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1
Lima, Verônica dos Santos. "Práticas exitosas e Currículo Contextualizado em escolas do campo no município de Teotônio Vilela, Alagoas." Revista Interseção 4, no. 1 (2023): 113–35. http://dx.doi.org/10.48178/intersecao.v4i1.429.
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RESUMO: O presente relato de experiências busca apresentar as práticas exitosas desenvolvidas com ênfase no currículo contextualizado nas instituições do campo na rede municipal de ensino de Teotônio Vilela-AL, em 2022. O mesmo faz uma discussão sobre a Educação do Campo e do currículo contextualizado, mostrando conceitos e possibilidades de contextualizar o currículo apresentando as contribuições da Rede de Educação Contextualizada do Agreste e Semiárido de Alagoas (Recasa) no agreste alagoano a inserção da Pedagogia Educacional de Apoio ao Desenvolvimento Sustentável (PEADS) no currículo das escolas do campo. Como instrumentos de pesquisa qualitativa, fora utilizada a ficha de monitoramento da prática da coordenação pedagógica e a observação direta das práticas contextualizadas dos/as professores/as durante o processo de ensino aprendizagem das escolas do campo do município de Teotônio Vilela, AL. Ao mesmo tempo que descrevemos como acontecem as práticas pedagógicas exitosas nas escolas do campo no município. O relato de experiências está ancorado a partir dos pressupostos teóricos de Arroio (2011) e Molina; Sá (2012) entre outros. E por fim, com base nos resultados da pesquisa foi possível denotar os bons resultados na aprendizagem, na não evasão, no índice de aprovação nas escolas, bons resultados no IDEB entre outros elementos que condicionam a harmonia entre as práticas pedagógicas e a execução do currículo enquanto suporte teórico norteador.
 Palavras-chave: Educação do Campo. Currículo. Contextualização.
 
 ABSTRACT: This experience report seeks to present the successful practices developed with emphasis on the contextualized curriculum in rural institutions in the municipal teaching network of Teotônio Vilela-AL, in 2022. The same makes a discussion about the Education of the Field and the contextualized curriculum, showing concepts and possibilities of contextualizing the curriculum presenting the contributions of the Network of Contextualized Education of the Agreste and Semi-arid of Alagoas (Recasa) in the agreste of Alagoas the insertion of the Educational Support Pedagogy to Sustainable Development (PEADS) in the curriculum of rural schools. As qualitative research instruments, the monitoring form of the practice of pedagogical coordination and the direct observation of the contextualized practices of the teachers during the teaching-learning process of the rural schools in the municipality of Teotônio Vilela, AL were used. At the same time we describe how successful pedagogical practices occur in rural schools in the municipality. The experience report is anchored from the theoretical assumptions of Arroio (2011) and Molina; Sá (2012) among others. And finally, based on the results of the research, it was possible to denote the good results in learning, in the non-evasion, in the approval rate in schools, good results in the IDEB, among other elements that condition the harmony between the pedagogical practices and the execution of the curriculum. as a guiding theoretical support.
 Keywords: Field Education. Curriculum. Contextualization.
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Sullivan, Brendan, Rick Archibald, Jahaun Azadmanesh, et al. "BraggNet: integrating Bragg peaks using neural networks." Journal of Applied Crystallography 52, no. 4 (2019): 854–63. http://dx.doi.org/10.1107/s1600576719008665.
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Neutron crystallography offers enormous potential to complement structures from X-ray crystallography by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing to the challenge of integrating peak shapes from pulsed-source experiments. To advance existing software, this article demonstrates the use of machine learning to refine peak locations, predict peak shapes and yield more accurate integrated intensities when applied to whole data sets from a protein crystal. The artificial neural network, based on the U-Net architecture commonly used for image segmentation, is trained using about 100 000 simulated training peaks derived from strong peaks. After 100 training epochs (a round of training over the whole data set broken into smaller batches), training converges and achieves a Dice coefficient of around 65%, in contrast to just 15% for negative control data sets. Integrating whole peak sets using the neural network yields improved intensity statistics compared with other integration methods, including k-nearest neighbours. These results demonstrate, for the first time, that neural networks can learn peak shapes and be used to integrate Bragg peaks. It is expected that integration using neural networks can be further developed to increase the quality of neutron, electron and X-ray crystallography data.
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Chen Benyong, 陈本永, 赵勇 Zhao Yong, 楼盈天 Lou Yingtian та ін. "基于卷积神经网络智能识别吸收峰的激光稳频方法". Chinese Journal of Lasers 51, № 17 (2024): 1701005. http://dx.doi.org/10.3788/cjl231308.
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Yao, Shunyu, Weike Lu, Lan Liu, and Guojing Hu. "The Peak Stability Analysis through Hysteresis Phenomenon on Heterogeneous Networks." Journal of Advanced Transportation 2024 (February 6, 2024): 1–13. http://dx.doi.org/10.1155/2024/4166921.
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The macroscopic fundamental diagram (MFD) is a nonuniversal changing process over network traffic status which indicates different shapes in different networks. Hysteresis is observed in the MFD of some urban networks. It is a unique phenomenon when the network remains at low stability level and usually appears around the congestion period. This paper analyzed network peak stability through focusing on hysteresis. The formation mechanism of hysteresis is deduced from the mathematical method based on previous research studies. The precondition of hysteresis and the changing process of network state can be figured by mathematical deduction. It indicates that hysteresis only occurs conditionally in the period of macroscopic congestion and is not a universal phenomenon. Heterogeneity is an important factor leading to network instability. The hysteresis patterns of different peaks in MFD are different due to the variation of network flow. Real data are collected from Atlanta’s urban network to verify the analysis of hysteresis. To discuss the changing process of hysteresis in different peaks, a three-stage division is proposed and time series is presented as a third dimension in MFD. It is worth noticing that the existence and form of hysteresis in morning and evening peaks are different. Although there is a higher peak flow in the morning peak, the stability of the evening peak performs better when hysteresis occurs in the network. The different fluctuations in the morning and evening peaks are exhibited through the 3D version of MFD. The otherness of hysteresis in different peaks is explained through a 3D coordinate system with cross-compared corresponding indexes.
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Martynenko, A. V., and O. N. Ie. "Modelling of natural development of interurban network of vehicular roads." Herald of the Ural State University of Railway Transport, no. 2 (2020): 4–12. http://dx.doi.org/10.20291/2079-0392-2020-2-4-12.
