Improving the prediction of wildfire susceptibility on Hawai'i Island, Hawai'i, using explainable hybrid machine learning models

This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawai'i Island, Hawai'i. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawai'i Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.

26 Virtual strabismus clinic: an alternative model of care during the COVID-19 pandemic

Addenbrooke's Hospital introduced a virtual strabismus clinic in March 2021 to manage patient care during the COVID-19 pandemic. This study aims to explore the feasibility and utility of this care model by evaluating its effectiveness in delivering patient care.Clinic data from April 2021 to April 2022 were retrospectively analysed, including patient demographics, referral information and outcomes. All patients underwent an initial assessment by a specialist orthoptist, preceding virtual review by a consultant ophthalmologist.The clinic saw 114 patients between the ages of 12 and 95 during this period, with an increasing number of patients seen per month. Within two months of the clinic's inception, wait times reduced by 59%: from 30.2 weeks to 12.5 weeks, remaining constant thereafter. Most referrals came from optometrists, with diplopia and identification of new or recurring strabismus being the most common complaint. Virtual review outcome varied significantly: 30.7% of patients were discharged, 16.7% listed for surgery, 34.2% received a repeat FTF review and a further 18.4% received a review virtually.Following its inception, the virtual clinic was able to effectively accommodate patients despite capacity restraints. This was partly achieved through the effective utilisation of specialised orthoptists. Subsequent virtual review by a consultant ophthalmologist achieved positive patient outcomes.Virtual clinics provide an opportunity to optimise patient care and maximise efficiency of clinical input. If applied appropriately, this model of patient care may reduce the NHS burden, improving wait times to facilitate faster intervention. Increasing consultant availability permits the treatment of a greater number of patients.

Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms

Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors. To optimally tune the hyper-parameters of GMDH and enhance the prediction accuracy of modelling radon distribution, the GMDH model was integrated with two metaheuristic optimization algorithms, namely the bat (BA) and cuckoo optimization (COA) algorithms. The goodness-of-fit and predictive performance of the models was quantified using the area under the receiver operating characteristic (ROC) curve (AUC), mean squared error (MSE), root mean square error (RMSE), and standard deviation (StD). The results indicated that the GMDH-COA model outperformed the other models in the training (AUC = 0.852, MSE = 0.058, RMSE = 0.242, StD = 0.242) and testing (AUC = 0.844, MSE = 0.060, RMSE = 0.246, StD = 0.0242) phases. Additionally, using metaheuristic optimization algorithms improved the predictive ability of the GMDH. The GMDH-COA model showed that approximately 7 % of the total area of Chungcheongnam-do consists of very high radon-prone areas. The information gain ratio method was used to assess the predictive ability of considered factors. As expected, soil properties and local geology significantly affected the spatial distribution of radon potential levels. The radon potential map produced in this study represents the first stage of identifying areas where large proportions of residential buildings are expected to experience significant radon levels due to high concentrations of natural radioisotopes in rocks and derived soils beneath building foundations. The generated map assists local authorities to develop urban plans more wisely towards region with less radon concentrations.

Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea

Landslides are a geological hazard that can pose a serious threat to human health and the environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting and mitigating landslides. This study aimed to investigate the application of deep learning algorithms based on convolutional neural networks (CNNs) with metaheuristic optimization algorithms, namely the grey wolf optimizer (GWO) and imperialist competitive algorithm (ICA), to landslide susceptibility mapping. The study area was Icheon City, South Korea, for which an accurate landslide inventory dataset was available. The landslide inventory map was prepared and randomly divided into datasets of 70% for training and 30% for validation. Additionally, 18 landslide-related factors, including geo-environmental and topo-hydrological factors, were considered as predictive variables. The models were compared using area under the curve (AUC) values in receiver operating characteristic (ROC) curve analysis. The validation results showed that optimized models based on CNN-GWO (AUC = 0.876, RMSE = 0.08) and CNN-ICA (AUC = 0.852, RMSE = 0.09) outperformed the standalone CNN model (AUC = 0.847, RMSE = 0.12). Nevertheless, the CNN model outperformed previous research that used a machine learning algorithm alone. Thus, the deep learning algorithm with optimization algorithms proposed in this study can generate more suitable models for landslide susceptibility mapping in the study area due to its improved accuracy.

