Optimal Network Research Articles

Emergence of Complex Network Topologies from Flow-Weighted Optimization of Network Efficiency

Transportation and distribution networks are a class of spatial networks that have been of interest in recent years. These networks are often characterized by the presence of complex structures such as central loops paired with peripheral branches, which can appear both in natural and manmade systems, such as subway and railway networks. In this study, we investigate the conditions for the emergence of these nontrivial topological structures in the context of human transportation in cities. We propose a simple model for spatial networks generation, where a network lattice acts as a planar substrate and edge speeds define an effective temporal distance which we aim to optimize and quantifies the efficiency in exploring the urban space. Complex network topologies can be recovered from the optimization of edges’ speeds and we study how the interplay between a flow probability between two nodes in space and the associated travel cost influences the resulting optimal network. In the perspective of urban transportation we simulate these flows by means of human mobility models to obtain origin-destination matrices. We find that when using simple lattices, the obtained optimal topologies transition from treelike structures to more regular networks, depending on the spatial range of flows. Remarkably, we find that branches paired to large loops structures appear as optimal structures when the network is optimized for an interplay between heterogeneous mobility patterns of small range travels and longer-range ones typical of commuting. Moreover, when congestion dynamics in traffic routing is considered, we study the emergence of additional edges from the tree structure to mitigate temporal delays. Finally, we show that our framework is able to recover the statistical spatial properties of the Greater London area subway network. Published by the American Physical Society 2024

The effects of policy uncertainty and risk aversion on carbon capture, utilization, and storage investments

Many model-based decarbonization pathways include substantial carbon capture, utilization, and storage (CCUS), and engineering cost estimates suggest that these technologies should be profitable to deploy under current incentives, such as the 45Q tax credits in the United States. However, CCUS investments have been slow to materialize. In this paper, we investigate the extent to which policy uncertainty and investor risk aversion may explain the limited buildout of CCUS infrastructure to date. To do so, we develop a two-stage stochastic programming model for optimal CCUS infrastructure network design that incorporates policy uncertainty and risk aversion as novel features. We then apply it to an empirically parameterized case study of the Texas-Louisiana Gulf Coast region. Our results show that the current 45Q incentive levels make a fair number of CCUS projects profitable even if they were to be discontinued after ten years. Moreover, while future policy uncertainty and risk aversion reduce CCUS development, the maximum effect size is only a 16% decline in expected total CO2 captured. We find policy uncertainty plays a more important role than investor risk aversion in the development of CCUS infrastructure, with increased uncertainty leading to reduced CO2 capture and investment.

Sustainable Municipal Solid Waste Management Strategies and Practices: A review

Purpose: This paper introduces a multi-period, multi-objective model for creating an integrated, sustainable municipal solid waste management supply chain. The model includes source separation and a reward-penalty mechanism, aiming to minimize total system costs, greenhouse gas emissions, and environmental impacts on residential areas. Many countries strive to develop efficient solid waste management systems (SWMS) that handle and dispose of daily waste cost-effectively while maintaining sustainability. These systems include waste sources, collection stations, landfills, incinerators, recycling plants, and a transportation network. Methodology: A systematic literature review was conducted on existing literature from various studies. Recent studies were used for this systematic review of sustainable municipal solid waste management strategies and practices. Findings: Decision-makers must design or reconfigure a sustainable MSWM to determine the optimal supply chain network for treating and disposing of daily waste effectively. This paper suggests sustainable waste management strategies and practices from various countries, aiming to determine the best number and locations for facilities and optimal waste flow within the system to minimize daily costs. The primary contribution of this review is the theoretical development of sustainable MSWM practices applicable worldwide. Unique contribution to theory, policy and practice: Cooperation between the private and public sectors involved in solid waste management and the circular economy is imperative to ensure the technical, financial, and social sustainability of waste management systems. Large-scale awareness programs on solid waste management and the circular economy should be implemented, focusing on fostering environmentally responsible behaviors and attitudes, promoting sorting, selective collection, and the recovery of materials and energy from waste.