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The main peculiarities of transport network functioning and structure are the result of reciprocal influence of the network and its service area. Namely, current transport needs of the area are the key factor, affecting any specific decision on creating a new network element. However, transport needs of the area significantly changes in the course of time and certain management decisions are poorly coordinated and made in conditions of incomplete information on future network state. Therefore, transport networks have a variety of features of natural objects and their development can be regarded as a result of implementation of some in-ner consistent patterns and mechanisms. The models of natural development of transport networks influenced by various external and internal factors are dealt with in the article. An approach to modelling of natural development of a transport network based on information on spatial location of its peaks is suggested. As an illustration of potentiality of the given approach Sverdlovsk region network of vehicular roads is used. A measure which indicates morphological similarity of the networks is introduced. The models of Sverdlovsk region vehicular road network are built for different representations of function, which characterize the extent of remoteness of one peak to the pair to other peaks. The generating network model which is sufficiently close to a real network is obtained. It is demonstrated that many fundamental features of a real network are the result of location of its peaks and for their explanation there is no need in additional social-economic and demographic information.
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Chesshire, Paige R., Lindsie M. McCabe, and Neil S. Cobb. "Variation in Plant–Pollinator Network Structure along the Elevational Gradient of the San Francisco Peaks, Arizona." Insects 12, no. 12 (2021): 1060. http://dx.doi.org/10.3390/insects12121060.
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The structural patterns comprising bimodal pollination networks can help characterize plant–pollinator systems and the interactions that influence species distribution and diversity over time and space. We compare network organization of three plant–pollinator communities along the altitudinal gradient of the San Francisco Peaks in northern Arizona. We found that pollination networks become more nested, as well as exhibit lower overall network specialization, with increasing elevation. Greater weight of generalist pollinators at higher elevations of the San Francisco Peaks may result in plant–pollinator communities less vulnerable to future species loss due to changing climate or shifts in species distribution. We uncover the critical, more generalized pollinator species likely responsible for higher nestedness and stability at the higher elevation environment. The generalist species most important for network stability may be of the greatest interest for conservation efforts; preservation of the most important links in plant–pollinator networks may help secure the more specialized pollinators and maintain species redundancy in the face of ecological change, such as changing climate.
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Meng, Yuanyuan, and Xiyu Liu. "Finding Central Vertices and Community Structure via Extended Density Peaks-Based Clustering." Information 12, no. 12 (2021): 501. http://dx.doi.org/10.3390/info12120501.
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Community detection is a significant research field of social networks, and modularity is a common method to measure the division of communities in social networks. Many classical algorithms obtain community partition by improving the modularity of the whole network. However, there is still a challenge in community division, which is that the traditional modularity optimization is difficult to avoid resolution limits. To a certain extent, the simple pursuit of improving modularity will cause the division to deviate from the real community structure. To overcome these defects, with the help of clustering ideas, we proposed a method to filter community centers by the relative connection coefficient between vertices, and we analyzed the community structure accordingly. We discuss how to define the relative connection coefficient between vertices, how to select the community centers, and how to divide the remaining vertices. Experiments on both real and synthetic networks demonstrated that our algorithm is effective compared with the state-of-the-art methods.
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Roberts-Lombard, Mornay, and Daniël Johannes Petzer. "Customer satisfaction/delight and behavioural intentions of cell phone network customers – an emerging market perspective." European Business Review 30, no. 4 (2018): 427–45. http://dx.doi.org/10.1108/ebr-03-2017-0061.
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PurposeThis study aims to investigate the extent to which the satisfaction/delight experienced by customers of cell phone network service providers is influenced by their perceptions of the networks’ employee service delivery skills and the value that the customers derive from the networks’ offerings. In turn, the influence of the extent of their satisfaction/delight on future behavioural intention (BI) is determined.Design/methodology/approachA descripto-explanatory research design is followed and data are collected from satisfied/delighted cell phone network service provider customers using self-administered questionnaires. A total of 593 responses were suitable for analysis. An exploratory factor analysis is used to uncover the interrelationships between the items measuring the study’s constructs. Furthermore, the measurement and structural models are assessed.FindingsPerceived employee service delivery skills (PESDS) and value significantly and positively influence customer satisfaction/delight experiences, whereas customer satisfaction/delight experiences significantly and positively influence their BIs.Research limitations/implicationsThe model tested confirms the hypothesised relationships between PESDS, perceived value, customer satisfaction/delight experiences and BIs of cell phone network customers. Customer satisfaction/delight experiences are linked to their two antecedents (PESDS and value) and their outcome, BI.Practical implicationsThe findings assist cell phone network service providers in understanding how PESDS and value can foster customer delight, ultimately leading to positive BIs from customers.Originality/valueThis study focuses only on satisfied customers and determines the interrelationships of the extent to which they encounter customer satisfaction/delight experiences and related constructs. Few research studies, however, have examined how customer satisfaction/delight experiences relate to its antecedents and outcome.
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Al Alam, Pamela, Joseph Constantin, Ibtissam Constantin, and Clelia Lopez. "Partitioning of Transportation Networks by Efficient Evolutionary Clustering and Density Peaks." Algorithms 15, no. 3 (2022): 76. http://dx.doi.org/10.3390/a15030076.
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Road traffic congestion has became a major problem in most countries because it affects sustainable mobility. Partitioning a transport network into homogeneous areas can be very useful for monitoring traffic as congestion is spatially correlated in adjacent roads, and it propagates at different speeds as a function of time. Spectral clustering has been successfully applied for the partitioning of transportation networks based on the spatial characteristics of congestion at a specific time. However, this type of classification is not suitable for data that change over time. Evolutionary spectral clustering represents a state-of-the-art algorithm for grouping objects evolving over time. However, the disadvantages of this algorithm are the cubic time complexity and the high memory demand, which make it insufficient to handle a large number of data sets. In this paper, we propose an efficient evolutionary spectral clustering algorithm that solves the drawbacks of evolutionary spectral clustering by reducing the size of the eigenvalue problem. This algorithm is applied in a dynamic environment to partition a transportation network into connected homogeneous regions that evolve with time. The number of clusters is selected automatically by using a density peak algorithm adopted for the classification of traffic congestion based on the sparse snake similarity matrix. Experiments on the real network of Amsterdam city demonstrate the superiority of the proposed algorithm in robustness and effectiveness.
