Optimization study of station track utilization in high-speed railroad based on constraints of control in random origin and process

Purpose-The purpose of this paper is to eliminate the fluctuations in train arrival and departure times caused by skewed distributions in interval operation times.These fluctuations arise from random origin and process factors during interval operations and can accumulate over multiple intervals.The aim is to enhance the robustness of high-speed rail station arrival and departure track utilization schemes.Design/methodologylapproach-To achieve this objective,the paper simulates actual train operations,incorporating the fluctuations in interval operation times into the utilization of arrival and departure tracks at the station.The Monte Carlo simulation method is adopted to solve this problem.This approach transforms a nonlinear model,which includes constraints from probability distribution functions and is difficult to solve directly,into a linear programming model that is easier to handle.The method then linearly weights two objectives to optimize the solution.Findings-Through the application of Monte Carlo simulation,the study successfully converts the complex nonlinear model with probability distribution function constraints into a manageable linear programming model.By continuously adjusting the weighting coefficients of the linear objectives,the method is able to optimize the Pareto solution.Notably,this approach does not require extensive scene data to obtain a satisfactory Pareto solution set.Originality/value-The paper contributes to the field by introducing a novel method for optimizing high-speed rail station arrival and departure track utilization in the presence of fluctuations in interval operation times.The use of Monte Carlo simulation to transform the problem into a tractable linear programming model represents a significant advancement.Furthermore,the method's ability to produce satisfactory Pareto solutions without relying on extensive data sets adds to its practical value and applicability in real-world scenarios.

Dilemmas and Optimization of Third-Party Assessment System in Chinese Fair Competition Review

The current predominant self-review mechanism by policy-making bodies suffers from deficiencies such as insufficient motivations, limited review capabilities, and weak external supervision. Third-party assessment, characterized by independence and specialization, is designed to mitigate these shortcomings. However, the implementation of third-party assessment faces challenges too. This paper intends to improve the third-party assessment system and to realize the legislative purpose of the system. Based on social research, discussions and exchanges with relevant parties, and the existing research results, this paper analyzes the challenges and possible optimization measures for the third-party assessment. The challenges include repulsion from policy-making bodies, insufficient independence of assessment bodies, disparity of assessment quality, and limited application of assessment outcomes. Possible optimization measures include promoting fair competition culture, increasing the acceptance of third-party assessment from policy-making bodies, enhancing the quality of third-party assessment, clarifying the relationship between policy-making bodies and assessment bodies, ensuring the independence of third-party assessments, and promoting the application of assessment results.

Assessment of Climate for Agricultural Suitability and Optimal Allocation of Agricultural Production in the Guanzhong Region, Shaanxi Province

Based on spatial climatic data of agriculture and the experiment data, the models of agro-ecological assessment of climate for agricultural suitability in this study were developed using the fuzzy mathematical method. Three coefficients, in- cluding the resource coefficient （Cr）, the efficiency coefficient （Ce）, and the utility co- efficient （K）, were used in the models, which were calculated based on temperature, moisture, and sunshine duration data of Guanzhong region, Shaanxi Province. The results indicated that resource coefficient was higher in west of the region than that in east, and higher in south （especially in the Central Shaanxi Plain） than that in the Weibei plateau. The value of Cr changed from 6.5 to 9.2 from north to plain area. Spatial change of efficiency coefficient was obvious, lower in the northeast than in the central plain, and the value of Ce changed from 2.3 to 6.5 from the northeast to the central plain. As for utility coefficient, it was lower in northeastern part of the Weibei plateau and in southern mountain areas than that in the central plain, showing significant latitudinal zonality. Furthermore, the value of K increased from 0.35 to 0.78 from northeast to the central plain, and decreased from 0.78 to 0.53 from the central plain to southern mountain areas. These indicated that climate resource in the central plain region was more abundant and potential, compared with other regions. GuanZhong region was classified into three larger agricultural zones and three small independent zones, according to agro-ecological assessment. Light, heat and water resources should be made use of in an efficient way in spatial allo- cation of agricultural production. For example, water facilities should also be im- proved in Weibei plateau region where highly-qualified fruit should be enhanced and fruit processing industrial chain should be shaped. Large-scale production area of wheat should be increased in central irrigation region and more vegetable bases should be developed around large and medium-scale cities. Thanks for outstanding water conservation function, the three-dimensional agriculture including medicine and other sideline production should be developed in Qinling Mountains and the special- ized commercial agriculture should be accelerated in independent small zones, ac- cording to local conditions. In the research, different crop varieties were developed in corresponding regions as per current eco-climatic conditions.

