Actual Demand Research Articles

Research on the Evaluation Model of Students' Foreign Language Learning Situation based on Oriented Online Teaching Collaboration Platform

Internet + Education makes online teaching gradually penetrate the education industry, and makes the industry enter a great revolution based on information technology. The traditional student learning evaluation system cannot satisfy the actual demand of current learning evaluation. This paper constructs an evaluation model for the foreign language learning of online students. Firstly, the DBSCAN algorithm with distance optimization is used to conduct cluster analysis on the description indicators of student behavior, and the student groups with different behavior characteristics are obtained. Then the ANOVA F-test was used to extract the features of different student groups. Finally, a novel N-Adaboost algorithm based on multiple classifiers is proposed and a model is constructed to evaluate students’ foreign language learning. The experimental results show that the accuracy of the evaluation model is 74.02% in the pass and fail groups and 73.74% in the excellent and non-excellent groups. Students’ listening, speaking, and reading abilities are in a state of upward development overall through the online teaching collaboration platform, but their writing ability is obviously declining. There is a great improvement in foreign language vocabulary. This study provides a new perspective of thinking for the improvement of the quality of school teaching management, the analysis of students’ behavior, and the evaluation of learning situations, and provides a new solution for the problem of students’ learning situations in modern information teaching.

Digital engine boosts the new vitality of rural education: The role and innovation of college students

Education informatization has entered a new stage, and education digitalization is of great significance to promoting the high-quality development of rural education and helping rural revitalization. The research shows that the digital transformation of rural education in China is facing important problems such as the great difference in infrastructure level, the overall lack of digital application awareness and ability of rural teachers, and the mismatch between funding input and actual demand. In order to solve this series of challenges, this paper proposes a new way, by giving full play to the advantages of college students in digital technology, especially focusing on multimedia courseware production, to promote the high-quality development of rural education digitalization and provide strong support for rural revitalization.

A joint model of infrastructure planning and smart charging strategies for shared electric vehicles

This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.

Algorithmic Complexity Research on Foreign-related Tourism English Education and Talent Cultivation Models in the Context of Free Trade Port

Abstract In today’s booming development of free trade ports, China, as a big country of inbound and outbound tourism, has put forward higher requirements for the talent cultivation mode of foreign-related tourism English. This paper takes the characteristics of foreign-related tourism English as the entry point and establishes the practical teaching system and the four-in-one talent cultivation mode of foreign-related tourism English. The multi-objective optimization model for cultivating foreign tourism English talents is proposed, with the objective function of enhancing the quality of foreign tourism English talents, and the corresponding constraints are set. The multi-objective optimization model is solved using the DQN model, and a strategy gradient algorithm-based model training optimization strategy is proposed. The test conditions of foreign-related tourism English talent cultivation are set, simulation experiments are carried out, and teaching experiments are compared based on the optimal scheme. The training loss obtained by the DQN model after 1700 iterations fluctuates between [0.015,0.019], and when the training period is set to 2 years, the cumulative return of foreign-related tourism English talent cultivation can reach 623.87%, and the maximum retracement is only - 5.457%. The optimal talent cultivation program’s average score is 14.1 points higher than the ordinary version. Foreign-related tourism English needs to be fully integrated with the actual demand for talent, and the faculty and policy guidelines help to enhance the quality of foreign-related tourism English talent training.

Sustainable Urban Development: A Sustainability Study of the Dhaka Megacity

Sustainable urban development is a precondition for positive and eco-centered development under the Urban Sustainability Framework (USF) (ADB, 2018). Climate change has played a vital role in global economic development. Meanwhile, the Earth is a roughcast multidimensional development dilemma, in which urbanization is one of them. There is a perceptual gap between the actual demand of the public and decision-makers’ perceptions. According to recent UN studies, approximately 55 per cent of the world’s population resides in urban areas, whereas it was 30 per cent in 1950. Currently, urbanization trends are very significant by population, for instance, in North America (82%), Latin America, and the Caribbean (81%), Europe (74%), and Oceania (68%). Urban growth in Asia is at an average of 50% and in Africa at 43% (UN, 2018). In the last two decades, the urbanization rate in Bangladesh has been 39.7% and the annual average growth rate is 3.21% (Knoema, 2023). However, this unplanned development has been destructive to natural growth, destroying the ecological environment, dishonouring historical monuments, and, finally, the cost is high in terms of money and sustainability. In this study, Dhaka, the capital city of Bangladesh, is the most vulnerable city in the world because of population, internal migration, air pollution, water pollution, and many more genuine reasons. Accordingly, this city is now unable to bear the load of this growing population’s weight, which is horrible (ADB, 1998). Unplanned development also helps to increase traffic, and people waste their productive time in a way that also affects the GDP of Bangladesh. Ultimately, unplanned, imbalanced, and unsustainable development is increasing hazard, death risk, and several negative impacts of urban development in Dhaka. Consequently, for the sake of God, this type of rapid urban development needs to be reconsidered by policymakers and implementers for the betterment of current and future generations.