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Wu, Shaolei, Jianing Wu, Di Lu, Hossein Azadi, and Jie Liu. "A Coupling Model for Measuring the Substitution of Subways for Buses during Snowstorms: A Case Study of Shenyang, China." Sustainability 16, no. 4 (2024): 1486. http://dx.doi.org/10.3390/su16041486.
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The development of integrated public transportation networks has received widespread attention in recent years. Especially in global northern cities, improving the substitution of subways for buses could meet population travel demand during snowstorms, which minimizes the impact of snowstorms on the public transportation network. Furthermore, the development of rail transit is conducive to the intensive and efficient use of land resources. Therefore, in this study, we selected a northern Chinese city, Shenyang, as a case study. For obtaining the population travel demand, we collected the actual population flow data in the morning and evening peaks during snowstorms. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively. A coupling model was built to measure the coupling value of each station in the subway and bus networks, according to its population travel demand and supply capacity, which was further used to measure the substitution of subways for buses in the morning and evening peaks during snowstorms. The results indicate that some subway stations were in a coupling state, while their surrounding bus stations were in a decoupling state. These subway stations could replace the bus stations to reduce the impact and damage of snowstorms on public transportation network. However, some subway stations and the surrounding bus stations were all in a decoupling state, which were under great pressure to meet the population commuting demand during snowstorms. This study can provide insight into optimizing public transportation network planning and design in many northern regions and help to coordinate land and transportation utilization.
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Li, Hui Feng, Jian Wang, Yun Hua Huang, and Yue Zhang. "Three-Dimensional Zinc Oxide Nanorod Networks." Advanced Materials Research 79-82 (August 2009): 457–60. http://dx.doi.org/10.4028/www.scientific.net/amr.79-82.457.
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Three-dimensional (3D) ZnO nanorod networks were synthesized through the direct evaporation of metal zinc with high purity via a chymical evaporation deposition (CVD) method in Ar and O2 at 910 °C without any catalyst. The nanorod networks of as-synthesized ZnO were characterized using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and X-ray diffraction (XRD). The branches within one network show very regular orientation relationships: either perpendicular or parallel to each other. The nanorods follow a growth direction [0001]. Photoluminescence (PL) spectroscopy were measured at room temperature and showed the different PL features of other nanostructures. Two typical emission peaks at -401 nm and at 452-495 nm were observed. Specially, the emission peak at 452-495 nm includes four subordinate peaks.
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Wang, Shuai, and Chunwu Liu. "Automatic Modulation Classification with Neural Networks via Knowledge Distillation." Electronics 11, no. 19 (2022): 3018. http://dx.doi.org/10.3390/electronics11193018.
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Deep learning is used for automatic modulation recognition in neural networks, and because of the need for high classification accuracy, deeper and deeper networks are used. However, these are computationally very expensive for neural network training and inference, so its utility in the case of a mobile with memory limitations or weak computational power is questionable. As a result, a trade-off between network depth and network classification accuracy must be considered. To address this issue, we used a knowledge distillation method in this study to improve the classification accuracy of a small network model. First, we trained Inception–Resnet as a teacher network, which has a size of 311.77 MB and a final peak classification accuracy of 93.09%. We used the method to train convolutional neural network 3 (CNN3) and increase its peak classification accuracy from 79.81 to 89.36%, with a network size of 0.37 MB. It was also used similarly to train mini Inception–Resnet and increase its peak accuracy from 84.18 to 93.59%, with a network size of 39.69 MB. When we compared all classification accuracy peaks, we discover that knowledge distillation improved small networks and that the student network had the potential to outperform the teacher network. Using knowledge distillation, a small network model can achieve the classification accuracy of a large network model. In practice, choosing the appropriate student network based on the constraints of the usage conditions while using knowledge distillation (KD) would be a way to meet practical needs.
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A. Alves, Luiz, Giuseppe Mangioni, Francisco Rodrigues, Pietro Panzarasa, and Yamir Moreno. "Unfolding the Complexity of the Global Value Chain: Strength and Entropy in the Single-Layer, Multiplex, and Multi-Layer International Trade Networks." Entropy 20, no. 12 (2018): 909. http://dx.doi.org/10.3390/e20120909.
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The worldwide trade network has been widely studied through different data sets and network representations with a view to better understanding interactions among countries and products. Here we investigate international trade through the lenses of the single-layer, multiplex, and multi-layer networks. We discuss differences among the three network frameworks in terms of their relative advantages in capturing salient topological features of trade. We draw on the World Input-Output Database to build the three networks. We then uncover sources of heterogeneity in the way strength is allocated among countries and transactions by computing the strength distribution and entropy in each network. Additionally, we trace how entropy evolved, and show how the observed peaks can be associated with the onset of the global economic downturn. Findings suggest how more complex representations of trade, such as the multi-layer network, enable us to disambiguate the distinct roles of intra- and cross-industry transactions in driving the evolution of entropy at a more aggregate level. We discuss our results and the implications of our comparative analysis of networks for research on international trade and other empirical domains across the natural and social sciences.
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Beghdad, Rachid, Mohamed Abdenour Hocini, Narimane Cherchour, and Mourad Chelik. "PEAS-LI: PEAS with Location Information for coverage in Wireless Sensor Networks." Journal of Innovation in Digital Ecosystems 3, no. 2 (2016): 163–71. http://dx.doi.org/10.1016/j.jides.2016.11.002.
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Li, Yipu, Yuan Rao, Xiu Jin, et al. "YOLOv5s-FP: A Novel Method for In-Field Pear Detection Using a Transformer Encoder and Multi-Scale Collaboration Perception." Sensors 23, no. 1 (2022): 30. http://dx.doi.org/10.3390/s23010030.
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Precise pear detection and recognition is an essential step toward modernizing orchard management. However, due to the ubiquitous occlusion in orchards and various locations of image acquisition, the pears in the acquired images may be quite small and occluded, causing high false detection and object loss rate. In this paper, a multi-scale collaborative perception network YOLOv5s-FP (Fusion and Perception) was proposed for pear detection, which coupled local and global features. Specifically, a pear dataset with a high proportion of small and occluded pears was proposed, comprising 3680 images acquired with cameras mounted on a ground tripod and a UAV platform. The cross-stage partial (CSP) module was optimized to extract global features through a transformer encoder, which was then fused with local features by an attentional feature fusion mechanism. Subsequently, a modified path aggregation network oriented to collaboration perception of multi-scale features was proposed by incorporating a transformer encoder, the optimized CSP, and new skip connections. The quantitative results of utilizing the YOLOv5s-FP for pear detection were compared with other typical object detection networks of the YOLO series, recording the highest average precision of 96.12% with less detection time and computational cost. In qualitative experiments, the proposed network achieved superior visual performance with stronger robustness to the changes in occlusion and illumination conditions, particularly providing the ability to detect pears with different sizes in highly dense, overlapping environments and non-normal illumination areas. Therefore, the proposed YOLOv5s-FP network was practicable for detecting in-field pears in a real-time and accurate way, which could be an advantageous component of the technology for monitoring pear growth status and implementing automated harvesting in unmanned orchards.
