First contact investigations and compliance to treatment in patients with uveitis

AIM:To ascertain the pattern of investigations at first contact in uveitic patients and evaluate compliance to treatment.METHODS:An observational study comprised of 201 uveitic patients presenting for the first time to our centre from January 2019 to June 2020.Detailed information regarding systemic investigations undertaken by specialists at the time of first contact and the cost of these investigations were reviewed on the first visit to our centre.Compliance with the treatment was determined and reasons behind non-compliance were evaluated on the first follow-up in patients who had no improvement in clinical signs and symptoms.RESULTS:The mean age of the study group was 35.35±14.1y and gender composition was 59.7%males and 40.3%females.Anterior uveitis was observed in 45.3%of patients,intermediate uveitis in 31.8%of patients,posterior uveitis in 14.9%of patients and panuveitis in 8.0%of patients.Association with a systemic disease was evident in 17.9%of patients.When compared with standard guidelines and uveitis patterns,systemic investigations were identified to be relevant only in 38.3%of patients.Non-compliance to treatment was documented in 22.4%of patients.Common reasons for non-compliance were inadequate counselling by the treating physician about treatment in 26.7%of patients and a busy schedule at work/school in 22.2%of patients.CONCLUSION:Significant number of investigations performed at first contact is found to be contrary to standard guidelines and are not contributory to the care.About a quarter of patients in this study are found to be non-compliant with the treatment.Compliance is more challenging to achieve in school-going children and working adults.The availability of comprehensive,periodically updated,evidence-based guidelines on the role of investigations and the use of trained counsellors may help to channelize proper evaluation and improve compliance to treatment,respectively,in patients with uveitis.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Investigations on the tensile deformation of pure Mg and Mg–15Gd alloy by in-situ X-ray synchrotron radiation and visco-plastic self-consistent modeling

In this study,the texture evolutions of two Mg materials during tension are explored.In-situ X-ray synchrotron and Visco-Plastic SelfConsistent(VPSC) modeling are employed to investigate the different deformation modes between pure Mg and Mg-15Gd(wt.%) alloy.These two materials with a strong extrusion texture show large different slip/twinning activity behaviors during tensile deformation.The basal(a) slip has the highest contribution to the initial stage of plastic deformation for pure Mg.During the subsequent plastic deformation,the prismatic slip is dominant due to the strong ED//(100) fiber texture.In contrast,the deformation behavior of Mg-15Gd alloy is more complex.Twinning and basal slip are dominant at the early stage of plastic deformation,but further deformation results in the increased activation of prismatic and pyramidal slips.In comparison to pure Mg,the ratios of the critical resolved shear stress(CRSS) between non-basal slip and basal slip of the Mg-15Gd alloy are much lower.

Analysis of Epidemiological Investigations and Responses to Human Rabies Deaths in Côte d’Ivoire, 2021

Rabies is an infectious disease that almost always leads to death as soon as the first signs appear, and is still endemic in several regions, including Côte d’Ivoire. Epidemiological investigations are carried out and response measures implemented in the event of any death from human rabies. However, the quality of these investigations does not always allow the adoption of response measures. The aim of this work was to evaluate the reports of epidemiological investigations and response actions carried out during the occurrence of human rabies deaths from 2016 to 2018. This was a cross-sectional study that took place from April to September 2021 at the National Institute of Public Hygiene and focused on human rabies deaths notified from 206 to 2018. Of sixty-one (61) human rabies deaths recorded, sixty (60) were investigated, among which fifty-nine (59) deaths had been subject to response. Most deaths occurred in rural areas (75%), and the main vector was the dog (98%). Several shortcomings were noted: response actions were not carried out promptly;joint interventions bringing together human and animal health professionals were rare, there was little communication about rabies to communities (36%), and very little vaccination of the vector, the source of the disease (12%);an almost total absence of capacity-building for local players (7%);the health districts of Abobo-Ouest, Bondoukou, Divo and Fresco, which had regularly recorded human rabies deaths, had not benefited from dog vaccination, which is supposed to break the circulation of the virus. The persistence of rabies deaths in the health districts could probably be linked to these shortcomings. Faced with these realities, it is essential to strengthen the epidemiological investigation capacities of the players involved, in order to improve the quality of investigations and ensure an effective response.