Predicting thermodynamic adhesion energies of membrane fouling in planktonic anammox MBR via backpropagation neural network model

Predicting thermodynamic adhesion energies was a critical strategy for mitigating membrane fouling. This study utilized a backpropagation (BP) neural network model to predict the thermodynamic adhesion energies associated with membrane fouling in a planktonic anammox MBR. Acid-base (ΔGAB), electrostatic double layer (ΔGEL), and Lifshitz–van der Waals (ΔGLW) energies were selected as output variables, the training dataset was collected by the advanced Derjaguin-Landau-Verwey-Overbeek (XDLVO) method. Optimization results identified “7-10-3″ as the optimal network structure for the BP model. The prediction results demonstrated a high degree of fit between the predicted and experimental values of thermodynamic adhesion energy (R2 ≥ 0.9278), indicating a robust predictive capability of the model in this study. Overall, the study presented a practical BP neural network model for predicting thermodynamic adhesion energies, significantly enhancing the prediction tool for adhesive fouling behavior in anammox MBRs.

Optimal Network Reconfiguration and Power Curtailment of Renewable Energy Sources to Eliminate Overloads of Power Lines

The increasing number of renewable energy sources in power systems contributes to overloads of power lines in emergency situations. Lines made with relatively small cross-section cables, which in the past were designed for an operating temperature of 40 °C, are particularly exposed to overloads. Currently, they constitute the so-called “bottlenecks” in network capacity. This is manifested in the fact that when carrying out expert opinions aimed at examining the impact of a source on the network, computational analyses show overloads of its elements. This article proposes a methodology for eliminating these overloads. It involves the use of two methods at the same time, namely optimal network reconfiguration combined with minimisation of the total power curtailment in RE sources. The search for the optimal network configuration will also allow for minimising power curtailment in renewable energy sources, and thus reduce the costs of this type of operation. With such a tool, network operators will be able to achieve the effect of relieving the line load with the lowest possible cost of redistribution. Based on the IEEE 118 bus test network, calculations were performed that confirmed the effectiveness of the proposed approach. The operation of the proposed methodology is presented with the example of two selected network failure states. The novelty of the proposed solution lies in the simultaneous use of two methods of eliminating line overloads. This streamlines the entire process and improves its effectiveness.

Neural network models and shapley additive explanations for a beam-ring structure

A combination of neural network modeling, SHapley Additive exPlanations (SHAP) and model simplification is proposed and applied to the system of a beam-ring structure in this paper. Based on the long short-term memory based encoder-decoder (LSTM ED), a new framework (P_LSTM ED) is presented to improve prediction accuracy. Several iterative methods are selected to optimize the neural network model under the new framework with LSTM EDs. The normalized root mean square error (NRMSE) is regarded as an evaluation metric for the prediction accuracy of neural network models. The SHAP method provides interpretability for the optimal neural network model and there are two applications through the SHAP analysis. One is a modification strategy for inputs of the neural network model, the other is a model simplification method for state equations of the nonlinear system. Numerical simulation shows that the P_LSTM ED has higher prediction accuracy while considering efficiency. The neural network model with the modification strategy applied achieves a maximum accuracy improvement of 24.15 % with less training time. The effectiveness and generality of model simplification are verified by numerical simulation comparison between simplified and original equations.

Application of ANN in Tourism Business Development for Demand Forecasting and Management of Tourism Headcount

With the rapid development of tourism, it is imperative to forecast tourism demand to maintain the long-term stable development of the tourism industry and make good planning for future tourism enterprises. The study uses the classical model of artificial neural network-BP neural network for tourism number demand prediction, given the problems of traditional BP neural networks, such as prematurity and poor convergence speed, this paper studies the iterative optimization of the algorithm of particle swarm fusion immune mechanism and finds out the optimal network parameters, to build an IAPSO-BP tourism demand prediction model. Tourist amounts from 2007 to 2017 in certain area-related data samples, the training model of iterative speed and fitting effect, and the rolling forecasting method will be used to predict the 2018-2022 years of travel. It can be seen from the convergence curve that the convergence speed of parameter optimization of the IAPSO algorithm is the fastest; the improvedIAPSO-BP network has the best training fitting effect, with a relative average error of 2.03% and an absolute average error of 4.37%, which is better than other forecasting methods. The IAPSO-BP prediction model has higher accuracy and better performance, which can provide an effective basis for the development planning of tourism enterprises and has higher practical application value.

