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Based on complex network theory, this paper establishes a Boolean competitive relationship network model and a weighted competitive relationship network model for building material enterprises based on big data and performs empirical analyses with construction prefabricated component (PC) enterprises in the Beijing–Tianjin–Hebei region as research samples. Results show the competitive relationship network of PC enterprises in the Beijing–Tianjin–Hebei region is characterised by the development of a small-world network, and its cumulative degree and out-degree (in-degree) intensity distributions are single-scale networks with fast-decaying tails. The network has strong network clustering and assortativity, and it can truly reflect the competitive status and dynamics of the enterprises.

Many patients that were infected with SARS-CoV-2 experience cognitive and affective symptoms weeks and months after their acute COVID-19 disease, even when acute symptoms were mild to moderate. For these patients, purely neurological explanations are struggling to explain the development and maintenance of the great variety of neuropsychiatric and cognitive symptoms occurring after COVID-19. We provide a psychological perspective based on the network theory of mental disorders as an added explanation that does not displace neurological mechanism but rather complements them. We suggest viewing the SARS-CoV-2 infection as a trigger that first activates nodes in a causally connected network of neuropsychiatric and cognitive symptoms. In the following, activation will spread throughout the network that will get in a self-sustaining stable and dysfunctional state manifesting in ongoing symptoms known as post-COVID-19 syndrome. The network perspective allows to generalize explanations for persistent neuropsychiatric and cognitive symptoms to patients that experienced mild or moderate acute courses of COVID-19, but also to similar phenomena following other viral infections. In addition, it could explain why some symptoms did not occur during acute COVID-19, but develop weeks or months after it. This network perspective shifts the focus from viewing persistent symptoms as a continuation of COVID-19 to acknowledging it as a complex syndrome that indeed originates from the disease but fully unfolds after it (post-COVID). To test the presented network perspective, we will need extensive cross-sectional as well as longitudinal data on cognitive and neuropsychiatric symptoms in post-COVID patients.

Background:: Contact tracing is one of the most effective non-pharmaceutical interventions in the COVID-19 pandemic. This study uses a multi-agent model to investigate the impact of four types of contact tracing strategies to prevent the spread of COVID-19. Methods:: In order to analyse individual contact tracing in a reasonably realistic setup, we construct an agent-based model of a small municipality with about 60.000 inhabitants (nodes) and about 2.8 million social contacts (edges) in 30 different layers. Those layers reflect demographic, geographic, sociological and other patterns of the TTWA (Travel-to-work-area) Hodonín in Czechia. Various data sources such as census, land register, transport data or data reflecting the shopping behaviour, were employed to meet this purpose. On this multi-graph structure we run a modified SEIR model of the COVID-19 dynamics. The parameters of the model are calibrated on data from the outbreak in the Czech Republic in the period March to June 2020. The simplest type of contact tracing follows just the family, the second tracing version tracks the family and all the work contacts, the third type finds all contacts with the family, work contacts and friends (leisure activities). The last one is a complete (digital) tracing capable of recalling any and all contacts.We evaluate the performance of these contact tracing strategies in four different environments. First, we consider an environment without any contact restrictions (benchmark); second with strict contact restriction (replicating the stringent non-pharmaceutical interventions employed in Czechia in the spring 2020); third environment, where the measures were substantially relaxed, and, finally an environment with weak contact restrictions and superspreader events (replicating the situation in Czechia in the summer 2020). Findings:: There are four main findings in our paper. 1. In general, local closures are more effective than any type of tracing. 2. In an environment with strict contact restrictions there are only small differences among the four contact tracing strategies. 3. In an environment with relaxed contact restrictions the effectiveness of the tracing strategies differs substantially. 4. In the presence of superspreader events only complete contact tracing can stop the epidemic. Interpretation:: In situations, where many other non-pharmaceutical interventions are in place, the specific extent of contact tracing may not have a large influence on their effectiveness. In a more relaxed setting with few contact restrictions and larger events the effectiveness of contact tracing depends heavily on their extent.

