A smartphone-based automated fluorescence analysis system for point-of-care testing of Hg(Ⅱ)

This work demonstrates a smartphone-based automated fluorescence analysis system(SAFAS)for point-of-care testing(POCT)of Hg(Ⅱ).This system consists of three modules.The smartphone module is used to provide an excitation light source,and to collect and analyze fluorescent images.The dark box module is applied to integrate the desired optical elements and offers a dark environment.The cost of the integrated dark box mainly includes the upper cover,box body,lower bottom,¯xture and some optical elements which is about$109.The chip module is used for fluorescence sensing,which is composed of an upper plate,bottom plate and cloth-based chip.Due to the integration of multiple smartphone functions,the SAFAS eliminates the need for additional power sources,light sources and analysis systems.The dark box and upper and bottom plates are made by 3D printer.The cloth-based chip(about$0.005 for each chip)is fabricated using the wax screenprinting technique,with no need for expensive and complex fabrication equipments.To our knowledge,the cloth-based microfluidic fluorescence detection method combined with smartphone functions is first reported.By using optimal conditions,the designed system can realize the quantitative detection of Hg(Ⅱ),which has a linear range of 0.001–100μgmL^(-1)and a detection limit of 0.5 ngmL^(-1).Additionally,the SAFAS has been successfully applied for detecting Hg(Ⅱ)in actual water samples,with recoveries of 100.1%–111%,RSDs of 3.88%–9.74%,and fast detection time of about 1 min.Obviously,the proposed SAFAS has the advantages of high sensitivity,wide dynamic range,acceptable reproducibility,good stability and low cost.Therefore,it is believed that the presented SAFAS has great potential to perform the POCT of Hg(II)in different water samples.

Automated detection and identification of white-backed planthoppers in paddy fields using image processing

A survey of the population densities of rice planthoppers is important for forecasting decisions and efficient control. Tra- ditional manual surveying of rice planthoppers is time-consuming, fatiguing, and subjective. A new three-layer detection method was proposed to detect and identify white-backed planthoppers （WBPHs, Sogatella furcifera （Horvath）） and their developmental stages using image processing. In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient （HOG） features and a support vector machine （SVM） classifier that was trained on Gabor and Local Binary Pattern （LBP） features to detect WBPHs and remove impurities. We achieved a detection rate of 85.6% and a false detection rate of 10.2%. In the third detection layer, a SVM classifier that was trained on the HOG features was used to identify the different developmental stages of the WBPHs, and we achieved an identification rate of 73.1%, a false identification rate of 23.3%, and a 5.6% false detection rate for the images without WBPHs. The proposed three-layer detection method is feasible and effective for the identification of different developmental stages of planthoppers on rice plants in paddy fields.

SVR-Miner:Mining Security Validation Rules and Detecting Violations in Large Software

For various reasons,many of the security programming rules applicable to specific software have not been recorded in official documents,and hence can hardly be employed by static analysis tools for detection.In this paper,we propose a new approach,named SVR-Miner(Security Validation Rules Miner),which uses frequent sequence mining technique [1-4] to automatically infer implicit security validation rules from large software code written in C programming language.Different from the past works in this area,SVR-Miner introduces three techniques which are sensitive thread,program slicing [5-7],and equivalent statements computing to improve the accuracy of rules.Experiments with the Linux Kernel demonstrate the effectiveness of our approach.With the ten given sensitive threads,SVR-Miner automatically generated 17 security validation rules and detected 8 violations,5 of which were published by Linux Kernel Organization before we detected them.We have reported the other three to the Linux Kernel Organization recently.

Characteristics of cyclone climatology and variability in the Southern Ocean

A new climatology of cyclones in the Southern Ocean is generated by applying an automated cyclone detection and tracking algorithm （developed by Hodges at the Reading University） for an improved and relatively high- resolution European Centre for Medium-Range Weather Forecasts atmospheric reanalysis during 1979-2013. A validation shows that identified cyclone tracks are in good agreement with a available analyzed cyclone product. The climatological characteristics of the Southern Ocean cyclones are then analyzed, including track, number, density, intensity, deepening rate and explosive events. An analysis shows that the number of cyclones in the Southern Ocean has increased for 1979-2013, but only statistically significant in summer. Coincident with the circumpolar trough, a single high-density band of cyclones is observed in 55^-67~S, and cyclone density has generally increased in north of this band for 1979-2013, except summer. The intensity of up to 70% cyclones in the Southern Ocean is less than 980 hPa, and only a few cyclones with pressure less than 920 hPa are detected for 1979-2013. Further analysis shows that a high frequency of explosive cyclones is located in the band of 45^-55~S, and the Atlantic Ocean sector has much higher frequent occurrence of the explosive cyclones than that in the Pacific Ocean sector. Additionally, the relationship between cyclone activities in the Southern Ocean and the Southern Annular Mode is discussed.