Parametrical synthesis of various radio devices with the set quantity of identical cascades of type «the nonlinear part – the resistive two-port network»

Background. Presence of possibility of analytical definition of a part of parametres of various radio devices, optimum by criterion of maintenance of preset values of modules and phases of transfer functions on necessary quantity of frequencies, considerably reduces time of numerical optimisation of other part of parametres by criterion of formation demanded PFC and FFC in a strip of frequencies. Till now such problems dared concerning radio devices only with one cascade of type «a nonlinear part – the coordination the device» or «the coordination the device – a nonlinear part». In quality согласующего devices were used the jet, resistive, complex or mixed two-port networks. The problem of multicascade radio devices with jet two-port networks is solved also. Change of basis for the coordination two-port networks and a place of inclusion of a nonlinear part leads to change of area of a physical realizability. Aim. Working out of algorithms of parametrical synthesis of radio devices with any quantity of identical and unequal cascades of type «a nonlinear part -the coordination the resistive two-port network» by criterion of maintenance of the set frequency characteristics. Nonlinear parts are presented in the form of a nonlinear element and parallel either consecutive on a current or pressure of a feedback. Methods. The theory of two-port networks, matrix algebra, a decomposition method, a method of synthesis of actuation devices microwave, numerical methods of optimisation. Results. In interests of achievement of the specified purpose systems of the algebraic equations are generated and solved. Models of optimum two-port networks in the form of mathematical expressions for definition of interrelations between elements of their classical matrix of transfer and for search of dependences of resistance of two-poles from frequency are received. It is shown, that at certain parities between quantity of cascades and values of resistance of a source of a signal and loading of the one-cascade radio device frequency characteristics of one-cascade and multicascade radio devices appear identical or similar. Such schemes are named by equivalent. Conclusion. The comparative analysis of theoretical results (PFC and FFC radio devices, value of parametres), received by mathematical modelling in system MathCad, and the experimental results received by circuit engineering modelling in systems OrCad and MicroCap, shows their satisfactory coincidence.

Methodology for Researching the Synthesis of Transport and Logistics Systems of the Kyrgyz Republic

The article discusses the research methodology for the synthesis of transport and logistics systems, adapted to the specific conditions of the Kyrgyz Republic. The relevance of the study is due to the need to increase the competitiveness of the national economy and realize the country’s transit potential through the creation of an effective logistics infrastructure. The proposed methodology is based on the integrated application of modern theories and concepts from the field of transport logistics, modeling, geographic information systems, intelligent transport systems, as well as the principles of green logistics and sustainable development. Particular attention is paid to taking into account the features of the mountainous terrain of Kyrgyzstan and the need to integrate various modes of transport into multimodal transportation. The methodology provides for the creation of an optimal network of logistics centers and transport hubs that concentrate cargo flows and ensure their efficient processing. The implementation of information technologies and systems is considered as a key factor in the integration of supply chain participants, monitoring of transport flows, modeling and optimization of logistics processes. Successful implementation will create a competitive transport and logistics system, integrated into global supply chains and meeting modern requirements for efficiency and sustainable development.

Hybrid Deep Optimal Network for Recognizing Emotions Using Facial Expressions at Real Time

Recognition of emotions by utilizing facial expressions is the progression of determining the various human facial emotions to infer the mental condition of the person. This recognition structure has been employed in several fields but more commonly applied in medical arena to determine psychological health problems. In this research work, a new hybrid model is projected using deep learning to recognize and classify facial expressions into seven emotions. Primarily, the facial image data is obtained from the datasets and subjected to pre-processing using adaptive median filter (AMF). Then, the features are extracted and facial emotions are classified through the improved VGG16+Aquila_BiLSTM (iVABL) deep optimal network. The proposed iVABL model provides accuracy of 95.63%, 96.61% and 95.58% on KDEF, JAFFE and Facial Expression Research Group 2D Database (FERG-DB) which is higher when compared to DCNN, DBN, Inception-V3, R-152 and Convolutional Bi-LSTM models. The iVABL model also takes less time to recognize the emotion from the facial image compared to the existing models.

