Effect of temperature and time on the precipitation ofκ-carbides in Fe-28Mn-10Al-0.8C low-density steels:Aging mechanism and its impact on material properties

In low-density steel,κ-carbides primarily precipitate in the form of nanoscale particles within austenite grains.However,their precipitation within ferrite matrix grains has not been comprehensively explored,and the second-phase evolution mechanism during aging remains unclear.In this study,the crystallographic characteristics and morphological evolution ofκ-carbides in Fe-28Mn-10Al-0.8C(wt%)low-density steel at different aging temperatures and times and the impacts of these changes on the steels’microhardness and properties were comprehensively analyzed.Under different heat treatment conditions,intragranularκ-carbides exhibited various morpho-logical and crystallographic characteristics,such as acicular,spherical,and short rod-like shapes.At the initial stage of aging,acicularκ-carbides primarily precipitated,accompanied by a few spherical carbides.κ-Carbides grew and coarsened with aging time,the spherical carbides were considerably reduced,and rod-like carbides coarsened.Vickers hardness testing demonstrated that the material’s hardness was affected by the volume fraction,morphology,and size ofκ-carbides.Extended aging at higher temperatures led to an increase in carbide size and volume fraction,resulting in a gradual rise in hardness.During deformation,the primary mechanisms for strengthening were dislocation strengthening and second-phase strengthening.Based on these findings,potential strategies for improving material strength are proposed.

Effects of heating temperature and atmosphere on element distribution and microstructure in high-Mn/Al austenitic low-density steel

The elemental distribution and microstructure near the surface of high-Mn/Al austenitic low-density steel were investigated after isothermal holding at temperatures of 900-1200℃ in different atmospheres,including air,N_(2),and N_(2)+CO_(2).No ferrite was formed near the surface of the experimental steel during isothermal holding at 900 and 1000℃ in air,while ferrite was formed near the steel sur-face at holding temperatures of 1100 and 1200℃.The ferrite fraction was larger at 1200℃ because more C and Mn diffused to the sur-face,exuded from the steel,and then reacted with N and O to form oxidation products.The thickness of the compound scale increased owing to the higher diffusion rate at higher temperatures.In addition,after isothermal holding at 1100℃ in N_(2),the Al content near the surface slightly decreased,while the C and Mn contents did not change.Therefore,no ferrite was formed near the surface.However,the near-surface C and Al contents decreased after holding at 1100℃in the N_(2)+CO_(2)mixed atmosphere,resulting in the formation of a small amount of ferrite.The compound scale was thickest in N_(2),followed by the N_(2)+CO_(2)mixed atmosphere,and thinnest in air.Overall,the element loss and ferrite fraction were largest after holding in air at the same temperature.The differences in element loss and ferrite frac-tion between in N_(2) and N_(2)+CO_(2)atmospheres were small,but the compound scale formed in N_(2) was significantly thicker.According to these results,N_(2)+CO_(2)is the ideal heating atmosphere for the industrial production of high-Mn/Al austenitic low-density steel.

Low-density lipoprotein receptor-related protein 2(LRP2)is required for lipid export in the midgut of the migratory locust,Locusta migratoria

Low-density lipoprotein receptor-related protein 2(LRP2)is a multifunctional endocytic receptor expressed in epithelial cells.In mammals,it acts as an endocytic receptor that mediates the cellular uptake of cholesterol-containing apolipoproteins to maintain lipid homeostasis.However,little is known about the role of LRP2 in lipid homeostasis in insects.In the present study,we investigated the function of LRP2 in the migratory locust Locusta migratoria(LmLRP2).The mRNA of LmLRP2 is widely distributed in various tissues,including integument,wing pads,foregut,midgut,hindgut,Malpighian tubules and fat body,and the amounts of LmLRP2 transcripts decreased gradually in the early stages and then increased in the late stages before ecdysis during the nymphal developmental stage.Fluorescence immunohistochemistry revealed that the LmLRP2 protein is mainly located in cellular membranes of the midgut and hindgut.Using RNAi to silence LmLRP2 caused molting defects in nymphs(more than 60%),and the neutral lipid was found to accumulate in the midgut and surface of the integument,but not in the fat body,of dsLmLRP2-treated nymphs.The results of a lipidomics analysis showed that the main components of lipids(diglyceride and triglyceride)were significantly increased in the midgut,but decreased in the fat body and hemolymph.Furthermore,the content of total triglyceride was significantly increased in the midgut,but markedly decreased in the fat body and hemolymph in dsLmLRP2-injected nymphs.Our results indicate that LmLRP2 is located in the cellular membranes of midgut cells,and is required for lipid export from the midgut to the hemolymphand fat body in locusts.

