Self-organizing Model Research Articles

SOP-MULTI: A Self-Organized Polymer-Based Coarse-Grained Model for Multidomain and Intrinsically Disordered Proteins with Conformation Ensemble Consistent with Experimental Scattering Data.

Multidomain proteins with long flexible linkers and full-length intrinsically disordered proteins (IDPs) are best defined as an ensemble of conformations rather than a single structure. Determining high-resolution ensemble structures of such proteins poses various challenges by using tools from experimental structural biophysics. Integrative approaches combining available low-resolution ensemble-averaged experimental data and in silico biomolecular reconstructions are now often used for the purpose. However, extensive Boltzmann weighted conformation sampling for large proteins, especially for ones where both the folded and disordered domains exist in the same polypeptide chain, remains a challenge. In this work, we present a 2-site per amino-acid resolution SOP-MULTI force field for simulating coarse-grained models of multidomain proteins. SOP-MULTI combines two well-established self-organized polymer models─: (i) SOP-SC models for folded systems and (ii) SOP-IDP for IDPs. For the SOP-MULTI, we introduce cross-interaction terms between the beads belonging to the folded and disordered regions to generate conformation ensembles for full-length multidomain proteins such as hnRNP A1, TDP-43, G3BP1, hGHR-ECD, TIA1, HIV-1 Gag, polyubiquitin, and FUS. When back-mapped to all-atom resolution, SOP-MULTI trajectories faithfully recapitulate the scattering data over the range of the reciprocal space. We also show that individual folded domains preserve native contacts with respect to solved folded structures, and root-mean-square fluctuations of residues in folded domains match those obtained from all-atom molecular dynamics simulation trajectories of the same folded systems. SOP-MULTI force field is made available as a LAMMPS-compatible user package along with setup codes for generating the required files for any full-length protein with folded and disordered regions.

Nanoripples evolution on tungsten surface induced by two-pulse configuration

Tuneable shapes and uniformity of the laser-induced periodic surface structures (LIPSS) attract interest because of their diverse applications in both scientific research and technological advancements. In this work, we investigate the progression of regular one-dimensional (1D) LIPSS on a tungsten surface, examining its evolution based on the time delay between two laser pulses that initiate the formation of nanoripples. 1D-LIPSS were formed in the case of single-beam laser ablation with approximately 84 laser pulses. Two-dimensional (2D) LIPSS, including triangle, hexagonal, and square shapes, were generated by employing two cross-polarized laser beams (with wavelengths of 1030 nm and pulse durations of 40 fs) with no delay between the pulses. However, introducing a time delay of 2 picoseconds (ps) between the two cross-polarized laser pulses resulted in the division of the initially cross-oriented 2D-LIPSS into square-shaped structures, particularly along the spatially overlapped region of the laser beams. The emergence of triangle- and hexagonal-shaped two-dimensional laser-induced periodic surface structures (2D-LIPSS) is analyzed within the framework of a self-organization model, particularly in the context of solidifying the molten layer under cold temperature conditions on the tungsten surface. We examine the formation mechanism of LIPSS, attributing it to a combination of the self-organization/hydrodynamic mechanism initially, which transitions to the electromagnetic mechanism as the effective number of pulses increases.

Belief-Rule-Based System with Self-organizing and Multi-temporal Modeling for Sensor-based Human Activity Recognition.

Smart environment is an efficient and cost- effective way to afford intelligent supports for the elderly people. Human activity recognition (HAR) is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based HAR model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.