Assessing environmental impact:Micro-energy network optimization in a Chinese industrial park

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources.This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and“cradle-to-gate”as the system boundary.Based on the baseline scenario,a natural gas generator set was added to replace central heating,and the light pipes were expanded to constitute the optimized scenario.The results showed that the key impact categories for both scenarios were global warming,fine particulate matter formation,human carcinogenic toxicity,and human non-carcinogenic toxicity.The overall environmental impact of the optimized scenario was reduced by 68%compared to the baseline scenario.A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios,followed by central heating and natural gas.Therefore,to reduce the environmental impact of network systems,it is necessary to further optimize the grid power structure.The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

An Extraction Process for Optimal Utilization of Naphtha Based on Molecule Management

In this paper, the separation of aromatics from light naphtha by using extraction process was investigated for improving the utilization efficiency of naphtha. It is indicated that, using a mixture of propylene carbonate-diethylene glycol as the solvent, the optimal extraction conditions cover: a volume fraction of propylene carbonate in the mixed solvent of 0.3, a solvent to feed ratio of 8, and an extraction temperature of 308 K. Through the extraction process, the aromatics mass fraction increases from 10.05% in naphtha to 27.74% in extract oil. It is found that the aromatics yield of extract oil, R_A, reaches 92.11%. As a result, in comparison with naphtha, the potential aromatics content of extract oil increases impressively by 18.03%. Meanwhile, the aromatics content of raffinate oil decreases to 1.33%, and the normal paraffin yield of raffinate oil, Rp, is 76.61%. Accordingly, higher total olefins yields can be obtained when using raffinate oil as the raw material for steam cracking. The present results show that the utilization efficiency of naphtha is improved through extraction process.

Optimal Design of Water Utilization Network with Energy Integration in Process Industries

Effective utilization of water and energy is the key factor of sustainable development in process industries, and also an important science and technology problem to be solved in systems engineering. In this paper,two new methods of optimal design of water utilization network with energy integration in process industries are presented, that is, stepwise and simultaneous optimization methods. They are suitable for both single contaminant and multi-contaminant systems, and the integration of energy can be carried out in the whole process system, not only limited in water network, so that energy can be utilized effectively. The two methods are illustrated by case study.

Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression

Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.

Assessment of the Content and Utilization of Antenatal Care Services in a Rural Community in Cameroon: A Cross-Sectional Study

Background: Pregnant women receive antenatal care (ANC) to ensure favorable pregnancy outcomes. Despite the high ANC coverage rate registered nationally in Cameroon;rural women, women with no formal education and those in the most deprived quintile still face difficulties in having access to quality ANC. The impact of the aforementioned factors on ANC use in the Muea Health Area (MHA) is unknown. The objective of this study was to determine the proportion of women attending ANC and the factors influencing ANC attendance in the MHA. Materials and Methods: This was a community based, analytical, cross sectional survey that involved pregnant women and women with children less than two years old who gave an informed consent or assent. Data were collected using a semi structured questionnaire. EPI info Version 7 and Stat Pac for Windows version 12??1998-2011 (Stat Pac Inc, Bloomington, USA) were used for data analysis. Associations were considered statistically significant for p values less than 0.05. Results: Two hundred and twenty women were interviewed. The mean age was 25 years (SD 5.28). Ninety-nine percent of women had at least one ANC visit meanwhile 84.8% had at least four ANC visits. Only 27.2% of women booked for ANC in the first trimester. Rural (Maumu) residence was associated with inappropriate ANC attendance (attending less than four times) (χ2?= 18.5;p = 0.001). Semi urban women (87%;95% C.I. = 85.10% - 89.0%) were more likely to attend four or more sessions than rural (Maumu) women (60.7%;95% C.I. = 44.2% - 77.3%) (p = 0.001). Participant’s educational level was a significant predictor of early booking for ANC (χ2?= 26.8;p = 0.0002). Semi urban wom- en (79.1%;95% C.I. = 76.0% - 82.2%) were significantly more likely to have a vaginal examination done than rural (Maumu) women (42.1%;95% C.I. = 17.5% - 68.2%) (p = 0.0001). Women who met a doctor during ANC (84.0%;95% C.I. = 80.8% - 87.0%) were more likely to have a vaginal examination done than women who only met a nurse or a midwife during ANC (65.0%;95% C.I. = 57.7% - 71.4%) (p = 0.002). One third of women did not have a vaginal examination performed?during ANC. 45.83% and 47.6% neither did a stool test nor received an insecticide treated net (ITN) respectively. Conclusion: Women in the MHA start their antenatal care late, so they should be encouraged to book early for antenatal surveillance. Furthermore, efforts should be made to increase the access of these women to quality ANC services and to adequately trained ANC providers.

