Reasonable Scheme Research Articles

Refining Al–20Si Alloy by Appending Trace Sr–Sc–Ce Nanoparticles and Ultrasonic Vibration Processing

In this study, Al–20Si alloy is modified by appending trace Sr–Sc–Ce nanoparticles and ultrasonic vibration processing (UVP). The consequences indicate that its microstructure is significantly refined and its mechanical characteristics are improved. The effect of the favorable refinements is achieved by appending 0.2% Sr–0.15% Sc–0.3% Ce and performing UVP. The primary Si is the minimum, the grain roundness coefficient and the strength of extension are the maximum, and the spheroidizing effect of α‐Al is the best. The elongation and hardness are the highest when 0.4% Ce is appended. The research consequences afford a reasonable process scheme for manufacturing high‐quality wear‐resistant material of the automotive and aerospace industries.

Research on the Prediction of Wax Deposition Thickness on Pipe Walls Based on the Optimal Weighted Combination Model

Wax deposition seriously affects the safe and economic operation of pipelines. Mastering the variation laws of wax deposition thickness is the premise of formulating reasonable pigging schemes. Although the GM (1,1) model (a kind of gray model) is an effective method for predicting wax deposition thickness on pipe walls, its prediction accuracy is easily affected by the smoothness of the original sequence. The improved GM (1,1) was established by introducing the idea of translation transformation, and an optimal weighted combination model based on the traditional gray model and a logarithmic function model was proposed. The differences in the predicted results of the established models were compared and analyzed through indoor wax deposition experimental data. The research results indicate that the optimal weighted combination model has the highest fitting accuracy, followed by the logarithmic function model and the improved GM (1,1), while the fitting accuracy of the traditional gray model is poor. When the number of modeling samples is five, the average relative error and root mean square error of the prediction results of the optimal weighted combination model are 1.313% and 0.021, respectively, which shows the highest prediction accuracy. When the number of modeling samples is six, the average relative error and root mean square error of the optimal weighted combination model are 2.143% and 0.031, respectively, and its prediction accuracy is still the highest. Overall, the optimal weighted combination model has the advantages of high accuracy and easy implementation, and has strong promotion and application value.

Stability Classification of Roadway Surrounding Rock Cased on Fuzzy Comprehensive Evaluation

In order to effectively predict the stability of roadway surrounding rock, a reasonable and effective support scheme was proposed. Fuzzy clustering analysis was introduced to classify the stability of roadway surrounding rock. Based on the data processing of 7 classification indexes, the weight and subordination degree of surrounding rock indexes of roadway are established. The results show that the upper roadway of No. 111070 is a Class IV unstable roadway. The research results can provide a certain reference for the subsequent determination of the stability classification of roadway surrounding rock.

Assessment of a new nutrient management strategy to control harmful cyanobacterial blooms in Lake Taihu using a hydrodynamic-ecological model

The external nutrient loading significantly affects large shallow lakes, particularly those with intricate rive networks. In Lake Taihu, pollutant discharge standards have traditionally been quantified based on water environmental capacity, while neglecting the response characteristics of algal growth in different regions to boundary inputs. For that analysis, this study first estimated the river pollutant loadings of 16 most significant inflow and outflow rivers of Lake Taihu from 2008 to 2020 and explored the correlations between inflow water quality parameters and lacustrine chlorophyll-a levels. Results highlighted the significant influence of high river input of permanganate values in spring on the chlorophyll-a levels in the lake. Based on this, this study proposed the hypothesis that reducing the inflow concentration of permanganate in spring would inhibit algal growth, which was further validated using coupled hydrodynamic and ecological models. The simulation results indicated that the reduction of permanganate inflow concentration during spring would significantly decrease chlorophyll-a concentration in spring and summer, especially leading to a notable impact on peak values. However, due to variations in background concentrations among rivers, the extent of reduction in lake chlorophyll-a levels showed significant spatial differences. Additionally, analysis of extracted algal bloom areas revealed that there still remained a relatively high risk of algal blooms occurring in the main regions, particularly during autumn when inflow pollutant concentrations increased rapidly. These findings emphasized the importance of formulating reasonable exogenous reduction schemes, which should consider the concentration and variation trend of inflow pollutants, as well as the response characteristics of algal bloom growth in different lake zones to the boundary.

