A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data-A New Method for Inner Mongolia Typical Grasslands

Grassland biomass is an important parameter of grassland ecosystems.The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge.Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass(AGB)estimation.In order to improve the accuracy of vegetation index inversion of grassland AGB,this study combined ground and Unmanned Aerial Vehicle(UAV)remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis.The narrow band vegetation indices were calculated,and ground and airborne hyperspectral inversion models were established.Finally,the accuracy of the model was verified.The results showed that:(1)The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB.(2)The comparison between measured R^(2) with the prediction R^(2) indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index(SAVI)as the independent variable in the analysis of AGB(fresh weight/dry weight)and four narrow-band vegetation indices.The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia.(3)The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data,where y=17.962e^(4.672x),the fitting R^(2) was 0.542,the prediction R^(2)was 0.424,and RMSE and REE were 57.03 and 0.65,respectively.Therefore,established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia.Compared with ground monitoring,airborne hyperspectral monitoring better reflects the inversion of actual surface biomass.It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management.

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index

Large-scale crop mapping using remote sensing data is of great significance for agricultural production, food security and the sustainable development of human societies. Winter rapeseed is an important oil crop in China that is mainly distributed in the Yangtze River Valley. Traditional winter rapeseed mapping practices are insufficient since they only use the spectral characteristics during the critical phenological period of winter rapeseed, which are usually limited to a small region and cannot meet the needs of large-scale applications. In this study, a novel phenology-based winter rapeseed index(PWRI) was proposed to map winter rapeseed in the Yangtze River Valley. PWRI expands the date window for distinguishing winter rapeseed and winter wheat, and it has good separability throughout the flowering period of winter rapeseed. PWRI also improves the separability of winter rapeseed and winter wheat, which traditionally have been two easily confused winter crops. A PWRI-based method was applied to the Middle Reaches of the Yangtze River Valley to map winter rapeseed on the Google Earth Engine platform. Time series composited Sentinel-2 data were used to map winter rapeseed with 10 m resolution. The mapping achieved a good result with overall accuracy and kappa coefficients exceeding 92% and 0.85, respectively. The PWRI-based method provides a new solution for high spatial resolution winter rapeseed mapping at a large scale.

Joint inversion of gravity and vertical gradient data based on modified structural similarity index for the structural and petrophysical consistency constraint

Joint inversion is one of the most effective methods for reducing non-uniqueness for geophysical inversion.The current joint inversion methods can be divided into the structural consistency constraint and petrophysical consistency constraint methods,which are mutually independent.Currently,there is a need for joint inversion methods that can comprehensively consider the structural consistency constraints and petrophysical consistency constraints.This paper develops the structural similarity index(SSIM)as a new structural and petrophysical consistency constraint for the joint inversion of gravity and vertical gradient data.The SSIM constraint is in the form of a fraction,which may have analytical singularities.Therefore,converting the fractional form to the subtractive form can solve the problem of analytic singularity and finally form a modified structural consistency index of the joint inversion,which enhances the stability of the SSIM constraint applied to the joint inversion.Compared to the reconstructed results from the cross-gradient inversion,the proposed method presents good performance and stability.The SSIM algorithm is a new joint inversion method for petrophysical and structural constraints.It can promote the consistency of the recovered models from the distribution and the structure of the physical property values.Then,applications to synthetic data illustrate that the algorithm proposed in this paper can well process the synthetic data and acquire good reconstructed results.

A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

On traceable iterated line graph and hamiltonian path index

Xiong and Liu[21]gave a characterization of the graphs G for which the n-iterated line graph L^(n)(G)is hamiltonian,for n≥2.In this paper,we study the existence of a hamiltonian path in L^(n)(G),and give a characterization of G for which L^(n)(G)has a hamiltonian path.As applications,we use this characterization to give several upper bounds on the hamiltonian path index of a graph.