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Ewert, Paweł. "Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor." Power Electronics and Drives 4, no. 1 (2019): 203–15. http://dx.doi.org/10.2478/pead-2019-0001.
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Abstract The paper presents the possibility of using neural networks in the detection of stator and rotor electrical faults of induction motors. Fault detection and identification are based on the analysis of symptoms obtained from the fast Fourier transform of the voltage induced by an axial flux in a measurement coil. Neural network teaching and testing were performed in a MATLAB–Simulink environment. The effectiveness of various neural network structures to detect damage, its type (rotor or stator damage) and damage levels (number of rotor bars cracked or stator winding shorted circuits) is presented.
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Kumar, Vijay, Dinushani Senarathna, Supraja Gurajala, et al. "Spectral analysis approach for assessing the accuracy of low-cost air quality sensor network data." Atmospheric Measurement Techniques 16, no. 21 (2023): 5415–27. http://dx.doi.org/10.5194/amt-16-5415-2023.
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Abstract. Extensive monitoring of particulate matter (PM) smaller than 2.5 µm, i.e., PM2.5, is critical for understanding changes in local air quality due to policy measures. With the emergence of low-cost air quality sensor networks, high spatiotemporal measurements of air quality are now possible. However, the sensitivity, noise, and accuracy of field data from such networks are not fully understood. In this study, we use spectral analysis of a 2-year data record of PM2.5 from both the Environmental Protection Agency (EPA) and PurpleAir (PA), a low-cost sensor network, to identify the contributions of individual periodic sources to local air quality in Chicago. We find that sources with time periods of 4, 8, 12, and 24 h have significant but varying relative contributions to the data for both networks. Further analysis reveals that the 8 and 12 h sources are traffic-related and photochemistry-driven, respectively, and that the contributions of both these sources are significantly lower in the PA data than in the EPA data. The presence of distinct peaks in the power spectrum analysis highlights recurring patterns in the air quality data; however, the underlying factors contributing to these peaks require further investigation and validation. We also use a correction model that accounts for the contribution of relative humidity and temperature, and we observe that the PA temporal components can be made to match those of the EPA over the medium and long term but not over the short term. Thus, standard approaches to improve the accuracy of low-cost sensor network data will not result in unbiased measurements. The strong source dependence of low-cost sensor network measurements demands exceptional care in the analysis of ambient data from these networks, particularly when used to evaluate and drive air quality policies.
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Iyer, Sridhar, and Shree Prakash Singh. "Effect of Traffic Uncertainities on the Design of Mixed Line Rate (MLR) Optical Networks." International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems 6, no. 2 (2017): 61. http://dx.doi.org/10.11601/ijates.v6i2.228.
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In the existing studies on Mixed Line Rate (MLR) optical networks, the network design methodology is based on the assumption of deterministic traffic, and hence, the effect of traffic uncertainty on the design of an MLR network remains an open problem of research. In this study, we upgrade our previously proposed cost-efficient mixed integer linear program (MILP) formulation for an MLR network, which considered a specific mean traffic for every network source-destination pair. Our upgraded model employs an optimization technique to account for the traffic uncertainties that an actual MLR optical network may encounter. Our simulation results show that (i) if the MLR network is cost-optimized under the assumption that approximately 10-20% of the demands are at their maximum (or peak) value then, the network demonstrates robustness to traffic peaks in approximately all the other demands, and (ii) the saturation of network cost for a number of source-destination pairs is network topology dependent.
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Naga, Naofumi, Yukie Uchiyama, Yuri Takahashi, and Hidemitsu Furukawa. "Synthesis and Structure of Organic-Inorganic Hybrid Semi-interpenetrating Polymer Network Gels." International Journal of Chemistry 8, no. 1 (2016): 165. http://dx.doi.org/10.5539/ijc.v8n1p165.
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Semi-interpenetrating polymer network (semi-IPN) gels have been synthesized using a hydrosilylation reaction of 1,3,5,7-tetramethylcyclotetrasiloxane (TMCTS) as a joint molecule, and a,w-nonconjugated dienes, 1,5-hexadiene (HD) or 1,9-decadiene (DD) as linker molecules in the presence of polystyrene (PS) as a liner polymers in toluene or cyclohexane. Network structure, mesh size and mesh size distribution, of the resulting semi-IPN gels was quantitatively characterized by means of a scanning microscopic light scattering (SMILS). The relaxation peaks derived from three kinds of structures were detected in the semi-IPN gels prepared in toluene by the SMILS analysis. One was derived from the mesh formed by TMCTS/a,w-nonconjugated dienes about 1-2 nm. Others were derived from transition networks about 20-150 nm and large clustered liner polymer chains about 700-2300 nm. Effect of concentration and molecular weight of the liner polymer on the network structure of the semi-IPN gels in toluene was investigated. The relaxation peaks derived from transition networks or random coils formed by aggregated PS chains were detected in the semi-IPN gels containing high concentration or high molecular weight PS. The semi-IPN gels containing PS were also prepared in cyclohexane as a poor solvent for PS at 40ºC, which was a higher temperature than the upper critical solution temperature (UCST = 34ºC) of PS in cyclohexane. The network structure of the semi-IPN gels was traced by SMILS on the cooling process. In the semi-IPN gel with the short linker molecule of HD, the relaxation peak derived from clustered PS chains was detected over the UCST. By contrast, the relaxation peak derived from transition network was observed in the semi-IPN gel with the long linker molecule of DD.
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Ahmad, Irshad, M. Hesham El Naggar, and Akhtar Naeem Khan. "Neural Network Based Attenuation of Strong Motion Peaks in Europe." Journal of Earthquake Engineering 12, no. 5 (2008): 663–80. http://dx.doi.org/10.1080/13632460701758570.