Optimal siting of substations of traction power supply systems considering seismic risk

The traction power supply system (TPSS) is a critical infrastructure that provides electrified railways with power. However, its safe and reliable operation is threatened by earthquake disasters. Due to the strong randomness, uneven spatial distribution, and destructive risk of earthquake disasters, it is crucial to develop reasonable planning schemes to mitigate seismic risks of the system. This paper proposes a substation optimal siting method considering seismic risks. By establishing the chain circuit model of the TPSS and employing the dynamic power flow calculation method, the power loss of the system is obtained. By integrating the earthquake scenario simulation and substation fragility analysis, the substation seismic risk is calculated through Monte Carlo simulation. On this basis, the optimization model for substation site selection is formulated, aiming to minimize both the system power loss and the substation seismic risk while adhering to constraints of construction cost, operating safety, and geographical locations. The Pareto optimal solutions are then obtained. Simulation analysis based on actual system parameters shows that compared with traditional site selection methods, the proposed method can effectively mitigate system seismic risks while considering system power loss, providing theoretical bases for the disaster prevention and mitigation in the TPSS planning.

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

DVNE-DRL: dynamic virtual network embedding algorithm based on deep reinforcement learning.

Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding decision and pursuing long-term average revenue goals. Most of the previous work ignored the dynamics in Virtual Network (VN) modeling, or could not automatically detect the complex and time-varying network state to provide a reasonable network embedding scheme. In view of this, we model a network embedding framework where the topology and resource allocation change dynamically with the number of network users and workload, and then introduce a deep reinforcement learning method to solve the VNE problem. Further, a dynamic virtual network embedding algorithm based on Deep Reinforcement Learning (DRL), named DVNE-DRL, is proposed. In DVNE-DRL, VNE is modeled as a Markov Decision Process (MDP), and then deep learning is introduced to perceive the current network state through historical data and embedded knowledge, while utilizing reinforcement learning decision-making capabilities to implement the network embedding process. In addition, we improve the method of feature extraction and matrix optimization, and consider the characteristics of virtual network and physical network together to alleviate the problem of redundancy and slow convergence. The simulation results show that compared with the existing advanced algorithms, the acceptance rate and average revenue of DVNE-DRL are increased by about 25% and 35%, respectively.

Study on energy-saving potential of lowering the emissivity of unheated surfaces for floor radiant heating

It has been demonstrated that the application of low emissivity materials to unheated surfaces improves the thermal comfort level of radiant heating. However, it remains unclear whether the emissivity of unheated surfaces can shorten the response time of floor radiant heating (FRH) and whether there is energy-saving potential. Numerical simulations were conducted to determine the influence of unheated surface emissivity and wall insulation methods on the dynamic performance of FRH during intermittent operation. The results indicate that decreasing the emissivity of the unheated surface can effectively shorten the response time of FRH by 26.8 %, but at the expense of an increased start-up load. The difference between start-up load and steady heating load is between 1.8 and 2.3 times, which increases with the area of the internal insulating wall. Two energy-saving measures exhibit a "substitution" relationship that must be combined with an economic analysis to determine a reasonable implementation scheme, with the roof occupying a key position. This study provides an alternative energy-saving solution for heating and air conditioning in the hot summer and cold winter zone of China, as well as a design foundation for the promotion and application of FRH.

A profit distribution model for the multimodal transport alliance considering participation rate and communication structure

The rationality of profit distribution affects the stability of the multimodal transport alliance. In the multimodal transport alliance, the participation rate of the carrier and the communication structure of the alliance are important influencing factors of profit distribution results. To get a reasonable profit distribution scheme, this paper constructs a profit distribution model considering the characteristics of multimodal transport, called the Choquet Cloud Gravity Center AT model. Firstly, considering the communication structure of the alliance, the Cloud Gravity Center Average Tree method is used as the base model for profit distribution. Secondly, considering the multimodal transport alliance is a fuzzy coalition, the profit for each alliance subset in the base model is calculated by the Choquet integral. Then, the profit distribution model considering participation rate and communication structure is obtained. Finally, a numerical example is given to illustrate the applicability of the model, and comparative analysis is conducted to verify the rationality of the model. This study provides a suitable profit-sharing model for multimodal transport alliances, which is conducive to the stable and efficient operation of alliances.

