A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage

Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.

Adaptive Modeling of Monthly Depression Levels in Terms of Daily Assessments of Opioid Medications Taken and Pain Levels for Cancer Patients

A research study collected intensive longitudinal data from cancer patients on a daily basis as well as non-intensive longitudinal survey data on a monthly basis. Although the daily data need separate analysis, those data can also be utilized to generate predictors of monthly outcomes. Alternatives for generating daily data predictors of monthly outcomes are addressed in this work. Analyses are reported of depression measured by the Patient Health Questionnaire 8 as the monthly survey outcome. Daily measures include numbers of opioid medications taken, numbers of pain flares, least pain levels, and worst pain levels. Predictors are averages of recent non-missing values for each daily measure recorded on or prior to survey dates for depression values. Weights for recent non-missing values are based on days between measurement of a recent value and a survey date. Five alternative averages are considered: averages with unit weights, averages with reciprocal weights, weighted averages with reciprocal weights, averages with exponential weights, and weighted averages with exponential weights. Adaptive regression methods based on likelihood cross-validation (LCV) scores are used to generate fractional polynomial models for possible nonlinear dependence of depression on each average. For all four daily measures, the best LCV score over averages of all types is generated using the average of recent non-missing values with reciprocal weights. Generated models are nonlinear and monotonic. Results indicate that an appropriate choice would be to assume three recent non-missing values and use the average with reciprocal weights of the first three recent non-missing values.

Utilizing Bayesian Modeling and MCMC for Accurate Characterization of Naturally Occurring Radionuclides Reference Background Levels in Mining Areas

Statistical biases may be introduced by imprecisely quantifying background radiation reference levels. It is, therefore, imperative to devise a simple, adaptable approach for precisely describing the reference background levels of naturally occurring radionuclides (NOR) in mining sites. As a substitute statistical method, we suggest using Bayesian modeling in this work to examine the spatial distribution of NOR. For naturally occurring gamma-induced radionuclides like 232Th, 40K, and 238U, statistical parameters are inferred using the Markov Chain Monte Carlo (MCMC) method. After obtaining an accurate subsample using bootstrapping, we exclude any possible outliers that fall outside of the Highest Density Interval (HDI). We use MCMC to build a Bayesian model with the resampled data and make predictions about the posterior distribution of radionuclides produced by gamma irradiation. This method offers a strong and dependable way to describe NOR reference background values, which is important for managing and evaluating radiation risks in mining contexts.

Cumulative effects of excess high-normal alanine aminotransferase levels in relation to new-onset metabolic dysfunction-associated fatty liver disease in China

BACKGROUND Within the normal range,elevated alanine aminotransferase(ALT)levels are associated with an increased risk of metabolic dysfunction-associated fatty liver disease(MAFLD).AIM To investigate the associations between repeated high-normal ALT measurements and the risk of new-onset MAFLD prospectively.METHODS A cohort of 3553 participants followed for four consecutive health examinations over 4 years was selected.The incidence rate,cumulative times,and equally and unequally weighted cumulative effects of excess high-normal ALT levels(ehALT)were measured.Cox proportional hazards regression was used to analyse the association between the cumulative effects of ehALT and the risk of new-onset MAFLD.RESULTS A total of 83.13%of participants with MAFLD had normal ALT levels.The incidence rate of MAFLD showed a linear increasing trend in the cumulative ehALT group.Compared with those in the low-normal ALT group,the multivariate adjusted hazard ratios of the equally and unequally weighted cumulative effects of ehALT were 1.651[95%confidence interval(CI):1.199-2.273]and 1.535(95%CI:1.119-2.106)in the third quartile and 1.616(95%CI:1.162-2.246)and 1.580(95%CI:1.155-2.162)in the fourth quartile,respectively.CONCLUSION Most participants with MAFLD had normal ALT levels.Long-term high-normal ALT levels were associated with a cumulative increased risk of new-onset MAFLD.

A method to calculate design tide levels on the basis of numerical model of tidal current and its application

In order to determine the design tide levels in the areas without measured tide level data, especially in the areas where it is difficult to measure tidal levels, a calculation method based on a numerical model of tidal current is proposed. The essentials of the method are described, and its application is illustrated with an example. The results of the application show that the design tide levels calculated by the method are close to those determined by long-time measured tide level data, and its calculation precision is high, so it is feasible to use the method to determine the design tide levels in the areas.

Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Methodology for local correction of the heights of global geoid models to improve the accuracy of GNSS leveling

At present,one of the methods used to determine the height of points on the Earth’s surface is Global Navigation Satellite System(GNSS)leveling.It is possible to determine the orthometric or normal height by this method only if there is a geoid or quasi-geoid height model available.This paper proposes the methodology for local correction of the heights of high-order global geoid models such as EGM08,EIGEN-6C4,GECO,and XGM2019e_2159.This methodology was tested in different areas of the research field,covering various relief forms.The dependence of the change in corrected height accuracy on the input data was analyzed,and the correction was also conducted for model heights in three tidal systems:"tide free","mean tide",and"zero tide".The results show that the heights of EIGEN-6C4 model can be corrected with an accuracy of up to 1 cm for flat and foothill terrains with the dimensionality of 1°×1°,2°×2°,and 3°×3°.The EGM08 model presents an almost identical result.The EIGEN-6C4 model is best suited for mountainous relief and provides an accuracy of 1.5 cm on the 1°×1°area.The height correction accuracy of GECO and XGM2019e_2159 models is slightly poor,which has fuzziness in terms of numerical fluctuation.

The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.

Research on the Digital Economy's Impact on China's Economic Growth:Based on the Variations in Urbanization Levels Across China's Eastern,Central,and Western Regions and Sectoral Heterogeneity

This paper uses inter-provincial panel data from 2011 to 2017,a linear regression model,and a threshold model to conduct empirical analyses of the impact of the digital economy on China's overall economic growth and the three main sectors of industry.The paper then investigates the impact and effects the digital economy has had on the economic growth of the three main sectors of industry in China's eastern,central,and western regions.Finally,the paper investigates the most significant differences among the various regions and the threshold effects of urbanization levels on the relationship between the digital economy and economic growth.The findings indicate a significantly positive correlation between the digital economy and regional economic growth.Moreover,geographical factors notably influence this correlation.The digital economy exerts a positive effect on all sectors of industry.It may not substantially impact industrial development in regions with highly developed infrastructure.Regarding the other regions,the digital economy exhibits varying degrees of impact due to the differences in the specific indicators.The conclusion drawn by the threshold model is that the magnitude of the threshold effect correlates with geographic factors.No threshold effect was observed in the eastern region,while the threshold effect occurred in the central region when the urbanization levels for the provinces were below 0.6645.Similarly,the threshold effect was noted in the western region when the urbanization level was below 0.3931.Considering all of this,the study also offers policy recommendations that will help balance the regional development of digital economies,accelerate the digital transformation of traditional industries,enhance digital infrastructure construction,refine the formulation and implementation of data policy,and establish relevant incentive mechanisms.

Inclusive Multiple Models(IMM)for predicting groundwater levels and treating heterogeneity

An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

Dynamic characteristics of coal specimens with varying static preloading levels under low-frequency disturbance load

The mechanical properties of residual coal pillars under the influence of upward mining disturbances significantly affect the safety of residual mining activities on working faces.This study conducted low-frequency disturbance dynamic uniaxial compression tests on coal specimens using a self-developed dynamic-static load coupling electro-hydraulic servo system,and studied the strength evolutions,surface deformations,acoustic emission(AE)characteristic parameters,and the failure modes of coal specimens with different static preloading levels were studied.The disturbance damage is positively correlated with the coal specimen static preload level.Specifically,the cumulative AE count rates of the initial accelerated damage stage for the coal specimens with static preloading level of 60%and 70%of the uniaxial compressive strength(UCS)were 2.66 and 3.19 times that of the 50%UCS specimens,respectively.Macroscopically,this behaviour manifested as a decrease in the compressive strength,and the mean strengths of the disturbance-damaged coal specimens with 60%and 70%of UCS static preloading decreased by 8.53%and 9.32%,respectively,compared to those of the specimens under pure static loading.The crack sources,such as the primary fissures,strongly control the dynamic response of the coal specimen.The difference between the dynamic responses of the coal specimens and that of dense rocks is significant.