Overexpression of low-density lipoprotein receptor prevents neurotoxic polarization of astrocytes via inhibiting NLRP3 inflammasome activation in experimental ischemic stroke

Neurotoxic astrocytes are a promising therapeutic target for the attenuation of cerebral ischemia/reperfusion injury.Low-density lipoprotein receptor,a classic cholesterol regulatory receptor,has been found to inhibit NLR family pyrin domain containing protein 3(NLRP3)inflammasome activation in neurons following ischemic stroke and to suppress the activation of microglia and astrocytes in individuals with Alzheimer’s disease.However,little is known about the effects of low-density lipoprotein receptor on astrocytic activation in ischemic stroke.To address this issue in the present study,we examined the mechanisms by which low-density lipoprotein receptor regulates astrocytic polarization in ischemic stroke models.First,we examined low-density lipoprotein receptor expression in astrocytes via immunofluorescence staining and western blotting analysis.We observed significant downregulation of low-density lipoprotein receptor following middle cerebral artery occlusion reperfusion and oxygen-glucose deprivation/reoxygenation.Second,we induced the astrocyte-specific overexpression of low-density lipoprotein receptor using astrocyte-specific adeno-associated virus.Low-density lipoprotein receptor overexpression in astrocytes improved neurological outcomes in middle cerebral artery occlusion mice and reversed neurotoxic astrocytes to create a neuroprotective phenotype.Finally,we found that the overexpression of low-density lipoprotein receptor inhibited NLRP3 inflammasome activation in oxygen-glucose deprivation/reoxygenation injured astrocytes and that the addition of nigericin,an NLRP3 agonist,restored the neurotoxic astrocyte phenotype.These findings suggest that low-density lipoprotein receptor could inhibit the NLRP3-meidiated neurotoxic polarization of astrocytes and that increasing low-density lipoprotein receptor in astrocytes might represent a novel strategy for treating cerebral ischemic stroke.

Emerging low-density polyethylene/paraffin wax/aluminum composite as a form-stable phase change thermal interface material

Thermal interface materials(TIMs) play a vital role in the thermal management of electronic devices and can significantly reduce thermal contact resistance(TCR). The TCR between the solid–liquid contact surface is much smaller than that of the solid–solid contact surface, but conventional solid–liquid phase change materials are likely to cause serious leakage. Therefore, this work has prepared a new formstable phase change thermal interface material. Through the melt blending of paraffin wax(PW) and low-density polyethylene(LDPE), the stability is improved and it has an excellent coating effect on PW. The addition of aluminum(Al) powder improves the low thermal conductivity of PW/LDPE, and the addition of 15wt% Al powder improves the thermal conductivity of the internal structure of the matrix by 67%. In addition, the influence of the addition of Al powder on the internal structure, thermal properties, and phase change behavior of the PW/LDPE matrix was systematically studied. The results confirmed that the addition of Al powder improved the thermal conductivity of the material without a significant impact on other properties, and the thermal conductivity increased with the increase of Al addition. Therefore, morphologically stable PW/LDPE/Al is an important development direction for TIMs.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method

Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Vehicle routing optimization algorithm based on time windows and dynamic demand

To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.

A Note on an Order Level Inventory Model with Varying Two-Phased Demand and Time-Proportional Deterioration

The main purpose of this paper is to generalize the effect of two-phased demand and variable deterioration within the EOQ (Economic Order Quantity) framework. The rate of deterioration is a linear function of time. The two-phased demand function states the constant function for a certain period and the quadratic function of time for the rest part of the cycle time. No shortages as well as partial backlogging are allowed to occur. The mathematical expressions are derived for determining the optimal cycle time, order quantity and total cost function. An easy-to-use working procedure is provided to calculate the above quantities. A couple of numerical examples are cited to explain the theoretical results and sensitivity analysis of some selected examples is carried out.