A quantitative analysis on the influence of temperature on head loss along PVC pipe and the choice of pipe diameter

PVC pipe is widely used in engineering. For instance, it can be used to transport hot water. However, in the process of transmission, a certain head loss will be produced, and the cost problem will be considered in the choice of pipe diameter [1]. Through the controlling of variable, this paper introduces and deduces the formula used to calculate the influence of temperature on head loss. By listing the results and making a graph after calculation, the influence of temperature parameter change on the head loss of PVC pipe is explored, and a reasonable pipe diameter selection scheme is discussed. The formulas and parameters used in this paper are all from the research of other scholars and experts. The results show that the head loss in PVC pipe decreases gradually and stably with the increase of temperature. In addition, with the increase of pipe diameter, the head loss will be reduced, the power consumption of the pump will be reduced, but the material cost will be increased. In conclusion, the water temperature can be increased in a certain range to reduce the head loss. When the distance of pipe distribution is short, a larger pipe diameter can be selected to reduce the power consumption cost. For longer distances, smaller pipe diameters can be selected to reduce material costs.

Sociological Trilogy of Cultural Community on Exposure Peril of Monsoon in Western Rural and Urban, Sierra Leone

Although there are crowds of cultural comprehension and interpretations, the mass of Sierra Leonean sociologists would consent that the anchor of Sierra Leonean sociology is the swotting of personage reciprocation (Bangura, 2019). Consequently, when exposure pounds, Sierra Leonean sociologists have asked, “how do Sierra Leoneans reciprocate?” The natural social standard of intermittence or frequency and ferocity of hydro-meteorological exposures stirring Western Rural and Urban is socially augmenting, preeminent to tough shivers shocks on socio-economic, educational and cultural growth (Jackson, 2023; Wouter Botzen et al, 2019). This social research pinpoints at charting the social judgements of the illustrative social history on the shock of cultural community-predicated social arteries to exposure foresight to the peril of typhoon activated monsoon in Western Rural and Urban, Sierra Leone. The judgements of this social research might bolster link social lacunae in cultural community foresight and imminent social antiphon to the peril of typhoon monsoon, consequently abridging social susceptibility and exposure. Fundamental dossier for social research was captured via online partially-structured social dialogues. Seventy-eight social research respondents were affianced for a profound sociological comprehension of the shock of cultural community predicated arteries on exposure foresight. Auxiliary dossier was collected via social evidence inquiry by scanning, appraising and reviewing briefs and inferences from previous social research discourses, monographs and peer reviewed journals. The Estimate-Talk-Estimate social technique was utilised to assess the impact of cultural community based arteries to exposure foresight. The extensive social research judgement was that cultural community-accustomed practice has a substantially lofty shock on individual threshold of exposure foresight. The social research suggested policy makers to proliferate the social accomplishment of cultural community accustomed exposure oversight practice blueprints as the maximum reasonable scheme to diminish the shocks of typhoon activated monsoons in Western Rural and Urban, Sierra Leone.

Research on the Comprehensive Evaluation Method of Driving Behavior of Mining Truck Drivers in an Open-Pit Mine

Trucking is an important production link in most open-pit mines, and its transportation cost accounts for more than 50% of the total production cost of open-pit mines. The quality of the driver’s driving behavior plays a crucial role in the fine control of the production cost of transportation. Different from the previous evaluation studies of drivers’ driving behavior in open-pit mines, which mainly took safety driving behavior index as a factor variable, this paper puts forward a comprehensive evaluation method of driving behavior of mining truck drivers, which takes both safety driving and transportation cost as factor variables. Taking the mining truck as the research object, firstly, a scientific and reasonable data collection scheme is established, and the data information characterizing the transport state of the mining truck is obtained through data collection and analysis. Secondly, the RKNN algorithm of time series prediction and the wavelet analysis method are used to achieve noise reduction and missing processing of the original data so as to obtain accurate sample data. Then, taking the principal component analysis method as the entry point, through constructing the principal component analysis theory model, the key index system representing safe driving behavior and transportation cost is established to realize the comprehensive evaluation of the driving behavior of mining truck drivers, and the evaluation system of “standard driving”, “prudent driving” and “aggressive driving” of mining truck drivers is formulated. The results show that after noise reduction, the accuracy of mining car operation data can be improved by 7~12%, and the transportation cost can be reduced by about 5% after the driver’s operation behavior is standardized.

