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.

The relation between funding by the National Institutes of Health and the burden of disease

Background:The Institute of Medicine has proposed that the amount of disease-specific research funding provided by the National Institutes of Health(NIH)be systematically and consistently compared with the burden of disease for society.Methods:We performed a cross-sectional study comparing estimates of disease-specific funding in 1996 with data on six measures of the burden of disease.

Generating Sets of the Complete Semigroup of Binary Relations Defined by Semilattices of the Class Σ8(X, 5)

In this article, we study generating sets of the complete semigroups of binary relations defined by X-semilattices of unions of the class Σ8(X, 5). Found uniquely irreducible generating set for the given semigroups and when X is finite set formulas for calculating the number of elements in generating sets are derived.

Special Relativity’s “Newtonization” in Complex “Para-Space”: The Two Theories Equivalence Question

Complex model, say C3, of “para-space” as alternative to the real M4 Minkowski space-time for both relativistic and classical mechanics was shortly introduced as reference to our previous works on that subject. The actual aim, however, is an additional analysis of the physical and para-physical phenomena’ behavior as we formally transport observable mechanical phenomena [motion] to non-real interior of the complex domain. As it turns out, such procedure, when properly set, corresponds to transition from relativistic to more classic (or, possibly, just classic) kind of the motion. This procedure, we call the “Newtonization of relativistic physical quantities and phenomena”, first of all, includes the mechanical motion’s characteristics in the C3. The algebraic structure of vector spaces was imposed and analyzed on both: the set of all relativistic velocities and on the set of the corresponding to them “Galilean” velocities. The key point of the analysis is realization that, as a matter of fact, the relativistic theory and the classical are equivalent at least as for the kinematics. This conclusion follows the fact that the two defined structures of topological vector spaces i.e., the structure imposed on sets of all relativistic velocities and the structure on set of all “Galilean” velocities, are both diffeomorphic in their topological parts and are isomorphic as the vector spaces. As for the relativistic theory, the two approaches: the hyperbolic (“classical” SR) with its four-vector formalism and Euclidean, where SR is modeled by the complex para-space C3, were analyzed and compared.

ARCADE 2 Spatial Roar, What Theory of Relation Reveals

The theory of Relation provides an explanation for the Arcade 2 excess. It assumes that the isotropic radio emission measured by the Arcade 2 Collaboration, which is 5 - 6 times brighter than the expected contributions from known extra-galactic sources, is the residue of an immense primitive energy of ordinary matter released by a relativistic bang almost 100 million years after the big bang, which gave the mass-energy the missing gravity to activate contraction. This relativistic bang, via a Lorentz energy transformation, would have released enormous energy held to be the source of the powerful radio noise detected by the NASA researchers. This transformation would have simultaneously triggered the formation of the first stars from dense gas and the reionization of less dense neutral gas. This departs from the idea that continuous reionization began after the formation of the first stars. We emphasize the importance of primordial magnetic fields, which would have generated significant density fluctuations during recombination and acted as a direct seed for cosmic structures. The first stars and galaxies were bathed in strong magnetic fields that gave rise to the radio microwave din (boom) discovered by Arcade 2. These intense magnetic fields alter the trajectory of charged particles zooming near the speed of light, triggering the space roar and emitting radiation that forms a synchrotron radio background. The theory of Relation offers an alternative to the Lambda-CDM cosmological model, which has become the standard model of the big bang, which leads straight to the vacuum catastrophe.