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Deng, Zheng-Hong, Hong-Hai Qiao, Ming-Yu Gao, Qun Song, and Li Gao. "Complex network community detection method by improved density peaks model." Physica A: Statistical Mechanics and its Applications 526 (July 2019): 121070. http://dx.doi.org/10.1016/j.physa.2019.121070.
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Gürsoy, Ömer, and Seref Naci Engin. "A wavelet neural network approach to predict daily river discharge using meteorological data." Measurement and Control 52, no. 5-6 (2019): 599–607. http://dx.doi.org/10.1177/0020294019827972.
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This paper reports some part of modelling and data analysis work carried out within the frame of a comprehensive project on the web-based development of watershed information system. This work basically aims to present the daily discharge predictions from the actual discharge along with the meteorological data using a wavelet neural network approach, which combines two methods: discrete wavelet transform and artificial neural networks. The wavelet–artificial neural network model developed provides a good fit with the measured data, in particular with zero discharge in the summer months and also with the peaks and sudden changes in discharge on the test data collected throughout the year. The results indicate that the wavelet–artificial neural network model based predictions are distinctly superior to that of conventional artificial neural network model that corresponds up to an 80% reduction in the mean-squared error between the artificial neural network model and measured data.
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Pallonetto, Fabiano, Marta Galvani, Agostino Torti, and Simone Vantini. "A Framework for Analysis and Expansion of Public Charging Infrastructure under Fast Penetration of Electric Vehicles." World Electric Vehicle Journal 11, no. 1 (2020): 18. http://dx.doi.org/10.3390/wevj11010018.
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The improvement commercial competitiveness of private electric vehicles supported by the European policy for the decarbonisation of transport and with the consumers awareness-raising about CO2 emissions and climate change, are driving the increase of electric vehicles on the roads. Therefore, public charging networks are facing the challenge of supply electricity to a fast increasing number of electric cars. The objective of this paper is to establish an assessment framework for analysis and monitor of existing charging networks. The developed methodology comprises modelling the charging infrastructure electricity profile, analysing the data by using machine learning models such as functional k-means clustering and defining a novel congestion metric. The described framework has been tested against Irish public charging network historical datasets. The analyses reveal a lack of reliability of the communication network infrastructure, frequent congestion events for commercial and shopping areas in specific clusters of charge points and the presence of power peaks caused by the high number of simultaneous charging events. Several recommendations for future network expansion have been highlighted.
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Liu, Yunchuan, Amir Ghasemkhani, and Lei Yang. "Drifting Streaming Peaks-over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast." Future Internet 15, no. 1 (2022): 17. http://dx.doi.org/10.3390/fi15010017.
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This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events.
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Pałczyński, Krzysztof, Sandra Śmigiel, Damian Ledziński, and Sławomir Bujnowski. "Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset." Sensors 22, no. 3 (2022): 904. http://dx.doi.org/10.3390/s22030904.
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The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I–XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was conducted by training Deep Convolutional Neural Networks to recognize 2, 5, and 20 different heart disease classes. The results of the FSL network were compared with the evaluation score of the neural network performing softmax-based classification. The neural network proposed for this task interprets a set of QRS complexes extracted from ECG signals. The FSL network proved to have higher accuracy in classifying healthy/sick patients ranging from 93.2% to 89.2% than the softmax-based classification network, which achieved 90.5–89.2% accuracy. The proposed network also achieved better results in classifying five different disease classes than softmax-based counterparts with an accuracy of 80.2–77.9% as opposed to 77.1% to 75.1%. In addition, the method of R-peaks labeling and QRS complexes extraction has been implemented. This procedure converts a 12-lead signal into a set of R waves by using the detection algorithms and the k-mean algorithm.
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Miao, Huajian, Menghuai Yu, and Shangxu Hu. "Artificial neural networks aided deconvolving overlapped peaks in chromatograms." Journal of Chromatography A 749, no. 1-2 (1996): 5–11. http://dx.doi.org/10.1016/0021-9673(96)00349-4.
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Cao, Yang, Zupeng Zhang, Xiaofeng Peng, Yuhan Wang, and Huaijun Qin. "Research on Orbital Angular Momentum Multiplexing Communication System Based on Neural Network Inversion of Phase." Electronics 11, no. 10 (2022): 1592. http://dx.doi.org/10.3390/electronics11101592.
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An adaptive optical wavefront recovery method based on a residual attention network is proposed for the degradation of an Orbital Angular Momentum multiplexing communication system performance caused by atmospheric turbulence in free-space optical communication. To prevent the degeneration phenomenon of neural networks, the residual network is used as the backbone network, and a multi-scale residual hybrid attention network is constructed. Distributed feature extraction by convolutional kernels at different scales is used to enhance the network’s ability to represent light intensity image features. The attention mechanism is used to improve the recognition rate of the network for broken light spot features. The network loss function is designed by combining realistic evaluation indexes so as to obtain Zernike coefficients that match the actual wavefront aberration. Simulation experiments are carried out for different atmospheric turbulence intensity conditions, and the results show that the residual attention network can reconstruct the turbulent phase quickly and accurately. The peaks to valleys of the recovered residual aberrations were between 0.1 and 0.3 rad, and the root means square was between 0.02 and 0.12 rad. The results obtained by the residual attention network are better than those of the conventional network at different SNRs.
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Petrus, Mioara, Cristina Popa, Ana Maria Bratu, et al. "A Synergistic Approach Using Photoacoustic Spectroscopy and AI-Based Image Analysis for Post-Harvest Quality Assessment of Conference Pears." Molecules 30, no. 11 (2025): 2431. https://doi.org/10.3390/molecules30112431.
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This study presents a non-invasive approach to monitoring post-harvest fruit quality by applying CO2 laser photoacoustic spectroscopy (CO2LPAS) to study the respiration of “Conference” pears from local and commercially stored (supermarket) sources. Concentrations of ethylene (C2H4), ethanol (C2H6O), and ammonia (NH3) were continuously monitored under shelf-life conditions. Our results reveal that ethylene emission peaks earlier in supermarket pears, likely due to post-harvest treatments, while ethanol accumulates over time, indicating fermentation-related deterioration. Significantly, ammonia levels increased during the late stages of senescence, suggesting its potential role as a novel biomarker for fruit degradation. The application of CO2LPAS enabled highly sensitive, real-time detection of trace gases without damaging the fruit, offering a powerful alternative to traditional monitoring methods. Additionally, artificial intelligence (AI) models, particularly convolutional neural networks (CNNs), were explored to enhance data interpretation, enabling early detection of ripening and spoilage patterns through volatile compound profiling. This study advances our understanding of post-harvest physiological processes and proposes new strategies for improving storage and distribution practices for climacteric fruits.
