i.e., position, size). Simultaneously, it drilled and self-organized into micro/nano-pore arrays by photo-dynamic plasma ablation and Marangoni removal confined under specific femtosecond-laser irradiation, as the negative feedback. The mechanisms and finite element modeling of the multi-physical transduction (based on the two-temperature model), the far-field/near-field coupling, and the polarization dependence during laser-matter interactions are studied. Large-area micro/nano-pore arrays (centimeter scale or larger) aremanufactured with tunable periods (1-5µm) and geometries (e.g., diameters of 500 nm-6µm using 343, 515, and 1030 lasers, respectively). Consequently, the mid/far-infrared reflectivity at 2.5-6.5µm iss decreased from ≈80% to ≈5%. The universality of multi-physical coupling and near-field enhancements makes this approach widely applicable, or even irreplaceable, in various applications.

Japanese ‘Scientific Diaspora’ in 21st Century: Features of Self-Organization and State Policy towards Japanese Researchers Overseas

This article examines the evolution of Japan’s policy towards the community of Japanese researchers working abroad, tentatively referred to as the scientific diaspora. It traces the formation characteristics of this community in the 21st century and questions the legitimacy of recognizing it as a ‘scientific diaspora’. The article presents a periodization of Japan’s course towards the ‘scientific diaspora’, encompassing four stages. To verify the collected data, an interview with a representative of the Japanese ‘scientific diaspora’ in Russia was conducted. Among the main motives for the emigration of Japanese researchers abroad, underfunding and bureaucratization of science in Japan, an excess of non-research-related tasks, and a general decline in the international prestige of national science were identified. The authors conclude that Japan’s policy towards the ‘scientific diaspora’ until the second half of the 2010s was overshadowed by general diaspora policies. Currently, the largest organization of Japanese scientists abroad, UJAW, is under close government scrutiny. The analysis confirms the status of communities of Japanese scientists abroad as a scientific diaspora. It is established that decision-makers in scientific policy consider that scientific workers do not completely sever ties with their homeland and can contribute to achieving Japan’s strategic development goals.

The research on the solutions of the energy consumption problem in WSN in medical field

In the last few years, the Wireless Sensor Networks technology has been a hot topic, and depending on its self-organization feature, it has been utilized in many fields. The WSN is a technology that collects different kinds of data from different sensor nodes and processes them to a database center which will provide information to the users. But, because the main tool to collect data is the sensor nodes, the problem of energy consumption will be caused. The article will mainly give the exact reason why the energy consumption problem shows up and pay attention to different solutions to this problem. The solutions are very effective in solving these problems and can be gradually applied in many fields, especially in the medical field, which the article focuses on.

Constructing the digital ark: How China utilizes shared media to foster online information collaboration and crisis management in public emergency response

With the prevalence of collaborative editing tools, such as shared documents, a digital rescue platform functionality has emerged to address major unforeseen events, displaying notable self-organizing characteristics. This study examines the transformative role of sharing technologies in crisis communication, using documents from the 2021 Zhengzhou flood and adopting an actor-network theory perspective. Investigating how sharing technology influences situational information, the findings highlight its creation of a real-time, collaborative, and detailed crisis information environment, fostering self-rescue initiatives and positive emotional support. The shift from a closed situational information pool to a dynamic digital network underscores the evolving landscape of crisis communication in the digital age. Self-negotiation and self-restraint mechanisms mitigate rumor transmission, providing robust information support for subsequent rescue efforts. This research contributes valuable insights into the intricate dynamics of sharing technologies in crisis communication.