Computational model design for road to waterway transportation conversion efficiency improvement based on deep learning algorithm

Abstract Path planning for combined transportation can improve the irrationality in the carrier task so as to achieve the purpose of cost reduction and efficiency. This study takes economic efficiency as the starting point, takes the transportation time window as a constraint, and constructs the total cost objective function in the transportation process. The objective function of carbon emission in the transportation process is designed to address the green and low-carbon problem. The Ant colony algorithm is used to solve the multi-objective joint transportation model, aiming to obtain a green and efficient transportation path that meets the actual demand. The ACO algorithm shows good convergence performance in the transportation task from starting point O to ending point D. The ACO algorithm is capable of solving the transportation task from starting point O to ending point D after 130 iterations. After 130 iterations of the algorithm, the cost objective function value converged to 3427.8 RMB, and the carbon emission objective function value converged to 5534 kg after 90 iterations of the algorithm. Furthermore, the ACO algorithm’s Pareto solution set exhibits a uniform spatial distribution and excellent ductility and approximation effects. Assigning the same weights to cost, carbon emissions, and transportation time, the optimal solution 4 is obtained. The scheme from the starting point O via nodes 1, 2, and 6 to the end point D, through the joint transportation of road and waterway for 162 hours, a total of 5549 kg CO2, and the cost of transporting each ton of goods is 4093.9 yuan.

Pivoting and pandemics: A game-theoretic framework for agile personal protective equipment supply chains

Supply chain models frequently tackle manufacturing issues but must also account for the distinctive nature of the disease. Conversely, most epidemiological models solely concentrate on the disease’s spread but must address logistical challenges. The medical supply chain encounters numerous problems during a pandemic, requiring adaptation through pivoting strategies. For instance, when the COVID-19 outbreak began, several nations prohibited the export of medical supplies, including personal protective equipment (PPE). Consequently, in times of crisis, many countries adopt a localization strategy that encourages domestic companies to adapt their operations and produce medical items. Nevertheless, an interconnected system is essential to align suppliers with the actual demand for medical supplies. This study focuses on the design of a game model for the supply chain that considers manufacturers’ equilibrium behaviors in response to the real demand for medical items. We propose a game model that incorporates both the medical supply chain and the unique characteristics of pandemics. Various decisions are taken into account, such as production volume, actual demand for medical products, price, distribution of medical supplies, and investment costs in manufacturing technologies. To determine the Nash Equilibrium solutions for the proposed game model, the Variational Inequality (VI) theory is implemented.

Improvement of Inventory Management and Demand Forecasting by Big Data Analytics in Supply Chain

Abstract Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.

Spatial effects of township health centers' health resource allocation efficiency in China.

China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China. This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency. The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume. To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Oral polio vaccine stockpile modeling: insights from recent experience

ABSTRACTBackgroundAchieving polio eradication requires ensuring the delivery of sufficient supplies of the right vaccines to the right places at the right times. Despite large global markets, decades of use, and large quantity purchases of polio vaccines by national immunization programs and the Global Polio Eradication Initiative (GPEI), forecasting demand for the oral poliovirus vaccine (OPV) stockpile remains challenging.Research design and methodsWe review OPV stockpile experience compared to pre-2016 expectations, actual demand, and changes in GPEI policies related to the procurement and use of type 2 OPV vaccines. We use available population and immunization schedule data to explore polio vaccine market segmentation, and its role in polio vaccine demand forecasting.ResultsWe find that substantial challenges remain in forecasting polio vaccine needs, mainly due to (1) deviations in implementation of plans that formed the basis for earlier forecasts, (2) lack of alignment of tactics/objectives among GPEI partners and other key stakeholders, (3) financing, and (4) uncertainty about development and licensure timelines for new polio vaccines and their field performance characteristics.ConclusionsMismatches between supply and demand over time have led to negative consequences associated with both oversupply and undersupply, as well as excess costs and potentially preventable cases.