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

The Effect of Doxycycline on Achilles Tendon Repair in a Rat Model

Introduction:

Doxycycline is a commonly used antibiotic that is also a potent inhibitor of matrix metalloproteinase (MMPs). The use of doxycycline in repairing tendon lesions has been previously investigated and conflicting findings have been reported on its effectiveness. In this study, we sought to evaluate the effects of exposure to doxycycline on Achilles tendon repair.

Materials and Methods:

Twenty healthy rats of the same breed and gender were randomly assigned to two groups of sham, and Doxycycline group therapy. The rats underwent a surgical intervention in which a 2mm incision was performed on the lateral sides of the right Achilles tendons. The treatment group received oral gavage administrations of 50mg/kg/day of doxycycline for 30 days. After this duration, tissue samples were taken from the site of the injuries, which were then histologically evaluated for alignment of the collagen fibres, inflammation reaction, cellular density, and fibroblastic activity.

Results:

The histological assessment of the tissue samples, revealed significant changes in the repaired tissues of the treatment group in comparison to the sham group; namely more irregularity in the alignment of the collagen fibres, increased cellular density, and increased fibroblastic activity. However, only the alignment of the collagen fibres reached the statistical significance.

Conclusion:

The results of this study indicate that exposure to doxycycline may result in the improvement of repair of the Achilles tendon injuries, especially collagen filament integrity.

Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms

Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon Township, South Korea, using hybrid Machin learning and metaheuristic algorithms, that is, the bee algorithm (Bee), the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and the grey wolf optimizer (GWO), and to compare their predictive accuracy. Based on identified landslide locations, an inventory map was prepared and divided into training and validation data sets (70%/30%). the predicated model outcomes were validated with root mean square error (RMSE), and area under receiver operating characteristic curve (AUC), and pairwise comparison values for the ANFIS, ANFIS-Bee, ANFIS-GWO, SVR, SVR-Bee, and SVR-GWO models were obtained. The area under the curve was obtained with the training and validation data sets. Based on the training data sets, AUC of 80%, 83%, 83%, 69%, 81%, and 80% were obtained for the SVR, SVR-GWO, SVR-Bee, ANFIS, ANFIS-GWO, and ANFIS-Bee models, respectively. For the validation data sets, values of 79%, 82%, 82%, 68%, 79%, and 79%, respectively, were obtained. The SVR-GWO and SVR-Bee models were the most predictive models in terms of constructing the exceptionally focused landslide susceptibility map, with little spatial variation in the highly susceptible classes. Furthermore, the MSE, RMSE, and pairwise comparisons indicated that the SVR-GWO and SVR-Bee models were superior models for this study township. In addition, ANFIS individually was not superior to the ensembles of ANFIS-GWO and ANFIS-Bee for landslide assessment. These landslide susceptibility maps provide a platform for land use planning with an eye toward sustainable development of infrastructure and damage reduction for Icheon Township.

Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia

Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUC_mean = 83.7%, RMSE_mean = 0.236), ANFIS-GA (AUC_mean = 81.62%, RMSE_mean = 0.247), ANFIS-ICA (AUC_mean = 82.12%, RMSE_mean = 0.247), and ANFIS-PSO (AUC_mean = 81.42%, RMSE_mean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUC_mean = 71.8%, RMSE_mean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans.

Multi-hazard probability assessment and mapping in Iran

Several areas of Iran are prone to numerous natural hazards. An effective multi-hazard risk reduction requires analysis of the individual hazards and their interplay. This research develops a multi-hazard probability map for three hazards (i.e. landslides, floods, and earthquakes) for the management of hazard-prone areas in Lorestan Province, Iran, using anew ensemble model named SWARA-ANFIS-GWO. First, based on flood and landslide occurrence maps, hazard-prone areas were identified and sub-divided into two subsets.70% of these locations were randomly chosen to be used for the construction of susceptibility maps, while the remaining 30% of the instances were used to assess the accuracy of the models. Then, eleven factors relating to terrain and land use were selected for the preparation of landslide and flood susceptibility maps. An earthquake map was prepared based on a probabilistic seismic hazard analysis (PSHA). The SWARA method was implemented for weighting contributing factors and evaluating spatial relationships between the three hazards and predisposing factors. Subsequently, the ANFIS approach was used to acquire weights for each value while using a gray Wolf metaheuristic algorithm. Finally, all weight values were further assessed using the MATLAB software. The predicated results from the models were validated with ROC (rate of change) curves. The resulting AUCs (area under the curve) of the validation data indicated accuracies of 84% and 80% for floods and landslides, respectively, and 87% and 82.6%for flood and landslides based on the training data, respectively. Finally, the flood, landslide, and earthquake maps were combined to create a multi-hazard probability map of the Lorestan Province. This multi-hazard map serves as a valuable tool for land use planning and sustainable infrastructure development for the Lorestan Province.

