Expert consensus on odontogenic maxillary sinusitis multi-disciplinary treatment

Odontogenic maxillary sinusitis (OMS) is a subtype of maxillary sinusitis (MS). It is actually inflammation of the maxillary sinus that secondary to adjacent infectious maxillary dental lesion. Due to the lack of unique clinical features, OMS is difficult to distinguish from other types of rhinosinusitis. Besides, the characteristic infectious pathogeny of OMS makes it is resistant to conventional therapies of rhinosinusitis. Its current diagnosis and treatment are thus facing great difficulties. The multi-disciplinary cooperation between otolaryngologists and dentists is absolutely urgent to settle these questions and to acquire standardized diagnostic and treatment regimen for OMS. However, this disease has actually received little attention and has been underrepresented by relatively low publication volume and quality. Based on systematically reviewed literature and practical experiences of expert members, our consensus focuses on characteristics, symptoms, classification and diagnosis of OMS, and further put forward multidisciplinary treatment decisions for OMS, as well as the common treatment complications and relative managements. This consensus aims to increase attention to OMS, and optimize the clinical diagnosis and decision-making of OMS, which finally provides evidence-based options for OMS clinical management.

A gated recurrent unit model to predict Poisson’s ratio using deep learning

Static Poisson’s ratio(vs)is crucial for determining geomechanical properties in petroleum applications,namely sand production.Some models have been used to predict vs;however,the published models were limited to specific data ranges with an average absolute percentage relative error(AAPRE)of more than 10%.The published gated recurrent unit(GRU)models do not consider trend analysis to show physical behaviors.In this study,we aim to develop a GRU model using trend analysis and three inputs for predicting n s based on a broad range of data,n s(value of 0.1627-0.4492),bulk formation density(RHOB)(0.315-2.994 g/mL),compressional time(DTc)(44.43-186.9 μs/ft),and shear time(DTs)(72.9-341.2μ s/ft).The GRU model was evaluated using different approaches,including statistical error an-alyses.The GRU model showed the proper trends,and the model data ranges were wider than previous ones.The GRU model has the largest correlation coefficient(R)of 0.967 and the lowest AAPRE,average percent relative error(APRE),root mean square error(RMSE),and standard deviation(SD)of 3.228%,1.054%,4.389,and 0.013,respectively,compared to other models.The GRU model has a high accuracy for the different datasets:training,validation,testing,and the whole datasets with R and AAPRE values were 0.981 and 2.601%,0.966 and 3.274%,0.967 and 3.228%,and 0.977 and 2.861%,respectively.The group error analyses of all inputs show that the GRU model has less than 5% AAPRE for all input ranges,which is superior to other models that have different AAPRE values of more than 10% at various ranges of inputs.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

Study on Ecological Change Remote Sensing Monitoring Method Based on Elman Dynamic Recurrent Neural Network

In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Investigation of risk factors in the development of recurrent urethral stricture after internal urethrotomy

BACKGROUND Urethral stricture is a condition that often develops with trauma and results in narrowing of the urethral lumen.Although endoscopic methods are mostly used in its treatment,it has high recurrence rates.Therefore,open urethroplasty is recommended after unsuccessful endoscopic treatments.AIM To investigate the risk factors associated with urethral stricture recurrence.METHODS The data of male patients who underwent internal urethrotomy for urethral stricture between January 2017 and January 2023 were retrospectively analyzed.Demographic data,comorbidities,preoperative haemogram,and biochemical values obtained from peripheral blood and operative data were recorded.Patients were divided into two groups in terms of recurrence development;recurrence and non-recurrence.Initially recorded data were compared between the two groups.RESULTS A total of 303 patients were included in the study.The mean age of the patients was 66.6±13.6 years.The mean duration of recurrence development was 9.63±9.84(min-max:1-39)months in the recurrence group.Recurrence did not occur in non-recurrence group throughout the follow-up period with an average time of 44.15±24.07(min-max:12-84)months.In the comparison of both groups,the presence of diabetes mellitus(DM),hypertension(HT),and multiple comorbidi-ties were significantly higher in the recurrence(+)group(P=0.038,P=0.012,P=0.013).Blood group,postoperative use of non-steroidal anti-inflammatory drugs,preoperative cystostomy,cause of stricture,iatrogenic cause of stricture,location and length of stricture,indwelling urinary cathater size and day of catheter removal did not differ between the two groups.No statistically significant difference was observed between the two groups in terms of age,uroflowmetric maximum flow rate value,hemo-gram parameters,aspartate aminotransferase(AST),alanine aminotransferase(ALT),fasting blood sugar,creati-nine,glomerular filtration rate,neutrophil-lymphocyte ratio,platelet-lymphocyte ratio,lymphocyte-monocyte ratio,monocyte-lymphocyte ratio and AST/ALT ratios.CONCLUSION In patients with urethral stricture recurrence,only the frequency of DM and HT was high,while inflammation marker levels and stricture-related parameters were similar between the groups.

