三色膏联合PRICE疗法治疗急性踝关节Ⅰ和Ⅱ度扭伤的临床效果

目的探讨三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤的临床效果。方法选取2022年2月至2023年5月上海市嘉定区中医医院骨伤科收治的52例Ⅰ、Ⅱ度急性踝关节扭伤患者作为研究对象,其中急诊患者35例,门诊患者17例,采用随机数字表法将其分为对照组和观察组,每组均失访1例,两组各25例纳入统计。对照组采用PRICE疗法治疗,观察组采用三色膏联合PRICE疗法,两组患者均治疗1周,治疗结束后,比较两组患者治疗前和治疗后第1、3周的视觉模拟评分法(VAS)评分及Mazur功能评分。结果52例患者中50例得到随访,随访时间不短于4周。治疗1、3周后,两组患者的VAS评分低于本组治疗前,Mazur功能评分高于本组治疗前,观察组的VAS评分低于对照组,Mazur功能评分高于对照组,差异有统计学意义(P<0.05)。结论三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤可以获得更好的疗效,具有重要的临床价值和推广意义。

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Cameroon Climate Predictions Using the SARIMA-LSTM Machine Learning Model: Adjustment of a Climate Model for the Sudano-Sahelian Zone of Cameroon

It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.

Economic Resilience in Bangladesh: Analyzing Household Well-Being amidst Price Hikes through ANOVA and Paired Sample t-Test Insights

This study delves into the multifaceted impact of price hikes on the standard of living in Bangladesh, with a specific focus on distinct socioeconomic segments. Amidst Bangladesh’s economic growth, the challenges of rising inflation and increased living costs have become pressing concerns. Employing a mixed-methods approach combines quantitative data from a structured survey with qualitative insights from in-depth interviews and focused group discussions to analyze the repercussions of price hikes. Stratified random sampling ensures representation across affluent, middle-class, and economically disadvantaged groups. Utilizing data [1] from 2020 to November 2023 on the yearly change in retail prices of essential commodities, analysis reveals significant demographic shifts, occupational changes, and altered asset ownership patterns among households. The vulnerable population, including daily wage laborers and low-income individuals, is disproportionately affected by adjustments in consumption, income generation, and living arrangements. Statistical analyses, including One-Way ANOVA and Paired Sample t-tests, illuminate significant mean differences in strategies employed during price hikes. Despite challenges, the prioritization of education remains evident, emphasizing its resilience in the face of economic hardships. The result shows that price hikes, especially in essential items, lead to substantial adjustments in living costs, with items like onions, garlic, and ginger experiencing significant increases of 275%, 108%, and 483%, respectively.

Adjustment mechanism of blasting dynamic-static action in the water decoupling charge

Water decoupling charge blasting excels in rock breaking,relying on its uniform pressure transmission and low energy dissipation.The water decoupling coefficients can adjust the contributions of the stress wave and quasi-static pressure.However,the quantitative relationship between the two contributions is unclear,and it is difficult to provide reasonable theoretical support for the design of water decoupling blasting.In this study,a theoretical model of blasting fracturing partitioning is established.The mechanical mechanism and determination method of the optimal decoupling coefficient are obtained.The reliability is verified through model experiments and a field test.The results show that with the increasing of decoupling coefficient,the rock breaking ability of blasting dynamic action decreases,while quasi-static action increases and then decreases.The ability of quasi-static action to wedge into cracks changes due to the spatial adjustment of the blast hole and crushed zone.The quasi-static action plays a leading role in the fracturing range,determining an optimal decoupling coefficient.The optimal water decoupling coefficient is not a fixed value,which can be obtained by the proposed theoretical model.Compared with the theoretical results,the maximum error in the model experiment results is 8.03%,and the error in the field test result is 3.04%.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