In the context of mathematical modeling, it is sometimes convenient to integrate models of different nature. These types of combinations, however, might entail difficulties even when individual models are well‐understood, particularly in relation to the well‐posedness of the ensemble. In this article, we focus on combining two classes of dissimilar diffusive models: the first one defined over a continuum and the second one based on discrete equations that connect average values of the solution over disjoint subdomains. For stationary problems, we show unconditional stability of the linked problems and then the stability and convergence of its discretized counterpart when mixed finite elements are used to approximate the model on the continuum. The theoretical results are highlighted with numerical examples illustrating the effects of linking diffusive models. As a side result, we show that the methods introduced in this article can be used to infer the solution of diffusive problems with incomplete data. [ABSTRACT FROM AUTHOR]

This article presents an approach for the construction of an expert system, which helps to make the decisions when parrying failures during information exchange in real-time systems based on a deterministic optical network. The authors analyze the requirements for deterministic data exchange environments and consider the trends for the development of software and hardware for deterministic networks. The main goal of this article is to propose a concept for the implementation of a deterministic control unit based on an all-optical network with spectral division multiplexing. Two main steps for the implementation of such a system are described. The first is the implementation of the DOS protocol stack and its model, which was used in several protocol development stages. This model is a part of a proposed concept for an automation design and development system for information exchanges. The second presented step is the development of an expert decision support system, based on the principles of the supervisory approach. This article presents a neurocontroller, which provides hardware support for the implementation of individual modes of operation of an expert system. [ABSTRACT FROM AUTHOR]

Evacuated tube collectors can achieve a much high efficiency and temperature for a much longer period compared to conventional single flat plate collector systems. However, they can be a lot more expensive compared to flat panel collectors. Hence, the building of a suitable model would make the analysis of the configuration of the systems more insightful to achieve higher efficiencies. In this paper, a thermal resistance network model of an evacuated tube heat pipe was developed. An analysis of its performance was done to understand the effect of adding an I-section geometry insert on the total thermal resistance, the heat transfer and the efficiency considering several working fluids such as water, ethanol, methanol, acetone and toluene. The proposed model was validated using data collected on a cloudy day from the SAURAN STATION located in UMLAZI, SOUTH AFRICA. This study reveals that the addition of an insert decreases the total thermal resistance, which results in improved efficiencies. Water exhibited higher efficiency compared to other working fluids with the efficiency of 62.24% and 54.27% corresponding to the configuration with and without a geometry insert. An improvement in the rate of heat transfer from 530 to 605 KW when introducing an insert into the heat pipe was observed. Interestingly, the solar collector exhibited better results, even on a cloudy day. The thermal resistance network model proposed in this paper was deemed appropriate to be used for the performance prediction of the heat pipe with an insert. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The relevance of this type of network is associated with the development and improvement of protocols, methods, and tools to verify routing policies and algorithmic models describing various aspects of SDN, which determined the purpose of this study. The main purpose of this work is to develop specialized methods to estimate the maximum end-to-end delay during packet transmission using SDN infrastructure. The methods of network calculus theory are used to build a model for estimating the maximum transmission delay of a data packet. The basis for this theory is obtaining deterministic evaluations by analyzing the best and worst-case scenarios for individual parts of the network and then optimally combining the best ones. It was found that the developed method of theoretical evaluation demonstrates high accuracy. Consequently, it is shown that the developed algorithm can estimate SND performance. It is possible to conclude the configuration optimality of elements in the network by comparing the different possible configurations. Furthermore, the proposed algorithm for calculating the upper estimate for packet transmission delay can reduce network maintenance costs by detecting inconsistencies between network equipment settings and requirements. The scientific novelty of these results is that it became possible to calculate the achievable upper data delay in polynomial time even in the case of arbitrary tree topologies, but not only when the network handlers are located in tandem. Doi: 10.28991/ESJ-2022-06-05-010 Full Text: PDF