Source and level of dietary iron influence semen quality by affecting inflammation, oxidative stress and iron utilization levels in boars

Background Boars fed a mixed form of inorganic and organic iron in excess of the NRC recommended levels still develop anemia,which suggested that the current level and form of iron supplementation in boar diets may be inappropriate.Therefore,56 healthy Topeka E line boars aged 15–21 months were randomly divided into 5 groups:basal diet supplemented with 96 mg/kg ferrous sulfate(FeSO_(4))and 54 mg/kg glycine chelated iron(Gly-Fe,control);80 mg/kg or 115 mg/kg Gly-Fe;80 mg/kg or 115 mg/kg methionine hydroxyl analogue chelated iron(MHA-Fe,from CalimetFe)for 16 weeks.The effects of dietary iron supplementation with different sources and levels on semen quality in boars were investigated.Results 1)Serum Fe and hemoglobin concentrations were not affected by reduced dietary iron levels in the 80 mg/kg or 115 mg/kg Gly-Fe and MHA-Fe groups compared with the control group(P>0.05).2)Serum interleukin-6(IL-6)and sperm malondialdehyde(MDA)levels in the 80 mg/kg or 115 mg/kg MHA-Fe groups were lower than those in the control group(P<0.05),and higher serum superoxide dismutase levels and lower MDA levels in the 115 mg/kg MHA-Fe group(P<0.05).3)Boars in the 80 mg/kg or 115 mg/kg Gly-Fe and MHA-Fe groups had lower serum hepcidin(P<0.01),ferritin(P<0.05),and transferrin receptor(P<0.01)concentrations,and boars in the 115 mg/kg MHA-Fe group had higher seminal plasma Fe concentrations compared with the control group.4)Boars in the 80 mg/kg and 115 mg/kg MHA-Fe groups had lower abnormal sperm rate and in situ oscillating sperm ratio compared to the control group at weeks 12 and/or 16 of the trial.However,the effect of Gly-Fe on improving semen quality in boars was not evident.5)Serum IL-6 level was positively correlated with hepcidin concentration(P<0.05),which in turn was significantly positively correlated with abnormal sperm rate(P<0.05).Furthermore,significant correlations were also found between indicators of iron status and oxidative stress and semen quality parameters.Conclusions Dietary supplementation with 80 mg/kg or 115 mg/kg MHA-Fe did not induce iron deficiency,but rather reduced serum inflammatory levels and hepcidin concentration,alleviated oxidative stress,increased body iron utilization,and improved semen quality in adult boars.

Antibody Levels and Infection Status of Pertussis in the Population under Pertussis Resurgence in Guangxi in 2018:A Cross-Sectional Survey

Objective Pertussis cases have increased markedly since 2018 in Guangxi.The aim of this study was to evaluate antibody levels and the infection status of pertussis in the resident population.Method A total of 10,215 serum samples from residents were collected from August-November 2018 and tested for anti-pertussis IgG and toxin IgG using the enzyme-linked immunosorbent assay(ELISA).Results Of the collected samples,1,833(17.94%)tested positive for anti-pertussis IgG,with the median concentration of 16.06 IU/mL.Antibody level<10 IU/mL accounted for more than 60%in children under 4 years of age,but declined with age,whereas the percentages of the other three levels(10-40,40-50,and≥50 IU/mL)increased almost with age(P<0.001).Moreover,7,924 samples were selected for anti-pertussis toxin IgG,of which 653(8.24%)tested positive(≥40 IU/mL)with the median concentration of 5.89 IU/mL,and 204 participants(2.56%)had recent pertussis infection(≥100 IU/mL).Among the different age groups,the highest rates of positivity and recent infection were observed at 11-20 years of age,the lowest positivity rate at 5 years of age,and the lowest recent infection rate at 4 years of age(P<0.001,P=0.005,respectively).Conclusion The survey results showed that all age groups in Guangxi lacked immunity against pertussis,which was one of the main factors contributing to the resurgence of pertussis in 2018.In addition,the prevalence of pertussis is relatively high in Guangxi,and its incidence is seriously underestimated,especially in adolescents and adults.