Visualization design of talent training paths in university education based on pattern knowledge

Abstract In the background of big data informatization, social and economic development needs talent support, and colleges and universities are the main positions of talent training. Using the features of self-organization, distributed multi-intelligence and positive feedback of the ant colony algorithm, a personalized learning path recommendation algorithm supporting pattern knowledge is proposed by combining the above learning feature model. The subject starts an independent solution search simultaneously at multiple points in the problem space, which increases the algorithm’s reliability and gives the algorithm a strong global search capability. When performing talent development path recommendation, the collaborative mechanism of distributed multi-subjects of the ant colony algorithm makes it possible to find an acceptable path quickly. Combined with the new knowledge representation model, the matching degree of students and learning objects is calculated from three dimensions, the heuristic information is obtained accordingly, and the Euclidean distance is used for matching degree calculation. Using the ant colony optimization algorithm to analyze the paths for cultivating talents in college education, it is concluded that in terms of the number of teachers: the total number of teachers with positive senior titles in five colleges and universities is 238, accounting for 8.88%, and the number of full-time teachers with senior associate titles is 618, accounting for 23.07%, all of which are lower than the average level in Fujian Province. This study has some reference significance to the cultivation of applied talents in local colleges and universities and has some reference value to the government in formulating the policy of cultivating talents in colleges and universities.

Acoustic Levitation-Assisted Contactless Printing of Microdroplets for Biomedical Applications

Abstract The cell is a microcapsule system wherein biological materials are encapsulated by a thin membrane, which provides valuable information on the metabolism, morphology, development, and signal transduction pathways of the studied cell. The cell-inspired microdroplet has the characteristics of efficient nanoscale substance transportation, self-organization, and morphological adaptation. However, it is extremely difficult to manufacture such systems. Mostly vesicles such as liposomes, polymersomes, and microcapsules are first produced by a high-pressure homogenizer and microfluidizer as an emulsion and then encapsulated microcapsules by the drop or emulsion method. Currently, acoustic levitation opens entirely new possibilities for creating bioinspired microdroplets because of its ability to suspend tiny droplets in an antigravity and noncontact manner. Herein, we propose contactless printing of single-core or multi-core cell-inspired microdroplets via acoustic levitation. First, the oscillation mode and microscopic morphology of the droplets under different ultrasonic vibration frequencies are shown by simulation, and the curing characteristics of the shell structure under different ultraviolet illumination conditions are quantitatively measured. The feasibility of manufacturing multi-core microdroplets and manufacturing submillimeter-scale particles based on oil trapping is extensively studied. To explore the morphological adaptability of microdroplets, ferromagnetic Fe3O4 nanoparticles are used to give magnetic-responsive properties to cells, and the microscopic deformation and motion in microfluidic channels under the magnetic field are characterized. Finally, the proposed printing method proves the versatility of in-space contactless printing of complex 3D beam structures and provides a powerful platform for developing biomedical devices and microrobots and studying morphogenesis and synthetic biological systems.

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Big data processing includes multiple processing flows of data. But data quality is the most important part of the entire process. Every data processing link will have an impact on the quality of big data. Collision detection is an important research content in many fields such as computer graphics and computer virtual reality. In layman's terms, it means that the computer detects the signal voltage on the channel while sending data. When the signal voltage swing value detected by a station exceeds a certain threshold value, it will be considered that at least two stations on the bus are sending data at the same time, indicating that a collision has occurred. As the number of questions increases, the level becomes higher, the target information becomes more diverse, and the algorithm becomes more complex. The traditional evolutionary algorithm is far from being able to deal with this situation effectively, and the optimization target algorithm emerges as the times require. Evolutionary computing is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It is not limited by the nature of the problem and can effectively deal with complex problems that are difficult to solve by traditional optimization algorithms. This paper aims to study the optimization algorithm of real-time collision detection based on Snake model in the field of big data. It is expected that, with the support of big data technology, the efficiency of real-time collision detection of Snake model will be improved and time will be saved. This paper proposes a multiline swarm particle swarm algorithm and combines it with the Snake model to improve the detection efficiency of the collision detection algorithm. It verifies the detection performance of traditional algorithms and tests their effectiveness in detection. The experimental results of this paper show that the frame rate of the Snake model algorithm is 15, the frame rate of the K-DOPs algorithm is 6.7, and the error of the algorithm is 1.04. It shows that the frame rate of Snake model algorithm is better.