Correlation between Different Factors of Non-point Source Pollution in Yangtze River Basin

This research used the improved export coefficient model to estimate non-point source load in sub-watersheds and a self-organizing map model to identify the correlation relationship of factors, that can be affected by non-point source load in Yangtze River Basin. Research results indicate that total nitrogen load is 4.87 - 15.53 kg/ha; total phosphorus load is 0.18 - 0.51 kg/ha; Dong Ting Lake sub-basin has the lowest contribution value of total nitrogen and phosphorus; Tai Lake sub-basin has the highest contribution load of total nitrogen and total phosphorus; Jinsha River sub-basin reveals the highest value of total nitrogen and phosphorus contribution on grassy land and desert land; Tai Lake sub-basin has the highest total nitrogen and total phosphorus load on forest, agricultural land, and urban construction land. The correlation relationship of factors reveals a very close correlation with each other as precipitation, total nitrogen load, and total phosphorus load factors have correlation in the highest value, in which total nitrogen load and total phosphorus load factors have a close correlation with each other from the lowest value to highest value; wetland and barren land factors correlate with the lowest value and the highest value; agricultural land, forestland, and grassy land factors correlate with one another in the high value and the highest value. The forest cover rate factor, as well as the population factor, does not correlate with other factors.

A self-organizing map clustering adaptive artificial neural network model for analysis critical success factors in different phases of MTO projects case study: electrical equipment manufacturer and supplier in Iran

PurposeThe purpose of this paper is to investigate the benefit of critical success factors (CSFs) clustering in different phases of make-to-order (MTO) projects and develop standards for management.Design/methodology/approachThis study is based on a questionnaire survey. First of all, collecting data by structured interviews, relying on a questionnaire and second from leader contractors who are active in the engineering and steel industry (in Iran). So, the requirements and objective of the research are presented to the top management of MTO projects to gain their support in data collection. Then 20 CSFs were identified by the literature review so a questionnaire survey was prepared for the CSFs assessment and interview with the experts. Finally analyzing the importance and performance of CSFs in project phases and cluster them in different project phases with self-organizing map as one of the artificial neural network (ANN) approaches due to high predictive accuracy. Review the research result with the top management of MTO project and examine the results obtained from neural networks and validation indices.FindingsCluster analysis shows that the implementation phase is the most important stage in MTO organizations and the other phases like feasibility and start-up, design and planning, delivery and end-phase should be also considered as effective phases in determining the level of organization performance. Different industries with additional data at different periodic times will verify the result. Furthermore, testing the other ANN model will improve risk analysis and could shift this classification approach to a regression type.Research limitations/implicationsThe main limitation of the research is related to the sample. Research findings are limited to the time of data collection so validity is limited to the mentioned time. Different industries with additional data will verify the result. Furthermore, testing different ANN models such as K-MEANS, non-negative matrix factorization (NMF) analyses will improve risk analysis and could meet different classification results to find gaps.Practical implicationsIn this paper, CSF and project phase dimensions are viewed together which is necessary to meet better results for simplifying social and economic benefits. Merge the new findings and latest technologies could prepare the best results and enable managers to create a better framework or implement key factors for minimizing waste.Originality/valueThis paper moves the definition of MTO organizations beyond measuring cost, complexity and financial variables by clustering CSFs in different phases of projects. So, the results enable managers to use this concept in their daily production to minimize waste and could be implemented to efficiently choose factors.

From kinetic flocking model of Cucker–Smale type to self-organized hydrodynamic model

We investigate the hydrodynamic limit problem for a kinetic flocking model. We develop a GCI-based Hilbert expansion method, and establish rigorously the asymptotic regime from the kinetic Cucker–Smale model with a confining potential in a mesoscopic scale to the macroscopic limit system for self-propelled individuals, which is derived formally by Aceves-Sánchez et al. in 2019. In the traditional kinetic equation with collisions, for example, Boltzmann-type equations, the key properties that connect the kinetic and fluid regimes are: the linearized collision operator (linearized collision operator around the equilibrium), denoted by [Formula: see text], is symmetric, and has a nontrivial null space (its elements are called collision invariants) which include all the fluid information, i.e. the dimension of Ker([Formula: see text]) is equal to the number of fluid variables. Furthermore, the moments of the collision invariants with the kinetic equations give the macroscopic equations. The new feature and difficulty of the corresponding problem considered in this paper is: the linearized operator [Formula: see text] is not symmetric, i.e. [Formula: see text], where [Formula: see text] is the dual of [Formula: see text]. Moreover, the collision invariants lies in Ker([Formula: see text]), which is called generalized collision invariants (GCI). This is fundamentally different with classical Boltzmann-type equations. This is a common feature of many collective motions of self-propelled particles with alignment in living systems, or many active particle system. Another difficulty (also common for active system) is involved by the normalization of the direction vector, which is highly nonlinear. In this paper, using Cucker–Smale model as an example, we develop systematically a GCI-based expansion method, and micro–macro decomposition on the dual space, to justify the limits to the macroscopic system, a non-Euler-type hyperbolic system. We believe our method has wide applications in the collective motions and active particle systems.

