Matching Degree Research Articles

Effect of BaTiO3 as a filling material with adjustable dielectric properties on improving the radio frequency heating uniformity in red jujubes

The non-uniformity of radio frequency (RF) heating is a major obstacle to develop successful large-scale disinfestation processes for agricultural products with large particles. To effectively improve the RF heating uniformity in red jujubes, the chemical material of BaTiO3 (BT) with different volume percentages was added into food-grade silicone (FGS) to form FGS/BT composite particles with various dielectric properties. The various FGS/BT composite particles were filled inside the rectangular container of red jujubes and used to evaluate the effect of matching degree of their dielectric constants on the RF heating uniformity. The results showed that the dielectric constant of the composite increased from 3.41 ± 0.14 to 8.31 ± 0.15 as the BT volume percentage increased from 0% to 20% at 25 °C and 27.12 MHz. Compared with FGS, the filled FGS/BT composite with a BT volume percentage of 20% not only improved the heat conduction but also optimized the temperature distributions among red jujube particles, which reduced the heating uniformity index in the top, middle, and bottom layers by 40%, 32%, and 27%, respectively. As the electrode gap increased from 160 mm to 180 mm, the heating rate and uniformity index in FGS/BT filled samples decreased significantly (p < 0.05). There was no significant (p > 0.05) difference in color and textural properties between the treated and control samples. This study provided the effective filling materials with matched dielectric constant in the RF treatment of red jujubes and a feasible method to further improve the RF heating uniformity in large-size agricultural products.

Constructing a student development model for undergraduate vocational universities in China using the Fuzzy Delphi Method and Analytic Hierarchy Process.

As the industrial structure changes, the severe shortage of high-quality technical and skilled talent in China is one of the most significant factors affecting the high-quality development of China's economy. Bridging the gap between cultivating talent from new undergraduate vocational universities and the demand for industrial talent is regarded as an efficient strategy to address the talent shortage. In addressing the gap, China is hindered by a lack of clarity regarding student development goals and effective assessment instruments. Thus, this study aimed to use the Fuzzy Delphi Method (FDM) and the Analytical Hierarchy Process (AHP) to overcome the above challenges. Specifically, we used the FDM to establish a five-level undergraduate vocational education student development model with two 2nd-level indicators, three 3rd-level indicators, eight 4th-level indicators, and 33 5th-level indicators to clarify student development goals. Then, the AHP was applied to determine the indicator weights, and a student development assessment instrument was developed to help universities acquire student development data and improve the matching degree between talent supply and demand. This study could help undergraduate vocational universities cultivate high-quality technical and skilled talent quickly to meet the demand for China's new economic system and to promote industry independence and global competitiveness.

Enhanced Performance in Cesium Tellurium Chlorine by Hafnium Alloying for X‐Ray Computed Tomography Imaging

AbstractThe term “light yield” (LY) refers to the ability of scintillator materials to convert high‐energy radiation into visible light. A higher LY corresponds to improved energy and spatial resolution in detectors. The enhancement of scintillator material performance is a crucial aspect of their development. In this study, the Cs2TeCl6 (CTC) double perovskite microcrystals is synthesized, and the broadband yellow emission shows a high degree of matching with the Charge‐coupled Device (CCD). However, the LY of CTC is poor for practical applications in X‐ray imaging. The scintillation performance is significantly enhanced through hafnium alloying. The LY increased from ≈4167 to 38 523 photons/MeV, and the detection limit decreased from 948 to 258 nGy s−1. The underlying mechanism of Te4+ ions emission is systematically explored through density function theory (DFT) analysis, along with investigations into pressure‐dependent photoluminescence, temperature‐dependent photoluminescence, and low‐temperature thermoluminescence. Furthermore, a flexible X‐ray scintillator screen with an outstanding high spatial resolution of 15.9 lp mm−1 is successfully fabricated. This work not only represents a substantial advancement in the scintillation performance of Te4+ ions emission double perovskite microcrystals but also provides an effective strategy for the development of the next generation of halide scintillators.