Association between the cumulative triglyceride-glucose index and the recurrence of atrial fibrillation after radiofrequency catheter ablation

BACKGROUND Triglyceride-glucose(TyG)index values are a new surrogate marker for insulin resistance.This study aimed to explore the relationship between cumulative TyG index values and atrial fibrillation(AF)recurrence after radiofrequency catheter ablation(RFCA).METHODS A total of 576 patients with AF who underwent RFCA at the Second Affiliated Hospital of Xi'an Jiaotong University were included in this study.The participants were grouped based on cumulative TyG index values tertiles within 3 months after ablation.Cox regression and restricted cubic spline analyses were used to determine the relationship between cumulative TyG index values and AF recurrence.The predictive value of all risk factors was assessed by receiver operating curve analysis.RESULTS There were 375 patients completed the study(age:63.23±10.73 years,64.27%male).The risk of AF recurrence increased with increasing cumulative TyG index values tertiles.After adjusting for potential confounders,patients in the medium cumulative TyG index group[hazard ratio(HR)=4.949,95%CI:1.778–13.778,P=0.002]and the high cumulative TyG index group(HR=8.716,95%CI:3.371–22.536,P<0.001)had a higher risk of AF recurrence than those in the low cumulative TyG index group.The restricted cubic spline regression model also showed an increased risk of AF recurrence with increasing cumulative TyG index values.When considering cumulative TyG index values,left atrial diameter,and lactate dehydrogenase levels as a comprehensive factor,the model could effectively predict AF recurrence after RFCA[area under the curve(AUC)=0.847,95%CI:0.797–0.897,P<0.001].CONCLUSIONS Cumulative TyG index values were a risk factor for AF recurrence after RFCA.Monitoring longitudinal TyG index values may assist with optimized for risk stratification and outcome prediction for AF recurrence.

Novel prognostic factors after radical resection of hepatocellular carcinoma: Updating an old issue

In this editorial,I comment on the article by Li et al published in the recent issue of the World Journal of Gastrointestinal Surgery in 2023,investigating the role of some novel prognostic factors for early survival after radical resection of liver cancer.Liver cancer is an important burden among Asian and Western popu-lations,despite recent advances in both medicine(from virus eradication to systemic target therapies)and surgery.However,survival after proven radical surgery remains poor,with recurrences being the rule.Many prognostic scores have been developed and validated to select those patients who will best benefit from radical liver surgery,although the final general and oncological outcomes continue to be highly jeopardized.Unfortunately,no single biomarker can resolve all these issues for hepatocellular carcinoma,and it remains to be proven whether some of them main-tain predictive power in the long-term follow-up.In the ongoing era of“preci-sion”medicine,the novel prognostic markers,including immune inflammatory and nutritional indexes could be of great help in better stratify surgical candi-dates.

Actuality and underlying mechanisms of systemic immuneinflammation index and geriatric nutritional risk index prognostic value in hepatocellular carcinoma

This editorial contains comments on the article“Correlation between preoperative systemic immune inflammation index,nutritional risk index,and prognosis of radical resection of liver cancer”in a recent issue of the World Journal of Gastrointestinal Surgery.It pointed out the actuality and importance of the article and focused primarily on the underlying mechanisms making the systemic immuneinflammation index(SII)and geriatric nutritional risk index(GNRI)prediction features valuable.There are few publications on both SII and GNRI together in hepatocellular carcinoma(HCC)and patient prognosis after radical surgery.Neutrophils release cytokines,chemokines,and enzymes,degrade extracellular matrix,reduce cell adhesion,and create conditions for tumor cell invasion.Neutrophils promote the adhesion of tumor cells to endothelial cells,through physical anchoring.That results in the migration of tumor cells.Pro-angiogenic factors from platelets enhance tumor angiogenesis to meet tumor cell supply needs.Platelets can form a protective film on the surface of tumor cells.This allows avoiding blood flow damage as well as immune system attack.It also induces the epithelial-mesenchymal transformation of tumor cells that is critical for invasiveness.High SII is also associated with macro-and microvascular invasion and increased numbers of circulating tumor cells.A high GNRI was associated with significantly better progression-free and overall survival.HCC patients are a very special population that requires increased attention.SII and GNRI have significant survival prediction value in both palliative treatment and radical surgery settings.The underlying mechanisms of their possible predictive properties lie in the field of essential cancer features.Those features provide tumor nutrition,growth,and distribution throughout the body,such as vascular invasion.On the other hand,they are tied to the possibility of patients to resist tumor progression and development of complications in both postoperative and cancer-related settings.The article is of considerable interest.It would be helpful to continue the study follow-up to 2 years and longer.External validation of the data is needed.