Influence of Carbon-Nitride Dot-Emitting Species and Evolution on Fluorescence-Based Sensing and Differentiation.

Carbon dots have attracted widespread interest for sensing applications based on their low cost, ease of synthesis, and robust optical properties. We investigate structure-function evolution on multiemitter fluorescence patterns for model carbon-nitride dots (CNDs) and their implications on trace-level sensing. Hydrothermally synthesized CNDs with different reaction times were used to determine how specific functionalities and their corresponding fluorescence signatures respond upon the addition of trace-level analytes. Archetype explosives molecules were chosen as a testbed due to similarities in substituent groups or inductive properties (i.e., electron withdrawing), and solution-based assays were performed using ratiometric fluorescence excitation-emission mapping (EEM). Analyte-specific quenching and enhancement responses were observed in EEM landscapes that varied with the CND reaction time. We then used self-organizing map models to examine EEM feature clustering with specific analytes. The results reveal that interactions between carbon-nitride frameworks and molecular-like species dictate response characteristics that may be harnessed to tailor sensor development for specific applications.

1/fα noise in the Robin Hood model

Abstract We consider the Robin Hood dynamics, a one-dimensional extremal self-organized critical model that describes the low-temperature creep phenomenon. One of the key quantities is the time evolution of the state variable (force noise). To understand the temporal correlations, we compute the power spectra of the local force fluctuations and apply finite-size scaling to get scaling functions and critical exponents. We find a signature of the 1 / f α noise for the local force with a nontrivial value of the spectral exponent 0 < α < 2 . We also examine temporal fluctuations in the position of the extremal site and a local activity signal. We present results for different local interaction rules of the model.

Pengaruh Model Pembelajaran Sole (Self Organized Learning Environment) terhadap Kemampuan Menulis Teks Eksposisi di Kelas VIII SMP Pahlawan Nasional Kota Medan

This research is a final assignment research (Thesis) and was conducted at the Medan City National Hero Middle School in class VIII with 2 classes and a total of 20 students in each class. Data in research using questionnaires and tests. Questionnaires containing questions will be distributed to class VIII students individually and submitted to researchers. Next, during the class VIII students' test, the researcher asked them to create an opinion or new idea that they knew about in the expository text material. Because, researchers looked at the process of writing ability of class VIII students in expository text material. The results of research that has been carried out on class VIII students show that: (1) To analyze students' ability to write exposition texts in Indonesian language subjects using a direct learning model from class VIII of National Hero Middle School. With a score of 66.42 vulnerable in this learning, students still have not mastered and are able to use the learning model that has been given by the facilitator (teacher) (2) To analyze how students are able to write expository texts in Indonesian language subjects using the Self-Organized learning model Learning Environment. With a maximum score of 84.85, it can be seen that it is very different from the learning model used by the teacher in that class. Therefore, researchers suggest that the SOLE model can attract students in the learning process. (3) To analyze whether there is an influence on the Self-Organized Learning Environment learning model on the writing ability of class VIII students of Medan National Hero Middle School.