Fuzzy Inventory Model under Selling Price Dependent Demand and Variable Deterioration with Fully Backlogged Shortages

The objective is to develop a model considering demand dependent on selling price and deterioration occurs after a certain period of time, which follows two-parameter Weibull distribution. Shortages are allowed and fully backlogged. Fuzzy optimal solution is obtained by considering hexagonal fuzzy numbers and for defuzzification Graded Mean Integration Representation Method. A numerical example is provided for the illustration of crisp and fuzzy, both models. To observe the effect of changes in parameters, sensitivity analysis is carried out.

Demand and Analysis of Clinical Research Curriculum in Medical Universities

Objective:To investigate the needs of medical students regarding clinical research curricula to provide scientifically sound offerings and cultivate their clinical research thinking.Methods:From June to October 2022,medical students at medical universities in Shaanxi Province were surveyed using online questionnaires.The survey covered their demographic information,awareness of their major,understanding of clinical research,and preferences for curriculum content.Results:A total of 341 valid questionnaires were analyzed.Medical students demonstrated a strong awareness of their majors but a relatively low awareness of clinical research.There was significant demand for clinical research courses,with preferences for professionally oriented(81.8%),market-oriented(100%),theoretically and practically integrated teaching(78.6%),and application-focused(73.0%)courses.Conclusion:Medical colleges and universities should align clinical research curricula with the actual needs of medical students and clinical practice.Reforms in curriculum design and teaching methods are essential to better prepare students for careers in public health.

Survey on the Demand for Elderly Care Services of Community Residents in Beijing and Analysis of Influencing Factors

Objective: To investigate the current situation of the demand for geriatric care services of community residents in Beijing and analyze the influencing factors to provide a reference basis for meeting the demand for diversified and professional geriatric care services. Methods: A self-made questionnaire was used to randomly survey 1558 elderly individuals at community health service centers in 8 urban districts where elderly care centers were planned to be built. The influencing factors of the different characteristics of elderly care service needs from three aspects were analyzed using a dichotomous logistic regression model: predisposing, enabling, and, need factors. Results: 69.7% of the elderly required home care services, 22.8% wanted to get care services at elderly care centers, 15.9% wanted to get care services at nursing homes, 12.3% required community care services, and 7.4% didn’t know where to access care services. 68.5% of the elderly required care services for disabilities/semi-disabilities, 58.0% for dementia, 54.7% for common diseases, 34.9% for rehabilitation training, 33.0% for plumbing care, and 7.5% for hospice care. At the same time, there were urban- rural differences in the demand for elderly care services, with suburban elderly having a higher demand for care services than those living in urban areas (P < 0.05). The elderly’s demand for care services was mainly related to age, place of residence, and gender in the causative factors, mode of residence and physical condition among able factors, and mode of care services and care needs among need factors (P < 0.05). Conclusion: The demand for elderly care services was differentiated by factors including place of residence, age, and gender. It is crucial to accurately match the demand for elderly care services, innovate the mode of elderly care services, and improve the service quality to improve the elderly health service system.

Research on Adaptability Analysis of Environmental Space Demand and Design Optimization of Daycare Centers: Taking Chongqing’s Community F as an Example

Currently,there is a lack of research on the detailed environmental spatial design of community daycare centers at the micro level.This study focuses on Community F in Chongqing,using the elderly’s“willingness to demand”as a central aspect.It examines indoor and outdoor environmental space needs at a micro level,considering both functional requirements and spiritual needs based on existing research.The analysis incorporates three adaptive elements:current construction,surrounding environment,and operational management.It explores the feasibility of restructuring spatial layouts,utilizing local resources,and integrating Bayu cultural characteristics.Finally,through design optimization practices,the study proposes three strategies for aging optimization:functional integration and interaction,user-friendly facilities,and emotional connections to place.

Dynamic Analysis of Customer Demand Based on Intuitionistic Fuzzy Number in Product Planning

To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers to represent the importance of each demand.Then,the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator,yielding a dynamic vector of the subjective importance of the demand index.Finally,the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.