A Signal Coordination Model for Long Arterials Considering Link Traffic Flow Characteristics

In order to improve the travel efficiency on a long arterial with massive signals, this paper proposed a mixed integer linear programming model, MaxBandLAM, to simultaneously optimize the arterial partition scheme and signal coordination schemes with the consideration of link traffic flow characteristics. The weighted sum of the two-way green bandwidths on links across the arterial was taken as its objective. The link volume to capacity rations were taken as the weights. The number of sub-zones, the allocation of signals and links, signal cycle, offset and phase sequence, the coordination speed, as well as the two-way green bandwidths of sub-zones and of links are the decision variables. A numerical test was carried out on a virtual arterial with twenty signals. The results indicate the scheme generated by the MaxBandLAM model can provide a more reasonable partition scheme and coordination schemes compared to those generated by Synchro and the models with no traffic flow characteristics of links consideration. For the scenario of dominating the main road through flows, the average delay, average stop number, and average travel speed at intersections for all-turning flows and main road through flows in the MaxBandLAM scheme, all performed well.

Spatiotemporal layout schemes of exclusive lanes for connected automated container trucks

Connected automated container trucks (CACTs) have been widely used at container ports in China to reduce labor costs and save energy. CACT lanes, as components of intelligent infrastructure, can accelerate the application of CACTs. However, the current layout schemes cannot effectively solve the spatiotemporal imbalance issues among traffic volumes during container transportation. In this study, optimal spatiotemporal layout schemes for exclusive CACT lanes are analyzed. Considering emission, construction and travel costs, a spatiotemporal optimization model of CACT lanes is established. A solution framework that combines the improved artificial bee colony algorithm and the simulation of urban mobility (SUMO) method is proposed. Finally, the road network of the Tianjin Port Container Terminal is used as a case study. The results indicate that establishing reasonable layout schemes for CACT lanes is important for enhancing traffic efficiency and reducing pollutant emissions.

Z-number based fuzzy neural network for system identification

In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) based on the integration of Z-valued fuzzy logic and neural networks is proposed. Z-valued fuzzy rule base is presented and its inference process is described using interpolative approximate reasoning. Accordingly, the structure of the Z-FNN is proposed using a distance measure and interpolative approximate reasoning scheme. Based on presented architecture the learning algorithm of Z-FNN is designed. The updating of the unknown parameters of the network is carried out using Genetic Algorithms (GA). The proposed Z-FNN system is utilized for dynamic plant identification. The effectiveness of Z-FNN has been tested by comparing its performance with the performances of other fuzzy systems available in the literature. The proposed approach has been proven to be a suitable alternative for the identification of nonlinear systems characterized by uncertain and imprecise information.

Main Steam Pipeline Support Hanger Failure and Pipeline Vibration Treatment

There are 19 faults in the main steam pipeline of a power plant, including structural failure, abnormal load bearing, abnormal displacement and other faults. Due to the low horizontal stiffness of the main steam pipe superheater header outlet, the pipe is subjected to periodic pulsation shock and resonates with the fixed frequency of the pipe. The failure of the main steam pipeline support hanger and pipeline vibration seriously affect the safe and stable operation of the unit. Through the calculation of piping stress and pipeline vibration test, a reasonable treatment scheme is formulated to solve the abnormal state of pipeline support and hanger and the pipeline vibration fault. It can provide reference for the maintenance of pipe system support and hanger and pipe vibration control of the same type of unit.

Capacity Configuration of Hybrid Energy Storage Power Stations Participating in Power Grid Frequency Modulation

To leverage the efficacy of different types of energy storage in improving the frequency of the power grid in the frequency regulation of the power system, we scrutinized the capacity allocation of hybrid energy storage power stations when participating in the frequency regulation of the power grid. Using MATLAB/Simulink, we established a regional model of a primary frequency regulation system with hybrid energy storage, with which we could obtain the target power required by the system when continuous load disturbance of the regional power grid causes frequency fluctuation. To optimize the variational mode decomposition, we proposed a capacity allocation method of hybrid energy storage power station based on the northern goshawk optimization algorithm based on the target power. Then, we adopted the northern goshawk optimization algorithm to optimize the number of modal decomposition and penalty factors of variational mode decomposition, avoiding subjectivity. The algorithm decomposition generated the number of modes, and we used the mode numbers to reconstruct the power components in various schemes. The power modal components were allocated to different types of energy storage systems according to the frequencies, namely, high, medium, and low, during which process the power and capacity of each type of energy storage were determined. The results show that the selection of a reasonable scheme can minimize the capacity allocation cost of a regional grid hybrid energy storage power station. Taking the 250 MW regional power grid as an example, a regional frequency regulation model was established, and the frequency regulation simulation and hybrid energy storage power station capacity configuration were carried out on the regional power grid disturbed by continuous load, verifying the rationality of the proposed capacity allocation method and providing certain reference significance for the capacity configuration of a hybrid energy storage power station.

