Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Evaluating the role of transportation system in community seismic resilience

The swift recuperation of communities following natural hazards heavily relies on the efficiency of transporta-tion systems,facilitating the timely delivery of vital resources and manpower to reconstruction sites.This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments,proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery.Focusing on optimizing urban population capacity in the aftermath of earthquakes,the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity.The methodology is demonstrated through an analysis of Beijing’s transportation sys-tem,elucidating how enhancements to transportation infrastructure fortify the resilience of built environments.Additionally,the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake.This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources,indicating a need for retrofitting.Through sensitivity analysis,this study explores how investments in the built environment or logistical systems can increase the resource supply rate,thereby contributing to more resilient urban areas in the face of seismic challenges.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

Imaging the Architecture of Mineral Systems and the Pathways of Ore-forming Fluids across Mongolia with Magnetotellurics

In the framework of a mineral system approach,a combination of components is required to develop a mineral system.This includes the whole-lithosphere architecture,which controls the transport of ore-forming fluids,and favorable tectonic and geodynamic processes,occurring at various spatial and temporal scales,that influence the genesis and evolution of ore-forming fluids(Huston et al.,2016;Groves et al.,2018;Davies et al.,2020).Knowledge of the deep structural framework can advance the understanding of the development of a mineral system and the emplacement of mineral deposits.Deep geophysical exploration carried out with this aim is increasingly important for targeting new ore deposits in unexplored and underexplored regions(Dentith et al.,2018;Dentith,2019).

Single-photon scattering and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system

We theoretically investigate coherent scattering of single photons and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system.Using the real-space Hamiltonian,analytical expressions are derived for the transport spectra scattered by these two giant atoms with four azimuthal angles.Fano-like resonance can be exhibited in the scattering spectra by adjusting the azimuthal angle difference.High concurrence of the entangled state for two atoms can be implemented in a wide angle-difference range,and the entanglement of the atomic states can be switched on/off by modulating the additional azimuthal angle differences from the giant atoms.This suggests a novel handle to effectively control the single-photon scattering and quantum entanglement.

Valleytronic topological filters in silicene-like inner-edge systems

Inner edge state with spin and valley degrees of freedom is a promising candidate for designing a dissipationless device due to the topological protection. The central challenge for the application of the inner edge state is to generate and modulate the polarized currents. In this work, we discover a new mechanism to generate fully valley-and spin–valley-polarized current caused by the Bloch wavevector mismatch(BWM). Based on this mechanism, we design some serial-typed inner-edge filters. By using once of the BWM, the coincident states could be divided into transmitted and reflected modes, which can serve as a valley or spin–valley filter. In particular, while with twice of the BWM, the incident current is absolutely reflected to support an off state with a specified valley and spin, which is different from the gap effect.These findings give rise to a new platform for designing valleytronics and spin-valleytronics.

Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems:Hierarchical Poisoning Attacks and Defenses in Federated Learning

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency.Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data.However,despite its privacy benefits,federated learning systems are vulnerable to poisoning attacks,where adversaries alter local model parameters on compromised clients and send malicious updates to the server,potentially compromising the global model’s accuracy.In this study,we introduce PMM(Perturbation coefficient Multiplied by Maximum value),a new poisoning attack method that perturbs model updates layer by layer,demonstrating the threat of poisoning attacks faced by federated learning.Extensive experiments across three distinct datasets have demonstrated PMM’s ability to significantly reduce the global model’s accuracy.Additionally,we propose an effective defense method,namely CLBL(Cluster Layer By Layer).Experiment results on three datasets have confirmed CLBL’s effectiveness.

Efficient Metal Recovery from Industrial Wastewater:Potential Oscillation and Turbulence Mode for Electrochemical System

Efficient metal recovery from industrial wastewater facilitates addressing of the environmental hazards and resource requirements of heavy metals.The conventional electrodeposition recovery method is hampered by the limitations of interfacial ion transport in charge-transfer reactions,creating challenges for simultaneous rapid and high-quality metal recovery.Therefore,we proposed integrating a transient electric field(TE)and swirling flow(SF)to synchronously enhance bulk mass transfer and promote interfacial ion transport.We investigated the effects of the operation mode,transient frequency,and flow rate on metal recovery,enabling determination of the optimal operating conditions for rapid and efficient sequential recovery of Cu in TE&SF mode.These conditions included low and high electric levels of 0 and 4 V,a 50%duty cycle,1 kHz frequency,and 400 L·h^(-1)flow rate.The kinetic coefficients of TE&SF electrodeposition were 3.5-4.3 and 1.37-1.97 times that of single TE and SF electrodeposition,respectively.Simulating the deposition process under TE and SF conditions confirmed the efficient concurrence of interfacial ion transport and charge transfer under TE and SF synergy,which achieved rapid and highquality metal recovery.Therefore,the combined deposition strategy is considered an effective technique for reducing metal pollution and promoting resource recycling.