Tunable Social Hierarchies in Self-Organizing Model with Chemotactic Agents

In the Bonabeau model, chemotaxis, which is observed in social insects, such as ants, was introduced into the movement rules of agents to control the collision frequency between agents, and its effect on the mechanism of hierarchical structure formation was investigated. Like an ant, this chemotactic agent makes stochastic decisions regarding its direction of movement depending on the intensity of its released chemicals. Because of this mechanism, the agent depends on its past location history. It can perform different motions from a random walk (RW) and asymmetric attractive or repulsive interactions with other agents via the diffusion of chemotactic substances. When there is an attractive interaction between these agents, they are more likely to aggregate, which increases the effective density; thus, the disparity in the agent winning ratio is more likely to form than in a conventional model with a RW. However, in the case of repulsive interactions, the agents became more distant from each other, the effective density decreased, and a disparity in the winning ratio was less likely to form. This indicates that the disparity in the winning ratio is tunable owing to the interactions between the introduced chemotactic agents.

The Big Role of Data Analytics in the Industrial Internet of Things

An IoT architecture is presented in the article which is a predictive way to maintain a group of buses using J1939 sensor data[1]. The authors adopted a minimally viable prototype (MVP) at the Socie´te´ de Transport de l’Outaouais (STO) garage in Gatineau, Canada, which was able to capture around 1 GB of uncompressed J1939 data per bus every day.[1] Data is a combination of the sensor reading and consists of data for the wheel speed, vehicle distance, driver pedal positions, engine information, oil information, coolant information, and transmission fluid information. The authors are trying to come up with a variant of Consensus Self-Organized Models (COSMO) named ICOSMO, which can self-fit the sensor selection for anomaly detection. The fourth industrial revolution has changed the face of ancient manufacturing by bringing in new technologies such as sensors, AI, IoT, and big data analytics. This equipment is used to convert factories into intelligent and interconnected systems that generate a huge amount of data. The data collected, when properly processed, can be utilized to improve decision making and reliability and safety. In addition, it makes predictive maintenance inevitable, thus minimizing the downtime and optimization of the performance[2]. Industrial big data is identified by the ”5V” elements: volume, velocity, variety, veracity, and value. Due to these characteristics, traditional data processing techniques become inadequate, so these circumstances require novel approaches to the treatment of the numerous and diverse data types that are produced in industry 4.0. The Proposed ICOSMO Infrastructure for IoT Predictive Maintenance consists of the following key activities: 1) finding out which sensor classes are the reasons for the faulty components, 2) adaptation of the sensor class (SC) and the sensor prediction capability (SPC) using fault detection abilities and 3) COSMO sensor configuration has to be rearranged after a certain period of time. ICOSMO has been verified with data from a minimally viable prototype (MVP) deployed on buses at the Socie´te´ de Transport de l’Outaouais in Canada. Furthermore, the interviewees said that the framework also includes techniques for the composition and the characterization of industrial big data, which is essential to make a practical predictive maintenance system that can work in smart manufacturing environments

Advancements in Research and Treatment Applications of Patient-Derived Tumor Organoids in Colorectal Cancer.

Colorectal cancer (CRC) remains a significant health burden globally, being the second leading cause of cancer-related mortality. Despite significant therapeutic advancements, resistance to systemic antineoplastic agents remains an important obstacle, highlighting the need for innovative screening tools to tailor patient-specific treatment. This review explores the application of patient-derived tumor organoids (PDTOs), three-dimensional, self-organizing models derived from patient tumor samples, as screening tools for drug resistance in CRC. PDTOs offer unique advantages over traditional models by recapitulating the tumor architecture, cellular heterogeneity, and genomic landscape and are a valuable ex vivo predictive drug screening tool. This review provides an overview of the current literature surrounding the use of PDTOs as an instrument for predicting therapy responses in CRC. We also explore more complex models, such as co-cultures with important stromal cells, such as cancer-associated fibroblasts, and organ-on-a-chip models. Furthermore, we discuss the use of PDTOs for drug repurposing, offering a new approach to identify the existing drugs effective against drug-resistant CRC. Additionally, we explore how PDTOs serve as models to gain insights into drug resistance mechanisms, using newer techniques, such as single-cell RNA sequencing and CRISPR-Cas9 genome editing. Through this review, we aim to highlight the potential of PDTOs in advancing our understanding of predicting therapy responses, drug resistance, and biomarker identification in CRC management.