Seasonal and inter-annual variations of Arctic cyclones and their linkage with Arctic sea ice and atmospheric teleconnections

The seasonal and inter-annual variations of Arctic cyclone are investigated. An automatic cyclone tracking algorithm developed by University of Reading was applied on the basis of European Center for Medium-range Weather Forecasts（ECMWF） ERA-interim mean sea level pressure field with 6 h interval for 34 a period. The maximum number of the Arctic cyclones is counted in winter, and the minimum is in spring not in summer.About 50% of Arctic cyclones in summer generated from south of 70°N, moving into the Arctic. The number of Arctic cyclones has large inter-annual and seasonal variabilities, but no significant linear trend is detected for the period 1979–2012. The spatial distribution and linear trends of the Arctic cyclones track density show that the cyclone activity extent is the widest in summer with significant increasing trend in CRU（central Russia）subregion, and the largest track density is in winter with decreasing trend in the same subregion. The linear regressions between the cyclone track density and large-scale indices for the same period and pre-period sea ice area indices show that Arctic cyclone activities are closely linked to large-scale atmospheric circulations, such as Arctic Oscillation（AO）, North Atlantic Oscillation（NAO） and Pacific-North American Pattern（PNA）. Moreover,the pre-period sea ice area is significantly associated with the cyclone activities in some regions.

Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver disease.This could be attributed to many factors,among which are human habits,awareness issues,poor healthcare,and late detection.To curb the growing threats from liver disease,early detection is critical to help reduce the risks and improve treatment outcome.Emerging technologies such as machine learning,as shown in this study,could be deployed to assist in enhancing its prediction and treatment.AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection,diagnosis,and reduction of risks and mortality associated with the disease.METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history.The data were collected from the state of Andhra Pradesh,India,through https://www.kaggle.com/datasets/uciml/indian-liver-patientrecords.The population was divided into two sets depending on the disease state of the patient.This binary information was recorded in the attribute"is_patient".RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36%and 73.24%,respectively,which was much better than the conventional method.The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis(scarring)and to enhance the survival of patients.The study showed the potential of machine learning in health care,especially as it concerns disease prediction and monitoring.CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease.However,relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.

QI-BRiCE: Quality Index for Bleeding Regions in Capsule Endoscopy Videos

With the advent in services such as telemedicine and telesurgery,provision of continuous quality monitoring for these services has become a challenge for the network operators.Quality standards for provision of such services are application specic as medical imagery is quite different than general purpose images and videos.This paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy(WCE)videos containing bleeding regions.Bleeding regions in gastrointestinal tract have been focused in this research,as bleeding is one of the major reasons behind several diseases within the tract.The method jointly estimates the diagnostic as well as perceptual quality of WCE videos,and accurately predicts the quality,which is in high correlation with the subjective differential mean opinion scores(DMOS).The proposed combines motion quality estimates,bleeding regions’quality estimates based on support vector machine(SVM)and perceptual quality estimates using the pristine and impaired WCE videos.Our method Quality Index for Bleeding Regions in Capsule Endoscopy(QI-BRiCE)videos is one of its kind and the results show high correlation in terms of Pearson’s linear correlation coefcient(PLCC)and Spearman’s rank order correlation coefcient(SROCC).An F-test is also provided in the results section to prove the statistical signicance of our proposed method.

Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure

Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil infrastructure.However,these tools are typically based on RGB images,which work well under ideal lighting conditions,but often have degrading performance in poor and low-light scenes.On the other hand,thermal images,while lacking in crispness of details,do not show the same degradation of performance in changing lighting conditions.The potential to enhance automated damage detection by fusing RGB and thermal images together within a deep learning network has yet to be explored.In this paper,RGB and thermal images are fused in a ResNET-based semantic segmentation model for vision-based inspections.A convolutional neural network is then employed to automatically identify damage defects in concrete.The model uses a thermal and RGB encoder to combine the features detected from both spectrums to improve its performance of the model,and a single decoder to predict the classes.The results suggest that this RGB-thermal fusion network outperforms the RGB-only network in the detection of cracks using the Intersection Over Union(IOU)performance metric.The RGB-thermal fusion model not only detected damage at a higher performance rate,but it also performed much better in differentiating the types of damage.