Impact of supply–demand matching on citizen satisfaction: analysis based on expectancy disconfirmation model

In the context of ensuring the well-being of citizens, the energy industry should predominantly adhere to the people-oriented approach. Investigation of citizen satisfaction through the lenses of power supply and societal demand is of utmost importance. This study constructs a satisfaction model based on expectancy disconfirmation theory, employs response surface analysis to explore the impact of matching degree between demand and supply on citizen satisfaction. The results show that citizen satisfaction is notably higher in case of a match between public expectations and supply quality compared to mismatch scenarios. Furthermore, instances involving both high public expectations and high supply quality, yield greater satisfaction compared to scenarios involving low public expectations and low supply quality. Importantly, citizen satisfaction experiences a dip when public expectations surpass supply quality, unlike situations where public expectations fall short of supply quality. Policy awareness does not moderate the effect of matching analysis on citizen satisfaction. This research enriches the scope of understanding the complex nature of public satisfaction, offering theoretical support for the high-quality development of power supply departments, catering to both energy needs and well-being of citizens. Thus, the current study significantly contributes to promoting the sustainable and high-quality development of the local social economy.

Analyses on Characteristics of Spatial Distribution and Matching of the Human–Land–Water–Heat System on the Yunnan Plateau

Water, soil, and heat are strategic supporting elements for human survival and social development. The degree of matching between human-land-water-heat elements directly influences the sustainable development of a region. However, the current evaluation of the matching of human-land-water-heat elements overlooks the influence of elevation factors on the matching results, especially evident in mountainous areas. Taking the Yunnan Plateau with distinctive mountainous features as the research subject, divided into 11 elevation ranges, the Lorenz Gini coefficient, asymmetry coefficient, matching distance, and imbalance index are used to assess the spatial matching and balance of human-land-water-heat elements. A projection tracing model is employed to analyze its water resource carrying capacity. Analyses revealed that the Gini coefficient of monthly precipitation from the 1950s to 2022 on the Yunnan Plateau increases with increasing latitude, whereas the correlation with elevation is notably lower. The asymmetry coefficient increases gradually from west to east with change in longitude. The mismatch of the human–land–water–heat system in regions at different elevations is in the order 1800–2000 m &gt; 2000–2200 m &gt; 1400–1600 m &gt; 800 m &gt; other areas. The matching of the human–land–water–heat system in different wet–dry years and seasons also fluctuates with elevation, resulting in serious seasonal drought and water shortage problems in mountainous areas with elevations of 1200–1600, 1800–2000 m, and &gt;2600 m. The spatial equilibrium of temperature and precipitation in regions of different elevations is best, followed by that of cultivated land, while that of the population is the worst. The Gini coefficients for different water cycle processes of precipitation, surface runoff, and regulating storage capacity for water supply continue to increase. Specifically, the Gini coefficient of industrial water supply is the highest, reaching 0.576, and that of agricultural irrigation is the lowest (0.424). Through artificial regulation of lake and reservoir water, seasonal changes in the demand for agricultural irrigation water are offset to achieve a demand–supply balance and matching of land and water resources. The water resource capacity of different elevation ranges is evenly underloaded. However, the potential of the water resource capacity varies obviously with elevation in the order 2000–2200 m &lt; 1800–2000 m &lt; 1600–8000 m &lt; 1400–1600 m &lt; other areas. It appears that the greater the human–land–water–heat system mismatch, the smaller the regional potential of the water resource capacity.

Surface slicing and toolpath planning for in-situ bioprinting of skin implants

Bioprinting has emerged as a successful method for fabricating engineered tissue implants, offering great potential for wound healing applications. This study focuses on an advanced surface-based slicing approach aimed at designing a skin implant specifically for in-situ bioprinting. The slicing step plays a crucial role in determining the layering arrangement of the tissue during printing. By utilizing surface slicing, a significant shift from planar fabrication methods is achieved. The developed methodology involves the utilization of a customized robotic printer to deliver biomaterials. A multilayer slicing and toolpath generation procedure is presented, enabling the fabrication of skin implants that incorporate the epidermal, dermal, and hypodermal layers. One notable advantage of using the approximate representation of the native wound site surface as the slicing surface is the avoidance of planar printing effects such as staircasing. This surface slicing method allows for the design of non-planar and ultra-thin skin implants, ensuring a higher degree of geometric match between the implant and the wound interface. Furthermore, the proposed methodology demonstrates superior surface quality of the in-situ bio-printed implant on a hand model, validating its ability to create toolpaths on implants with complex surfaces.