End-to-End Self-organizing Intelligent Security Model for Wireless Sensor Network based on a Hybrid (AES–RSA) Cryptography

End-to-End Self-organizing Intelligent Security Model for Wireless Sensor Network based on a Hybrid (AES–RSA) Cryptography

DECODING THE SEMANTIC VOLUME OF NOUNS WITH THE VERBAL MODALITY MÜSSEN: THE SYNERGISTIC DIMENSION OF LEAST EFFORT

The presented work is one of the stages of noun research within a certain verbal modality. The focus of the article lies in the study of the generating noun content in relation to the verbal modality müssen in German from the perspective of linguosynergetic scientific paradigm, which considers language as a complex dynamic system capable of self-organization and self-regulation. In this study methodologically important is the position that the synergetic law of making the least effort and conservation of speech energy is the driving factor of language development. In a synergetic sense, the author of the article draws parallels between the dictionary as a representative of the linguistic generalization of the structured amount of knowledge of the learned extraverbal reality and the mental lexicon, which is not an arbitrary accumulation of contributions, but constitutes a structured hierarchical system of such contributions. If lexical units in the synergetic sense and their meanings at the language level contain coded structured information, then in the same aspect the paper considers the implementation of the word in one of its meanings at the speech level as information decoding. The linguosynergetic format of the research proposed in this article assumes that language as a self-regulating system under the influence of external energy and information has developed a mechanism of different representations of nouns in syntactic constructions with different verbal modality in German-language fiction and press. The results obtained on the basis of the synergetic-quantitative approach are extrapolated to linguosynergetic models of self-organization of noun names according to the principle of minimization of efforts, which directs the human mental lexicon to optimal decoding of semantic volume of polysemic or monosemic nouns in relation to the verbal modality müssen in German-language fiction and press. It was proved that since the closest distances in the lexical structure of a word at the linguistic level are the closest in comparison with the structures of knowledge of the human mental lexicon, the construction of noun content in modern German on the synergetic principle of conservation of speech energy and minimization of efforts is the most optimal way of directing the mental lexicon of a person to decoding the semantic scope of the closets (main) meanings of the polysemic and unambiguous monosemic word models.

A self-organization reconstruction method of ESN reservoir structure based on reinforcement learning

The dynamic reservoir of the randomly generated Echo State Network (ESN) contains numerous redundant neurons, resulting in collinearity in the high-dimensional state space matrix. This collinearity impacts the prediction performance of the network. In order to address this issue, this paper introduces a self-organizing ESN structure optimization model that is based on reinforcement learning, called SR-ESN. The SR-ESN model employs pruning methods to reconstruct the reservoir using contribution and decision mechanisms. To mitigate potential instability caused by high coupling among neurons in a single reservoir, the concept of ensemble learning is applied to create multiple initial reservoir pools, thereby enhancing screening diversity. Simultaneously, the model utilizes reinforcement learning's decision mechanism to identify effective neurons. Neurons with low contribution are pruned, while those with high contribution are retained for self-organizing reconstruction. This optimization of the network structure enhances its prediction performance. Based on both artificial and real datasets, the proposed SR-ESN model demonstrates superior prediction performance with minimal structural complexity compared to other prediction models.

Information content and optimization of self-organized developmental systems

A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction-diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.

Finite-size and finite-time scaling for kinetic rough interfaces.