Formulation the problem. Large-scale projects of the modern society include a large number of different types of work. The network graph is the main document for planning and managing such projects. This is an information-dynamic model of the sequence of works and the relationships between them. From a mathematical point of view, the network model is a finite-oriented graph. The construction of such a schedule begins with the division of the project into clearly defined works, for which the duration is specified. It is the mathematical approach that can and should replace the still widespread mechanical approach to work planning with a scientifically sound division of the production program between departments. Most authors reveal the essence of mathematical modeling through a system of complex mathematical formulas or emphasize the use of information systems and technologies. The conducted research allowed us to draw a conclusion that some discrete process management issues (namely time management) need further consideration and research. Materials and Methods. During the study, a set of theoretical, empirical, and modeling methods were applied, in particular: systematic analysis of scientific and methodological sources, the results of domestic and foreign experience on the problem, review of existing software to determine the problem solution state and select development tools of web service, analysis of methods and technologies network graphs construction, generalization of information on the problem. Results. A review of previous research in this area has been conducted. The results of the pedagogical experiment and theoretical and methodological bases of construction of network graphs in works planning tasks are analyzed. The necessity of using the apparatus of graph theory to solve the problems of optimal work planning is substantiated. Based on the methodological strategy, a web application was developed that allows you to plan the progress of work in complex projects by building an appropriate network schedule. A brief description of the functionality and user interface of the proposed web application is provided. Conclusions. A feature of the created application is the solution to the problem of planning the implementation of complex works, involving network models, which simplifies the understanding of the model as a whole and provides optimization of the developed graph-based on mathematical methods. The program displays the results of planning in graphical and textual representation, this facilitates and makes the choice of solution obvious, allows you to monitor the progress of events, and makes adjustments to the model to improve optimization. The study was conducted within the Norwegian-Ukrainian Project CPEA-ST-2019/10067 Development of students' mathematical competencies through Digital Mathematical Modelling (DeDiMaMo) in partnership between the Ternopil Volodymyr Hnatiuk National Pedagogical University and the University of Agder (Norway), Borys Grinchenko Kyiv University.

Traditional neural networks have the disadvantages of over-reliance on hardware resources and high requirements for application equipment performance. Therefore, they cannot be deployed on edge devices and mobile terminals with limited computing power. The application development of artificial intelligence technology is limited to a certain extent. However, with the advent of the technological age, artificial intelligence, which is affected by user requirements, urgently needs to be able to successfully perform operations such as computer vision applications on portable devices. For this reason, this paper takes the convolution of popular lightweight neural networks in recent years as the research object. Firstly, by introducing the concept of lightweight neural network, the development status of lightweight neural networks and the problems faced by convolution in the network are introduced. Secondly, the convolution is divided into three aspects: lightweight of convolution structure, lightweight of convolution module and lightweight of convolution operation, specifically through the study of the convolution design in various lightweight neural network models, the lightweight effects of different convolutions are demonstrated, and the advantages and disadvantages of the optimization methods are explained. Finally, the main ideas and usage methods of all lightweight model convolutional design in this paper are summarized and analyzed, and their possible future development is prospected.

In this paper we tried to study the values of radiant temperatures (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) for areas occupied by buildings and green spaces. The area affected by the Urban Heat Island (UHI) was also determined. Study Area, Iasi, the largest city in eastern Romania, is geographically situated on latitude 47°12'N to 47°06'N and longitude 27°32'E to 27°40'E. LST is an estimate of ground temperature and is important to identify change in environment. An important parameter in global climate change is rapid urbanization which leads to an increase in Land Surface Temperature (LST). The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural zones due to urbanization. It also been found that night UHI is more powerful than day. At night the LST values for SUHI varies between 24.5°C-25.9°C, and during the day between 35°C-38.7°C. With the development of remote sensing technology, it has become an important approach to urban heat island research. MODIS and Landsat data were used to estimate the LST and NDVI. From the analysis of the images it can be seen that the temperatures in SUHI are lower where there are green spaces around the buildings, and temperatures are higher in the non-UHI area, where inside or around the green spaces there are surfaces built or covered with concrete. Statistical data show very average temperatures for areas affected by UHI, 37.8°C for daytime and 24.6°C for night.