Nyalep Narjhe Wedding Tradition in Bindang Village, Pasean District, Pamekasan Regency

Marriages between indigenous peoples with one another, between one tribe with another, between Muslims with one another, as well as there are differences in urban and village marriage customs. Customs that have become customary law will be more difficult and stronger because violations of them will meet a sanction according to the regulations that are enforced and obeyed in society, from here the custom is not just an ancestral heritage but becomes a rule that must be obeyed as happened in Bindang Village, Pasean District, Pamekasan Regency, it is forbidden for a younger brother to get married before his older sister, even though the younger brother is physically and mentally ready to marry. This is not allowed, because if this happens according to the prevailing belief and it is believed that a disaster will occur to the household that will be fostered and the family, especially the older brother he is stepping on. The results of the study were to prove whether the customs regarding the marriage of Nyalep Narjhê (preceding his older sister) in Bindang Village, Pasean District, Pamekasan Regency did not conflict with the actual demands and teachings of Islam, and whether it was considered fair for a younger sister who would marry first to crawl under a brother three times, so that this is no longer a continuous dark history.

지역연계 대학재정지원사업에 나타난 지방자치단체의 역할과 쟁점

This study analyses how financial support projects for universities led by the government have evolved and the implications of such projects. The research critically examines the progression of government-led financial support projects for universities that are integrated with local regions. It identifies the changing role of local governments in these projects, which now focus on structural adjustments and enhancing local university competitiveness. The study reveals that local governments have taken on diverse roles, including forming linkages, fostering cooperation, and making joint investments. However, their participation and policy involvement has been largely declarative, with actual demands and expectations for regional-university connections not fully met. Universities also lack clarity in their roles, leading to confusion and scattered investment efforts. A significant obstacle identified is the insufficient resources available to local governments, alongside the absence of a legal framework for independent budget management.

윤석열 정부 지방시대 계획과 교육발전특구 제도의 지역 정주형 인재육성 가치와 교육 문화과제

Since the beginning of the 21st century, Korea has come up with many policies and used the budget to realize decentralization and balanced national development. However, as of 2023, the concentration of population in the metropolitan area and the hollowing out of the non-metropolitan area have resulted, and the Yoon Sukyeol government has established a comprehensive plan for the local era that integrates the two major policy concepts. This paper critically diagnoses the government's comprehensive plan for the local era and the special educational development zone system, emphasizing the timely value of fostering local resident-type talents, which is the main goal of the policy, and argues that the task of improving Korea's educational culture should be added. To this end, the background and meaning of the Yoon Suk Yeoln government's introduction of the 'First Comprehensive Plan for the Local Era' are examined, and the special education development zone system and the regional innovation university support system are compared. It points out the practice that has been disconnected from elementary and secondary schools, just as the academic administration and policies of local universities did not meet the actual demand of local industries due to the university ranking culture that has been overemphasized in the process. In the conclusion, it emphasizes the timely value of this system and presents it as a task that at the same time should include the education-cultural dilemma in the planning stage. This study is an attempt to combine cultural and historical perspectives with existing educational administration and policy considerations, and it is significant in illuminating the direction of national projects that are expected to invest a huge budget.

Tracking two decades of global gallium stocks and flows: A dynamic material flow analysis