A Transparent Photoresist Made of Titanium Dioxide Nanoparticle-Embedded Acrylic Resin with a Tunable Refractive Index for UV-Imprint Lithography

Transparent photoresists with a high refractive index(RI)and high transmittance in visible wavelengths have promising functionalities in optical fields.This work reports a kind of tunable optical material composed of titanium dioxide nanoparticles embedded in acrylic resin with a high RI for ultraviolet(UV)-imprint lithography.The hybrid film exhibits a tunable RI of up to 1.67(589 nm)after being cured by UV light,while maintaining both a high transparency of over 98%in the visible light range and a low haze of less than 0.05%.The precision machining of optical microstructures can be imprinted easily and efficiently using the hybrid resin,which acts as a light guide plate(LGP)to guide the light from the side to the top in order to conserve the energy of the display device.These preliminary studies based on both laboratory and commercial experiments pave the way for exploiting the unparalleled optical properties of nanocomposite resins and promoting their industrial application.

Systemic immune-inflammation index,neutrophilto-lymphocyte ratio,and platelet-to-lymphocyte ratio in patients with type 2 diabetes at different stages of diabetic retinopathy

AIM:To investigate systemic immune-inflammation index(SII),neutrophil-to-lymphocyte ratio(NLR),and plateletto-lymphocyte ratio(PLR)levels in patients with type 2 diabetes at different stages of diabetic retinopathy(DR).METHODS:This retrospective study included 141 patients with type 2 diabetes mellitus(DM):45 without diabetic retinopathy(NDR),47 with non-proliferative diabetic retinopathy(NPDR),and 49 with proliferative diabetic retinopathy(PDR).Complete blood counts were obtained,and NLR,PLR,and SII were calculated.The study analysed the ability of inflammatory markers to predict DR using receiver operating characteristic(ROC)curves.The relationships between DR stages and SII,PLR,and NLP were assessed using multivariate logistic regression.RESULTS:The average NLR,PLR,and SII were higher in the PDR group than in the NPDR group(P=0.011,0.043,0.009,respectively);higher in the NPDR group than in the NDR group(P<0.001 for all);and higher in the PDR group than in the NDR group(P<0.001 for all).In the ROC curve analysis,the NLR,PLR,and SII were significant predictors of DR(P<0.001 for all).The highest area under the curve(AUC)was for the PLR(0.929 for PLR,0.925 for SII,and 0.821 for NLR).Multivariate regression analysis indicated that NLR,PLR,and SII were statistically significantly positive and independent predictors for the DR stages in patients with DM[odds ratio(OR)=1.122,95%confidence interval(CI):0.200–2.043,P<0.05;OR=0.038,95%CI:0.018–0.058,P<0.05;OR=0.007,95%CI:0.001–0.01,P<0.05,respectively).CONCLUSION:The NLR,PLR,and SII may be used as predictors of DR.

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.

Fabrication of UV-Curing Linalool-Polysiloxane Hybrid Films with High Refractive Index and Transparency

In this article,a series of high refractive indices(1.50-1.53)thiol phenyl polysiloxane(TPS)were synthesized via hydrolytic sol-gel reaction.The Fourier transform infrared spectra(FT-IR)and nuclear magnetic resonance spectra(NMR)results showed that TPS conformed to the predicted structures.Natural terpene linalool was exploited as photocrosslinker to fabricate UV-curing linalool-polysiloxane hybrid films(LPH)with TPS via photoinitiated thiol-ene reaction.LPH rapidly cured under UV irradiation at the intensity of 80 mW/cm^(2) in 30 s,exhibiting good UV-curing properties.The optical transmittance of LPH in the wavelength of 300-800 nm was over 90%,exhibiting good optical transparency.The water contact angle and water vapor permeability results showed that the introduction of phenyl groups enhance the hydrophobicity and water vapor barrier properties of LPH.The results indicated the potential of LPHs in the applications of optical functional coatings.