Glutamine transporters as effective targets in digestive system malignant tumor treatment

Glutamine is one of the most abundant non-essential amino acids in human plasma and plays a crucial role in many biological processes of the human body.Tumor cells take up a large amount of glutamine to meet their rapid proliferation requirements,which is supported by the upregulation of glutamine transporters.Targeted inhibition of glutamine transporters effectively inhibits cell growth and proliferation in tumors.Among all cancers,digestive system malignant tumors(DSMTs)have the highest incidence and mortality rates,and the current therapeutic strategies for DSMTs are mainly surgical resection and chemotherapy.Due to the relatively low survival rate and severe side effects associated with DSMTs treatment,new treatment strategies are urgently required.This article summarizes the glutamine transporters involved in DSMTs and describes their role in DSMTs.Additionally,glutamine transportertarget drugs are discussed,providing theoretical guidance for the further development of drugs DSMTs treatment.

A Dynamical System-Based Framework for Dimension Reduction

We propose a novel framework for learning a low-dimensional representation of data based on nonlinear dynamical systems,which we call the dynamical dimension reduction(DDR).In the DDR model,each point is evolved via a nonlinear flow towards a lower-dimensional subspace;the projection onto the subspace gives the low-dimensional embedding.Training the model involves identifying the nonlinear flow and the subspace.Following the equation discovery method,we represent the vector field that defines the flow using a linear combination of dictionary elements,where each element is a pre-specified linear/nonlinear candidate function.A regularization term for the average total kinetic energy is also introduced and motivated by the optimal transport theory.We prove that the resulting optimization problem is well-posed and establish several properties of the DDR method.We also show how the DDR method can be trained using a gradient-based optimization method,where the gradients are computed using the adjoint method from the optimal control theory.The DDR method is implemented and compared on synthetic and example data sets to other dimension reduction methods,including the PCA,t-SNE,and Umap.

Existing Case of Transportation System in Turkey and Transportation Trends in Istanbul

Transportation sector is one of the most important elements of a country’s economy with its highway,railway,airway and seaway modes,besides the information and communication infrastructure.Transportation sector has a pattern that affects the society continuously with its economic and social inputs that has a significant role in economies of countries in terms of being an important part of manufacturing process and effects of sizable investments on economy.Demands of more comfortable,more reliable,more safe and more punctual transport in developing economy is an arising trend worldwide and this shows an increase the importance of the transportation sector.Establishment of an efficient and functional transportation system is closely related with traffic safety,intermodal integration and balanced modal distribution.In Turkey,an important improvement has been achieved in these issues,but also some basic constitutive problems are still continuing.These constitutional problems can be summarized as providing traffic safety,integration of innovative implementations to transportation system,enhancing of infrastructure and an effective usage of existing infrastructure.

Smart Parking Management System “MOTAH”

With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.

A Study on Optimizing the Double-Spine Type Flow Path Design for the Overhead Transportation System Using Tabu Search Algorithm

Optimizing Flow Path Design(FPD)is a popular research area in transportation system design,but its application to Overhead Transportation Systems(OTSs)has been limited.This study focuses on optimizing a double-spine flow path design for OTSs with 10 stations by minimizing the total travel distance for both loaded and empty flows.We employ transportation methods,specifically the North-West Corner and Stepping-Stone methods,to determine empty vehicle travel flows.Additionally,the Tabu Search(TS)algorithm is applied to branch the 10 stations into two main layout branches.The results obtained from our proposed method demonstrate a reduction in the objective function value compared to the initial feasible solution.Furthermore,we explore howchanges in the parameters of the TS algorithm affect the optimal result.We validate the feasibility of our approach by comparing it with relevant literature and conducting additional tests on layouts with 20 and 30 stations.