On calculating glacial isostatic adjustment

Modeling the earth's fluid and elastic response to the melting of the glaciers of the last ice age is the most direct way to infer the earth's radial viscosity profile.Here,we compare two methods for calculating the viscoelastic response to surface loading.In one,the elastic equation of motion is converted to a viscoelastic equation using the Correspondence Principle.In the other,elastic deformation is added to the viscous flow as isostatic adjustment proceeds.The two modeling methods predict adjustment histories that are different enough to potentially impact the interpretation of the observed glacial isostatic adjustment(GIA).The differences arise from buoyancy and whether fluid displacements are subjected to hydrostatic pre-stress.The methods agree if they use the same equations and boundary conditions.The origin of the differences is determined by varying the boundary conditions and pre-stress application.

Early clinical outcome with lens position adjustment following implantable collamer lens surgery

AIM:To observe early clinical outcome with lens position adjustment following the implantable collamer lens(ICL)surgery.METHODS:Sixty patients were selected for this retrospective study.One eye from each patient received Toric ICL for astigmatism correction,and the other received non-astigmatic ICL surgery using horizontal position.Patients with higher postoperative arch height were selected,and their non-astigmatic eye clinical outcome were observed after ICL surgery at 1wk,1,and 3mo.The clinical measurements included uncorrected visual acuity(UCVA),intraocular pressure(IOP),refractive state,corneal endothelium cell count,and arch height.Three months later,the ICL in each patient’s non-astigmatic eye was adjusted to the vertical from the horizontal position.The results were compared before and 1wk,1,and 3mo after adjustment.RESULTS:UCVA and IOP were significantly reduced 1wk after position adjustment compared to 1wk after ICL implantation(P<0.05).The patients demonstrated significantly reduced arch height and corneal endothelium cell count 1wk,1,and 3mo after adjusting position compared to 1wk,1,and 3mo after ICL implantation(P<0.05).However,there was no significant difference in refraction between 1wk,1,and 3mo after ICL implantation and position adjustment(P>0.05).CONCLUSION:Early positioning adjustment postphakic ICL implantation can benefit patients with adjusted arch height or higher IOP.Despite the good clinical effects,the doctors should pay attention to the potential for adverse effects on UCVA and corneal endothelium cells following early position adjustment after posterior chamber phakic ICL implantation.

Production Chain Length and PPI-CPI Divergence:Analysis Based on the Global Input-output Price Model

Productivity and international energy price shocks are reflected in PPI and CPI via industrial chains.China’s in-depth participation into the global value chains has increasingly lengthened its industrial production chains.The question is how the changing length of production chains will affect CPI and PPI,as well as CPI-PPI correlation?By constructing a global input-output price model,this paper offers a theoretical discussion on the impact of production chain length on the CPI-PPI divergence.Our findings suggest that the price shock of international bulk commodities has a greater impact on China’s PPI than that on CPI.The effects on both China’s PPI and CPI estimated by using the single-country input-output model are higher than the results estimated with the global input-output model.However,the difference between CPI and PPI variations estimated with the global input-output model is greater than the result estimated with the single-country input-output model,which supports the view that the lengthening of production chains,especially international production chains,leads to a divergence between CPI and PPI.Empirical results based on cross-national panel data also suggest that the lengthening of production chains has reduced the CPI-PPI correlation for countries,i.e.the lengthening of production chains has increased the PPI-CPI divergence.That is to say,policymakers should target not just CPI in maintaining price stability,but instead focus on the stability of both PPI and CPI.Efforts can be made to proactively adjust the price index system,and formulate the industrial chain price index.