Gallium (Ga) has recently garnered significant attention as a crucial byproduct element due to its crucial role in advancing low-carbon technologies and its globally imbalanced supply-demand. Understanding the global Ga flows and stocks can contribute to discovering ways to mitigate potential supply risks. In this study, we applied a material flow analysis to map the global Ga cycle from 2000 to 2021. Our results reveal the following results: (1) Over the past two decades, a cumulative 15.3% (313240.8 tons) of the total Ga reserves has been consumed through host metals (i.e., bauxite and zinc ores) mining. However, affected by actual demand, only 5549.7 tons of Ga have been extracted. Moreover, China has gradually become the dominant supplier of primary Ga globally, relying on its robust alumina industry. (2) The consumption pattern of global Ga is becoming increasingly complex. Driven by the diffusion of low-carbon technologies, primary Ga consumption has grown from 101 tons in 2000 to 432 tons in 2021. With a low material efficiency, only 2143.4 tons of Ga ultimately enter final products from semi-products. (3) The global Ga stock reached 956.7 tons in 2021 with significant recycling potential. However, commercial recycling systems were not established, and Ga resources in end-of-life (EoL) final products are mostly wasted. Given the anticipated growth of Ga demand, more coordinated efforts among different regions to promote technological innovation and improve recycling and circularity rates are urgently needed to ensure global Ga supply and a shared green future.

A novel two-stage dynamic pricing model for logistics planning using an exploration–exploitation framework: A multi-armed bandit problem

Dynamic pricing is utilized as a tool for determining pricing strategies in different sectors, such as the food industries, airline companies, and transportation services. Dynamic pricing aims to maximize revenue based on learning the price-demand relationship. It is imperative to perform price change testing within a short time frame since rapid changes can negatively impact the market. Therefore, there are two conflicting objectives: maximizing revenue and learning the demand function, which is called the exploration–exploitation concept. In this study, we propose an exploration–exploitation framework, focused on the domestic delivery service of Tipax Express Pars Company (TEPC), to determine the optimal selling price. In the exploration process, the initial demand function is proposed based on previous sales data using weighted linear regression. Then, the selling price and delivery lead-time parameters are incorporated into the demand function hypotheses. After that, the value of the optimal demand function parameters is determined based on the Z-Price Changes Algorithm (ZPCA). In the exploitation process, price optimization is designed as a Multi-armed Bandit (MAB) problem and solved using (i) Upper Confidence Bound Method (UCBM) (i.e., deterministic approach), and (ii) Thompson Sampling Method (TSM) (i.e., stochastic approach). The proposed exploration–exploitation framework is evaluated in 361 days, of which the exploration and exploitation durations are 31 and 330 days, respectively. In this study, 10,000 demand functions are investigated, which leads to the complexity of the developed model. The cross-validation approach, based on Mean Squared Error (MSE), is developed to decrease the complexity of the proposed model. A simulation study of 500 simulations demonstrates that TSM is more useful than UCBM because its solution is more robust. Furthermore, the results demonstrate that TEPC can gain maximum total expected revenue, maximum total number of selections during the exploitation stage, and maximum actual demand using a gaining market share strategy.

Pricing competition in maritime transportation with blockchain technology and empty container repositioning

A duopoly maritime transportation system with two ocean carriers providing cargo transport services between two sea ports is investigated in this paper. Pricing game models are developed to describe the competitive relationship between the two ocean carriers with/without blockchain adoption. Empty container repositioning is characterized in the models. Through model analysis, the pricing equilibria are derived. Using theoretical analysis and numerical studies, we find that (1) only when the fixed cost of blockchain is low do carriers adopt blockchain technology; (2) regardless of whether or not the competitor adopts blockchain technology, the carrier is better off by adopting it; (3) when both participants choose to adopt blockchain technology, the impact of blockchain on actual demand is not always positive unless the original operational costs are relatively high; and (4) sometimes, empty containers caused by demand imbalance in the two transportation directions are helpful in increasing the carrier’s profit.

Ostrakinda: A Game-Based Learning Toolkit for Ancient Mediterranean Cities

The study develops the 'Ostrakinda' toolkit for teaching morphologies of ancient Mediterranean cities. The methodology consists of a triple structure involving design, play, and analysis phases. In the design phase, the level of interest and actual demands of students regarding game-based learning (GBL) and ancient cities were explored through pre-experiments. The play phase involved experimental studies conducted in courses and workshops, where both physical and digital toolkits were tested by a total of 331 students. The analysis phase aimed to evaluate the impact of the game by systematically addressing five research questions. The results of the paired t-test confirm a significant difference in scores before and after playing the game, thus validating the effectiveness of the proposed strategy. Additionally, Chi-square (X2) tests revealed significant relationships between the form of play and the level of abstraction in the game outputs. Individual and group experiences during gameplay were associated with various interactions, and group experiences particularly fostered competitive and collaborative learning. The extensive analysis using the GBL Design Scale, comprising ten critical factors (GBL-Fn), showed that the challenge factor (F8) had the weakest values, while fantasy (F5) and narrative (F6) had the highest values in the correlation matrix. Overall, the study contributes methodologically and contextually to the existing literature, offering valuable findings for the future development of similar strategies in architectural education. However, it is important to acknowledge the limitations of this interdisciplinary study, particularly with regards to technical, cognitive, and archaeological aspects. Nevertheless, the clear and effective structure of the game holds promise as a valuable tool for learning.