High-Precision Detection for Sandalwood Trees via Improved YOLOv5s and StyleGAN

An algorithm model based on computer vision is one of the critical technologies that are imperative for agriculture and forestry planting. In this paper, a vision algorithm model based on StyleGAN and improved YOLOv5s is proposed to detect sandalwood trees from unmanned aerial vehicle remote sensing data, and this model has excellent adaptability to complex environments. To enhance feature expression ability, a CA (coordinate attention) module with dimensional information is introduced, which can both capture target channel information and keep correlation information between long-range pixels. To improve the training speed and test accuracy, SIOU (structural similarity intersection over union) is proposed to replace the traditional loss function, whose direction matching degree between the prediction box and the real box is fully considered. To achieve the generalization ability of the model, StyleGAN is introduced to augment the remote sensing data of sandalwood trees and to improve the sample balance of different flight heights. The experimental results show that the average accuracy of sandalwood tree detection increased from 93% to 95.2% through YOLOv5s model improvement; then, on that basis, the accuracy increased by another 0.4% via data generation from the StyleGAN algorithm model, finally reaching 95.6%. Compared with the mainstream lightweight models YOLOv5-mobilenet, YOLOv5-ghost, YOLOXs, and YOLOv4-tiny, the accuracy of this method is 2.3%, 2.9%, 3.6%, and 6.6% higher, respectively. The size of the training sandalwood tree model is 14.5 Mb, and the detection time is 17.6 ms. Thus, the algorithm demonstrates the advantages of having high detection accuracy, a compact model size, and a rapid processing speed, making it suitable for integration into edge computing devices for on-site real-time monitoring.

Assessment of Urban Sustainability and Coupling Coordinated Development: An Empirical Study in Anhui Province, China

The conflict between economic development and environmental degradation has significantly impeded urban development. Currently, the Chinese government is actively promoting the comprehensive transformation of new-type urbanization. This study measured the quality of urbanization and its degree of coupling coordination in 16 cities in Anhui Province from 2010 to 2020, utilizing 32 indicators across six dimensions: economic development (ED), infrastructure (INF), population development (PD), social development (SD), environmental protection (EP) and urban–rural integration (URI). Additionally, a novel method of sequential relationship analysis, oriented towards “interaction–change” was introduced to calculate indicator weights, which take the interactive degree among various indices and dependencies between neighboring cities into account. The results indicate that urban development in Anhui Province is imbalanced due to a low degree of resource matching. However, there is a positive trend in the relationship between urbanization quality and economic development. Furthermore, although the coupling coordination degree of urbanization quality in most cities fluctuates at different scales, it is still necessary to narrow and optimize the gap between subsystems. For one thing, the above achievements may provide reference for breaking through the traditional static weighting method from the theoretical level. For another, it can also provide decision-makers with valuable insights to establish an intercity complementary management system, so as to achieve sustainable development across the entire region.

Study on the Path of Cultivating “New Abilities” of Contemporary College Students’ Employment

With the development of the society and the increasing enrolment rate of higher education, the requirements for the employment ability of college students have been gradually improved, especially the college students with the “new ability” consistent with the needs of the society have obvious employment advantages, this paper analyses the “new ability” of college students from the three dimensions of professional ability, humanistic ability and basic ability. This paper analyses the “new ability” in three dimensions: professional ability, humanistic ability and basic ability, and combines the current cultivation mode and cultivation path of colleges and universities to analyze the matching degree of the ability demand of the society for college students, the “new ability” of college students and the cultivation path of colleges and universities to propose that colleges and universities cultivate the “new ability” that is agreed by the society for college students. Then, it proposes effective methods for colleges and universities to cultivate socially acceptable “new competences” necessary for college students.