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Yousif, Muhammad, Qian Ai, Yang Gao, Waqas Ahmad Wattoo, Ziqing Jiang, and Ran Hao. "Application of Particle Swarm Optimization to a Scheduling Strategy for Microgrids Coupled with Natural Gas Networks." Energies 11, no. 12 (2018): 3499. http://dx.doi.org/10.3390/en11123499.
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This article focuses on the minimization of operational cost and optimal power dispatch associated with microgrids coupled with natural gas networks using particle swarm optimization (PSO). Introducing a natural gas turbine in a microgrid to overcome the drawbacks of renewable energy resources is a recent trend. This results in increased load and congestion in the gas network. To avoid congestion and balance the load, it is necessary to coordinate with the electric grid to plan optimal dispatch of both interactive networks. A modification is done in applying PSO to solve this coupled network problem. To study the proposed approach, a 7-node natural gas system coupled with the IEEE bus 33 test system is used. The proposed strategy provides the optimal power dispatch. Moreover, it indicates that power sharing between the main grid and microgrid is reduced in such a way that it may help the main grid to shave the load curve peaks.
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Veltz, Romain, and Terrence J. Sejnowski. "Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling." Neural Computation 27, no. 12 (2015): 2477–509. http://dx.doi.org/10.1162/neco_a_00786.
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Inhibition-stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from inhibitory interneurons, a circuit element found in the hippocampus and the primary visual cortex. In their working regime, ISNs produce damped oscillations in the [Formula: see text]-range in response to inputs to the inhibitory population. In order to understand the properties of interconnected ISNs, we investigated periodic forcing of ISNs. We show that ISNs can be excited over a range of frequencies and derive properties of the resonance peaks. In particular, we studied the phase-locked solutions, the torus solutions, and the resonance peaks. Periodically forced ISNs respond with (possibly multistable) phase-locked activity, whereas networks with sustained intrinsic oscillations respond more dynamically to periodic inputs with tori. Hence, the dynamics are surprisingly rich, and phase effects alone do not adequately describe the network response. This strengthens the importance of phase-amplitude coupling as opposed to phase-phase coupling in providing multiple frequencies for multiplexing and routing information.
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Tremblay, Antoine, Elissa Asp, Anne Johnson, Malgorzata Zarzycka Migdal, Tim Bardouille, and Aaron J. Newman. "What the Networks Tell us about Serial and Parallel Processing." Mental Lexicon 11, no. 1 (2016): 115–60. http://dx.doi.org/10.1075/ml.11.1.06tre.
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A large literature documenting facilitative effects for high frequency complex words and phrases has led to proposals that high frequency phrases may be stored in memory rather than constructed on-line from their component parts (similarly to high frequency complex words). To investigate this, we explored language processing during a novel picture description task. Using the magneto-encephalographam (MEG) technique and generalised additive mixed-effects modelling, we characterised the effects of the frequency of use of single words as well as two-, three-, and four-word sequences (N-grams) on brain activity during the pre-production stage of unconstrained overt picture description. We expected amplitude responses to be modulated by N-gram frequency such that if N-grams were stored we would see a corresponding reduction or flattening in amplitudes as frequency increased. We found that while amplitude responses to increasing N-gram frequencies corresponded with our expectations about facilitation, the effect appeared at low frequency ranges and for single words only in the phonological network. We additionally found that high frequency N-grams elicited activity increases in some networks, which may be signs of competition or combination depending on the network. Moreover, this effect was not reliable for single word frequencies. These amplitude responses do not clearly support storage for high frequency multi-word sequences. To probe these unexpected results, we turned our attention to network topographies and the timing. We found that, with the exception of an initial ‘sentence’ network, all the networks aggregated peaks from more than one domain (e.g. semantics and phonology). Moreover, although activity moved serially from anterior ventral networks to dorsal posterior networks during processing, as expected in combinatorial accounts, sentence processing and semantic networks ran largely in parallel. Thus, network topographies and timing may account for (some) facilitative effects associated with frequency. We review literature relevant to the network topographies and timing and briefly discuss our results in relation to current processing and theoretical models.
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Zhang, Caili, Yali Li, Longgang Xiang, Fengwei Jiao, Chenhao Wu, and Siyu Li. "Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories." Sensors 21, no. 1 (2021): 235. http://dx.doi.org/10.3390/s21010235.
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With the popularity of portable positioning devices, crowd-sourced trajectory data have attracted widespread attention, and led to many research breakthroughs in the field of road network extraction. However, it is still a challenging task to detect the road networks of old downtown areas with complex network layouts from high noise, low frequency, and uneven distribution trajectories. Therefore, this paper focuses on the old downtown area and provides a novel intersection-first approach to generate road networks based on low quality, crowd-sourced vehicle trajectories. For intersection detection, virtual representative points with distance constraints are detected, and the clustering by fast search and find of density peaks (CFDP) algorithm is introduced to overcome low frequency features of trajectories, and improve the positioning accuracy of intersections. For link extraction, an identification strategy based on the Delaunay triangulation network is developed to quickly filter out false links between large-scale intersections. In order to alleviate the curse of sparse and uneven data distribution, an adaptive link-fitting scheme, considering feature differences, is further designed to derive link centerlines. The experiment results show that the method proposed in this paper preforms remarkably better in both intersection detection and road network generation for old downtown areas.
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Zhu, Nan, Lei Rao, Xue Liu, Jie Liu, and Haibin Guan. "Taming power peaks in mapreduce clusters." ACM SIGCOMM Computer Communication Review 41, no. 4 (2011): 416–17. http://dx.doi.org/10.1145/2043164.2018497.
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Fouad Aly, Wael Hosny. "A Novel Controller Placement Using Petri-Nets for SDNs." WSEAS TRANSACTIONS ON COMPUTERS 19 (January 29, 2021): 268–76. http://dx.doi.org/10.37394/23205.2020.19.32.