Molecular spectroscopy and optical effect of Albendazole chemical compound behaves as liquid crystal: A theoretical and experimental approach

In the present work, we have investigated the Albendazole (ABZ) molecule which behaves as a liquid crystal or mesophase under the impact of temperature as well as the external effect of the electric field. Under the electric field's impact, the ABZ molecule demonstrates the mesophase phase stability in order parameter, birefringence, director angle and refractive index. At the higher temperature, the ABZ molecule shifted to the mesophase. Theoretical IR spectrosctrum of the ABZ molecule has been correlated with the experimental evidence. The XRD pattern of ABZ at 250 °C exhibits crystalline nature and at higher temperatures (300 °C and 400 °C) it reveal the amorphous nature. Tauc plots indicate optical bandgaps 4.73, 1.38, 6.02 and 5.80 eV at room temperature, 250 °C, 300 °C and 400 °C, respectively. Such variations in bandgap indicate self-organizing characteristic of the nematic liquid crystalline phase in the sample. FESEM images at higher temperatures exhibit pores with various structures in the ABZ.

Paving the Way for Synthetic Intrinsically Disordered Polymers for Soft Robotics.

Nature is full of examples of processes that, through evolution, have been perfected over the ages to effectively use matter and sustain life. Here, we present our strategies for designing intrinsically disordered smart polymers for soft robotics applications that are bio-inspired by intrinsically disordered proteins. Bio-inspired intrinsically disordered smart and soft polymers designed using our deep understanding of intrinsically disordered proteins have the potential to open new avenues in soft robotics. Together with other desirable traits, such as robustness, dynamic self-organization, and self-healing abilities, these systems possess ideal characteristics that human-made formations strive for but often fail to achieve. Our main aim is to develop materials for soft robotics applications bio-inspired by intrinsically disordered proteins to address what we see as the largest current barriers in the practical deployment of future soft robotics in various areas, including defense. Much of the current literature has focused on the de novo synthesis of tailor-made polymers to perform specific functions. With bio-inspired polymers, the complexity of protein folding mechanisms has limited the ability of researchers to reliably engineer specific structures. Unlike existing studies, our work is focused on utilizing the high flexibility of intrinsically disordered proteins and their self-organization characteristics using synthetic quasi-foldamers.

A mechanics model based on information entropy for identifying influencers in complex networks.

The network, with some or all characteristics of scale-free, self-similarity, self-organization, attractor and small world, is defined as a complex network. The identification of significant spreaders is an indispensable research direction in complex networks, which aims to discover nodes that play a crucial role in the structure and function of the network. Since influencers are essential for studying the security of the network and controlling the propagation process of the network, their assessment methods are of great significance and practical value to solve many problems. However, how to effectively combine global information with local information is still an open problem. To solve this problem, the generalized mechanics model is further improved in this paper. A generalized mechanics model based on information entropy is proposed to discover crucial spreaders in complex networks. The influence of each neighbor node on local information is quantified by information entropy, and the interaction between each node on global information is considered by calculating the shortest distance. Extensive tests on eleven real networks indicate the proposed approach is much faster and more precise than traditional ways and state-of-the-art benchmarks. At the same time, it is effective to use our approach to identify influencers in complex networks.