Analysis of Factors Related to Vasovagal Response in Apheresis Blood Donors and the Establishment of Prediction Model Based on BP Neural Network Algorithm

Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to intervene in high-risk VVR blood donors,improve the blood donation experience,and retain blood donors.Methods:A total of 316 blood donors from the Xi'an Central Blood Bank from June to September 2022 were selected to statistically analyze VVR-related factors.A BP neural network prediction model is established with relevant factors as input and DRVR risk as output.Results:First-time blood donors had a high risk of VVR,female risk was high,and sex difference was significant(P value<0.05).The blood pressure before donation and intergroup differences were also significant(P value<0.05).After training,the established BP neural network model has a minimum RMS error of o.116,a correlation coefficient R=0.75,and a test model accuracy of 66.7%.Conclusion:First-time blood donors,women,and relatively low blood pressure are all high-risk groups for VVR.The BP neural network prediction model established in this paper has certain prediction accuracy and can be used as a means to evaluate the risk degree of clinical blood donors.

The Relationship Between the Communist Party of China and the Japanese Communist Party During the War of Resistance Against Japanese Aggression

It is important to clarify the historical facts and summarize the characteristics of the communication and cooperation between the CPC and the JCP during the War of Resistance against Japanese Aggression,as it holds historical and contemporary relevance.The research findings indicate that the Comintern played a crucial role as the key intermediary in facilitating the establishment of connections between the CPC and the JCP and in driving the deepening of their relationship.During their exchanges,both parties adhered to a flexible and multi-layered friendship based on the unified values of patriotism and internationalism.Their efforts played a significant role in the establishment of an international anti-fascist front in the East.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

Relationships Between Fractal Road and Drainage Networks in Wuling Mountainous Area:Another Symmetric Understanding of Human-Environment Relations

Symmetrical relationships between humans and their environment have been referred to as an extension of symmetries in the human geographical system and have drawn great attention.This paper explored the symmetry between physical and human systems through fractal analysis of the road and drainage networks in Wuling mountainous area. We found that both the road and drainage networks reflect weak clustering distributions. The evolution of the road network shared a significant self-organizing composition, while the drainage network showed obvious double fractal characteristics The geometric fractal dimension of the road network was larger than that of the drainage network. In addition, when assigned a weight relating to hierarchy or length, neither the road network nor drainage network showed a fractal property. These findings indicated that the fractal evolution of the road network shared certain similarities with fractal distribution of the drainage network. The symmetry between the two systems resulted from an interactive process of destroying symmetry at the lower order and reconstructing symmetry at the higher order. The relationships between the fractal dimensions of the rural-urban road network, the drainage network and the urban system indicated that the development of this area was to achieve the symmetrical isomorphism of physical-human geographical systems.

The chemical characteristics of different sodium iron ethylenediaminetetraacetate sources and their relative bioavailabilities for broilers fed with a conventional corn‑soybean meal diet

Background Our previous studies demonstrated that divalent organic iron(Fe)proteinate sources with higher complexation or chelation strengths as expressed by the greater quotient of formation(Qf)values displayed higher Fe bioavailabilities for broilers.Sodium iron ethylenediaminetetraacetate(NaFeEDTA)is a trivalent organic Fe source with the strongest chelating ligand EDTA.However,the bioavailability of Fe when administered as NaFeEDTA in broilers and other agricultural animals remains untested.Herein,the chemical characteristics of 12 NaFeEDTA products were determined.Of these,one feed grade NaFeEDTA(Qf=2.07×10^(8)),one food grade NaFeEDTA(Qf=3.31×10^(8)),and one Fe proteinate with an extremely strong chelation strength(Fe-Prot ES,Qf value=8,590)were selected.Their bioavailabilities relative to Fe sulfate(FeSO_(4)·7H_(2)O)for broilers fed with a conventional corn-soybean meal diet were evaluated during d 1 to 21 by investigating the effects of the above Fe sources and added Fe levels on the growth performance,hematological indices,Fe contents,activities and gene expressions of Fe-containing enzymes in various tissues of broilers.Results NaFeEDTA sources varied greatly in their chemical characteristics.Plasma Fe concentration(PI),transferrin saturation(TS),liver Fe content,succinate dehydrogenase(SDH)activities in liver,heart,and kidney,catalase(CAT)activity in liver,and SDH mRNA expressions in liver and kidney increased linearly(P<0.05)with increasing levels of Fe supplementation.However,differences among Fe sources were detected(P<0.05)only for PI,liver Fe content,CAT activity in liver,SDH activities in heart and kidney,and SDH mRNA expressions in liver and kidney.Based on slope ratios from multiple linear regressions of the above indices on daily dietary analyzed Fe intake,the average bioavailabilities of Fe-Prot ES,feed grade NaFeEDTA,and food grade NaFeEDTA relative to the inorganic FeSO_(4)·7H_(2)O(100%)for broilers were 139%,155%,and 166%,respectively.Conclusions The bioavailabilities of organic Fe sources relative to FeSO_(4)·7H_(2)O were closely related to their Qf values,and NaFeEDTA sources with higher Qf values showed higher Fe bioavailabilities for broilers fed with a conventional corn-soybean meal diet.