A neural network-based prediction model for cultivating skilled personnel in higher vocational education

Abstract In this paper, the neurons in the BP neural network are used to represent the skill-based talent cultivation feature vectors, and the feature vectors are trained on the network to obtain the vector transformation function. On the basis of the vector transformation function, after constructing the technical talent cultivation prediction model by using the backpropagation algorithm, the data indicators are determined according to the requirements of skill-based talent cultivation, and the initial data are normalized by overfitting for the technical talent cultivation prediction results. The empirical research on the synthesis of technically skilled talents in higher vocational education is designed by means of questionnaires, and the data analysis software is used to analyze the examples of skilled talent cultivation in vocational education under the background of big data technology. The results show that from 2015 to 2020, the predicted values of the number of technically skilled talents demanded in a province are 92,130, 105,396, 160,946, 225,045, 232,313 and 216,150 respectively, and the relative error values are less than 0.05 compared with the actual demand values of technically skilled talents in the same period, indicating that based on the BP neural network-based technical talent cultivation prediction model outputs a good fit between the extrapolation test prediction value and the real value. This study guides the cultivation of skilled talents in higher vocational education.

Net zero carbon rural integrated energy system design optimization based on the energy demand in temporal and spatial dimensions

The energy demand of rural residential buildings has not received sufficient attention in previous research on rural integrated energy system (IES) design. The predominant "full-time and full-space" design principle results in the IES capacity not corresponding to the actual energy demand of rural buildings, and the impact of energy demand on IES design capacity has not been quantified. Therefore, energy demands for four scenarios ("full-time and full-space", "full-time and partial-space", "partial-time and full-space", and "partial-time and partial-space") were obtained through field investigations and simulation. Meanwhile, a rural IES capacity configuration optimization model was constructed with the economic and net zero carbon emissions constraints. The two-stage fast and elitist multi-objective genetic algorithm (Top-NSGAII) was employed to determine the optimal design for four scenarios. Results showed that the design based on "partial-time and partial-space" performs best in terms of economics, carbon emissions, and autonomy, reducing annual total costs by 6.96 %, net carbon emissions by 3492.30kgCO2, and external electricity ratio by 8.32percentage pionts, compared with the "full-time and full-space". The study quantifies the impact of energy demand on capacity design and provides a new path to realizing net zero carbon rural residential buildings.

A novel in-tube reformer for solid oxide fuel cell for performance improvement and efficient thermal management: A numerical study based on artificial neural network and genetic algorithm

The pursuit of higher power density and compact structure presents a critical challenge to the thermal management of solid oxide fuel cell. In this study, a novel in-tube reformer is proposed and a Multi-physics simulation-Artificial neural network-Multi-objective genetic algorism based optimization framework is developed to improve the output performance and reduce the internal temperature difference in solid oxide fuel cell. First, a validated multi-physics model is developed for parametric simulation and generating dataset. Afterwards, a surrogate model is obtained by training an artificial neural network to predict the output performance and internal temperature field of solid oxide fuel cell. Finally, multi-objective genetic algorithm optimizations based on the surrogate model are performed to maximize the output performance and minimize the internal temperature difference under different operation strategies. It is found that compared to the conventional configuration (without in-tube reformer), the use of in-tube reformer can effectively promote the electrochemical reactions, increase the fuel utilization (up to 34.2%) and current density (up to 14.5%) while significantly reducing the maximum temperature difference (up to 85.5%) in the cell, resulting in a uniform current density and temperature distribution along the cell. The proposed novel in-tube reformer and optimization framework are demonstrated to be highly powerful and can be easily applied to other fuel cell/electrolyzer systems to effectively improve system performance and realize efficient thermal management under actual demands.