Inter- and sub-harmonics estimation: Treatise on raptor-inspired Harris Hawks collective wisdom

In contemporary power systems, harmonics as undesired electrical phenomena, have drawn considerable attention of the research community investigating in design and development of optimal electrical networks. Harmonics affect the power electronics devices with a number of negative impacts, including higher power losses, worse power quality, and interference with other delicate grid connected equipment. An objective function of inter- and sub-harmonics is constructed for joint estimation of amplitude and phase with known frequency. Accurate estimation of the harmonics model is carried out with stupendous knacks of raptor-inspired Harris Hawks optimization algorithm (HHOA), a revolutionary population-based, nature-motivated optimization approach. The primary sources of inspiration for HHOA are Harris Hawks cooperative demeanor and surprise pounce chasing style in nature. The HHOA’s performance is assessed for harmonics parameter estimation with several noise conditions, differing particle sizes and iterations. The proposed HHOA-based estimation technique provides accurate, convergent and robust performance for harmonics estimation. The reliable level of fitness is achieved consistently in the range of 10−3 to 10−10, an evidence of valuable precision, stability and robustness of the proposed algorithm.

Rolling Element Bearing Fault Diagnosis Based on Multi-objective Optimized Deep Auto-encoder

Abstract Bearing fault diagnosis holds significant importance, with widespread attention focused on enhancing its accuracy and efficiency. Existing diagnostic methods based on deep learning and transfer learning typically tackle this issue by introducing new function modules and diagnostic strategies, such as attention mechanism, adversarial domain adaptation, etc. However, most studies do not consider the structure and hyperparameters optimization of the network to improve the diagnostic performance of the network itself. To address this limitation, a novel multi-objective optimized deep auto-encoder (MODAE) is proposed in this paper. The optimal network structure and hyperparameters is determined by a multi-objective particle swarm optimization algorithm. Crucially, the method is based on a data-driven approaches to automatically search for network structures with stronger generalization and feature extraction capabilities to address engineering problems in different scenarios. Finally, this method is examined in both multi-fault classification diagnosis and transfer diagnosis scenarios, demonstrating strong self-adaptability through experimental results. In comparison with typical deep learning fault diagnosis methods, the proposed method demonstrates higher diagnostic accuracy and superior generalization ability.

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.

On the Existence of Optimal Shallow Feedforward Networks with ReLU Activation

On the Existence of Optimal Shallow Feedforward Networks with ReLU Activation

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

Road Network-based Determinants of Travel Demand

Road network has a key role in defining the urban structure of a city. The question of how travel demand varies with the road network design is particularly important, as it is the slowest urban system to alter. As the network structure prevails for a long duration, and its modification in a short time is rather difficult, it is essential to obtain an optimal network design. This paper examines the effect of the road network on travel demand, in the realm of developing nations, based on the characteristics of Calicut city, Kerala. Various network characteristics including fractal dimension, have been quantified so as to identify their influence on travel. This study, being the first of its kind in India, confirms that the network characteristics influence the number of trips and travel by various modes. The estimation results are expected to shed light on how network design can reduce travel.

Transmission Pipeline Planning from Kregan Water Treatment Plant to Watu Gadjah Tank by PERUMDA PDAM Sleman Using EPANET 2.2

Clean water is vital as it directly impacts health and influences various aspects of life, including social, economic, and cultural activities. To address the clean water supply requirements in the area, PERUMDA PDAM Sleman, Yogyakarta, is currently planning for a transmission network linking the Kregan WTP to the Watu Gadjah Tank. The pipeline planning is carried out to ensure efficient and optimal network performance. The pipeline planning use both primary and secondary data. The primary data was obtained from surveys, while the secondary data was obtained though previous research and planning, regulation, and from the other sources. The pipeline network analysis carried out using EPANET 2.2. software. EPTANET was selected because of its cost-effectiveness and robust capabilities in modeling drinking water networks, with the advantage of being re-programmable. The results of the analysis are then adjusted to the planning standards that applicable in Indonesia. Risk analysis was carried out based on experience and expert opinion. The results of the planning showed that the maximum pressure on the transmission network was 84.21m on Pendowo Road. The velocity between 1.03 m/s and 1.63 m/ss with the highest headloss was 10.33 m/km. Based on the results of the planning, the entire parameter meets the standard of the applicable planning criteria. The results of the analysis indicate that the technical specifications of the pipe used must be able to withstand a pressure of 92,63m. There was a potential risk that network performance may be disrupted both in terms of network performance, implementation of development, and operational stages. The re-examination and the development of operational standard procedures is needed to ensure that planning has been in line with the expectations, can be implemented, and can be operated efficiently.