Analysis of Epidemiological Investigations and Responses to Human Rabies Deaths in Côte d’Ivoire, 2021

Rabies is an infectious disease that almost always leads to death as soon as the first signs appear, and is still endemic in several regions, including Côte d’Ivoire. Epidemiological investigations are carried out and response measures implemented in the event of any death from human rabies. However, the quality of these investigations does not always allow the adoption of response measures. The aim of this work was to evaluate the reports of epidemiological investigations and response actions carried out during the occurrence of human rabies deaths from 2016 to 2018. This was a cross-sectional study that took place from April to September 2021 at the National Institute of Public Hygiene and focused on human rabies deaths notified from 206 to 2018. Of sixty-one (61) human rabies deaths recorded, sixty (60) were investigated, among which fifty-nine (59) deaths had been subject to response. Most deaths occurred in rural areas (75%), and the main vector was the dog (98%). Several shortcomings were noted: response actions were not carried out promptly;joint interventions bringing together human and animal health professionals were rare, there was little communication about rabies to communities (36%), and very little vaccination of the vector, the source of the disease (12%);an almost total absence of capacity-building for local players (7%);the health districts of Abobo-Ouest, Bondoukou, Divo and Fresco, which had regularly recorded human rabies deaths, had not benefited from dog vaccination, which is supposed to break the circulation of the virus. The persistence of rabies deaths in the health districts could probably be linked to these shortcomings. Faced with these realities, it is essential to strengthen the epidemiological investigation capacities of the players involved, in order to improve the quality of investigations and ensure an effective response.

Numerical investigation of friction-heating-pressurization and its control parameters in the shear band of high-speed landslides

High-speed sliding often leads to catastrophic landslides,many of which,in the initial sliding phase before disintegration,experience a friction-induced thermal pressurization effect in the bottom shear band,accelerating the movement of the overlying sliding mass.To quantitatively investigate this complex multiphysical phenomenon,we established a set of equations that describe the variations in temperature and excess pore pressure within the shear band,as well as the conservation of momentum equation for the overlying sliding mass.With a simplified landslide model,we investigated the variations of temperature and excess pore pressure within the shear band and their impacts on the velocity of the overlying sliding mass.On this basis,we studied the impact of seven key parameters on the maximum temperature and excess pore pressure in the shear band,as well as the impact on the velocity of the overlying sliding mass.The simulation results of the standard model show that the temperature and excess pore pressure in the shear band are significantly higher than those in the adjacent areas,and reach the maximum values in the center.Within a few seconds after the start,the maximum excess pore pressure in the shear zone is close to the initial stress,and the shear strength loss rate exceeds 90%.The thermal pressurization mechanism significantly increases the velocity of the overlying sliding mass.The results of parameter sensitivity analysis show that the thermal expansion coefficient has the most significant impact on the temperature and excess pore pressure in the shear band,and the sliding surface dip angle has the most significant impact on the velocity of the overlying sliding mass.The results of this study are of great significance for clarifying the mechanism of thermal pressurization-induced high-speed sliding.

Experimental investigation on the permeability of gap-graded soil due to horizontal suffusion considering boundary effect

The boundary condition is a crucial factor affecting the permeability variation due to suffusion.An experimental investigation on the permeability of gap-graded soil due to horizontal suffusion considering the boundary effect is conducted,where the hydraulic head difference(DH)varies,and the boundary includes non-loss and soil-loss conditions.Soil samples are filled into seven soil storerooms connected in turn.After evaluation,the variation in content of fine sand(ΔR_(f))and the hydraulic conductivity of soils in each storeroom(C_(i))are analyzed.In the non-loss test,the soil sample filling area is divided into runoff,transited,and accumulated areas according to the negative or positive ΔR_(f) values.ΔR_(f) increases from negative to positive along the seepage path,and Ci decreases from runoff area to transited area and then rebounds in accumulated area.In the soil-loss test,all soil sample filling areas belong to the runoff area,where the gentle-loss,strengthened-loss,and alleviated-loss parts are further divided.ΔR_(f) decreases from the gentle-loss part to the strengthened-loss part and then rebounds in the alleviated-loss part,and C_(i) increases and then decreases along the seepage path.The relationship between ΔR_(f) and Ci is different with the boundary condition.Ci exponentially decreases with ΔR_(f) in the non-loss test and increases with ΔR_(f) generally in the soil-loss test.