Background: Running participation/performance is a complex system. Understanding the variables associated with these behaviors may help to increase population physical activity and improve performance. This study aimed to investigate social and training variables important for running performance using a network approach. Methods: This cross-sectional study sampled 1151 non-professional Brazilian runners of both sexes (women, 38.2%; men, 61.7%). A questionnaire was available for eligible participants using an online platform, and information regarding training (volume and running pace) and social variables (participation in a running event, participation in a running group, influence on running, runners in the family, and childhood sport) related to runners' performance was obtained. The Chi-square test and network model were estimated by sex. Results: Training characteristics tend to be clustered. For both sexes, the training volume node presented the highest expected influence in the network (1.69 for women and 2.16 for men). Centrality indicators of social variables show that sports childhood participation and the presence of runners in the family were the most important nodes of network connection for women and men, respectively. Conclusion: Based on these findings, it was concluded that sports participation during childhood and the practice of running by other family members were important factors to connect variables in the network. These findings have practical applications for health policymakers, highlighting the need to develop educational programs to increase sports participation during childhood and within families. Key points: Sports participation during childhood was the most important social variable in female runners' performance; Running practice in the family was related to male runners' performance; Among social and training variables, volume for the week presented the strongest expected influence for runners of both sexes. [ABSTRACT FROM AUTHOR]

In the cellular response to stresses, the tumor suppressor p53 is activated to maintain genomic integrity and fidelity. As a transcription factor, p53 exhibits rich dynamics to allow for discrimination of the type and intensity of stresses and to direct the selective activation of target genes involved in different processes including cell cycle arrest and apoptosis. In this review, we focused on how stresses are encoded into p53 dynamics and how the dynamics are decoded into cellular outcomes. Theoretical modeling may provide a global view of signaling in the p53 network by coupling the encoding and decoding processes. We discussed the significance of modeling in revealing the mechanisms of the transition between p53 dynamic modes. Moreover, we shed light on the crosstalk between the p53 network and other signaling networks. This review may advance the understanding of operating principles of the p53 signaling network comprehensively and provide insights into p53 dynamics-based cancer therapy. [ABSTRACT FROM AUTHOR]

This paper presents the network-based modeling, validation and analysis of the nonlinear liquid spring damper model under vertical landing conditions of reusable launch vehicle. The impedance function of damper model is derived first. Then, its mechanical and hydraulic networks are newly established based on the hydro-mechanical analogy and network-based analysis. By comparing the networks between the corresponding symmetric and asymmetric structures, the meaning of each branch in the network is elucidated. After that, the validity of the network-based model for the liquid spring damper is confirmed by comparison against the experimentally verified nonlinear model in both frequency and time domain. The force and energy absorption characteristics of the damper model are further decomposed, and, specifically, the influence of the orifice area and orifice length on the attenuation performance is studied. The results show that the network-based model provides predictions consistent with those generated by the nonlinear model. The main discrepancy is attributed to the inaccuracy caused by the equivalent fluid bulk modulus. The network-based analysis indicates that the orifice area mainly influences the damping force in the network, which further affects the loads and efficiency of the damper. The orifice length mainly influences the inertia force in the network, which should be limited to a small value. The proposed novel interpretation of the damper models and responses under impact conditions constitutes a framework suitable for systematic design of typically highly nonlinear landing systems in reusable launch vehicles. [ABSTRACT FROM AUTHOR]

Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree distributions were neither Poisson distributions nor pure power-law distributions. We have proposed a model of network evolution with different initial attractiveness for in-degrees and out-degrees of nodes and preferential attachment, which reproduces the asymmetry between in-degrees and out-degrees and similar degree distributions via the tuning of the initial attractiveness values. In this study, we present the well-preserved structural properties of C. elegans neuronal networks across development, and provide some insight into understanding the evolutionary processes of biological neural networks through a simple network model. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lack of progress in treating major depressive disorder (MDD) along with critiques of the reductionist biomedical model have motivated psychiatric researchers to develop alternative models of mental disorders. Over the last decade, network models have been developed that conceptualise mental disorders as the product of dynamic causal interactions between psychiatric symptoms, especially when these symptoms form self-reinforcing feedback loops. This article discusses how problems with the biomedical model have motivated research into network models of MDD. The network approach is then compared with critical realist (CR) theorising on MDD within the sociology of mental health. This article argues that critical realism and network approaches overlap significantly in their ontological and epistemological assumptions about mental disorders. Therefore, CR and network models are potentially complementary approaches for sociological theorising about MDD. The final section of this article discusses tensions between these two approaches and limitations of current network models of MDD from a CR perspective. [ABSTRACT FROM AUTHOR]

This paper is concerned with a dynamic inventory control system described by a network model where the nodes are warehouses and the arcs represent production and distribution activities. We assume that an uncertain demand may take any value in an assigned interval and we allow that the system is disturbed by noise inputs. These assumptions yield a model with a mix of interval and stochastic demand uncertainties. We use the method of model predictive control to derive the control strategy. To deal with interval uncertainty we use the interval analysis tools and act according to the interval analysis theory. The developed results are illustrated using a numerical example. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is a crucial work direction in speech recognition. To increase the performance of speech emotion detection, researchers have worked relentlessly to improve data augmentation, feature extraction, and pattern formation. To address the concerns of limited speech data resources and model training overfitting, A-CapsNet, a neural network model based on data augmentation methodologies, is proposed in this research. In order to solve the issue of data scarcity and achieve the goal of data augmentation, the noise from the Noisex-92 database is first combined with four different data division methods (emotion-independent random-division, emotion-dependent random-division, emotion-independent cross-validation and emotion-dependent cross-validation methods, abbreviated as EIRD, EDRD, EICV and EDCV, respectively). The database EMODB is then used to analyze and compare the performance of the model proposed in this paper under different signal-to-noise ratios, and the results show that the proposed model and data augmentation are effective. [ABSTRACT FROM AUTHOR]

The project was carried out to implement a network monitoring system to establish the baseline of optimal performance and to know the status of devices, links, resources and services that make up the computer network infrastructure of the UFPS Cúcuta and Campos Elíseos headquarters. The first step was to gather information about the network infrastructure, which allowed defining the needs and requirements to be met by the monitoring tool. Subsequently, the most appropriate tool was determined, and the monitoring system was designed and implemented. Finally, tests and adjustments were carried out to validate and verify the implemented solution and the installation, configuration and administration manuals for the monitoring system were prepared to guide the unit in charge of these procedures. These activities were developed under the PPDIOO (Prepare, Plan, Design, Implement, Operate and Optimize) methodology proposed by Cisco. The project's development contributes to improving the availability of the data network at UFPS, since, by alerting and notifying the network administrator immediately, he/she can act quickly in case of an eventuality. It also contributes as a tool to improve the mathematical model that supports the computer network of the UFPS for its continuous study according to changing needs. [ABSTRACT FROM AUTHOR]

Paper simulations that resolve the entire microscopic fiber structure are typically time-consuming and require extensive resources. Several such modeling approaches have been proposed to analyze different properties in paper. However, most use non-linear and time-dependent models resulting in high computational complexity. Resolving these computational issues would increase its usefulness in industrial applications. The model proposed in this work was developed in collaboration with companies in the papermaking industry within the Innovative Simulation of Paper (ISOP) project. A linear network model is used for efficiency, where 1-D beams represent the fibers. Similar models have been proposed in the past. However, in this work, the paper models are three-dimensional, a new dynamic bonding technique is used, and more extensive simulations are evaluated. The model is used to simulate tensile stiffness, tensile strength, and bending resistance. These simulated results are compared to experimental and theoretical counterparts and produce representable results for realistic parameters. Moreover, an off-the-shelf computer accessible to a paper developer can evaluate these models structural properties efficiently. [ABSTRACT FROM AUTHOR]