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Software defined networking (SDN) separates the control and the data planes. This separation brings flexibility to the network. But the decoupling has some drawbacks such as the controller placement problem (CPP). Controller placement is a crucial task which affects the overall networks’ performance. This paper proposes a novel controller placement model that is based on petri-nets to place the SDN’s controllers. The proposed model is called controller placement using petri-nets for SDNs (CPPNSDN). CPPNSDN aims to reduce the average propagation latency among switches and their associated controllers. CPPNSDN divides the network into sub-networks. Each sub-network is governed by a controller. Experiments were conducted on the Internet2/OS3E topology to evaluate the performance of CPPNSDN. Experiments show that CPPNSDN reduces the average latency significantly compared to two reference models. The first reference model is the Modified Density Peaks Clustering (MDPC) and the Optimized Kmeans model. In terms of the overall average latency, the CPPNSDN has shown promising results as it outperformed the MDPC and optimized Kmeans reference models by 7% and 17% respectively. Confidence Interval (CI) used was 90%. This is an ongoing work and the results are promising for more future investigation.
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Gao, Hongyan, Yuanye Liu, Gen Li, Haodong Liu, Yuxi Shang, and Zheng Ma. "On-Demand Design of Terahertz Metasurface Sensors for Detecting Plant Endogenous and Exogenous Molecules." Agriculture 15, no. 14 (2025): 1481. https://doi.org/10.3390/agriculture15141481.
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This study presents a neural-network-based method for on-demand design of terahertz metasurface sensors, aimed at detecting plant endogenous and exogenous molecules. The approach uses target performance indicators (constructed via fingerprint peaks) as inputs and structural parameters as outputs, employing a neural network to map the complex relationship between them. Two single-resonant-peak metasurface sensors were developed to detect abscisic acid and gibberellic acid. The abscisic acid metasurface sensor achieved an average MSE of 5.66 × 10−6 and RER of 0.167%, while the gibberellic acid metasurface sensor had an average MSE of 8 × 10−7 and RER of 0.086%. Their resonant peaks highly matched the substance fingerprint peaks, enabling specific detection. Metasurface sensors’ sensitivities were effectively controlled using correlation analysis and neural networks, achieving remarkable levels of 156.7 and 150.1 GHz/RIU, allowing trace detection. Three dual-resonant-peak metasurface sensors were designed to improve the detection specificity for chlorophyll and folpet and to detect chlorophyll and folpet simultaneously. These metasurface sensors exhibited average MSEs of 1.4 × 10−5, 1.6 × 10−6, 1.35 × 10−5 and RERs of 0.27%, 0.088%, 0.20%. The model also worked for four other plant-related molecules, proving its strong generalization ability. Overall, for different application scenarios of exogenous and endogenous molecules in plants, the on-demand design methodology offers a whole new set of ideas for quickly designing and widely applying metasurface sensors with suitable performance indicators.
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Bocaz-Beneventi, Gaston, Rosa Latorre, Marta Farková, and Josef Havel. "Artificial neural networks for quantification in unresolved capillary electrophoresis peaks." Analytica Chimica Acta 452, no. 1 (2002): 47–63. http://dx.doi.org/10.1016/s0003-2670(01)01445-3.
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Lange, B. Markus. "Integrative analysis of metabolic networks: from peaks to flux models?" Current Opinion in Plant Biology 9, no. 3 (2006): 220–26. http://dx.doi.org/10.1016/j.pbi.2006.03.003.
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Kantz, Edward D., Saumya Tiwari, Jeramie D. Watrous, Susan Cheng, and Mohit Jain. "Deep Neural Networks for Classification of LC-MS Spectral Peaks." Analytical Chemistry 91, no. 19 (2019): 12407–13. http://dx.doi.org/10.1021/acs.analchem.9b02983.
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Zheng, Muhua, Wei Wang, Ming Tang, Jie Zhou, S. Boccaletti, and Zonghua Liu. "Multiple peaks patterns of epidemic spreading in multi-layer networks." Chaos, Solitons & Fractals 107 (February 2018): 135–42. http://dx.doi.org/10.1016/j.chaos.2017.12.026.
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Latorre, Rosa M., Santiago Hernández-Cassou, and Javier Saurina. "Artificial neural networks for quantification in unresolved capillary electrophoresis peaks." Journal of Separation Science 24, no. 6 (2001): 427–34. http://dx.doi.org/10.1002/1615-9314(20010601)24:6<427::aid-jssc427>3.0.co;2-1.
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Li, Shuyang, Enrico Magli, Gianluca Francini, and Giorgio Ghinamo. "Deep learning based prediction of traffic peaks in mobile networks." Computer Networks 240 (February 2024): 110167. http://dx.doi.org/10.1016/j.comnet.2023.110167.
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Agrawala, Pranav. "Analyzing Carbon-13 NMR Spectra to Predict Chemical Shifts of Carbon Compounds using Machine Learning Algorithms." Morganton Scientific 1 (June 27, 2024): 5–9. http://dx.doi.org/10.62329/vrhv7044.
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Nuclear Magnetic Resonance (NMR) is a fundamental tool in chemistry for elucidating the molecular structure of unidentified substances. The evaluation of 13C NMR spectra can be challenging due to the numerous factors that affect the peaks and their locations (chemical shifts). Chemists can use NMR spectroscopy in synthesizing new molecules to confirm the identity of the molecule produced. Since the NMR spectrum for the molecule does not exist, the chemists cannot compare their spectra with preexisting spectra to verify their results. To address this, an algorithm that predicts the chemical shifts of the 13C NMR spectra for compounds based on their molecular structure emerges as a solution, generating artificial spectra for comparison with real ones. This paper delineates a method to formulate such an algorithm using machine learning techniques. Multiple graph neural networks and a classical neural network underwent training on an NMR database to predict the chemical shifts of the 13C NMR spectra for several molecules. The accuracy of each neural network was tested by analyzing the difference between the predictions and the actual chemical shift values in the database using Mean Absolute Error (MAE). Notably, the graph neural networks had a higher accuracy and precision than the classical neural network. Among them, the Graph Transformer Network emerges as the most proficient performer. Chemists can utilize the Graph Transformer Network model to validate the synthesis of new compounds within a margin of error of approximately 2.599 ppm.
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Amana, A. L., F. I. Onah, N. B. Ngang, and M. Ogharandukun. "Enhancing Sensor Network Performance through an Intelligent Smart Multi-Attribute Decision-Making Approach." Journal of Computer Science Review and Engineering 8, no. 1 (2024): 1–19. https://doi.org/10.5281/zenodo.12780058.