Surface phonon resonance:A new mechanism for enhancing photonic spin Hall effect and refractive index sensor

Metal-based surface plasmon resonance(SPR)plays an important role in enhancing the photonic spin Hall effect(SHE)and developing sensitive optical sensors.However,the very large negative permittivities of metals limit their applications beyond the near-infrared regime.In this work,we theoretically present a new mechanism to enhance the photonic SHE by taking advantage of SiC-supported surface phonon resonance(SPhR)in the mid-infrared regime.The transverse displacement of photonic SHE is very sensitive to the wavelength of incident light and the thickness of SiC layer.Under the optimal parameter setup,the calculated largest transverse displacement of SiC-based SPhR structure reaches up to 163.8 ym,which is much larger than the condition of SPR.Moreover,an NO_(2) gas sensor based on the SPhR-enhanced photonic SHE is theoretically proposed with the superior sensing performance.Both the intensity and angle sensitivity of this sensor can be effectively manipulated by varying the damping rate of SiC.The results may provide a promising paradigm to enhance the photonic SHE in the mid-infrared region and open up new opportunity of highly sensitive refractive index sensors.

Carpel Tunnel Syndrome: A Link with Vitamin D, Body Mass Index and Hyperlipidemia

Introduction: Carbai tunnel syndrome [CTS] is compression of the median nerve at the wrist , this causes tingling in the hands, pain, numbness, tingling in the fingers particularly the thumb, index and middle fingers, loss of sensation in the hands and fingers, also weakness in the hands. The aim of the present study was to study a possible association which could be found between electrophysiological data in CTS, BMI, hyperlipidemia, and vitamin D [Vit D] levels. Methods: We used a sample of 40 females of the same age group, who were divided into Group 1 as a control consists of 18 healthy females and Group II consisted of 22 age matched females with clinical and electrophysiological evidence of CTS. We measured atherogenic index [AI] as a marker of hyperlipidemia, body mass index [BMI], Vit D status and electrophysiological tests of CTS. Results: Subjects with CTS had deficient Vit D status, they had significantly high atherogenic index (AI), and significant high BMI all compared to control Group I. Median sensory conduction velocity was significantly correlated negatively with BMI and atherogenic index, and positively correlated significantly with Vit D status. But median sensory and motor action potential latency were significantly correlated positively with BMI and atherogenic index, and negatively correlated significantly with Vit D status The analysis revealed BMI, atherogenic index and Vit D status as predictors of median nerve sensory and motor action potential latency and sensory nerve conduction velocity in CTS. Conclusion: The results of this study suggest that obesity and hyperlipidemia are potent CTS risk factors and declared the direct association between Vit D status and CTS occurrence. Our study supports the notion of the compensatory neuroprotective role of Vit D which could have a direct impact on the nerves integrity as it has an anti-inflammatory property which acts in relieving nervous insults and stress. .

Preoperative prognostic nutritional index predicts long-term outcomes of patients with ampullary adenocarcinoma after curative pancreatoduodenectomy

BACKGROUND The prognostic nutritional index(PNI),a marker of immune-nutrition balance,has predictive value for the survival and prognosis of patients with various cancers.AIM To explore the clinical significance of the preoperative PNI on the prognosis of ampullary adenocarcinoma(AC)patients who underwent curative pancreaticoduodenectomy.METHODS The data concerning 233 patients diagnosed with ACs were extracted and analyzed at our institution from January 1998 to December 2020.All patients were categorized into low and high PNI groups based on the cutoff value determined by receiver operating characteristic curve analysis.We compared disease-free survival(DFS)and overall survival(OS)between these groups and assessed prognostic factors through univariate and multivariate analyses.RESULTS The optimal cutoff value for the PNI was established at 45.3.Patients with a PNI≥45.3 were categorized into the PNI-high group,while those with a PNI<45.3 were assigned to the PNI-low group.Patients within the PNI-low group tended to be of advanced age and exhibited higher levels of aspartate transaminase and total bilirubin and a lower creatinine level than were those in the PNI-high group.The 5-year OS rates for patients with a PNI≥45.3 and a PNI<45.3 were 61.8%and 43.4%,respectively,while the 5-year DFS rates were 53.5%and 38.3%,respectively.Patients in the PNI-low group had shorter OS(P=0.006)and DFS(P=0.012).In addition,multivariate analysis revealed that the PNI,pathological T stage and pathological N stage were found to be independent prognostic factors for both OS and DFS.CONCLUSION The PNI is a straightforward and valuable marker for predicting long-term survival after pancreatoduodenectomy.The PNI should be incorporated into the standard assessment of patients with AC.