Method for determining the position of landslide slip-surface with a typical inclinometric curves

In the actual monitoring of deep hole displacement,the identification of slip surfaces is primarily based on abrupt changes observed in the inclinometric curve.In conventional identification methods,inclinometric curves exhibiting indications of sliding can be categorized into three types:B-type,D-type,and r-type.The position of the slip surface is typically determined by identifying the depth corresponding to the point of maximum displacement mutation.However,this method is sensitive to the interval of measurement points and the observation scale of the coordinate axes and suffers from unclear sliding surfaces and uncertain values.Based on the variation characteristics of these diagonal curves,we classified the landslide into three components:the sliding body,the sliding interval,and the immobile body.Moreover,three different generalization models were established to analyze the relationships between the curve form and the slip surface location based on different physical indicators such as displacement rate,relative displacement,and acceleration.The results show that the displacement rate curves of an r-type slope exhibit a clustering feature in the sliding interval,and by solving for the depth of discrete points within the step phase,it is possible to determine the location of the slip surface.On the other hand,D-type slopes have inflection points in the relative displacement curve located at the slip surface.The acceleration curves of B-type slopes exhibit clustering characteristics during the sliding interval,while the scattered acceleration data demonstrate wandering characteristics.Consequently,the slip surface location can be revealed by solving the depth corresponding to the maximum acceleration with cubic spline interpolation.The approach proposed in this paper was applied to the monitoring data of a landslide in Yunnan Province,China.The results indicate that our approach can accurately identify the slip surface location and enable computability of its position,thereby enhancing applicability and reliability of the deep-hole displacement monitoring data.

Hard life for sons in the nest?Sex-dependent offspring mortality in Great Tits in urban and forest areas

Sex-biased mortality can occur in birds during development,for example due to sexual differences in energy requirement and/or environmental sensitivity,or the effects of sex hormones or sex differences in the expression of mutations linked to sex chromosomes.The extent of sex-bias in mortality may also be related to environmental conditions that influence offspring development and survival.Urban areas often provide poorer conditions for nestling development resulting in higher offspring mortality compared to natural areas,which may accelerate sex differences in offspring mortality in cities.To test this hypothesis,we examined the sex ratio of dead offspring in Great Tits(Parus major),using 427 samples of unhatched eggs and dead nestlings collected in two urban and two forest sites between 2013 and 2019.The ratio of males in the whole sample of dead offspring(56.9%)was significantly higher than expected by an 1:1 ratio,and the strongest sex biases were detected in urban areas(57.6%males)and in young nestlings(<14 days old,59.0%males).However,the sex ratios of dead offspring did not differ significantly among study sites and between offspring developmental stages.29.3%of unhatched eggs contained a visible embryo,and the proportion of embryo-containing unhatched eggs did not differ significantly between urban and forest study sites.These results suggest male-biased offspring mortality in Great Tits,and highlight the need of large datasets to detect subtle differences between habitats and developmental stages.

How to implement a knowledge graph completeness assessment with the guidance of user requirements

In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.

Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients

BACKGROUND Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications.Machine learning models offer a promising approach to predict the occurrence of intraoperative hypothermia.AIM To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.METHODS This retrospective study enrolled 220 patients who were admitted with multiple injuries between June 2018 and December 2023.Of these,154 patients were allocated to a training set and the remaining 66 were allocated to a validation set in a 7:3 ratio.In the training set,53 cases experienced intraoperative hypothermia and 101 did not.Logistic regression analysis was used to construct a predictive model of intraoperative hypothermia in patients with polytrauma undergoing laparoscopic surgery.The area under the curve(AUC),sensitivity,and specificity were calculated.RESULTS Comparison of the hypothermia and non-hypothermia groups found significant differences in sex,age,baseline temperature,intraoperative temperature,duration of anesthesia,duration of surgery,intraoperative fluid infusion,crystalloid infusion,colloid infusion,and pneumoperitoneum volume(P<0.05).Differences between other characteristics were not significant(P>0.05).The results of the logistic regression analysis showed that age,baseline temperature,intraoperative temperature,duration of anesthesia,and duration of surgery were independent influencing factors for intraoperative hypothermia during laparoscopic surgery(P<0.05).Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence(P>0.05).The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets,respectively.CONCLUSION Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery,which improved surgical safety and patient recovery.

The Impact of Finerenone on Changes in Pulse Wave Velocity, Arterial Pressure and Heart Related Deaths in Hemodialysis Patients—Study Perspective

The description in the abstract lacks clear logic and a comprehensive summary of this study, so please revise and improve it according to the design theme and main content of this study, and describe it in the order of (research background), purpose/aim, method, results and conclusions. The introduction of the abstract and preface is rather lengthy, but the summary of the whole study and the presentation of the research background are not perfect (mainly because the logic of the context is not clear and orderly), so it will appear a bit messy. Hope to be able to modify (this has been mentioned in the preliminary opinion). Cardiovascular events (CVE) pose a significant threat to individuals with end-stage renal disease (ESRD), yet these patients are often excluded from cardiovascular clinical trials, leaving prognostic factors associated with CVE in ESRD patients largely unexplored. Recent human studies have demonstrated elevated circulating aldosterone levels in ESRD patients, correlating with left ventricular hypertrophy. Additionally, animal models have shown improvements in uremic cardiomyopathy with spironolactone therapy, prompting interest in assessing the efficacy of spironolactone or eplerenone in reducing mortality and improving cardiovascular function in dialysis patients. Clinicians have historically been cautious about prescribing mineralocorticoid receptor antagonists (MRAs) to congestive heart failure patients with chronic kidney disease (CKD) due to hyperkalemia risk. However, the emergence of finerenone, a novel MR antagonist with a favorable safety profile and lower hyperkalemia risk, has renewed interest in MRA therapy in this population. Heart disease, including coronary artery disease, hypertension, and left ventricular failure, is alarmingly prevalent in dialysis patients, contributing significantly to elevated mortality rates compared to the general population. Arterial stiffness, as indicated by pulse wave velocity (PWV), progressively worsens with advancing CKD stages, peaking in severity among ESRD patients undergoing dialysis. High PWV serves as a crucial risk stratification tool in ESRD. Elevated NT-proBNP and BNP levels in ESRD patients are well-documented, with significant associations observed between baseline peptide concentrations and cardiovascular morbidity and mortality. By incorporating finerenone into our study, we aim to investigate its potential benefits in reducing arterial stiffness, lowering blood pressure, and ultimately mitigating heart-related mortality among hemodialysis patients. This study holds substantial implications for hypertension and cardiovascular risk management in this vulnerable patient population. Eligible participants must have been on chronic hemodialysis for at least three months, with ACE inhibitors or angiotensin receptor blockers included in their therapy at maximum tolerable doses. Serum potassium levels 5.7 mmol/L, left ventricular ejection fraction 50%, and PWV higher than age-estimated values are also prerequisites for study entry. Randomized allocation will be conducted using a permuted block design, stratified by center, with allocation communicated via signed study forms during initial examinations. All steps of this research will be conducted in accordance with the principles of the Helsinki Declaration.