Study on the future development prospects of biomass energy in towns, townships, and subdistricts under multi-scenarios —A case study of Harbin City

Biomass energy is the most likely new energy source to replace fossil fuels. This study presents a prediction method for biomass energy supply-demand and changes in supply-demand patterns that connects the past and future. In the study, carbon peaking and carbon neutrality are taken as the time base. The FLUS model is used to simulate future land allocation in four basic scenarios: natural development scenario, agricultural development scenario, low-carbon development scenario and industrial development scenario; and townships, towns, and subdistricts are taken as the basic statistical unit. Based on this, we have examined the suitability of future development of biomass energy in rural and urban areas in each scenario, as well as its contribution to the future energy system transformation that substitutes for fossil energy. This method has been applied to the case study of townships, towns, and subdistricts in Harbin City in China. The study results show that: The degrees to which biomass energy potential can substitute for fossil energy in the four scenarios in 2030 reach 4.21%, 5.62%, 5.47%, and 3.82%, respectively. Under agricultural development scenario, the supply-demand balance areas account for 48.94%, meaning this scenario has the largest potential for energy development and transition; low-carbon development scenario sees the highest degree of spatial supply-demand match, and thus enjoys the most advantages in energy transportation. The degrees to which biomass energy potential can substitute for fossil energy under the four scenarios in 2060 reach 2.01%, 6.96%, 5.35%, and 1.26% respectively. Agricultural development scenario still has the largest development potential, with the proportion of supply-demand balance areas jumping to 50.46%; Finally, low supply-high demand mismatch clusters are mainly concentrated in the main urban area and the central areas of townships, towns, and subdistricts. The high supply-low demand mismatch clusters are mainly distributed in the northeast. Under low-carbon development scenario, the low supply-high demand mismatch ratio has been reduced to 0.92%, the lowest supply-demand mismatch among all scenarios. Therefore, low-carbon development scenario enjoys the most spatial match advantages. This study will serve as an assessment method to advance the spatial development of biomass, and provide a geospatial basis for optimizing the layout of future energy development. Compared with previous studies, it has more spatial attributes, better reflects the complexity of biomass energy changes and is more connected with the measurement results and future policy making. In future research, we will combine multiple types of clean energy into the comparison and combined use to form a more comprehensive and specific research framework for new energy conversion. Longer-term macro-planning will go beyond the application boundaries of this research.

In situ preparation of oriented microbial consortium-based compound enzyme strengthens food waste disintegration and anaerobic digestion: Performance, mechanism, microbial communities and global metabolic pathways

This study used food waste (FW) as the substrate, under the co-culture system, innovatively developed the oriented and high matching degree microbial consortium-based compound enzyme (MCE) used for promoting FW hydrolysis and anaerobic digestion (AD). It was found that the highest hydrolysis efficiency and substrate biodegradability could be obtained after 1:100 MCE (i.e. the inoculation ratio of Aspergillus oryzae CICC 40214 as protease generator and Aspergillus niger CICC 40294 as amylase generator was 1:100) pretreatment, thereby harvesting the highest biomethane yield of 522.61 mL/g VS. The dissection of FTIR results indicated that the structure of main components of starch, protein and lipid in FW was destructed after MCE pretreatment, of which the protein was more susceptible to MCE and disintegrated first, while its structure also became looser. XPS C1s spectra and N1s spectra showed that the C-(C/H) and protein-N of the enzyme pretreated solid were respectively decreased from 77.82 % to 69.19 % and 69.43 % to 40.07 %, which further implied that more carbohydrates and proteins were released from solid phase to liquid phase after MCE pretreatment. The microbial community and metagenome analysis illustrated that MCE pretreatment regulated the microbial communities lead to a shift in the favorable direction of biomethane production. While based on the changes in gene abundance, the deciphering of global metabolic pathways revealed that the 80 %, 60 %, 75 % and 86 % of the functional genes related to glycolysis, amino acid metabolism, TCA cycle and methane metabolism were respectively intensified after 1:100 MCE pretreatment, thereby achieving the maximized biomethane yield.