Rationale of a Cross Sectional Analytic Study on Determinants of Recurrent Preeclampsia at University Clinics of Kinshasa (Democratic Republic of Congo) and at Victor Dupouy Hospital Center (France)

Research Background: Pre-eclampsia is one of main causes of materno-foetal mortality and morbidity worldwide, with a prevalence of 3% - 7%. Although considered a primiparous condition, it can nevertheless recur. Several factors appear to be associated with risk of recurrence of pre-eclampsia, such as the term of delivery of previous pregnancy, severity of disease, the existence of co-morbidities and the inter-genital space. Purpose: The aim of our study will be to analyse and identify in a population of pregnant women with a history of preeclampsia risk factors associated with occurrence of recurrent preeclampsia at University clinics of Kinshasa (Democratic Republic of Congo) and at Victor Dupouy Hospital Center (France). Methods: In this study, pregnant women with an history of preeclampsia who will give birth between November 2018 and October 2024 at University Clinics of Kinshasa (UCK) and Victor Dupouy Hospital Center (VDHC) will be included. This will be a cross-sectional analytical study, data from previous and subsequent pregnancies will be studied. Expected Result: The prevalence of recurrent preeclampsia in the study population will be determined. And we will highlight the factors that will determine the recurrence of preeclampsia by analysing the risk factors. Conclusion: Knowledge of the factors associated with recurrent preeclampsia could be an excellent tool for predicting and preventing the disease.

Recorded recurrent deep reinforcement learning guidance laws for intercepting endoatmospheric maneuvering missiles

This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.

Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

This paper focuses on wireless-powered communication systems,which are increasingly relevant in the Internet of Things(IoT)due to their ability to extend the operational lifetime of devices with limited energy.The main contribution of the paper is a novel approach to minimize the secrecy outage probability(SOP)in these systems.Minimizing SOP is crucial for maintaining the confidentiality and integrity of data,especially in situations where the transmission of sensitive data is critical.Our proposed method harnesses the power of an improved biogeography-based optimization(IBBO)to effectively train a recurrent neural network(RNN).The proposed IBBO introduces an innovative migration model.The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation.This is accomplished by integrating tactics such as advancing towards a random habitat,adopting the crossover operator from genetic algorithms(GA),and utilizing the global best(Gbest)operator from particle swarm optimization(PSO)into the IBBO framework.The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters,resulting in significant outage probability reduction.Through comprehensive simulations,we showcase the superiority of the IBBO-RNN over existing approaches,highlighting its capability to achieve remarkable gains in SOP minimization.This paper compares nine methods for predicting outage probability in wireless-powered communications.The IBBO-RNN achieved the highest accuracy rate of 98.92%,showing a significant performance improvement.In contrast,the standard RNN recorded lower accuracy rates of 91.27%.The IBBO-RNN maintains lower SOP values across the entire signal-to-noise ratio(SNR)spectrum tested,suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

Ocular rosacea without facial erythema involvement manifesting as bilateral multiple recurrent chalazions:A case report

BACKGROUND In addition to the non-specific symptomatology of ocular rosacea,currently,there are no reliable diagnostic tests for the disease,which may lead to its misdiagnosis.Here,we report a case of ocular rosacea presenting with multiple recurrent chalazion on both eyelids.CASE SUMMARY A 63-year-old female patient presented with multiple chalazion and dry eyes in both eyes,with no facial erythema.Initial management done were application of steroid eye ointment on both eyelids,hot compresses,and eyelid margin cleaning;noting that there was no relief of symptoms.Surgical excision of the chalazion was done on both eyes,however,bilateral recurrence occurred post-operatively.The pathological studies showed infiltration of a small amount of fibrous tissue with many chronic inflammatory cells.Immunohistochemistry studies were positive for LL-37.Resolution of the chalazion occurred after oral administration of doxycycline and azithromycin.CONCLUSION Our findings show that ophthalmologists should recognize the ocular manifestations of skin diseases.

Embryo Transfer Strategies for Women with Recurrent Implantation Failure During the Frozen-thawed Embryo Transfer Cycles:Sequential Embryo Transfer or Double-blastocyst Transfer?

