Search Results (14)

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development. Full article

In this paper, a controller design method based on deep reinforcement learning is proposed for a wide-range variable cycle engine with a turbine interstage mixed architecture. The PID controller is subject to limitations, including single-input single-output limitations, low regulation efficiency, and poor adaptability when confronted with contemporary variable cycle engines that exhibit complex and multi-variable operating conditions. To solve this problem, this paper adopts a deep reinforcement learning method based on a deep deterministic policy gradient algorithm, and it applies an action space pruning technique to optimize the controller, which significantly improves the convergence speed of network training. In order to verify the control performance, two typical flight conditions are selected for simulation experiments as follows: in the first scenario, H = 0 km and Ma = 0, while in the second scenario, H = 10 km and Ma = 0.9. A comparison of the simulation results shows that the proposed deep reinforcement learning controller effectively addresses the engine’s multi-variable coupling control problem. In addition, it reduces response time by an average of 44.5%, while maintaining a similar overshoot level to that of the PID controller. Full article

This paper studies a continuous-review stochastic replenishment model for a multi-component system with regular and emergency orders. The system consists of N parallel and independent components, each of which has a finite life span. In addition, there is a warehouse with a limited stock of new components. Each broken component is replaced by a new component from the stock. When no component is available, an emergency supply is ordered. The stock is managed according to an $((s,S),(0,Q^e))$ policy, which is a combination of an $(s,S)$ policy for the regular order and a $(0,Q^e)$ policy for the emergency order. The regular order is delivered after an exponentially distributed lead time, whereas the emergency order is delivered immediately. We study three sub-policies for emergency orders, which differ from each other in size and in relation to the regular order. Applying the results from queueing theory and phase-type properties, we derive the optimal thresholds for each sub-policy and then compare the economic benefit of each one. Full article

In order to continuously promote water conservation efforts to alleviate the pressure of water diversion, water footprint (WF) is used as an effective tool to measure water utilization in the water-receiving areas of the Middle Route and Eastern Route of the South-to-North Water Diversion Project (SNWDP). The tempo-spatial variations of WF and spatial equilibrium of water footprint intensity (WFI) in the study area are quantified using the Mann–Kandle trend test, Sen’s slope, and Dagum Gini coefficient decomposition method for the years of 2005–2020. The results show that WF has a significant decreasing trend (Sen’s slop < 0, significant level < 0.05) in 17 cities in the study area, whereas WF shows a significant increasing trend (Sen’s slop > 0, significant level < 0.05) in 10 cities. Cities in the water-receiving areas are categorized into three types based on the contribution of the water utilization sector to changes in WF as follows: agriculture water-dominated city (AD), domestic and ecological water-dominated city (DED), and virtual water trade-dominated city (VWTD). Accordingly, targeted water conservation recommendations are made for these three kinds of cities, and it is suggested that AD, DED, and VWTD cities need to focus on advanced irrigation technologies, water reuse, and trade restructuring, respectively. The overall Gini coefficient of WFI fluctuates between 0.219 and 0.267 in the water-receiving areas of the Middle Route, which is dominated by the differences in city level. However, it fluctuates between 0.412 and 0.278 in the water-receiving areas of the Eastern Route, which is dominated by the differences in provincial level. Accordingly, water conservation hotspots are determined at the city level in the Middle Route and at the provincial level in the Eastern Route with different water management policies. These results provide a scientific support for water conservation management in the water-receiving areas of the SNWDP, as well as a methodological reference for the tempo-spatial characteristics of WF and their implications for water conservation. Full article

Based on China’s summary of three years of experience and measures in the prevention and control of the COVID-19 epidemic, we have built a COVID-19 prevention and control model integrating health and medical detection, big data information technology to track the trend of the epidemic throughout the whole process, isolation of key epidemic areas, and dynamic prevention and control management throughout the whole process. This model provides a simple, feasible, and theoretically reliable prevention and control model for future large-scale infectious disease prevention and control. The Lyapnov functional method is replaced by the global exponential attractor theory, which provides a new mathematical method for studying the global stability of the multi parameter, multi variable infectious disease prevention and control system. We extracted mathematical methods and models suitable for non-mathematical infectious disease researchers from profound and difficult to understand mathematical theories. Using the results of the global exponential Attractor theory obtained in this paper, we studied the global dynamics of the COVID-19 model with an epidemiological investigation. The results demonstrated that the non-constant disease-free equilibrium is globally asymptotically stable when ${\lambda }^{*}<0$, and the COVID-19 epidemic is persisting uniformly when ${\lambda }^{*}>0$. In order to understand the impact of the epidemiological investigation under different prevention and control stages in China, we compare the control effects of COVID-19 under different levels of epidemiological investigation policies. We visually demonstrate the global stability and global exponential attractiveness of the COVID-19 model with transferors between regions and epidemiological investigation in a temporal-spatial heterogeneous environment with the help of numerical simulations. We find that the epidemiological investigation really has a significant effect on the prevention and control of the epidemic situation, and we can also intuitively observe the relationship between the flow of people (including tourism, shopping, work and so on) and epidemiological investigation policies. Our model is adapted to different stages of prevention and control; the emergency “circuit breaker” mechanism of the model is also consistent with actual prevention and control. Full article