Exploring strategies to optimise outcomes in hepatitis-associated aplastic anaemia patients following haematopoietic stem cell transplantation

This study aimed to assess haematopoietic stem cell transplantation (HSCT) safety and efficacy while exploring strategies for optimising outcomes in patients with hepatitis-associated aplastic anaemia (HAAA). We retrospectively reviewed 35 HAAA patients who underwent HSCT at a large Chinese blood disease hospital between 2008 and 2022. HAAA patients receiving HSCT typically presented with severe (28.6%) and very severe (65.7%) AA. Male patients predominated (68.6%), with a median onset age of 23 years (range, 9–44). Haploidentical donor-HSCT and matched sibling donor-HSCT were in comparable proportions. The 5-year overall survival (OS) rate was 74.0%, with cumulative incidences of grade II–IV acute and chronic graft-versus-host disease (GVHD) at 37.1% and 22.4%, respectively. A diagnosis-to-HSCT interval ≥ 75 days, acute GVHD, and post-HSCT liver events (e.g., hepatic GVHD and a three-fold increase in aminotransferase or bilirubin) significantly worsened 5-year OS. In the multivariate models, recipients with sex-matched grafts had better OS, and those with younger male donors had a lower incidence of II–IV aGVHD. Higher HLA matching degree (HLA > = 7/10) was an independent prognostic factor associated with better OS and GFFS. A diagnosis-to-HSCT interval ≥ 75 days was predictive of post-transplant liver events in HAAA patients. In conclusion, HSCT was a safe and effective treatment for HAAA. Early transplantation, careful donor selection and improving post-transplant liver events were crucial to optimise outcomes.

User Identification Across Multiple Social Networks Based on Naive Bayes Model.

Recently, the problem of user identification across multiple social networks (UIAMSNs) has attracted considerable attention since it is a prerequisite for many downstream tasks and applications. Although substantial network feature-based approaches have been proposed to solve the UIAMSNs' problem, the matching degree in most of the current works is given by experience, which lacks a solid theoretical basis. To alleviate the above predicament, we propose a user identification algorithm based on naive Bayes model (UI-NBM) within the network feature-based framework. First, a matching degree index is designed based on the naive Bayes model, which can accurately measure the contributions of different common matched node pairs (MNPs) to the connection probability of unmatched node pairs (UMNPs). Second, the matching degrees of all UMNPs are formulated as the product of matrices, giving rise to the great reduction of the time complexity and the compact expression; Finally, with the idea of recursion process, more UMNPs can be iteratively predicted even when only a small amount of prior information (i.e., a few number of MNPs) is known. The experimental results on the synthetic and real cross platforms demonstrate that the method outperforms the baseline methods within the feature-based framework.

Application of a Three-Stage Procedure for Extracting a Small-Sized Anchor Object on a Noisy Image

The purpose of the article is to develop a three-stage procedure for identifying an anchor object in a noisy current image. This goal is achieved by determining the sampling threshold levels at which the greatest similarity of the compared images is ensured; development of a procedure for refining the maximum of the decision function by quantizing the current image. The solution to the first problem is based on the formation of a correlation field of radio brightness temperatures and the choice of a sampling threshold. It is proposed to use the cross-correlation coefficient as a criterion for the degree of image matching. The effectiveness of the procedure for selecting a fragment of a reference image is assessed based on the criterion of the probability of selecting a fragment of a reference image. It is shown that image noise can lead to a decrease in the probability of selecting a fragment of a reference image, down to 0.4. It is proposed to refine the maximum of the decision function based on the iteration method. The most significant results are the obtained dependences of the probability of choosing a fragment of the reference image on the threshold value and the signal-to-noise ratio, as well as the analytical relation for the asymptote of this dependence. The novelty of the work lies in the fact that the procedure for forming the decisive function has been further developed. This will significantly improve the operating efficiency of unmanned aerial vehicles, especially in conditions of interference.