Value Composition and Optimal Utilization of Religious Cultural Landscape Resources

Multiple value structure of religious cultural landscape resources influences and determines development and regulation of cultural landscape resources. In the multiple value structure consisting of cultural inheritance, spiritual needs, industrial expansion, leisure and entertainment, development and utilization of religious cultural landscape resources should adhere to scientific classification, relatively independent policy orientation, make and implement specific countermeasures, so as to meet various requirements of the public and other relevant subjects using more refined and improved policy measures, and to realize the optimal utilization and comprehensive value of religious cultural landscapes.

Study and application on product design system orienting to optimal utilization of material resources

Product design plays a decisive role in material resource consumption in manufacturing systems. So it is significant to study optimal utilization of material resources of manufacturing system from the perspective of product design. This paper firstly defines concept of product design, then after an analysis of design objectives the author proposes a target system of product design with three subsystems: structural system, functional system, and technical system. Finally, a product design system on Architectural Metal Structure Enterprises is developed and used in light of the great consumption of material resources in Metal Structure Enterprises. The system has got an obvious effect on improving comprehensive optimal using rate of material resources of enterprises, reducing design cycle, improving management of enterprises.

Determining Optimal Strategies to Reduce Maternal and Child Mortality in Rural Areas in Western China: an Assessment Using the Lives Saved Tool

China, as a whole, is about to meet the Millennium Development Goals for reducing the maternal mortality ratio (MMR) and infant mortality rate (IMR), but the disparities between rural area and urban area still exists. This study estimated the potential effectiveness of expanding coverage with high impact interventions using the Lives Saved Tool (LiST). It was found that gestational hypertension, antepartum and postpartum hemorrhage, preterm birth, neonatal asphyxia, and neonatal childhood pneumonia and diarrhea are still the major killers of mothers and children in rural area in China. It was estimated that 30% of deaths among 0-59 month old children and 25% of maternal deaths in 2008 could be prevented in 2015 if primary health care intervention coverage expanded to a feasible level. The LiST death cause framework, compared to data from the Maternal and Child Mortality Surveillance System, represents 60%-80% of neonatal deaths, 40%-50% of deaths in 1-59 month old children and 40%-60% of maternal deaths in rural areas of western China.

Optimal Quota-Share and Excess-of-Loss Reinsurance and Investment with Heston’s Stochastic Volatility Model

An optimal quota-share and excess-of-loss reinsurance and investment problem is studied for an insurer who is allowed to invest in a risk-free asset and a risky asset.Especially the price process of the risky asset is governed by Heston's stochastic volatility(SV)model.With the objective of maximizing the expected index utility of the terminal wealth of the insurance company,by using the classical tools of stochastic optimal control,the explicit expressions for optimal strategies and optimal value functions are derived.An interesting conclusion is found that it is better to buy one reinsurance than two under the assumption of this paper.Moreover,some numerical simulations and sensitivity analysis are provided.

Optimal consumption-leisure, portfolio and retirement selection based on α-maxmin expected CES utility with ambiguity

This article studies optimal consumption-leisure, portfolio and retirement selection of an infinitely lived investor whose preference is formulated by a-maxmin expected CES utility which is to differentiate ambiguity and ambiguity attitude. Adopting the recursive multiple- priors utility and the technique of backward stochastic differential equations （BSDEs）, we transform the （^-maxmin expected CES utility into a classical expected CES utility under a new probability measure related to the degree of an investor＇s uncertainty. Our model investi- gates the optimal consumption-leisure-work selection, the optimal portfolio selection, and the optimal stopping problem. In this model, the investor is able to adjust her supply of labor flex- ibly above a certain minimum work-hour along with a retirement option. The problem can be analytically solved by using a variational inequality. And the optimal retirement time is given as the first time when her wealth exceeds a certain critical level. The optimal consumption-leisure and portfolio strategies before and after retirement are provided in closed forms. Finally, the distinctions of optimal consumption-leisure, portfolio and critical wealth level under ambiguity from those with no vagueness are discussed.

Mutual optimization of water utilization structure and industrial structure in arid inland river basins of Northwest China

Water is a key restricting factor of the economic development and eco-environmental protection in arid inland river basins of Northwest China. Although water supplies are short, the water utilization structure and the corresponding industrial structure are unbalanced. We constructed a System Dynamic Model for mutual optimization based on the mechanism of their interaction. This model is applied to the Heihe River Basin where the share of limited water resources among ecosystem, production and human living is optimized. Results show that, by mutual optimization, the water utilization structure and the industrial structures fit in with each other. And the relationships between the upper, middle and lower reaches of the Heihe River Basin can be harmonized. Mutual benefits of ecology, society and economy can be reached, and a sustainable ecology-production-living system can be obtained. This study gives a new insight and method for the sustainable utilization of water resources in arid inland river basins.