Developing and validating a predictive model of delivering large-forgestational-age infants among women with gestational diabetes mellitus

BACKGROUND The birth of large-for-gestational-age(LGA)infants is associated with many shortterm adverse pregnancy outcomes.It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus(GDM)is significantly higher than that born to healthy pregnant women.However,traditional methods for the diagnosis of LGA have limitations.Therefore,this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants.AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM,and provide strategies for the effective prevention and timely intervention of LGA.METHODS The multivariable prediction model was developed by carrying out the following steps.First,the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses,for which the P value was<0.10.Subsequently,Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations,and the optimal combination factors were se-lected by choosing lambda 1se as the criterion.The final predictors were deter-mined by multiple backward stepwise logistic regression analysis,in which only the independent variables were associated with LGA risk,with a P value<0.05.Finally,a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve,calibration curve and decision curve analyses.RESULTS After using a multistep screening method,we establish a predictive model.Several risk factors for delivering an LGA infant were identified(P<0.01),including weight gain during pregnancy,parity,triglyceride-glucose index,free tetraiodothyronine level,abdominal circumference,alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks.The nomogram’s prediction ability was supported by the area under the curve(0.703,0.709,and 0.699 for the training cohort,validation cohort,and test cohort,respectively).The calibration curves of the three cohorts displayed good agreement.The decision curve showed that the use of the 10%-60%threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit.CONCLUSION Our nomogram incorporated easily accessible risk factors,facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Impact of body mass index on adverse kidney events in diabetes mellitus patients: A systematic-review and meta-analysis

BACKGROUND The incidence of chronic kidney disease among patients with diabetes mellitus(DM)remains a global concern.Long-term obesity is known to possibly influence the development of type 2 diabetes mellitus.However,no previous meta-analysis has assessed the effects of body mass index(BMI)on adverse kidney events in patients with DM.AIM To determine the impact of BMI on adverse kidney events in patients with DM.METHODS A systematic literature search was performed on the PubMed,ISI Web of Science,Scopus,Ovid,Google Scholar,EMBASE,and BMJ databases.We included trials with the following characteristics:(1)Type of study:Prospective,retrospective,randomized,and non-randomized in design;(2)participants:Restricted to patients with DM aged≥18 years;(3)intervention:No intervention;and(4)kidney adverse events:Onset of diabetic kidney disease[estimated glomerular filtration rate(eGFR)of<60 mL/min/1.73 m2 and/or microalbuminuria value of≥30 mg/g Cr],serum creatinine increase of more than double the baseline or end-stage renal disease(eGFR<15 mL/min/1.73 m2 or dialysis),or death.RESULTS Overall,11 studies involving 801 patients with DM were included.High BMI(≥25 kg/m2)was significantly associated with higher blood pressure(BP)[systolic BP by 0.20,95%confidence interval(CI):0.15–0.25,P<0.00001;diastolic BP by 0.21 mmHg,95%CI:0.04–0.37,P=0.010],serum albumin,triglycerides[standard mean difference(SMD)=0.35,95%CI:0.29–0.41,P<0.00001],low-density lipoprotein(SMD=0.12,95%CI:0.04–0.20,P=0.030),and lower high-density lipoprotein(SMD=–0.36,95%CI:–0.51 to–0.21,P<0.00001)in patients with DM compared with those with low BMIs(<25 kg/m2).Our analysis showed that high BMI was associated with a higher risk ratio of adverse kidney events than low BMI(RR:1.22,95%CI:1.01–1.43,P=0.036).CONCLUSION The present analysis suggested that high BMI was a risk factor for adverse kidney events in patients with DM.