Objective Both sequential embryo transfer(SeET)and double-blastocyst transfer(DBT)can serve as embryo transfer strategies for women with recurrent implantation failure(RIF).This study aims to compare the effects of SeET and DBT on pregnancy outcomes.Methods Totally,261 frozen-thawed embryo transfer cycles of 243 RIF women were included in this multicenter retrospective analysis.According to different embryo quality and transfer strategies,they were divided into four groups:group A,good-quality SeET(GQ-SeET,n=38 cycles);group B,poor-quality or mixed-quality SeET(PQ/MQ-SeET,n=31 cycles);group C,good-quality DBT(GQ-DBT,n=121 cycles);and group D,poor-quality or mixed-quality DBT(PQ/MQ-DBT,n=71 cycles).The main outcome,clinical pregnancy rate,was compared,and the generalized estimating equation(GEE)model was used to correct potential confounders that might impact pregnancy outcomes.Results GQ-DBT achieved a significantly higher clinical pregnancy rate(aOR 2.588,95%CI 1.267–5.284,P=0.009)and live birth rate(aOR 3.082,95%CI 1.482–6.412,P=0.003)than PQ/MQ-DBT.Similarly,the clinical pregnancy rate was significantly higher in GQ-SeET than in PQ/MQ-SeET(aOR 4.047,95%CI 1.218–13.450,P=0.023).The pregnancy outcomes of GQ-SeET were not significantly different from those of GQ-DBT,and the same results were found between PQ/MQ-SeET and PQ/MQ-DBT.Conclusion SeET relative to DBT did not seem to improve pregnancy outcomes for RIF patients if the embryo quality was comparable between the two groups.Better clinical pregnancy outcomes could be obtained by transferring good-quality embryos,no matter whether in SeET or DBT.Embryo quality plays a more important role in pregnancy outcomes for RIF patients.

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

Populational change of CD4^(+)CD25^(+)Treg cells is responsible for the synergistic effect of the combination of RAMP2 with baicalin in treating recurrent spontaneous abortion mouse models

Background: The absence of a safe and effective therapy for recurrent spontaneous abortion due to a maternofetal failure in immunological tolerance remains an intractable clinical obstacle for surgeons. Recently, traditional Chinese medicine has become a feasible alternative for certain diseases, including recurrent spontaneous abortion. However, because of the complex composition of the traditional Chinese medicine formula, its action mechanism remains unclear. Methods: We selected two isolated active ingredients (RAMP and baicalin) from the traditional Chinese medicine formula and used an abortion-prone CBA/J × DBA/2 model to simulate human RSA and compared the changes in fetal resorption rate, Treg cell percentage, and relevant cytokines before and after combination therapy. In addition, The mechanisms were preliminarily discussed using in vitro differentiation models. Results: In CBA/J × DBA/2 abortion-prone mice, the combination therapy resulted in a lower embryo resorption rate compared to that obtained with individual delivery of either RAMP or baicalin, thereby playing an embryo-protective role through the increase in Treg cells for the maintenance of maternal-fetal immune tolerance. In in vitro primary cell differentiation experiments, the concentration of Treg cells significantly increased from 11% to 17.9% after the combination therapy compared to that of the single administration group. Conclusion: the synergistic effects of RAMP and baicalin were responsible for Treg differentiation. The present study provides a solid basis for improving the applicability of traditional Chinese herbs in the treatment of recurrent spontaneous abortion.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

Association of Comorbid Asthma and the Efficacy of Bioabsorbable Steroid-eluting Sinus Stents Implanted After Endoscopic Sinus Surgery in Patients with Chronic Rhinosinusitis with Nasal Polyps

Objective To identify factors affecting the efficacy of steroid-eluting sinus stents implanted after endoscopic sinus surgery(ESS)in patients with chronic rhinosinusitis with nasal polyps(CRSwNP).Methods We performed a post-hoc analysis of a randomized self-controlled clinical trial on post-operative implantation of bioabsorbable steroid-eluting stents in patients with CRSwNP.Univariate logistic regression analysis was conducted to identify which of the following factors affect the response to post-operative stent implantation:sex,serum eosinophil levels,history of prior surgery,endoscopic scores,and comorbid conditions(asthma and allergic rhinitis).The primary outcome was the rate of post-operative intervention on day 30,and the secondary outcome was the rate of polypoid tissue formation(grades 2–3)on days 14,30,and 90.Results A total of 151 patients with CRSwNP were included in the post-hoc analysis.Asthma was identified as the only risk factor for a poor response to steroid-eluting sinus stents on post-operative day 30,with an odds ratio of 23.71(95%CI,2.81,200.16;P=0.004)for the need for post-operative intervention and 19(95%CI,2.20,164.16;P=0.003)for moderate-to-severe polypoid tissue formation.In addition,the asthmatic group showed higher rates of post-operative intervention and polypoid tissue formation than the non-asthmatic group on post-operative day 30.Blood eosinophil levels were not identified as a risk factor for poor outcomes after stent implantation.Conclusion Comorbid asthma,but not blood eosinophil level,impairs the efficacy of steroid-eluting sinus stents in the short term after ESS in patients with CRSwNP.