We consider discrete models of kinetic rough interfaces that exhibit space-time scale invariance in height-height correlation. We use the generic scaling theory of Ramasco et al. [Phys. Rev. Lett. 84, 2199 (2000)0031-900710.1103/PhysRevLett.84.2199] to confirm that the dynamical structure factor of the height profile can uniquely characterize the underlying dynamics. We apply both finite-size and finite-time scaling methods that systematically allow an estimation of the critical exponents and the scaling functions, eventually establishing the universality class accurately. The finite-size scaling analysis offers an alternative way to characterize the anomalous rough interfaces. As an illustration, we investigate a class of self-organized interface models in random media with extremal dynamics. The isotropic version shows a faceted pattern and belongs to the same universality class (as shown numerically) as the Sneppen model (version A). We also examine an anisotropic version of the Sneppen model and suggest that the model belongs to the universality class of the tensionless Kardar-Parisi-Zhang (tKPZ) equationin one dimension.

Emergence of Diverse Epidermal Patterns via the Integration of the Turing Pattern Model with the Majority Voting Model

Animal skin patterns are increasingly explained using the Turing pattern model proposed by Alan Turing. The Turing model, a self-organizing model, can produce spotted or striped patterns. However, several animal patterns exist that do not correspond to these patterns. For example, the body patterns of the ornamental carp Nishiki goi produced in Japan vary randomly among individuals. Therefore, predicting the pattern of offspring is difficult based on the parent fish. Such a randomly formed pattern could be explained using a majority voting model. This model is a type of cellular automaton model that counts the surrounding states and transitions to high-number states. Nevertheless, the utility of these two models in explaining fish patterns remains unclear. Interestingly, the patterns generated by these two models can be detected among very closely related species. It is difficult to think that completely different epidermal formation mechanisms are used among species of the same family. Therefore, there may be a basic model that can produce both patterns. Herein, the Turing pattern and majority voting method are represented using cellular automata, and the possibility of integrating these two methods is examined. This integrated model is equivalent to both models when the parameters are adjusted. Although this integrated model is extremely simple, it can produce more varied patterns than either one of the individual models. However, further research is warranted to determine whether this model is consistent with the mechanisms involved in the formation of animal fish patterns from a biological perspective.

The Clock and Wavefront Self-Organizing model recreates the dynamics of mouse somitogenesis in vivo and in vitro.

During mouse development, presomitic mesoderm cells synchronize Wnt and Notch oscillations, creating sequential phase waves that pattern somites. Traditional somitogenesis models attribute phase waves to a global modulation of the oscillation frequency. However, increasing evidence suggests that they could arise in a self-organizing manner. Here, we introduce the Sevilletor, a novel reaction-diffusion system that serves as a framework to compare different somitogenesis patterning hypotheses. Using this framework, we propose the Clock and Wavefront Self-Organizing model that considers an excitable self-organizing region where phase waves form independent of global frequency gradients. The model recapitulates the change in relative phase of Wnt and Notch observed during mouse somitogenesis and provides a theoretical basis for understanding the excitability of mouse presomitic mesoderm cells in vitro.

Local government behavior in rural construction land marketization in China: An archetype analysis

There are various rural construction land marketization (RCLM) patterns in China. Explorations on the factors influencing the differences between patterns can provide policy inspirations for establishing a unified land market in China. Archetype analysis enables us to reveal the structural relationships between various factors and elucidate reappearing patterns in the cases. Based on 29 cases, this study analyzes the effects of different factor combinations on the behavioral choices of local governments using the archetype analysis approach. Six archetypes are extracted, and the main findings from the archetype analysis are as follows: 1) The combination of active rural land markets and the effective operation of village autonomy or informal dialogue mechanisms led by rural elites promote the emergence of self-organization models for RCLM. Local governments mainly play guiding, supervisory, and service roles. 2) When the self-organization model is challenging to sustain, local governments participate in the process of RCLM with different mechanisms due to social benefits orientation. 3) In certain well-developed rural areas with a high level of industrialization and limited land resources, extensive government intervention has emerged as a new governance model to achieve regional coordinated development goals. Our work reveals the mechanisms through which local governments play a role in the reform and innovation of RCLM, and it contributes to a better understanding of local government positioning in the future RCLM reform process.