Technological innovation is closely related to the green total factor productivity (GTFP), which has played an important role in China’s sustainable development goals. However, the relationship between technological innovation and GTFP may change due to the influence of economic factors such as the service-oriented upgrading of industrial structure. This study used a panel dual-threshold regression model to perform an empirical analysis in order to explore this change. We introduced dummy variables to divide the samples into three categories according to the threshold value for group regression. The results show that technological innovation will still promote GTFP under the influence of the service-oriented upgrading of industrial structure. However, this positive influence has a double threshold effect; that is, it led to a nonlinear nexus. The role of technological innovation in promoting GTFP will decrease when the service-oriented upgrading of industrial structures crosses the first and the second thresholds. Additionally, the promotion effect of technological innovation on GTFP in provinces with high levels of service-oriented upgrades of industrial structures is smaller than that in provinces with a low degree of service-oriented upgrading of industrial structures, and even tends to be 0. When the government guides technological innovation to promote the improvement of GTFP, it needs to reasonably consider the composition of industrial structure and coordinate with effective industrial policies. Full article

We study an inventory control problem with two storage facilities: a primary warehouse (PW) of limited capacity M, and a subsidiary one (SW) of sufficiently large capacity. Two types of customers are considered: individual customers arriving at (positive and negative) linear rates governed by a Markov chain, and retailers arriving according to a Markov arrival process and bringing a (positive and negative) random number of items. The PW is managed according to a triple-parameter band policy $(M,S,s),$$0\le s<S\le M,$ under a lost sales assumption. Under this policy, as soon as the stock level at the PW falls below s, a refilling to S is performed by a distributor after a random lead-time. However, if the stock exceeds level S when the distributor arrives, no refilling is carried out, and only maintenance services are performed. Items that exceed level M are transferred to the SW at a negligible amount of time for those used in related products. Our cost structure includes a fixed order cost, a variable cost for each item supplied by the distributor, a cost for the additional maintenance, a salvage payment for each transferred item from the PW to the SW, and a loss cost for each unsatisfied item due to demands. We seek to determine the optimal thresholds that minimize the expected overall cost under the discounted criterion. Applying first-passage time results, we present a simple set of equations that provide managers with a useful and an efficient tool to derive the optimal thresholds. Sensitivity analysis and fruitful conclusions along with future scope of research directions are provided. Full article

COVID-19 is the name of the new infectious disease which has reached the pandemic stage and is named after the coronavirus (COVs) which causes it. COV is a single-stranded RNA virus which in humans leads to respiratory tract symptoms which can lead to death in those with low immunities, particularly older people. In this study, a standard dynamic model for COVID-19 was proposed by comparing a simple model and the optimal control model to reduce the number of infected people and become a guideline to control the outbreak. Control strategies are the vaccination rate and vaccine-induced immunity. An analysis was performed to find an equilibrium point, the basic reproduction number $(R_0)$, and conditions that generate stability by using Lyapunov functions to prove the stability of the solution at the equilibrium point. Pontryagin’s maximum principle was used to find the optimal control condition. Moreover, sensitivity analysis of the parameters was performed to learn about the parameters that might affect the outbreak in order to be able to control the outbreak. According to the analysis, it is seen that the efficacy of vaccines $\left(b\right)$ and the infection rate $({\beta }_{an},{\beta }_{sn},{\beta }_{av},{\beta }_{sv})$ will affect the increased (decreased) incidence of the outbreak. Numerical analyses were performed on the Omicron variant outbreak data collected from the Thailand Ministry of Health, whose analyses then indicated that the optimal control strategy could lead to planning management and policy setting to control the COVID-19 outbreak. Full article