Data-Driven Learning Control Algorithms for Unachievable Tracking Problems

For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

Data-driven prediction of dimensionless quantities for semi-infinite target penetration by integrating machine-learning and feature selection methods

This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Experimental investigation on coal pore-fracture variation and fractal characteristics synergistically affected by solvents for improving clean gas extraction

Chemical solvents instead of pure water being as hydraulic fracturing fluid could effectively increase permeability and improve clean methane extraction efficiency.However,pore-fracture variation features of lean coal synergistically affected by solvents have not been fully understood.Ultrasonic testing,nuclear magnetic resonance analysis,liquid phase mass spectrometry was adopted to comprehensively analyze pore-fracture change characteristics of lean coal treated by combined solvent(NMP and CS_(2)).Meanwhile,quantitative characterization of above changing properties was conducted using geometric fractal theory.Relationship model between permeability,fractal dimension and porosity were established.Results indicate that the end face fractures of coal are well developed after CS2and combined solvent treatments,of which,end face box-counting fractal dimensions range from 1.1227 to 1.4767.Maximum decreases in ultrasonic longitudinal wave velocity of coal affected by NMP,CS_(2)and combined solvent are 2.700%,20.521%,22.454%,respectively.Solvent treatments could lead to increasing amount of both mesopores and macropores.Decrease ratio of fractal dimension Dsis 0.259%–2.159%,while permeability increases ratio of NMR ranges from 0.1904 to 6.4486.Meanwhile,combined solvent could dissolve coal polar and non-polar small molecules and expand flow space.Results could provide reference for solvent selection and parameter optimization of permeability-enhancement technology.

Serological Investigation into the Infected Genotypes of Patients with Japanese Encephalitis in the Coastal Provinces of China

Objective Genotypes(G)1,3,and 5 of the Japanese encephalitis virus(JEV)have been isolated in China,but the dominant genotype circulating in Chinese coastal areas remains unknown.We searched for G5 JEV-infected cases and attempted to elucidate which JEV genotype was most closely related to human Japanese encephalitis(JE)in the coastal provinces of China.Methods In this study,we collected serum specimens from patients with JE in three coastal provinces of China(Guangdong,Zhejiang,and Shandong)from 2018 to 2020 and conducted JEV cross-neutralization tests against G1,G3,and G5.Results Acute serum specimens from clinically reported JE cases were obtained for laboratory confirmation from hospitals in Shandong(92 patients),Zhejiang(192 patients),and Guangdong(77 patients),China,from 2018 to 2020.Seventy of the 361 serum specimens were laboratory-confirmed to be infected with JEV.Two cases were confirmed to be infected with G1 JEV,32 with G3 JEV,and two with G5 JEV.Conclusion G3 was the primary infection genotype among JE cases with a definite infection genotype,and the infection caused by G5 JEV was confirmed serologically in China.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

Systematic investigation of Radix Salviae for treating diabetic peripheral neuropathy disease based on network Pharmacology

BACKGROUND Diabetic peripheral neuropathy(DPN)is a debilitating complication of diabetes mellitus with limited available treatment options.Radix Salviae,a traditional Chinese herb,has shown promise in treating DPN,but its therapeutic mech-anisms have not been systematically investigated.AIM Radix Salviae(Danshen in pinin),a traditional Chinese medicine(TCM),is widely used to treat DPN in China.However,the mechanism through which Radix Salviae treats DPN remains unclear.Therefore,we aimed to explore the mechanism of action of Radix Salviae against DPN using network pharmacology.METHODS The active ingredients and target genes of Radix Salviae were screened using the TCM pharmacology database and analysis platform.The genes associated with DPN were obtained from the Gene Cards and OMIM databases,a drug-com-position-target-disease network was constructed,and a protein–protein inter-action network was subsequently constructed to screen the main targets.Gene Ontology(GO)functional annotation and pathway enrichment analysis were performed via the Kyoto Encyclopedia of Genes and Genomes(KEGG)using Bioconductor.RESULTS A total of 56 effective components,108 targets and 4581 DPN-related target genes of Radix Salviae were screened.Intervention with Radix Salviae for DPN mainly involved 81 target genes.The top 30 major targets were selected for enrichment analysis of GO and KEGG pathways.CONCLUSION These results suggested that Radix Salviae could treat DPN by regulating the AGE-RAGE signaling pathway and the PI3K-Akt signaling pathway.Therefore,Danshen may affect DPN by regulating inflammation and apoptosis.

Investigation and Analysis of Main Diseases of Landscape Plants in the Main Urban Area of Lu'an City

To figure out the disease occurrence of landscape plants in the main urban area of Lu'an City,the author investigated the disease occurrence of landscape plants in park green space,residential green space,unit attached green space and main road in the area under administration.The survey results showed that there were 29 species of urban landscape plant diseases,mainly powdery mildew and spot diseases.According to the characteristics of the diseases,the causes and problems of the diseases were analyzed,and the corresponding prevention and control measures were put forward.

A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.