Impact of an official accountability audit on industrial structure adjustment:a case study of environmental regulation in China

As a unique environmental regulation in China,the official accountability audit was piloted in 2014.With a focus on prioritizing the ecological environment,officials in pilot districts have implemented economic construction,adjusted industrial structures,and promoted coordinated development between the economy and environment.The effects of implementation have garnered widespread attention from society.However,there is limited research on the impact of an accountability audit on industrial structure adjustments.Using the“Accountability Audit of Officials for Natural Resource Assets(Trial)”released in 2015 as a quasi-natural experiment,this study collected panel data from 279 cities between 2013 and 2017.It then empirically analyzed the impact mechanism and effects of the accountability audit on industrial structure adjustment using the Propensity Score Matching and Difference-in-Differences model.The research findings indicate that the accountability audit directly impacted industrial structure adjustment,promoting the upgrading of the primary industry to the secondary industry and restricting the development of the tertiary industry.In addition,the audit is beneficial for enterprise entry,but not conducive to technological innovation,and has no significant impact on foreign direct investment.This conclusion fills a gap in the existing research and provides valuable insights for policymakers.

Price Mechanism,Government Constraints and Carbon Trading Pilot Policy for Emission Reduction

Based on the data of 247 cities at the prefecture level in China from 2007 to 2019,this paper analyzes the impact of the carbon emissions trading(CET)pilot policy on carbon emission reduction from the perspective of the price mechanism and government constraints.The results show that the carbon emissions and carbon intensity in the pilot areas are significantly reduced by adjusting the industrial structure and promoting green technology innovation.In terms of regions,the emission reduction effect of the pilot policy in regions with a high proportion of industry is obviously weaker than that in other regions.The aim of the carbon emission trading policy in China that achieve carbon emission reduction is by coordinating the carbon emission trading price that fail to fulfill this aim independently and the degree of government punishment for enterprises.

A Hybrid Channel Stock Model for Stock Price Forecasting with Multifaceted Feature Fusion

Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we propose a Hybrid Channel Stock model that incorporates multiple features of basic stock data,K-line charts and technical indicator factors for predicting the closing price of a stock on day n+1.The model combines multiple aspects of data and uses a multi-channel structure including improved CNN-TW,bidirectional LSTM and Transformer network.First,we construct the multi-channel branches of the multi-faceted feature fusion input network model;second,in this paper,we will use the concatenate method to stitch the output of each branch as the input of the rest of the network;the last layer in the network is the fully connected layer,which combines the linear activation function regression to output the predicted prices.Finally,we conducted extensive experiments on the Dow 30,SSH 50 and CSI100 indices.The experimental results show that the Hybrid Channel Stock method has the best performance with the smallest MSE,RMSE,MAE and MAPE compared with existing models.in addition,the experiments on different trading days validate the stability and effectiveness of the model,providing an important reference for investors to make stock investment decisions.

Cross-Cultural Adjustment and Coping:U.S.Students in Taiwan Residents Higher Education

The aim of this study was to investigate the adjustment problems of students from the United States enrolled in universities in the East,specifically in Taiwan,their problems related to cultural adaptation,and the process of adjustment to student life in Taiwan.Under investigation were cultural adjustment and coping skills as these students transitioned from West to East.Qualitative data were collected from interviews with participants and faculty members as well as participant observations.Results indicated that U.S.students found their own ways to acclimate to their new academic setting as well as to social relations,cross-cultural issues,and the linguistic environment in Taiwan to achieve effective adaptation.They made changes in themselves to cope with all situations they encountered.This study provides suggestions for international students abroad in Taiwan,for the Taiwan Residents government,and for universities or colleges in terms of what they should offer to current and future international students.

Optimized CUDA Implementation to Improve the Performance of Bundle Adjustment Algorithm on GPUs