The purpose of this article is to examine the server activation policy (SAP) in a multi-server queuing-inventory system (MQIS). The queue has a total of c number of multi-threshold stages as well as c-homogeneous servers. The activation of each server begins one by one if there is an adequate queue length and inventory in the system; otherwise, they remain idle. The server deactivation process continues until the queue length exceeds the manageable level (predetermined stages) or there is insufficient stock. In addition, when we assume the length of the two successive threshold levels is one, the server activation policy model becomes a regular multi-server model. The Neuts matrix geometric approach is used to discuss the stability condition, stationary probability vector. The Laplace–Stieltjes transform (LST) is used to analyse the waiting time distributions of the queue and orbital customers. Additionally, significant system performance metrics and sensitivity analysis are used to investigate the effects of various parameters and cost values. In the comparative result between the server activation model (SAM) and without the server activation model (WSAM) on the expected total cost, we obtain the minimised cost in the SAM. Moreover, the results are obtained by assuming that the length of the intervals between the two successive threshold levels is to be taken into account as the non-uniform length. The expected inventory level, reorder rate, and waiting time of a customer in the waiting hall and orbit were explored numerically by the parameter analysis. Full article

In large projects such as dams, embankments, and seawalls, it is sometimes important to determine the compressive properties of a mixture containing soil and gravel with particle sizes exceeding 75 mm. The Saemangeum Renewable Energy Vision Proclamation Ceremony, held in October 2018 in Korea, confirmed and promulgated the plan to build a total of 4.0 GW of renewable energy power generation complex in the Saemangeum area. The project will be carried out on an area of 31.95 km², and a 1.0 GW offshore wind power development plan is in progress. Since most of the Saemangeum area has a soft ground layer that has been reclaimed, a key research institute is absolutely necessary to lead in the stabilization of the supporting structures for power generation facilities and to achieve the renewable energy 3020 policy in extreme environments. Hence, it is meaningful to investigate the effect of gravel content (P) on the ground strength characteristics. However, such investigation cannot be routinely performed due to the limited size of the equipment available. Several equations have been proposed in the literature to modify the compaction properties of gravel-mixed soils containing coarse aggregates. Among these is the proposed equation by Walker and Holtz, which has widely been used. However, the use of this equation in the case of high gravel content is not appropriate because the physical meaning of this equation is not clear and does not apply to materials with gravel content exceeding 40%. Therefore, a better quantitative evaluation method in determining material characteristics according to gravel content must be established through laboratory tests on samples of acceptable particle size for the experimental equipment. To obtain the compressive properties of decomposed granite soil (D-G-S), in this study, the results from large-scale one-dimensional compression tests on samples compacted at various gravel concentrations, constant compaction energy, and constant water content were analyzed. To quantitatively evaluate the properties of D-G-S according to the gravel content, a modified formula based on the two-phase mixing theory was utilized. It was shown that the degree of mixing between the gravel and sand for the conditions of D-G-S used in the experiments was high, at 0.85. To estimate the compression curves of D-G-S at various gravel content, the compression curves of purely sand (P = 0%) and purely gravel (P = 100%) materials, and the value of R_m = 0.85 were utilized, and it was shown that the compression index and swelling index curves estimated using the method presented in this study were in good agreement with the experimental results. To confirm the engineering applicability of the presented method, finite element analysis was performed, and as a result, it was revealed that it can be sufficiently applied in the simulation of embankment settlement. In order to obtain more reliable results in the future, verification using various samples is required. Full article

Climate change is an important issue that increasingly affects our lives. One of the proposals for mitigating climate change is fighting biodiversity loss, which can support climate mitigation and adaptation actions. In Poland, the possibility of excluding large tracts of forest areas from use is being considered. The discussed the exclusion of forest land from use will affect the timber supply and market, especially for broadleaved wood. The main purpose of this analysis is to present a timber supply forecast, with a particular focus on the possibility of obtaining broadleaved hardwood timber in Poland from forests managed by State Forests National Forest Holding under three scenarios that assume different criteria for selecting forest areas for protection. The work was divided into two main phases: (1) the analysis of historical sales volume of wood products and average sale prices of hardwood during the period 2011–2020; (2) the preparation of a forecast of the potential possibility of maintaining broadleaved hardwood production in the three decades between 2020 and 2049. In the forecast, it was assumed that about 2.7 million hectares of planted and production forests are excluded from use in order to implement the provisions of the 2030 Biodiversity Strategy. In Scenario “I”, the supply of merchantable broadleaved roundwood volume will be reduced to 14%–18% that of Scenario “0”. In Scenario “II”, 55% of the “0” scenario is harvested, and in Scenario “III”, 33%–37% of the “0” scenario merchantable broadleaved roundwood is harvested. The introduction of restrictions on timber harvesting as a result of Poland’s compliance with European Union requirements in the area of environmental protection will lead to a significant reduction in the supply of timber on the market. This may lead to a further increase in timber prices and an increase in the importance of large timber buyers at the expense of local buyers. The recommendations contained in the policy objectives that the EU sets for the states should be supported by a thorough analysis when selecting areas for strict protection. Full article