At present, there are many ways to build the network model of C4ISR, most of which are from the perspective of support personnel, and it is difficult to directly reflect the support characteristics of C4ISR for combat tasks from the perspective of commanders. To solve this problem, a network model of combined brigade C4ISR based on support chain is constructed. Firstly, starting with the operational support characteristics of the C4ISR, four functional chains, namely, observation chain, control chain, attack chain and support chain, are analyzed. Secondly, the nodes and element edges are extracted from the support chain, the network model is constructed, and the index of node chain degree and node support rate is put forward. Finally, taking a typical combined brigade C4ISR as an example, the simulation results show that the network model based on the support chain can reflect the support capability of the C4ISR, verify the feasibility and effectiveness of the model, and provide a new idea for the analysis of the support characteristics of the C4ISR. [ABSTRACT FROM AUTHOR]

Conventional deep brain stimulation (DBS) for movement disorders is a well-established clinical treatment. Over the last few decades, over 200,000 people have been treated by DBS worldwide for several neurological conditions, including Parkinson's disease and Essential Tremor. DBS involves implanting electrodes into disorder-specific targets in the brain and applying an electric current. Although the hardware has developed in recent years, the clinically used stimulation pattern has remained as a regular frequency square pulse. Recent studies have suggested that phase-locking, coordinated reset or irregular patterns may be as or more effective at desynchronising the pathological neural activity. Such studies have shown efficacy using detailed neuron models or highly simplified networks and considered one frequency band. We previously described a population level model which generates oscillatory activity in both the beta band (20 Hz) and the tremor band (4 Hz). Here we use this model to look at the impact of applying regular, irregular and phase dependent bursts of stimulation, and show how this influences both tremor- and beta-band activity. We found that bursts are as or more effective at suppressing the pathological oscillations compared to continuous DBS. Importantly however, at higher amplitudes we found that the stimulus drove the network activity, as seen previously. Strikingly, this suppression was most apparent for the tremor band oscillations, with beta band pathological activity being more resistant to the burst stimulation compared to continuous, conventional DBS. Furthermore, our simulations showed that phase-locked bursts of stimulation did not convey much improvement on regular bursts of oscillation. Using a genetic algorithm optimisation approach to find the best stimulation parameters for regular, irregular and phase-locked bursts, we confirmed that tremor band oscillations could be more readily suppressed. Our results allow exploration of stimulation mechanisms at the network level to formulate testable predictions regarding parameter settings in DBS., (Creative Commons Attribution license.)

Outlier detection aims to reveal data patterns different from existing data. Benefit from its good robustness and interpretability, the outlier detection method for numerical dataset based on $k$ -Nearest Neighbor ( $k$ -NN) network has attracted much attention in recent years. However, the datasets produced in many practical contexts tend to contain both numerical and categorical attributes, that are, the datasets with mixed-valued attributes (DMAs). And, the selection of $k$ is also an issue that is worthy of attention for unlabeled datasets. Therefore, an unsupervised outlier detection method for DMA based on an adaptive $k$ -NN global network is proposed. First, an adaptive search algorithm for the appropriate value of $k$ considering the distribution characteristics of datasets is introduced. Next, the distance between mixed-valued data objects is measured based on the Heterogeneous Euclidean-Overlap Metric, and the $k$ -NN of a data object is obtained. Then, an adaptive $k$ -NN global network is constructed based on the neighborhood relationships between data objects, and a customized random walk process is executed on it to detect outliers by using the transition probability to limit behaviors of the random walker. Finally, the effectiveness, accuracy, and applicability of the proposed method are demonstrated by a detailed experiment.