The 3D reconstruction pipeline uses the Bundle Adjustment algorithm to refine the camera and point parameters. The Bundle Adjustment algorithm is a compute-intensive algorithm, and many researchers have improved its performance by implementing the algorithm on GPUs. In the previous research work, “Improving Accuracy and Computational Burden of Bundle Adjustment Algorithm using GPUs,” the authors demonstrated first the Bundle Adjustment algorithmic performance improvement by reducing the mean square error using an additional radial distorting parameter and explicitly computed analytical derivatives and reducing the computational burden of the Bundle Adjustment algorithm using GPUs. The naïve implementation of the CUDA code, a speedup of 10× for the largest dataset of 13,678 cameras, 4,455,747 points, and 28,975,571 projections was achieved. In this paper, we present the optimization of the Bundle Adjustment algorithm CUDA code on GPUs to achieve higher speedup. We propose a new data memory layout for the parameters in the Bundle Adjustment algorithm, resulting in contiguous memory access. We demonstrate that it improves the memory throughput on the GPUs, thereby improving the overall performance. We also demonstrate an increase in the computational throughput of the algorithm by optimizing the CUDA kernels to utilize the GPU resources effectively. A comparative performance study of explicitly computing an algorithm parameter versus using the Jacobians instead is presented. In the previous work, the Bundle Adjustment algorithm failed to converge for certain datasets due to several block matrices of the cameras in the augmented normal equation, resulting in rank-deficient matrices. In this work, we identify the cameras that cause rank-deficient matrices and preprocess the datasets to ensure the convergence of the BA algorithm. Our optimized CUDA implementation achieves convergence of the Bundle Adjustment algorithm in around 22 seconds for the largest dataset compared to 654 seconds for the sequential implementation, resulting in a speedup of 30×. Our optimized CUDA implementation presented in this paper has achieved a 3× speedup for the largest dataset compared to the previous naïve CUDA implementation.

Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

The Influence of Price Discrimination from Airports on the Route Development Behavior of Airlines

Given the prominence and magnitude of airport incentive schemes,it is surprising that literature hitherto remains silent as to their effectiveness.In this paper,the relationship between airport incentive schemes and the route development behavior of airlines is analyzed.Because of rare and often controversial findings in the extant literature regarding relevant influencing variables for attracting airlines at an airport,expert interviews are used as a complement to formulate testable hypotheses in this regard.A fixed effects regression model is used to test the hypotheses with a dataset that covers all seat capacity offered at the 22 largest German commercial airports in the week 46 from 2004 to 2011.It is found that incentives from primary choice,as well as secondary choice airports,have a significant influence on Low Cost Carriers.Furthermore,Low Cost Carriers,in general,do not leave any of both types of airports when the incentives cease.In the case of Network Carriers,no case is found where one joins a primary choice airport and receives an incentive.Insufficient data between Network Carriers and secondary choice airports in the time when incentives have ceased means that no statement can be given.

Adjustment of Administrative Divisions and Upgrading of Industrial Structure in Hefei Metropolitan Area:Taking"Partitions of Chaohu"as a Quasi-natural Experiment

Administrative regions are an important environment for the operation of China's market economy.The relevant economic subjects cannot predict the policy of adjustment of administrative divisions and carry out conscious migration behavior,adjustment of administrative divisions can be regarded as a quasi-natural experiment.The three cities of Hefei,Wuhu,and Ma'anshan,which are directly related to the adjustment of the administrative division of Chaohu,are taken as the treatment group,and the seven adjacent cities of Lu'an,Huainan,Chuzhou,Bengbu,Anqing,Chizhou,and Tongling are taken as the control group.Differences-in-Differences method and relevant control variables affecting the upgrading of industrial structure are used to test.The test results show that"Partitions of Chaohu"has a significant industrial structure upgrading effect by promoting the optimization of spatial layout,the cross-regional flow of production factors and the effective management of Chaohu Lake Basin.At the same time,the increase of total retail sales of consumer goods,urban fixed assets investment,public utility expenses in science,education,culture and health,and population plays a significant positive role in promoting the upgrading of industrial structure,while foreign direct investment plays a certain inhibition role in the upgrading of industrial structure.In order to meet the ever-developing space demands and enhance the impact on surrounding areas,the Hefei metropolitan area should be driven by technological innovation,strengthen the integration of industrial chains,improve the business environment and transportation network,and continuously promote the upgrading of industrial structure and the formation and development of new productive forces.

From Finnish Assortment Pricing to Market Economy Using Prices for Sawn Wood and Chips in Reference Bucking

Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.