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<em>With the increasing integration of sensor networks in various domains, optimizing their performance has become a crucial challenge. This paper presents an intelligent Smart Multi-Attribute Decision-Making (SMADM) approach designed to enhance sensor network performance. By leveraging advanced algorithms and real-time data processing, the proposed approach aims to improve decision-making processes, ensuring efficient sensor network management. The study highlights the effectiveness of the SMADM approach through simulation results, showcasing significant improvements in network reliability, data accuracy, and overall system efficiency. The consistent poor performance in our communication network occurs when the wireless sensor does not effectively reduce packet loss and interference, leading to high costs. This issue is addressed by improving sensor network performance using the SMADM approach. This involves characterizing increased energy consumption, interference, and packet loss, which contribute to reduced performance, and designing a rule base for the SMADM approach to mitigate these issues. A SIMULINK model for wireless sensor networks (WSN) is developed, along with an algorithm to implement the process. Validation results indicate that conventional packet loss peaks at 30Kb/s on day 4, while sensor 1 experiences a reduced loss of 27.98Kb/s. Incorporating sensor 2 decreases packet loss to 28.6Kb/s, and sensor 3 further reduces it to 27.39Kb/s, demonstrating that sensor 3 is the most effective for improved network performance. Additionally, conventional power consumption is highest at 0.5W on day 1. With sensor 1, it drops to 0.4663W, with sensor 2 to 0.4767W, and with sensor 3, it reduces to 0.456W, making sensor 3 very efficient.</em>
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Cheng, Haoyuan, and Qian Ai. "Integrated Demand Response Design of Integrated Energy System with Mobile Hydrogen Energy Storage in Time-Domain Two-Port Model." Electronics 11, no. 24 (2022): 4083. http://dx.doi.org/10.3390/electronics11244083.
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With the development of energy integration technology, demand response (DR) has gradually evolved into integrated demand response (IDR). In this study, for the integrated energy system (IES) on the distribution grid side with electricity, heat, natural gas network, and hydrogen energy equipment, the analogy relationship between the thermal and mobile hydrogen energy storage networks is proposed. Moreover, a unified model that reflects network commonalities across different energy forms is established. Then, considering the time delay of the IES in the nontransient network, a time-domain two-port model of the IES considering the time delay is established. This model shows the joint effect of time and space on system parameters. Finally, this study validates the model in the application of DR. The verification results show that in DR, the time-domain two-port model can accurately “cut peaks and fill valleys” for the IES and effectively reduce the operating cost of the IES system.
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Liu, Hongtao, and Gege Li. "Overlapping Community Detection Method Based on Network Representation Learning and Density Peaks." IEEE Access 8 (2020): 226506–14. http://dx.doi.org/10.1109/access.2020.3041472.
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Kang, Kukjin, Michael Shelley, James Andrew Henrie, and Robert Shapley. "LFP spectral peaks in V1 cortex: network resonance and cortico-cortical feedback." Journal of Computational Neuroscience 29, no. 3 (2009): 495–507. http://dx.doi.org/10.1007/s10827-009-0190-2.
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Kjær, Mogens, and Flemming M. Poulsen. "Identification of 2D 1H NMR antiphase cross peaks using a neural network." Journal of Magnetic Resonance (1969) 94, no. 3 (1991): 659–63. http://dx.doi.org/10.1016/0022-2364(91)90157-o.
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Li, Jia, Bo Zhao, Jincan Wu, Shuaiyang Zhang, Feiyun Wang, and Chengxu Lv. "MBNet: A multi-branch network for detecting the appearance of Korla pears." Computers and Electronics in Agriculture 206 (March 2023): 107660. http://dx.doi.org/10.1016/j.compag.2023.107660.
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Sudha Letha, Shimi, Angela Espin Delgado, Sarah K. Rönnberg, and Math H. J. Bollen. "Evaluation of Medium Voltage Network for Propagation of Supraharmonics Resonance." Energies 14, no. 4 (2021): 1093. http://dx.doi.org/10.3390/en14041093.
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Power converters with high switching frequency used to integrate renewable power sources to medium and low voltage networks are sources of emission in the supraharmonic range (2 to 150 kHz). When such converters are connected to a medium voltage (MV) network these supraharmonics propagate through the MV network and can impact network and customer equipment over a wide range. This paper evaluates an existing Swedish MV electrical network and studies the pattern of supraharmonic resonance and the propagation of supraharmonics. The MV network consists of eight feeders including a small wind farm. Simulations reveal that, the bigger the MV network, the more resonant frequencies, but also the lower the amplitude of the resonance peaks in the driving point impedance. It was also identified that for short feeders as length increases, the magnitude of the transfer impedance at supraharmonic frequency decreases. For further increment in feeder length, the magnitude increases or becomes almost constant. For very long feeders, the transfer impedance further starts decreasing. The eight feeders in the network under study are similar but show completely different impedance versus frequency characteristics. Measurements at the MV side of the wind farm show time varying emissions in the supraharmonic range during low power production. The impact of these emissions coupled with system resonance is examined.
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Tan, Fu, Xiaolong Chen, Rui Chen, Ruijie Wang, Chi Huang, and Shimin Cai. "Identifying Influential Nodes Based on Evidence Theory in Complex Network." Entropy 27, no. 4 (2025): 406. https://doi.org/10.3390/e27040406.
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Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster–Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster–Shafer evidence theory quantifies uncertainty through its basic belief assignment function and combines evidence from different information sources, enabling it to effectively handle uncertainty. Secondly, Dempster–Shafer evidence theory processes conflicting evidence using Dempster’s rule of combination, enhancing the reliability of decision-making. Lastly, in complex networks, information may come from multiple dimensions, and the Dempster–Shafer theory can effectively integrate this multidimensional information. To verify the effectiveness of the proposed method, extensive experiments are conducted on real-world complex networks. The results show that, compared to the other algorithms, attacking the influential nodes identified by the DS method is more likely to lead to the disintegration of the network, which indicates that the DS method is more effective for identifying the key nodes in the network. To further validate the reliability of the proposed algorithm, we use the visibility graph algorithm to convert the GBP futures time series into a complex network and then rank the nodes in the network using the DS method. The results show that the top-ranked nodes correspond to the peaks and troughs of the time series, which represents the key turning points in price changes. By conducting an in-depth analysis, investors can uncover major events that influence price trends, once again confirming the effectiveness of the algorithm.