To solve the problems of low security and low reliability in data sharing, this paper proposes data sharing network model and mechanism of power Internet of Things in virtualized environment. Due to the different coverage of the power Internet of Things, this paper proposes a node model based on the node network theory of blockchain technology. This model divides network nodes into data consumer nodes, data storage nodes, routing node, and coordination nodes according to business requirements. Through the cooperation of the four types of nodes, data sharing between multiple power Internet of Things can be realized efficiently. To solve the problem of low security in data sharing, this paper constructs a data transmission contribution analysis model of network nodes and a data access authorization model. The data transmission contribution degree model can fairly evaluate the behavior of network nodes based on the contribution degree, so as to quickly find malicious nodes. The data access authorization model calculates the entropy weight of each Internet of Things according to the trust level, thereby calculating the credibility of data sharing. To solve the problem of low transmission reliability of network nodes, a set of alternative links is constructed for data sharing routes. With load balancing as the goal, a relative cost value evaluation model is constructed for each route. The shortest route with the best relative cost value is regarded as the optimal data sharing route. In the simulation experiment, the related mechanisms are first improved based on the mechanism of this paper, which verifies that the mechanism of this paper improves the utilization and reliability of network resources in terms of data sharing routing. Secondly, it is verified that the mechanism of this paper improves the success rate and availability rate of network resources in terms of data security sharing.

The management of street vendors that interferes with city governance has not been effective because it is sporadically done by the displacement or seizure of trading facilities. The pattern is thus ineffective in its construction because it does not reduce the amount, nor the behavior of the street vendors. This research aims to determine the pattern of coaching and explore the model of handling the use of street vendors through a synergic approach between agencies in coordination. This study uses descriptive type with a qualitative approach. The research site is the location of the street vendors trade in the city of Makassar, the research informant consists of the street vendors, government officials, and public citizens. Data is collected through direct interviews with an informant, field observations, and document analysis. Data analysis is done at the same time when collecting data using the interactive models of Huberman and Miles is data collection, data reduction, display data, and conclusion drawing and verifying. Data, source, and time triangulating data. The results showed the construction of the street vendors still oriented to the regulatory offenders and carried out partially by the agency according to its interests so that the handling has not synergized between government agencies and other related parties. Construction of street vendors is institutionally implemented by sub-district offices and assisted by Makassar public order police, while for the utilization of street vendors needed a model coordinated centrally by the mayor to be structured, programmatic, and integrated so that the street vendors can be empowered and contribute to the development of the city.

Conserving genetic connectivity is fundamental to species persistence, yet rarely is made actionable into spatial planning for imperilled species. Climate change and habitat degradation have added urgency to embrace connectivity into networks of protected areas. Our two-step process integrates a network model with a functional connectivity model, to identify population centres important to maintaining genetic connectivity then to delineate those pathways most likely to facilitate connectivity thereamong for the greater sage-grouse (Centrocercus urophasianus), a species of conservation concern ranging across eleven western US states and into two Canadian provinces. This replicable process yielded spatial action maps, able to be prioritized by importance to maintaining range-wide genetic connectivity. We used these maps to investigate the efficacy of 3.2 million ha designated as priority areas for conservation (PACs) to encompass functional connectivity. We discovered that PACs encompassed 41.1% of cumulative functional connectivity—twice the amount of connectivity as random—and disproportionately encompassed the highest-connectivity landscapes. Comparing spatial action maps to impedances to connectivity such as cultivation and woodland expansion allows both planning for future management and tracking outcomes from past efforts.

Zinc oxide (ZnO) nanocatalyst was doped with nickel (Ni2+) and silver (Ag2+) by co-precipitation method for effective degradation of acid blue 113 dye (AB113) from textile industries wastewater. The synthesized co-doped nanocatalyst was characterised using a scanning electron microscope, and its size was around 50-200 nm. The elemental constituents of Ag2+ and Ni2+ in the co-doped zinc oxide nanocatalyst have analysed the results of energy dispersive analysis. The effects of the process parameters pH, initial dye concentration and catalyst loading were optimized to get the maximum dye removal from the wastewater. The acid blue 113 dye gets degraded entirely once it was subjected to irradiation of 120 min. The co-doped and un-doped zinc oxide nanocatalyst were used to degrade dye molecules. The co-doped ZnO nanocatalyst was twice effective as the un-doped ZnO nanocatalyst in the entire dye degrading phenomena upon exposure to the sunlight. The kinetic models have been applied to predict kinetic behaviour, and its mechanism. The present study promotes the importance of co-doped ZnO nanocatalysts in the treatment of azo dyes discharged from the textile industry.