Atmospheric coherence time measurement by four-aperture DIMM defocus velocity technique

In this work, we estimate the atmospheric Fried parameter $ {r_0} $r₀, average wind speed $ \bar v $v¯, and subsequently, atmospheric coherence time $ {\tau _0} $τ₀ by experimental measurement via a four-aperture differential image motion monitor (DIMM) instrument at the Iranian National Observatory (INO) site. The experimental approach is based on the four-aperture DIMM defocus velocity theory, which uses the angle-of-arrival fluctuation measurement of starlight propagation through atmospheric turbulence in the form of a four-spot configuration provided by the four-aperture DIMM telescope. Here, we measure the defocus variance $ \sigma _{{C_4}}^2 $σC₄2 and its velocity variance $ \sigma _{\partial {C_4}/\partial t}^2 $σ∂C₄/∂t2 and use the preceding theory to estimate the atmospheric turbulence parameters. We have implemented the data sampling at the INO site at an altitude of 3600 m above sea level with a 12-inch Meade Cassegrain telescope consisting of a four-aperture mask at its entrance pupil and a fast CCD camera recording short-exposure images with frame rates in the range of 480 fps to 620 fps from the Capella star. The experimental recorded data sets are analyzed and the results compared to those of our simulation and other methods and demonstrate good agreement.

Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm

Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA). A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30%) sets. To perform the proposed framework, the step-wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS-BBO and ANFIS-ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), root-mean-square error (RMSE). The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS-BBO and ANFIS-ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS-BBO and ANFIS-ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition, as regards ANFIS-BBO and ANFIS-ICA, a total areas of 39.30% and 35.39% were classified as highly susceptible to flooding. Therefore, the proposed ensemble framework can be used for flood susceptibility mapping in other sites with similar geo-environmental characteristics for taking measures to manage and prevent flood damages.

A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

Use of a 4-aperture DIMM instrument for atmospheric coherence time estimation: an analytical development

We report on an analytic method to estimate the Fried parameter r₀, average wind speed v¯, and subsequently the atmospheric coherence time τ₀ via a 4-aperture differential image motion monitor (DIMM) instrument. The theory developed here shows that the velocity of defocus aberration is statistically related to atmospheric turbulence parameters which are measured by means of angle of arrival (AA) fluctuations. Then, using the variance of the defocus velocity of four spots and the derived analytic relation, the atmospheric coherence time can be estimated. In parallel to the analytic work, some sequences of a star image with 700 Hz acquisition frequency are considered to simulate the atmospheric defocus and its variations by the 4-aperture DIMM instrument for the first 10 km near the ground in both one- and three-layer atmospheric models. The estimations from the analytic method are found to be in good agreement with the simulation data obtained for starlight propagating through different atmospheric conditions.

Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.

GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models

The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.

Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution

Floods are among Earth's most common natural hazards, and they cause major economic losses and seriously affect peoples' lives and health. This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS. An adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling. The model considers thirteen hydrologic, morphologic and lithologic parameters for the flood susceptibility assessment, and Hengfeng County in China was chosen for the application of the model due to data availability and the 195 total flood events. The flood locations were randomly divided into two subsets, namely, training (70% of the total) and testing (30%). The Step-wise Weight Assessment Ratio Analysis (SWARA) approach was used to assess the relation between the floods and influencing parameters. Subsequently, two data mining techniques were combined with the ANFIS model, including the ANFIS-Genetic Algorithm and the ANFIS-Differential Evolution, to be used for flood spatial modelling and zonation. The flood susceptibility maps were produced, and their robustness was checked using the Receiver Operating Characteristic (ROC) curve. The results showed that the area under the curve (AUC) for all models was >0.80. The highest AUC value was for the ANFIS-DE model (0.852), followed by ANFIS-GA (0.849). According to the RMSE and MSE methods, the ANFIS-DE hybrid model is more suitable for flood susceptibility mapping in the study area. The proposed method is adaptable and can easily be applied in other sites for flood management and prevention.