FEATURES OF THE DESIGN OF STEEL EARTHQUAKE-RESISTANT STRUCTURES OF HIGH-RISE BUILDINGS

The design, construction and operation of buildings in Ukraine is associated with the need to take into account additional special loads and impacts, namely: seismic, in complex en-gineering and geological conditions, on weak soils, artificial territories and subsidence soils, and in the conditions of the war with Russia during missile and artillery shelling and bom-bardment of populated areas – effects from explosions, blast waves, spread of fires, etc. [3].
 The territory of Ukraine is located on the outskirts of the powerful Azores-Mediterranean-Alpine-Trans-Asian seismogenic belt of the planet. In general, Ukraine does not belong to particularly seismically dangerous regions of the planet. Low- and medium-magnitude (magnitude 3...6) earthquakes were recorded only within three of its regions: the Ukrainian Carpathians and the Crimean Moun-tains, the Azov region. But observations of the consequences of numerous earthquakes have shown that in different parts of the same seis-mic area they differ significantly in intensity. Thus, the intensity of the earthquake on the surface of the earth in areas with loose soils is 15 times greater than in areas with rocky ones. Therefore, during the design of buildings and structures, one should take into account the peculiarities of construction in the complex engineering and geological conditions of the territory of Ukraine, which are associated with research, design and arrangement of bases and foundations on weak water-saturated, clayey and peaty soils, peat and silt, subsidence, swell-ing, saline, swelling and unevenly compacted soils, loose sands and floating karst and forged territories, taking into account seismic and dy-namic action in compliance with the require-ments of DBN B.1.1-12:2014 "Construction in seismic regions of Ukraine" [5] and DBN V.1.1-45:2017 "Buildings and structures in difficult engineering and geological conditions. General Provisions" [6].
 The design of modern buildings in seismic areas is developing in two directions that corre-spond to the main principles of seismic protec-tion - traditional (passive) and special (active). Complex systems of seismic protection of buildings combine passive and active systems.
 With traditional seismic protection, the load-bearing capacity of the main load-bearing structures of buildings is increased (the dimen-sions of cross-sections, their reinforcement, strengthening of joints, etc.) to absorb addition-al forces caused by seismic influences. At the same time, the nature of the buildings does not change. Special (active) measures to improve the seismic resistance of buildings consist in reducing loads due to modifications of their dynamic work schemes. Active seismic protec-tion of buildings is a new direction, which con-sists in the implementation of additional con-structive measures to prevent dangerous reso-nant oscillations and thereby reduce seismic impacts. It is achieved by changing the dynam-ic stiffness or periods of natural oscillations of buildings during earthquakes as a result of the use of special structural devices: sliding belts, connections that can be turned on or off, instal-lation of dynamic vibration dampers, kinematic or pile foundations, which have dissipative characteristics of self-organization, frame-linking systems with complex stiffness dia-phragms, rubber-steel cylindrical supports, etc.The article analyzes the features of design-ing earthquake-resistant steel structures of high-rise buildings. The schemes of earthquake-resistant high-rise buildings with steel frames were studied. The feasibility of using steel en-ergy-absorbing elements is substantiated and schemes for their installation in earthquake-resistant buildings with steel frames are pre-sented.

RANCE: A Randomly Centralized and On-Demand Clustering Protocol for Mobile Ad Hoc Networks

LEACH-like clustering protocols focus mainly on the low-power, low-rate, and low-wakeup network applications, and work in a multiround clustering strategy that causes frequent handovers of cluster heads (CHs), thus less support for real-time services that require stable cluster topologies. Besides, these protocols are faced with respective drawbacks, such as suboptimality of selected heads, costly node-base station (BS) energy overheads, lack of runtime cluster maintenance, etc. This article proposes RANCE, a randomly centralized and on-demand clustering protocol, aiming at prolonging nodes’ clustered time to support internodes collaboration while being energy efficient in mobile ad hoc networks. First, RANCE designs a randomly centralized CH selection mechanism in which every node in the local wireless network is eligible to initiate the centralized CH selection, so that the self-organizing characteristics of mobile ad hoc nodes can be utilized for head selection optimization. Second, taking into account the wireless volatility caused by changes of topology, obstacles, signal strength, etc., the fine-grained cluster relationships maintenance is provided by means of multilevel aliveness and adaptive bidirectional heartbeat packets. Third, RANCE works in an event-driven and on-demand manner instead of a time-triggered manner in LEACH-like protocols, to reduce the impact on continuous services caused by frequent CH handovers among all nodes. Simulation results show that RANCE provides longer clustered time (over 99% of nodes’ lifetime in networks more than 100 nodes) and good clustering scalability with high consistency at minimum energy cost, and exhibits good potentials in mobile wireless environments that are infrastructureless/poor for continuous missions.