Levels of evidence and grades of recommendation supporting European society for medical oncology clinical practice guidelines

Background:The European Society for Medical Oncology(ESMO)guidelines are among the most comprehensive and widely used clinical practice guidelines(CPGs)globally.However,the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated.This study assessed ESMO CPG levels of evidence(LOE)and grades of recommendations(GOR),as well as their trends over time across various cancer settings.Methods:We manually extracted every recommendation with the Infectious Diseases Society of America(IDSA)classification from each CPG.We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types.Results:Among the 1,823 recommendations in the current CPG,30%were classified as LOEⅠ,and 43%were classified as GOR A.Overall,there was a slight decrease in LOEⅠ(−2%)and an increase in the proportion of GOR A(+1%)in the current CPG compared to previous versions.The proportion of GOR A recommendations based on higher levels of evidence such as randomized trials(LOEⅠ–Ⅱ)shows a decrease(71%vs.63%,p=0.009)while recommendations based on lower levels of evidence(LOEⅢ–Ⅴ)show an increase(29%vs.37%,p=0.01)between previous and current version.In the current versions,the highest proportion of LOEⅠ(42%)was found in recommendations related to pharmacotherapy,while the highest proportion of GOR A recommendations was found in the areas of pathology(50%)and diagnostic(50%)recommendations.Significant variability in LOEⅠand GOR A recommendations and their changes over time was observed across different cancer types.Conclusion:One-third of the current ESMO CPG recommendations are supported by the highest level of evidence.More well-designed randomized clinical trials are needed to increase the proportion of LOEⅠand GOR A recommendations,ultimately leading to improved outcomes for cancer patients.

Assessment of Water Scarcity Levels in the Srepok River Basin

Water scarcity has become a pressing global issue, worsening food security, hindering economic development, compromising environmental quality, and threatening human health and other fundamental societal needs. Viet Nam is among the countries severely affected by water scarcity. This study comprehensively assesses the extent and scale of water scarcity in the Srepok River Basin, considering the impacts of water resource allocation, balance, and environmental flows. The areas heavily affected by water scarcity include Ea Hleo, Ea Krong Ana, and several Srepok River branches, with water scarcity periods mainly concentrated in February, March, and April. The influence of climate change has increased the extent and level of water scarcity in the river, affecting an estimated 1.4 million people for at least one month and about 1 million for at least three months. The agricultural sector is significantly affected by water scarcity, with water shortages of 50% according to the baseline scenario and over 60% according to climate change scenarios.

Geriatric nursing competence of clinical nursing staff at different hospital levels in Chongqing,China:A cross-sectional study

Objectives:This study aimed to survey the geriatric nursing competencies of clinical nursing staff in Chongqing City,China,and provide suggestions to enhance these competencies.Methods:This study was conducted in 204 hospitals in Southwest China from December 24,2022 to January 7,2023.The“Geriatric Nursing Competence of Clinical Nurse Investigation Tool”was used to explore factors that influence geriatric nurses’competencies via stratified sampling.The survey was conducted by distributing and collecting questionnaires through the online platform Wenjuanxing.Results:A total of 10,692 nurses answered the questionnaires.Of these questionnaires,9,442 were valid.The total geriatric nursing competence score of the clinical nursing staff was 2.29±0.81,the secondary hospital score was 2.23±0.78,and the tertiary hospital’s overall mean score was 2.33±0.83.The factors that influenced secondary hospitals included the department of medicine,age of nurses and total length of career(P<0.05).The factors that influenced tertiary hospitals included the department of medicine,age of nurses,nurses’professional title,and geriatric practical advanced nurses’certification(P<0.05).Conclusions:Geriatric nursing competence among clinical nursing staff is imbalanced at a lower-middle level and is influenced by various factors.Thefindings highlight the need for further clinical training in geriatric nursing.The training of geriatric nurses should focus on necessary clinical skills and on preparing them to adequately manage comprehensive geriatric syndromes.