Short Communication: Enhancing the Drying Process of Microbial-Based Products with a Dehumidifier

The development of microbial-based products requires certain criteria for them to be successfully commercialized. The product must meet the following desirable criteria: effectiveness, contamination free, stability, cost-effectiveness, and a prolonged shelf life. Controlling the drying process is crucial for ensuring the stability and durability of the product. The traditional approach, which involved mechanical and natural drying, led to decreased productivity and quality. The objective of this research endeavour was to achieve a dry process enhancement while preserving the microbial quality of Trichoderma asperellum (M103). The temperature and relative humidity during the drying period were monitored under two conditions: with and without a dehumidifier. The results demonstrate that the dehumidifier increases drying period efficiency by up to 63%.

Graph Convolutional Networks Embedding Textual Structure Information for Relation Extraction

Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information Graph Convolutional Networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.

An Experimental Study on the Effect of a Nanofluid on Oil-Water Relative Permeability

The low porosity and low permeability of tight oil reservoirs call for improvements in the current technologies for oil recovery.Traditional chemical solutions with large molecular size cannot effectively flow through the nanopores of the reservoir.In this study,the feasibility of Nanofluids has been investigated using a high pressure high temperature core-holder and nuclear magnetic resonance(NMR).The results of the experiments indicate that the specified Nanofluids can enhance the tight oil recovery significantly.The water and oil relative permeability curve shifts to the high water saturation side after Nanofluid flooding,thereby demonstrating an increase in the water wettability of the core.In the Nanofluid flooding process the oil recovery was enhanced by 15.1%,compared to waterflooding stage.The T2 spectra using the NMR show that after Nanofluid flooding,a 7.18%increment in oil recovery factor was gained in the small pores,a 4.9%increase in the middle pores,and a 0.29%increase in the large pores.These results confirm that the Nanofluids can improve the flow state in micro-sized pores inside the core and increase the ultimate oil recovery factor.

Explanation of Two Important Empirical Relations for Galaxies

The phenomenon of “missing mass” in galaxies has triggered new theoretical exploration, forming a competition between dark matter assumption, modified Newtonian dynamics and modified gravity. Over the past forty years, various versions of the modified scenario have been proposed to simulate the effects of missing mass. These schemes replace the dynamic effect of dark matter by introducing some tiny extra force terms in the dynamic equations. Such extra forces have mainly interactions on large scales of galaxies, such as fitting the Tully-Fisher relation or asymptotically flat rotation curves. The discussion in this paper shows that the evidence of taking the modified schemes as fundamental theory is still insufficient. In this paper, we display a system of simplified galactic dynamical equations derived from weak field and low-speed approximations of Einstein field equations, and then we use it to discuss two important empirical relations in galactic dynamics, namely the Faber-Jackson relation and Tully-Fisher relation, as well as the related fundamental plane. These discussions provide a reference scheme for improving the dispersion of the empirical relations, and also provide a theoretical foundation to analyze the properties of dark matter and galactic structures.

A Germplasm Resource Repository:The Book Review of Illustrated Flora of Food Crops and Their Wild Related Plants in China

Illustrated Flora of Food Crops and Their Wild Related Plants in China systematically examines the botanical and morphological characteristics of China's major food crops,such as rice,wheat,corn,sweet potato,potato,mung bean,and buckwheat.Featuring more than 5000 color photos and 200 line drawings,it offers a comparative study that highlights the kinship and internal connections between cultivated and wild species.The book is an invaluable resource for breeders,offering a comprehensive morphological and genetic database that aids in the development of high-yielding,high-quality,and disease-resistant crop varieties.