Consumers initiating returns online may produce secondary packaging, while most of the packages are produced by plastics. The more products are returned, the more plastics are used. Existing research indicates that the plastic packages can contribute to the micro-plastics pollution of the environment. As consumer environmental awareness (CEA) improves, more and more consumers are willing to pay extra fees to change the materials of packages from plastics to others in order to protect the environment, prompting enterprises to adjust to their return policies. In this context, this paper takes environmental quality improvement effort and the environmental coefficient as decision variables, and compares the manufacturer’s optimal decisions under with and without return policy. Our results show as follows: (1) There is a positive correlation between CEA and environmental quality improvement effort and the environmental coefficient; that is, environmental quality improvement effort and the environmental coefficient increase with an increase in CEA; (2) When CEA is high ($k\le \tau \le d$), there is a threshold for manufacturers to invest in environmental effort. However, when CEA is low ($0\le \tau \le k$), regardless of the return policy the manufacturer implements, its profit increases with the promotion of CEA, and when the manufacturer allows consumer returns, the relationship is more obvious; (3) The manufacturer should adopt an appropriate return policy according to the changes in CEA. When CEA is low ($0\le \tau \le k$), the manufacturer should adopt a without return policy; when CEA is high ($k\le \tau \le d$), the manufacturer should adopt a full refund ($r=p$) return policy, which is the optimal profit, and increase investment in environmental protection. From the above conclusions, we suggest that the government should increase the publicity of environmental protection, consumers should establish the awareness of green consumption, and enterprises should increase investment in environmental quality improvement to achieve sustainable development. Full article

With the continuous and rapid rise of urbanization in China, land use transition research has been carried out extensively. Multiple cropping is the content of land use recessive morphology research, and it is also a common agricultural system in China. Accordingly, further research on multiple cropping index (MCI) can enrich the land use transition research and help to evaluate China’s food security. In order to examine the spatiotemporal changes and factors influencing the MCI of cultivated land in China, we collected MODIS remote sensing image data and land use classification data and conducted a remote sensing inversion on China’s MCI from 2000, 2005, 2010, and 2015. The spatial distributions and evolution processes of the MCI were explored through spatial mapping, statistical analysis, and processing with the Geographic Information System; moreover, the influencing factors of MCI were explored quantitatively with principal component regression. The results were as follows: (1) at the provincial scale, the average MCI across Guangdong, Guangxi, Hainan, Henan, Anhui, and Jiangsu was high; meanwhile, the average MCI across Heilongjiang, Inner Mongolia, Ningxia, and Qinghai was low. Between 2000 and 2015, the number of provinces with low MCI increased gradually, and the average MCI decreased greatly in the southern provinces. (2) At the county scale, the Taihang Mountains, Qinling Mountains, and Hengduan Mountains formed the boundary of China’s single cropping and multiple cropping indices. Dynamic changes in China’s MCI were obvious, and the number of counties with MCI change values lower than 0 increased gradually. Last, (3) natural conditions, nonagricultural process, cultivated land quality, and agricultural intensification demonstrated different degrees of impact on the MCI; in particular, the influence of nonagricultural industries, pesticides, and agricultural plastic film on the MCI proved especially important. Future research should strengthen the existing work on related transformations in farmers’ livelihoods, especially in terms of the return of rural labor force, the body of agricultural production, agricultural ecological issues, and the balance between increased crop production and reduced environmental pollution. In addition, agricultural policy design should pay more attention to cultivated land quality, the farmer who cultivates the land, and the multiple cropping potential of cultivated land. Full article

Several studies have found rising ambient particulate matter (PM ${}_{2.5}$ ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM ${}_{2.5}$ ambient concentrations and assessing whether urban expansion can explain rise of PM ${}_{2.5}$ ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM ${}_{2.5}$ concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM ${}_{2.5}$ emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory. Full article