Evaluation of the Effect of Four Levels of Shade on the Growth and Development of Desmodium adscendens with a View to Its Domestication as a Cover Crop in Côte d’Ivoire

Desmodium adscendens is a plant in the Fabaceae family that is very important to man, both agronomically and medicinally. It is an important cover crop that contributes to improving soil fertility, reducing the risks associated with crop pests and weeds. However, very little research has been carried out into its domestication for use as a cover crop in banana plantations in Côte d’Ivoire. The general objective of this study was to evaluate, under semi-controlled conditions, the effect of different levels of shade on the growth and development of Desmodium adscendens. The studies were carried out on the experimental site of the UP-Plant Physiology Laboratory. Four shading levels were evaluated: 0 (NO), 20% (N1), 40% (N2) and 60% (N3) light levels. The Desmodium adscendens cuttings used were collected from a 3-month nursery. For each treatment, 10 tubs containing sterilized soil were used, and 50 cuttings were planted. After planting, growth and development parameters were assessed weekly on 10 plants. In addition, yield and fresh and dry mass were measured. Finally, the reproductive cycle of Desmodium adscendens was determined. The results showed that plant growth and development were greatest in plants grown under the 40% shade level, followed by the 20% and 60% shade levels. In contrast, plants in full light showed poor growth and development. The 40% shade level was the most favorable for growing Desmodium adscendens cuttings, with a cycle length of 67 days. Growing Desmodium adscendens under shade with 20% and 40% light levels could be recommended to farmers.

Depression and anxiety,peripheral blood inflammatory factors,and stress levels on therapeutic outcomes in patients with chronic wounds

BACKGROUND The incidence of chronic wounds is rising due to an aging population and lifestyle changes in our country.In addition,as the disease spectrum evolves,chronic wounds have become common clinical issues that seriously threaten health and impose significant social and economic burdens.AIM To investigate how depression,anxiety,peripheral blood inflammatory factors,and stress levels affect therapeutic outcomes in patients with chronic wounds.METHODS Retrospectively collected clinical data from 110 patients with chronic wounds treated at Changde Hospital,Xiangya School of Medicine,Central South University(The First People’s Hospital of Changde City)between January 2021 and December 2023,categorizing them into effective and ineffective groups based on treatment effects.Differences between both groups were analyzed using univariate analysis,independent risk factors identified via logistic regression,and their predictive value assessed through receiver operating characteristic analysis.RESULTS Following treatment,95 cases were classified as the effective group(cured or improved),while 15 cases with improvement formed the ineffective group.Significant differences between both groups were noted in wound area,infection status,daily bed time,Hamilton Anxiety Rating Scale(HAMA)scores,Hamilton Depression Rating Scale(HAMD)scores,and levels of interleukin-6,tumor necrosis factor-alpha,and superoxide dismutase(P<0.05).Logistic regression analysis identified a wound area≥7 cm^(2),HAMA≥9 scores,and HAMD≥8 scores were independent risk factors for ineffective treatment in patients with chronic wounds(P<0.05).The receiver operating characteristic curve analysis revealed that the area under the curve for ineffective treatment based on wound area,HAMA,and HAMD was 0.767,0.805,and 0.768 respectively.CONCLUSION Wound size,anxiety,and depression are significant factors influencing the therapeutic outcomes in patients with chronic wounds that require careful attention,alongside the development of appropriate strategies.

Evaluating Mitigation Plans over Traffic Sector to Improve NO₂Levels in Andalusia (Spain) Using a Regional-Local Scale Photochemical Modelling System

In this contribution, we present an evaluation of different mitigation plans to improve NO2 levels in Andalusia, a region in the south of Spain. Specifically, we consider four possible mitigation plans: the effects over NO2 concentration of apply changes in the distribution of Vehicles Park;the effect of realize traffic restrictions (affecting to the density flow of vehicles) over highways and main roads;the effect of replacement of diesel use by natural gas in urban areas;and the effect of applying new velocity limits to access to urban areas. A sophisticated air quality modelling (AQM) system has been used to evaluate these mitigation plans. AQM implemented is composed on WRF meteorological model, an emission model created by the authors and CMAQ photochemical model. AQM analyzes mitigation plans during fifteen episodes of 2011 where NO2 levels were the highest of the year;so we analyze the effect of mitigation plans in worst conditions. Results provided by the AQM system show that: 1-h maximum daily NO2 is reduced to 10μg.m-3 near circulation roads when traffic restrictions and velocity limits plans are applied (NOx emissions are reduced in 9% - 15%);1-h maximum daily NO2 is reduced to 12 μg.m-3 affecting all municipalities when changes in the distribution of Vehicles Park are applied (NOx emissions are reduced in 25% - 26%);and the replacement of fuel of urban buses does not affect considerably NO2 levels.