Optimizing the frequency ratio method for landslide susceptibility assessment: A case study of the Caiyuan Basin in the southeast mountainous area of China

Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio(FR) method is one of the most popular. However, the results of such assessments are dominated by the number of classes and bounds of landslide-related causative factors, and the optimal assessment is unknown. This paper optimizes the frequency ratio method as an example of bivariate statistical analysis for landslide susceptibility mapping based on a case study of the Caiyuan Basin, a region with frequent landslides, which is located in the southeast coastal mountainous area of China. A landslide inventory map containing a total of 1425 landslides(polygons) was produced, in which 70% of the landslides were selected for training purposes, and the remaining were used for validationpurposes. All datasets were resampled to the same 5 m × 5 m/pixel resolution. The receiver operating characteristic(ROC) curves of the susceptibility maps were obtained based on different combinations of dominating parameters, and the maximum value of the areas under the ROC curves(AUCs) as well as the corresponding optimal parameter was identified with an automatic searching algorithm. The results showed that the landslide susceptibility maps obtained using optimal parameters displayed a significant increase in the prediction AUC compared with those values obtained using stochastic parameters. The results also showed that one parameter named bin width has a dominant influence on the optimum. In practice, this paper is expected to benefit the assessment of landslide susceptibility by providing an easy-to-use tool. The proposed automatic approach provides a way to optimize the frequency ratio method or other bivariate statistical methods, which can furtherfacilitate comparisons and choices between different methods for landslide susceptibility assessment.

Conceptual carbon-reduction process design and quantitative sustainable assessment for concentrating high purity ethylene from wasted refinery gas

The direct emission of waste refinery gas after combustion will cause a severe greenhouse effect.Recovering high-value-added ethylene from wasted refinery gas has fundamental economic and environmental significance. Due to the complexity of the composition of refinery waste gas, designing and optimizing the whole recovery process is still a challenging task. Herein, a novel process(SCOAS) was proposed to obtain polymer-grade ethylene from wasted refinery gas through a direct separation process,and heat pump-assisted thermal integration optimization(HPSCOAS) was carried out. The unique feature of the novel approach is that a new stripper and ethylene reabsorber follow the dry gas absorber to ensure ethylene recovery and methane content. An industrial model, shallow cooling oil absorption(SCOA), and concentration combined cold separation system of ethylene unit using wasted refinery gas was established to analyze the technology and environment. Based on the detailed process modeling and simulation results, the quantitative sustainability assessment of economy and environment based on product life cycle process is carried out. The results show that compared with the traditional process when the same product is obtained, the total annual cost of the HPSCOAS process is the lowest, which is 15.4% lower than that of the SCOA process and 6.1% lower than that of the SCOAS process. In addition,compared with the SCOA process and the HPSCOAS process, the SCOAS process has more environmental advantages. The non-renewable energy consumed by SCOAS is reduced by about 24.8% and 6.1%, respectively. The CO_(2) equivalent is reduced by about 38.6% and 23.7%.

AN EFFICIENT ASSESSMENT METHOD FOR INTELLIGENT DESIGN RESULTS OF SHEAR WALL STRUCTURE BASED ON MECHANICAL PERFORMANCE,MATERIAL CONSUMPTION,AND EMPIRICAL RULES

Efficient methods for incorporating engineering experience into the intelligent generation and optimization of shear wall structures are lacking,hindering intelligent design performance assessment and enhancement.This study introduces an assessment method used in the intelligent design and optimization of shear wall structures that effectively combines mechanical analysis and formulaic encoding of empirical rules.First,the critical information about the structure was extracted through data structuring.Second,an empirical rule assessment method was developed based on the engineer's experience and design standards to complete a preliminary assessment and screening of the structure.Subsequently,an assessment method based on mechanical performance and material consumption was used to compare different structural schemes comprehensively.Finally,the assessment effectiveness was demonstrated using a typical case.Compared to traditional assessment methods,the proposed method is more comprehensive and significantly more efficient,promoting the intelligent transformation of structural design.

Analysis of Optimal Portfolio with Different Utility Function

The question of optimal portfolio is that finds the trading strategy satisfying the maximal expected utility function subject to some constraints. There is the optimal trading strategy under the risk neutral probability measure （martingale measure） if and only if there is no-arbitrage opportunity in the market. This paper argues the optimal wealth and the optimal value of expected utility with different utility function.