INTEGRATION OF APPLIED IPD MODULES INTO THE COMPOSITION OF ORGANISATIONAL AND TECHNOLOGICAL TOOLS FOR CONSTRUCTION

Modern construction projects are characterised by complex multifunctional relationships that require processing a large amount of information. The main task of innovative development of construction development is to create competitive advantages in the strategic perspective, which form a safe and comfortable environment for human life that meets high international quality standards, ensuring sustainable socio-economic development of the country's construction industry. Integrated Project Delivery (IPD) is a completely new idea based on Building Information Modelling (BIM) technology. The main idea is to work closely with the investor, designer and executors of a construction project. Achievement of competitive advantages should be based on innovative re-equipment of the construction industry, development of innovative competences, engineering schemes for life cycle management of construction projects, application of information technologies to increase productivity, reduce energy intensity, material consumption and cost of construction products. The article analyses the benefits of using information modelling and the integrated project implementation method in construction. A mathematical model for choosing the optimal network organisational structure of a project is developed. The main factors affecting the synergistic effect of the construction project implementation (costs of making changes to the project, increasing the implementation time, transaction and operating costs) are identified. An economic and mathematical model for assessing the synergistic effect of the joint implementation of integrated construction project implementation and information modelling has been built and tested in practice. A simulation model has been developed for a comprehensive study and optimisation of the communication network of participants in the implementation of a construction project. The results obtained will allow the top management of contractors to effectively monitor, structure and manoeuvre assets in their operations, substantiate the economic strategy and parameters of the production and business portfolio of construction companies.

Development of novel artificial intelligence functions based on 3D finite element method using February 6 Kahramanmaraş Seismic Records for earthquake effects prediction in various soils

The seismic events on February 6, 2023, in the province of Kahramanmaraş/Türkiye, caused severe damage and the collapse of numerous structures due to underlying soil issues. This catastrophe revealed the inevitable requirement to evaluate the effect of soil profile on structural safety. In the present study, novel artificial intelligence (AI) functions based on the three-dimensional finite element (3D FE) method considering various soil parameters were developed to predict the effects of earthquakes. A 3D FE model of the ten-story building with a known soil profile and structural elements was created in the first stage, accounting for the soil-pile-structure interaction. After model validation, numerous parametric time history earthquake analyses were performed using the February 6 Pazarcık/Kahramanmaraş (Mw = 7.7) earthquake records. Therefore, the effects of soil parameters on acceleration, settlement, and lateral deformations were investigated. An innovative coding infrastructure, leveraging the power of AI, was developed to generate optimal network solutions automatically for creating high-order regression prediction functions. The 3D FE data was integrated into the code, and subsequently, an artificial neural network was utilized to formulate a function that yielded statistically significant outcomes. The created function accurately predicted the accelerations, settlements, and deformations. A novel method for indicating the potential deformations and accelerations inflicted by earthquakes based on soil parameters was introduced. This methodology can serve as a practical guide for researchers and project implementers in the initial design phases.

Optimal multimodal multi-echelon vaccine distribution network design for low and medium-income countries with manufacturing infrastructure during healthcare emergencies

In the event of routine immunizations for containing disease outbreaks, the Expanded Programme on Immunization (EPI) led Low and Medium-Income Countries (LMICs) follow a four-tier distribution network. Such a network is suitable for LMICs importing vaccines rather than manufacturing and may not be suitable for healthcare emergencies. Recent shift from routine immunizations to pandemic-orientation, calls for large-scale multimodal distribution models. This paper proposes a cold chain network for LMICs with manufacturing hubs during health emergencies, integrating vaccine manufacturers and incorporating multiple transportation modes. The problem is formulated as a Mixed Integer Quadratically Constrained Programming (MIQCP) problem, to minimize ‘Cumulative Transportation Time’ (CTT) with the minimal number of trips. To solve large-scale problems the paper also proposes a novel algorithm that divides certain parts of the network and retains the edges that are likely to give better solutions. The proposed model extends the existing body of the literature by introducing features such as vehicle choice between locations, generalized hierarchical structure, network decomposition, and appropriateness with LMIC networks. We conduct numerical experiments to validate the proposed algorithm using data from India. The results show that the proposed model saves cumulative transportation time along the optimally generated network when compared with the actual vaccine supply network in India during healthcare emergencies. The proposed algorithm is also faster than the formulation giving comparable results while solved in a commercial solver.