Existing Case of Transportation System in Turkey and Transportation Trends in Istanbul

Transportation sector is one of the most important elements of a country’s economy with its highway,railway,airway and seaway modes,besides the information and communication infrastructure.Transportation sector has a pattern that affects the society continuously with its economic and social inputs that has a significant role in economies of countries in terms of being an important part of manufacturing process and effects of sizable investments on economy.Demands of more comfortable,more reliable,more safe and more punctual transport in developing economy is an arising trend worldwide and this shows an increase the importance of the transportation sector.Establishment of an efficient and functional transportation system is closely related with traffic safety,intermodal integration and balanced modal distribution.In Turkey,an important improvement has been achieved in these issues,but also some basic constitutive problems are still continuing.These constitutional problems can be summarized as providing traffic safety,integration of innovative implementations to transportation system,enhancing of infrastructure and an effective usage of existing infrastructure.

Research on the Interactive Impact of Transportation Systems on Socio-Economic Development

This paper aims to explore the interactive impact between transportation systems and socio-economic development,employing Structural Equation Modeling(SEM)to analyze data from 31 provincial-level administrative regions in China from 2013 to 2022.It comprehensively considers key indicators from the economic,social,and transportation sectors.The paper constructs a model encompassing 5 latent variables and 15 observed variables.Through in-depth analysis,it reveals the promoting role of transportation systems on economic growth and social development,as well as the demand for transportation system construction and optimization driven by socio-economic development levels.The results indicate that an efficient transportation system can not only directly drive economic growth but also indirectly promote social development by improving social welfare and enhancing quality of life.This paper provides new insights into understanding the complex relationship between transportation systems and socio-economic development and holds significant implications for policymakers in optimizing transportation infrastructure to foster economic and social development.

Review of Key Technologies and Applications in Intelligent Transportation Systems

The development of Intelligent Transportation Systems(ITS)is closely intertwined with the growth of every city,serving as a critical component of smart city construction.This paper provides a concise overview of the concept and overall framework of smart transportation.It emphasizes the application of key technologies,including Traffic Element Identification and Perception,data mining,and Smart Transportation System Integration Technology,in the field.Furthermore,the paper elucidates the current practical applications of smart transportation,showcasing its advancements and implementations in real-world scenarios.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Transportation mobility during COVID-19:a systematic review and bibliometric analysis

The COVID-19 pandemic has significantly affected global transportation mobility,presenting unprecedented challenges to transportation management.Public transit and ride-hailing services saw a drastic reduction in ridership,leading to an increased inclination towards private vehicles.The pandemic also altered travel patterns and individual mobility due to various COVID-19 protocols.This study conducted a comprehensive review of 96 academic papers spanning from January 1,2020,to December 31,2022,focusing on transportation and mobility using the Scopus database.Three major themes were identified:‘Impact on Ride-Hailing Services',‘Impact on Mode Preference',and‘Impact on Trip Purpose',with subdivisions based on keywords and key findings extracted using VOSviewer.The pandemic significantly impacted ride-hailing services,altering demand,usage,and safety measures.Mode preference shifted towards private vehicles due to safety concerns.The present study underscores the long-term implications of the pandemic,emphasizing recovery strategies for ride-hailing services and mode preferences post-pandemic.It highlights the need for sustainable transportation policies,advocating for enhanced public transportation systems,promoting active travel modes,and addressing socioeconomic disparities in mobility patterns.The findings emphasize the need for resilient transportation strategies in the face of future disruptions.

Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems(ITSs).Numerous studies have been conducted for traffic flow forecasting during the past decades.However,most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy.For real-world ITS applications,the interpretability of the developed models is extremely important but has largely been ignored.This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms.The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline,enabling the seamless incorporation of various algorithms and tools.To evaluate the effectiveness of the framework,the developed tree-ensemble models and another three typical categories of baseline models,including statistical time series,shallow learning,and deep learning,were compared on three datasets collected from different types of roads(i.e.,arterial,expressway,and freeway).Further,the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods.Experimental results highlight the potential of the proposed framework.The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models.Meanwhile,these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques.The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

Street Lamp Status Warning System Based on Internet of Things Technology

This paper proposes a street light warning system based on Internet of Things(IoT)technology,which uses cameras to detect moving targets such as vehicles and pedestrians around the system and adjust the brightness of street lights according to road conditions to reduce unnecessary power waste.The system has a mature self-fault detection mechanism and is equipped with a wireless communication device for data exchange and timely communication with the host computer terminal.The intelligent street lamp system in this paper can be used to reduce the occurrence of pedestrian and vehicle accidents at intersections,and at the same time reduce the consumption of manpower and material resources for street lamp troubleshooting,to achieve energy conservation and emission reduction.