A sequential scheme including PTT and 2′3′-cGAMP/CQ-LP reveals the antitumor immune function of PTT through the type I interferon pathway

Photothermal therapy (PTT) is a promising antitumor treatment that is easy to implement, minimally invasive, and precisely controllable, and evokes strong antitumor immunity. We believe that a thorough elucidation of its underlying antitumor immune mechanisms would contribute to the rational design of combination treatments with other antitumor strategies and consequently potentiate clinical use. In this study, PTT using indocyanine green (ICG) induced STING-dependent type I interferon (IFN) production in macrophages (RAW264.7 and bone marrow-derived macrophages (BMDMs)), as proven by the use of a STING inhibitor (C178), and triggered STING-independent type I IFN generation in tumor cells (CT26 and 4T1), which was inhibited by DNase pretreatment. A novel liposome coloaded with the STING agonist 2′3′-cGAMP (cGAMP) and chloroquine (CQ) was constructed to achieve synergistic effect with PTT, in which CQ increased cGAMP entrapment efficiency and prevented STING degradation after IFN signaling activation. The sequential combination treatment caused a significant increase in tumor cell apoptosis, probably due to interferon stimulating gene products 15 and 54 (ISG15 and ISG 54), and achieved a more striking antitumor inhibition effect in the CT26 tumor model than the 4T1 model, likely due to higher STAT1 expression and consequently more intense IFN signal transduction. In the tumor microenvironment, the combination treatment increased infiltrating CD8+T cells (4-fold) and M1-like TAMs (10-fold), and decreased M-MDSCs (over 2-fold) and M2-like TAMs (over 4-fold). Above all, in-depth exploration of the antitumor mechanism of PTT provides guidance for selecting sensitive tumor models and designing reasonable clinical schemes.

Game-Based Demand Feedback Reservation Parking Pricing

Reservation parking is a new type of parking that allows users to obtain a parking space for an appropriate time slot by reservation. This paper pioneers a high degree-of-freedom reservation strategy and rules for commercial districts in which users can arbitrarily select parking time slots. The nested logit is applied to build a parking revenue model by analyzing the cost, demand, and price factors sequentially according to the user’s choice behavior. The parking lot price game in the region is dissected and a nonlinear pricing model is proposed for maximizing regional returns. A model validation of a parking lot in an actual commercial area shows that compared to the traditional parking method, the parking lot revenue increased by 69.63% upon the application of this reservation strategy and pricing scheme, while the parking lot traffic increased by 22.4%. This model can provide strong support for reservation parking pricing in commercial areas. In the comparison of scenarios, the scientific validity of this model using dynamic pricing and regional game competition is verified. In the application, a reasonable price scheme can be formulated based on the parking lot traffic, cost, and surrounding parking strategies, which can result in higher parking revenue while providing users with great parking convenience.

An improved bi-level programming model for water resources allocation under multiple uncertainties

Nowadays, with the scarcity of water resources, competition for water resources among different levels and water sectors is growing increasingly fierce. Furthermore, uncertainties are unavoidable in the water resources system. To address the aforementioned issues, a fuzzy max-min decision bi-level multi-objective interval programming model was proposed, which can not only focus on water conflicts at the same level or between different levels, but also pay attention to optimal allocation of water resources under uncertainty. The developed model was then applied to a case study in Wuwei City, Gansu Province, China, which selected fairness of water distribution and agricultural economic benefits as planning objectives. Based on the developed model, different water resources optimal allocation schemes under different representative hydrological years were provided. From the result, as representative hydrological years changed from wet (P = 25%) to dry (P = 75%), agricultural economic benefit and Gini coefficient of agriculture would vary from [35.19, 37.78] × 108 yuan to [31.12, 31.99] × 108 yuan and from [0.468, 0.429] to [0.505, 0.503], which indicates that as available water resources decrease, agricultural economic benefit would decrease and fairness of water distribution would also decrease. And the water distribution fairness of the upper bound water allocation scheme is higher than that of the lower bound water allocation scheme when in the same representative hydrological year. In addition, no matter what representative hydrological year, the results of the established bi-level programming model are always in the middle of the results of the upper and lower level individual objective, which means that the developed bi-level programming model has great advantage to deal with water competing conflict among different levels. Furthermore, based on the results of developed model, the reasonable water resources optimization schemes can be determined by the decision-makers when faced with multi-objective, bi-level and multiple uncertainties problems.