Electric current and magnetization distributions’ self-organization features in a magnetoresistive film nanoelement under the influence of an external magnetic field

The new generalized Laplace’s equation for an electric potential is presented, which in the general case, is nonlinear for media with resistance anisotropy caused by the anisotropic magnetoresistance (AMR) effect and determined by the orientation of the magnetization vector relative to the current density vector at an arbitrary point of the sample. The solution to this equation is obtained for an anisotropic medium with a non-uniform distribution of current density and magnetization in the case of an oblique-shaped plate of nanoscale thickness. In addition, the analytical solution was obtained for the current density distribution in such a medium using conformal mapping, which confirms the numerical solution in the case of an isotropic conductor. An analysis of the results showed that the presence of the AMR effect leads to a significant change in the current density distribution in the sample compared to the isotropic case, which is expressed in the deviation of the current density vector in perpendicular to the magnetization vector direction in the plate. It has been established that the presence of an external magnetic field in the film plane, which leads to the emergence of an inhomogeneous magnetization distribution, entails self-organization of the electric current distribution. The graphs of the plate resistance as a function of applied magnetic field demonstrate asymmetry for the cases of the direct and reverse directions of the external magnetic field, which cannot be obtained within the framework of simplified models of current flow in media with the AMR effect.

Cationic Polyelectrolytes Containing Perfluorinated Groups: Synthesis and Self-Assembly Properties in Aqueous Solutions

In this work, the synthesis and solution self-assembly of partially fluorinated cationic polyelectrolytes based on post-polymerization-modified poly(2-(dimethylamino) ethyl methacrylate), incorporating hydrophobic fluorinated groups along the polymer chain, is reported. The post-polymerization modification aims to establish amphiphilic characteristics and strong polyelectrolyte properties to the polymer. Therefore, the self-organization characteristics in aqueous media are expected to be affected. The poly(2-(dimethylamino) ethyl methacrylate) (PDMAEMA) homopolymer precursor is synthesized using RAFT polymerization. A quaternization reaction on the amine side groups using heptadecafluoro-10-iododecane results in the poly((2-dimethylamino) ethyl methacrylate-co-quaternized heptadecafluoro-10-iododecane (2-dimethylamino) ethyl methacrylate) (PQFD) amphiphilic statistical copolymers. We intentionally study statistical copolymers in our research of DMAEMA-based polyelectrolytes as there are not many studies about such materials. We have also chosen the incorporation of highly hydrophobic perfluorinated groups to study the changes in the solution properties of the initial hydrophilic homopolymer. The successful synthesis is supported by size-exclusion chromatography (SEC), 1H-NMR, and FTIR experiments. The self-assembly of the copolymers in aqueous solutions along with the dependence of the properties of the resulting aggregates on the pH, temperature, and ionic strength of the solution are studied using light scattering methods (DLS, ELS) and fluorescence spectroscopy (FS). The structural characteristics of the aggregates depend on composition, solution preparation protocol and solution pH, and temperature.

An Enhanced Bio-Inspired Routing Algorithm for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes. In this context, this work presents an enhanced bio-inspired routing algorithm (EBIRA) to provide reliable communication. In EBIRA, enhanced ant colony optimization (EACO) finds the optimal long-life short-distance routes with the minimum hops based on distance, received signal strength metric, hop count, and evaporation rate. In EBIRA, the selected path has a short distance and a high level of connectivity at the link level with minimum hops. Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles. Simulation results show that the performance of EBIRA is better than reliable route discovery by using ant colony optimization (RDACO) and road-aware geographic routing protocol (RAGR) in terms of packet delivery ratio, throughput, and latency. Furthermore, variations of the received signal strength based on vehicle density and speed are evaluated, and the EBIRA route discovery success ratio is estimated and shown based on vehicle density at speed.
 HIGHLIGHTS
 
 Vehicular ad hoc networks (VANETs) demands reliable communication mechanisms for time-critical communication between vehicles. In VANETs, communication links between vehicles are prone to frequent breaks due to high mobility and topology changes
 VANET routing faces various challenges due to its unique characteristics such as high mobility, dynamic topology, unlimited network size, no infrastructure, and wireless communication
 Choosing the shortest path through minimum hops with high connectivity level links improves route lifetime and reduces frequent link breaks between vehicles
 The Less complexity, adaptability and self-organizing characteristics of ACO can cope with frequent topology changes, high mobility, absence of infrastructure, and wireless communication. The robustness feature of the ACO often helps to overcome network interruptions in the form of disconnections
 
 GRAPHICAL ABSTRACT

Space-Air-Ground Integrated Multi-Domain Network Resource Orchestration Based on Virtual Network Architecture: A DRL Method

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN’s heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.