Comment on "Modeling the electrical conduction in DNA nanowires: Charge transfer and lattice fluctuation theories"

In a recent paper [S. Behnia and S. Fathizadeh, Phys. Rev. E 91, 022719 (2015)10.1103/PhysRevE.91.022719] an analytical approach is proposed for the investigation of the conductivity properties of DNA. The authors use mean Lyapunov exponent methods as the backbone of their approach and try to interpret properties of the system based on its behavior. Their interpretation regarding the change in nature of the mean Lyapunov exponent at the denaturation temperatures and discussions of stability and instability based on the mean Lyapunov exponent method are questioned. Moreover there is misunderstanding between mean Lyapunov exponent and Lyapunov exponent.

All-optical switching between optical bistability and multistability in a defect dielectric medium doped with a multiple quantum well nanostructure

In this paper, optical bistability (OB) and multistability (OM) properties of a defect dielectric slab are studied. A GsAs multiple quantum well nanostructure (MQW) with 17.5 nm GaAs wells and 15 nm Al(0.3) Ga(0.7) barriers is used in the dielectric medium as a defect layer. Therefore, properties of the refractive index of the slab can be changed in the presence of MQWs. It is observed that switching from OB to OM can be obtained by controlling some external parameters. We find that the exciton spin relaxation and thickness of the slab have essential roles in adjusting the OB and OM properties of the probe light through the slab. Moreover, transmission, reflection, and absorption properties of the propagating pulse through the slab are also discussed. We show that the subluminal and superluminal light transmission or reflection can be obtained via amplification of the probe pulse through the medium. We hope that our proposed model may be suitable for the development of nanoscale devices in all-optical quantum information technology.

Pulmonary tuberculosis in migratory nomadic populations: the missing link in Iran's National Tuberculosis Programme

OBJECTIVE

To detect pulmonary tuberculosis (PTB) in migratory nomadic populations in Fars Province, southern Iran.

DESIGN

Cross-sectional study.

RESULTS

In this study, 5506 (82.8%) of a total nomad population of 6650 from 1337 tents were screened for PTB. The mean age was 27.4 ± 18.2 years (range 1-109). Based on clinical symptoms, 141/5506 (2.6%) were identified as TB suspects. One male and three female adult new smear-positive PTB cases were detected, giving an incidence rate of 0.7/1000 population compared to 0.08/1000 in the general population of the region, and 28.4/1000 TB suspects. The median time to onset of symptoms in detected cases was 82.5 days. Tribal stigma against female TB patients was one of the main barriers to appropriate health-seeking behaviour.

CONCLUSIONS

The incidence of smear-positive PTB among migratory nomads is approximately nine-fold higher than in the general population. Active screening of TB in migratory nomads should be integrated into Iran's national TB control programme. The issue of destigmatisation, particularly among female TB patients, should also be addressed.

Mortality and morbidity in patients with X-linked agammaglobulinaemia

BACKGROUND

X-linked agammaglobulinaemia (XLA) is a genetic disorder characterised by a defect in the generation of mature B cells, lack of antibodies production, and susceptibility to recurrent bacterial infections. Understanding of the risk factors responsible for morbidity and mortality in these patients can help in a better management of this disorder. However, there is a lack of specific studies in the literature regarding the morbidity and mortality of XLA patients. This study is designed to evaluate morbidities and mortality and survival rates in Iranian patients with XLA diagnosis during the past 20 years.

METHODS

We have registered the clinical data of the XLA patients and followed them up until 2010. At the time of diagnosis, a four-page questionnaire including complete medical information was filled out for all patients. Follow-up information was gathered either by reviewing the patients' hospital records or regularly visiting the patients.