Mapping and assessing supply–demand matching of recreational ecosystem services in Shandong province, China

Globally, natural landscapes and their recreational ecosystem services (RES) are both under threat from anthropogenic activities and environmental changes. But the demand for RES increases with the development of human living standards. Therefore, mapping and assessing the supply–demand relationships of RES is crucial for landscape planning, management, and human well-being. This study aimed to study the relationships between supply and demand of RES in Shandong province, China, quantify the supply, demand, and supporting capacity of RES, analyze the degree of matching and coupling between the supply and demand of RES, and explore the effect of terrain gradient to RES. The results indicated that the average value of coupling coordination (CCD) between supply–demand of RES in Shandong province was 0.45, which is a mild balance. According to the supply–demand matching types, areas with low supply-high demand accounted for 6.62%, indicating a serious shortage matching of supply and demand. Using hotel quantity and roads as indicators of the supporting capacity of RES, the overall coordination between landscape aesthetic quality (LAQ) and regional hotel quantity shows a mild imbalance (0.38), while that with the regional road density shows a good balance (0.60). Furthermore, the scenery spots density, RES supply, RES supply–demand CCD and support capacity CCD shows an upward trend with the increase of elevation and slope, an “increased-decreased-increased” trend with the increase of terrain niche index. This study provides spatial matching of RES supply and demand in Shandong province. Optimizing land-use structures and improving natural resource allocation were proposed to boost coordination of RES supply and demand.

Resilience Assessment and Improvement Strategies for Urban Haze Disasters Based on Resident Activity Characteristics: A Case Study of Gaoyou, China

The popularisation of mobile information technology has provided access to the living habits and activity trajectories of residents and enabled the accurate measurement of the impact of urban haze disasters on residents’ lives, supporting urban haze risk response. Using the main urban area of Gaoyou City as a case study, this study identifies the spatial range and trajectory characteristics of the daily activities of residents in a haze disaster environment, based on air pollution monitoring and resident travel positioning data. We constructed an evaluation index system to measure the corresponding relationship between residential activities and haze disasters. The results indicate that the interference with residential activities and the adaptability of built environments are key indicators for evaluating urban resilience in haze environments, with weights of 0.57 and 0.43, and correlation indices of 0.67 and 0.81, respectively. The interference with residential activities and the adaptability of built environments exhibit spatial characteristics of cold and hot ‘multi-core’ agglomeration and ‘strip’ agglomeration, respectively. Specific indicators show that the residential activity exposure index is significantly influenced by the built environment factor index, with the vegetation coverage index showing a significant positive correlation (0.837) and the public transportation facility accessibility index showing a significant negative correlation (−1.242). Planning should focus on improving the adaptability of the built environment or reducing the interference with residential activities and enhancing the matching degree of the two at the spatial facility level.

Revealing the Influencing Factors of an Oxygenated Volatile Organic Compounds (OVOCs) Source Apportionment Model: A Case Study of a Dense Urban Agglomeration in the Winter

AbstractUnderstanding sources of oxygenated volatile organic compounds (OVOCs) in the atmosphere still has large uncertainties. In this study, an improved OVOC source apportionment model was developed by principal component analysis and multiple linear regression based on the online monitoring of nonmethane hydrocarbons (NMHCs) and OVOCs in a dense urban agglomeration in the winter. The modeled concentrations were in good agreement with the measured concentrations (R2 = 0.56–0.97). The concentrations of major OVOCs, except for 2‐methylacrolein, were greatly affected by anthropogenic sources (15.8%–76.8%) and secondary generation (0.0%–51.7%), while transport and natural sources contributed to 0.0%–26.8% and 0.0%–32.0%, respectively. The selection of isoprene as the natural tracer led to an underestimation of the OVOC species from primary emission and an overestimation from natural sources. In addition, photochemical reactions significantly reduced the simulation accuracy of the model for NMHCs in the afternoon, with the R2 of 0.60 ± 0.23, which was lower than the overall value of 0.82 ± 0.11. However, the R2 for OVOCs (0.83 ± 0.14) did not decrease significantly in the afternoon due to the compensation of secondary oxidation. Furthermore, the concentration gradient distribution of the species gradually changes from a normal distribution to an exponential normal distribution with a decrease in concentration, the accuracy of the model was influenced by the degree of matching between tracer and species concentration gradient as species concentration change. Developing models with additional tracers at different concentration levels may enhance the robustness of the OVOC source apportionment model without increasing its complexity.