Contagion dynamics in self-organized systems of self-propelled agents

We investigate the susceptible–infectious–recovered contagion dynamics in a system of self-propelled particles with polar alignment. Using agent-based simulations, we analyze the outbreak process for different combinations of the spatial parameters (alignment strength and Peclet number) and epidemic parameters (infection-lifetime transmissibility and duration of the individual infectious period). We show that the emerging spatial features strongly affect the contagion process. The ordered homogeneous states greatly disfavor infection spreading, due to their limited mixing, only achieving large outbreaks for high values of the individual infectious duration. The disordered homogeneous states also present low contagion capabilities, requiring relatively high values of both epidemic parameters to reach significant spreading. Instead, the inhomogeneous ordered states display high outbreak levels for a broad range of parameters. The formation of bands and clusters in these states favor infection propagation through a combination of processes that develop inside and outside of these structures. Our results highlight the importance of self-organized spatiotemporal features in a variety of contagion processes that can describe epidemics or other propagation dynamics, thus suggesting new approaches for understanding, predicting, and controlling their spreading in a variety of self-organized biological systems, ranging from bacterial swarms to animal groups and human crowds.

Stochastic termination of spiral wave dynamics in cardiac tissue.

Rotating spiral waves are self-organized features in spatially extended excitable media and may play an important role in cardiac arrhythmias including atrial fibrillation (AF). In homogeneous media, spiral wave dynamics are perpetuated through spiral wave breakup, leading to the continuous birth and death of spiral waves, but have a finite probability of termination. In non-homogeneous media, however, heterogeneities can act as anchoring sources that result in sustained spiral wave activity. It is thus unclear how and if AF may terminate following the removal of putative spiral wave sources in patients. Here, we address this question using computer simulations in which a stable spiral wave is trapped by an heterogeneity and is surrounded by spiral wave breakup. We show that, following ablation of spatial heterogeneity to render that region of the medium unexcitable, termination of spiral wave dynamics is stochastic and Poisson-distributed. Furthermore, we show that the dynamics can be accurately described by a master equation using birth and death rates. To validate these predictions in vivo, we mapped spiral wave activity in patients with AF and targeted the locations of spiral wave sources using radiofrequency ablation. Targeted ablation was indeed able to terminate AF, but only after a variable delay of up to several minutes. Furthermore, and consistent with numerical simulations, termination was not accompanied by gradual temporal or spatial organization. Our results suggest that spiral wave sources and tissue heterogeneities play a critical role in the maintenance of AF and that the removal of sources results in spiral wave dynamics with a finite termination time, which could have important clinical implications.

BR-FRL: A Belief Rule-Based Fault Recognition and Location Model for Bus Network Systems

For a control system with bus network topology, it is significant while difficult to accurately recognize and locate the fault of transmission medium. The reason is that the occurrence of one fault can lead to the abnormality of multiple terminal nodes. How to use these abnormal signals to realize effective fault recognition and fault location is the key to solving the problem. Inspired by the manual inference in engineering practice, this paper proposes a belief rule-based fault recognition and location model (BR-FRL) for transmission medium in bus network systems. The core idea is fault location based on the results of fault recognition. To adapt to the flexible expansion of bus topology, the designed BR-FRL model has self-organization characteristics. The highlights of this paper include three aspects: 1) The progressive reasoning of fault recognition and location based on belief rules is realized in one model; 2) A new model architecture is proposed, which integrates the belief rules, the group method of data handling, and the cautious conjunctive rule (CCR); 3) The parameter learning based on stochastic gradient descent is applied to BR-FRL, and the sensitivity analysis of evidence weight in CCR of BR-FRL is performed. The effectiveness of BR-FRL is verified by an experiment of gigabit-capable passive optical network.