A Novel Parameter-Optimized Recurrent Attention Network for Pipeline Leakage Detection

Accurate detection of pipeline leakage is essential to maintain the safety of pipeline transportation.Recently,deep learning(DL)has emerged as a promising tool for pipeline leakage detection(PLD).However,most existing DL methods have difficulty in achieving good performance in identifying leakage types due to the complex time dynamics of pipeline data.On the other hand,the initial parameter selection in the detection model is generally random,which may lead to unstable recognition performance.For this reason,a hybrid DL framework referred to as parameter-optimized recurrent attention network(PRAN)is presented in this paper to improve the accuracy of PLD.First,a parameter-optimized long short-term memory(LSTM)network is introduced to extract effective and robust features,which exploits a particle swarm optimization(PSO)algorithm with cross-entropy fitness function to search for globally optimal parameters.With this framework,the learning representation capability of the model is improved and the convergence rate is accelerated.Moreover,an anomaly-attention mechanism(AM)is proposed to discover class discriminative information by weighting the hidden states,which contributes to amplifying the normalabnormal distinguishable discrepancy,further improving the accuracy of PLD.After that,the proposed PRAN not only implements the adaptive optimization of network parameters,but also enlarges the contribution of normal-abnormal discrepancy,thereby overcoming the drawbacks of instability and poor generalization.Finally,the experimental results demonstrate the effectiveness and superiority of the proposed PRAN for PLD.

Socioeconomic Status Impacts the Prognosis of Chronic Rhinosinusitis Treated by Endoscopic Sinus Surgery:An Observational Cohort Study in Northeast China

Objective To explore the association between socioeconomic status(SES)and postoperative outcomes in patients with chronic sinusitis(CRS)after functional endoscopic sinus surgery(ESS).Methods We conducted an observational cohort study of 1,047 patients with CRS undergoing ESS.Discharged patients were followed up to 72 weeks for all-cause recurrence events.Baseline SES was established based on occupation,education level,and family income of the patients 1 year before the operation.Kaplan–Meier method was used to calculate the recovery rate after ESS,and Cox proportional hazards regression analysis was used to evaluate the relationship between SES and prognosis.Results Patients of middle SES had lower unadjusted all-cause recurrence than those of low or high SES;24-week overall recovery rate was 90.4%[95%confidence interval(CI):89.6%–91.2%]in patients of middle SES,13.5%(95%CI:12.8%–14.2%)in patients of low SES,and 31.7%(95%CI:30.7%–32.7%)in patients of high SES(both log-rank P<0.001).After adjustment for covariates,hazard ratios(HRs)were7.69(95%CI:6.17–9.71,Ptrend<0.001)for all-cause recurrence for low SES versus middle SES,and 6.19(95%CI:4.78–7.93,Ptrend<0.001)for middle SES versus high SES.Conclusion Low SES and high SES were more associated with the worse prognosis of CRS patients after ESS than middle SES.

Soil NOx Emission Prediction via Recurrent Neural Networks

This paper presents designing sequence-to-sequence recurrent neural network(RNN)architectures for a novel study to predict soil NOx emissions,driven by the imperative of understanding and mitigating environmental impact.The study utilizes data collected by the Environmental Protection Agency(EPA)to develop two distinct RNN predictive models:one built upon the long-short term memory(LSTM)and the other utilizing the gated recurrent unit(GRU).These models are fed with a combination of historical and anticipated air temperature,air moisture,and NOx emissions as inputs to forecast future NOx emissions.Both LSTM and GRU models can capture the intricate pulse patterns inherent in soil NOx emissions.Notably,the GRU model emerges as the superior performer,surpassing the LSTM model in predictive accuracy while demonstrating efficiency by necessitating less training time.Intriguingly,the investigation into varying input features reveals that relying solely on past NOx emissions as input yields satisfactory performance,highlighting the dominant influence of this factor.The study also delves into the impact of altering input series lengths and training data sizes,yielding insights into optimal configurations for enhanced model performance.Importantly,the findings promise to advance our grasp of soil NOx emission dynamics,with implications for environmental management strategies.Looking ahead,the anticipated availability of additional measurements is poised to bolster machine-learning model efficacy.Furthermore,the future study will explore physical-based RNNs,a promising avenue for deeper insights into soil NOx emission prediction.