RESULTS

Among 41 patients, 26.8% died during the follow up period. All of the complications before the initiation of treatment such as pneumonia, otitis media and diarrhoea were reduced after immunoglobulin replacement, except sinusitis and conjunctivitis. There were significant associations between some immunological and clinical characteristics such as lymphocyte subsets, consanguinity marriage and mortality.

CONCLUSION

Despite recent advances in the treatment of XLA, these patients still suffer from severe complications. Associations between poor prognosis and clinical and some immunological characteristics of the patients may help physicians to select poor prognoses patients at higher risk of mortality to develop prevention strategies for them.

Multifractal analysis of thermal denaturation based on the Peyrard-Bishop-Dauxois model

The theory of DNA dynamics is exceedingly complex and not easily explained. In the past two decades, by adapting methods of statistical physics, the dynamics of DNA in contact with a thermal bath is widely studied. In this paper, the thermal denaturation of DNA in the framework of the Peyrard-Bishop-Dauxois (PBD) model through the Rényi dimension is investigated. As a result, the Rényi dimension spectrum of the melting transition process reveals the multifractal nature of the dynamics of the Peyrard-Bishop-Dauxois model. Also, it can be concluded that the Rényi dimension (D(q)) at negative values of q is the characteristic signature of pre-melting and thermal denaturation of DNA. Furthermore, this approach is in excellent agreement with previous experimental studies.

The neuroprotective effects of progesterone on experimental diabetic neuropathy in rats

This study was conducted to investigate the neuroprotective effects of progesterone (PROG) on electrophysiological and histomorphometrical alternation in STZ-induced diabetic neuropathy starting from 4 weeks after the diabetic induction. Thirty adult male Sprague-Dawley rats were randomly divided into 3 groups (with 10 rats in each), control (nondiabetic), untreated diabetic and diabetic PROG-treated. Diabetes was induced in adult male rats by a single dose injection of streptozotocin (STZ, 55 mg kg(-1), i.p.). In the PROG-treated group, 4 weeks after induce of diabetes; rats were treated with PROG (8 mg kg(-1), i.p., every two days) for 6 weeks. Diabetic rats showed a significant reduction in motor nerve conduction velocity (MNCV), mean myelinated fibers (MFs) diameter, axon diameter and myelin sheath thickness in the sciatic nerve after 6 weeks. In the untreated diabetic group endoneurial edema was observed in sciatic nerve and the numbers of MFs with infolding into the axoplasm, irregularity of fibers, myelin sheath with unclear boundaries and alteration in myelin compaction were also increased. Long-term treatment with PROG increased MNCV significantly and prevented all these abnormalities in treated diabetic rats. Our findings indicated that PROG as a therapeutic approach can protect neurophysiologic and histomorphologic alterations induced by peripheral diabetic neuropathy.

Effects of methyl-beta-cyclodextrin and cholesterol on cryosurvival of spermatozoa from C57BL/6 mouse

MBCD and Cholesterol-Loaded-Cyclodextrin (CLC) were examined for their abilities to increase the cryosurvival of C57BL/6 mouse sperm, the main strain of genetically engineered mice. The intactness of acrosome and motility of frozen/thawed spermatozoa were used to monitor cryosurvival. In this experimental study, male mice were randomly divided in 6 groups: control 1, experimental 1, experimental 2, control 2, experimental 3 and experimental 4. In experimental groups 1 and 2 spermatozoa were exposed to 0.75 and 1 mM MBCD and in experimental groups 3 and 4 were exposed to two different concentrations of CLC (1 and 2 mg mL(-1)) over a period of 1 h and were subsequently cryopreserved. Spermatozoa in control 1 group were frozen without any exposure to CLC or MBCD and in control 2 (vehicle), sperms were incubated with 4 mM MBCD. The post-thaw sperms were evaluated for their motility and acrosomal status. The values of the intact acrosome and motility increased significantly with concentration of CLC compared to controls and MBCD experimental groups (p<0.05). These results indicate that cryosurvival of C57BL/6 mouse spermatozoa is enhanced by exposure to MBCD which loaded with cholesterol (CLC) before freezing and MBCD alone can not protect sperm from freeze-thaw damage efficiently compare to CLC.