A New Water Film Depth Prediction Model for Pavement Surface Drainage

The prediction of the water film depth (WFD) on the road surface can help with road skid resistance research and reduce the risk associated with driving on rainy days. At present, there are many empirical and analytical models based on drainage length, slope, rainfall intensity and other parameters. Considering the influence of road surface runoff and starting from the Reynolds number formula of road surface water flow, a new road surface WFD calculation formula that considers the movement state of laminar water flow is derived. The results show that the changing trends of various parameters in the prediction model (drainage length, rainfall intensity, road slope) affecting WFD are consistent with those of the existing model. It is also found that the initial water film depth, initial speed of rainwater, and rainfall angle have little impact on WFD. The predicted value of the model has a suitable matching degree compared with the classical empirical model, which provides a new approach to the prediction of road water film depth.

A Joint Optimization Algorithm for Trajectory Planning and Resource Allocation of Vehicle Mobile Base Stations for On-Demand Coverage Networks

In today’s urban hotspot regions, service traffic exhibits dynamic variations in both time and location. Traditional fixed macro base stations (FMBSs) are unable to meet these dynamic demands due to their fixed coverage and capacity. Therefore, this paper introduces a novel algorithm for the joint optimization of the placement of terrestrial vehicle-mounted mobile micro base stations (mBSs), the correlation of service clusters (SCs) with mBSs, and resource assignments. The objective is to maximize the matching degree between network capacity and service demands while adhering to constraints related to the power, coverage, and bandwidth of mBSs, as well as the data rate required for the services. Additionally, we investigate the mobility of the mBSs towards the SCs in the spatiotemporal changing service demand network and obtain optimal trajectories for the mBSs. We begin by formulating the problem of maximizing the matching degree by analyzing the capacity provided by the base stations and the network service demand. Subsequently, we derive solutions to the optimization problem using our algorithm. The simulation results demonstrate that the proposed algorithm can effectively meet the capacity demand of dynamically changing hotspot regions and achieve on-demand, resilient coverage of hotspot regions in the network.

A Spatiotemporal Hierarchical Analysis Method for Urban Traffic Congestion Optimization Based on Calculation of Road Carrying Capacity in Spatial Grids

Traffic congestion is a globally widespread problem that causes significant economic losses, delays, and environmental impacts. Monitoring traffic conditions and analyzing congestion factors are the first, challenging steps in optimizing traffic congestion, one of the main causes of which is regional spatiotemporal imbalance. In this article, we propose an improved spatiotemporal hierarchical analysis method whose steps include calculating road carrying capacity based on geospatial data, extracting vehicle information from remote sensing images to reflect instantaneous traffic demand, and analyzing the spatiotemporal matching degree between roads and vehicles in theory and in practice. First, we defined and calculated the ratio of carrying capacity in a regional road network using a nine-cell-grid model composed of nested grids of different sizes. By the conservation law of flow, we determined unbalanced areas in the road network configuration using the ratio of the carrying capacity of the central cell to that of the nine grid cells. Then, we designed a spatiotemporal analysis method for traffic congestion using real-time traffic data as the dependent variables and five selected spatial indicators relative to the spatial grids as the independent variables. The proposed spatiotemporal analysis method was applied to Chengdu, a typical provincial capital city in China. The relationships among regional traffic, impact factors, and spatial heterogeneity were analyzed. The proposed method effectively integrates GIS, remote sensing, and deep learning technologies. It was further demonstrated that our method is reliable and effective and enhances the coordination of congested areas by virtue of a fast calculation speed and an efficient local balance adjustment.