Regions Of High Susceptibility Research Articles

Characterization of Antimicrobial Activity of NiO Nanoparticles Against Pathogenic Bacteria Isolated from Biofilm of Water Lines in Medical Devices

The major goal of this research was to determine how well NiO nanoparticles (NPs) inhibited the growth of harmful bacteria that were separated from biofilm layers that had developed inside of medical equipment used on a regular basis in hospitals and dental offices. Chia seed extract was previously used in the preparation and physical characterization of NiO nanoparticles. Thirty isolates of Pseudomonas aeruginosa (n=16) and Staphylococcus aureus (n=14) were found in separate swab samples taken from dental office bottles, syringe tubes, spittoons, ventilation machine tubes, and emergency room bottles. Using conventional bacteriological techniques, moist swab samples were inoculated and identified on culture medium, and subsequently antibiotic susceptibility was assessed in accordance with NCCLs. Using two different techniques—well diffusion and microdilution, respectively—the antibacterial activity of NiO nanoparticles against contaminated bacteria was assessed both qualitatively and quantitatively. The antibacterial susceptibility of suggested antibiotics was determined using a Muller Hinton agar (MHA) plate. Antibacterial susceptibility of bacterial isolates revealed considerable resistance to the antibiotics utilized in the current investigation. According to these results, NiO nanoparticles showed the greatest effectiveness when used at a concentration of 30 mg/mL in well diffusion, and the mean inhibition zones for P. aeruginosa and S. aureus isolates ranged from 16 to 20 mm, as opposed to 16 mm for the positive control (ofloxacin disk). In this work, the inhibition zone diameters of NiO nanoparticles were within the region of high susceptibility of positive control (16 mm/ Ofloxacin disk breakpoint). However, the minimum inhibitory concentration (MIC) of NiO nanoparticles was 1.875 mg/ml for 57.1% of S. aureus isolates and 0.468 mg/ml for 31.2% of P. aeruginosa isolates, with no isolates inhibited at high doses. The MIC findings revealed that the lowest concentration of NiO nanoparticles had in vitro efficiency and proved to be a good antibacterial agent against contaminated bacteria.

Leveraging Multi-Echo EPI to Enhance BOLD Sensitivity in Task-based Olfactory fMRI.

Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast relies on gradient echo echo-planar imaging (GE-EPI) to quantify dynamic susceptibility changes associated with the hemodynamic response to neural activity. However, acquiring BOLD fMRI in human olfactory regions is particularly challenging due to their proximity to the sinuses where large susceptibility gradients induce magnetic field distortions. BOLD fMRI of the human olfactory system is further complicated by respiratory artifacts that are highly correlated with event onsets in olfactory tasks. Multi-echo EPI (ME-EPI) acquires gradient echo data at multiple echo times (TEs) during a single acquisition and can leverage signal evolution over the multiple echo times to enhance BOLD sensitivity and reduce artifactual signal contributions. In the current study, we developed a ME-EPI acquisition protocol for olfactory task-based fMRI and demonstrated significant improvement in BOLD signal sensitivity over conventional single-echo EPI (1E-EPI). The observed improvement arose from both an increase in BOLD signal changes through a T 2 * -weighted echo combination and a reduction in non-BOLD artifacts through the application of the Multi-Echo Independent Components Analysis (ME-ICA) denoising method. This study represents one of the first direct comparisons between 1E-EPI and ME-EPI in high-susceptibility regions and provides compelling evidence in favor of using ME-EPI for future task-based fMRI studies.

Using a time-varying LCAR-based strategy to investigate the spatiotemporal pattern of association between short-term exposure to particulate matter and COVID-19 incidence: a case study in the continental USA.

Numerous studies have demonstrated that short-term exposure to particulate matter less than 10 μm (PM10) is positively associated with the COVID-19 incidence. However, no study has investigated the spatiotemporal pattern in this association, which plays important roles in identifying high-susceptibility regions and stages of epidemic. In this work, taking the 49 native states in America as an example, we used an advanced strategy to investigate this issue. First, time-series generalized additive model (GAM) were independently constructed to obtain the state-specific associations between short-term exposure to PM10 and the daily COVID-19 cases from 1 April 2020 to 31 December 2021. Then, a Leroux-prior-based conditional autoregression (LCAR) was used to spatially smoothen the associations. Third, the temporal variation of association and the reasons underlying the spatiotemporal heterogeneity were investigated by incorporating the time-varying GAM into LCAR. Results showed that PM10 was adversely associated with COVID-19 incidence in all the states. On average, a 10 μg/m3 increase of PM10 was associated with a 7.38% (95% CI 5.20-9.64%) increase in COVID-19 cases. A substantial spatial heterogeneity was observed, with strong associations in the middle and northeastern regions and weak associations in the western regions. The temporal trend of association presented a U shape, with the strongest association in the end of 2021. The vaccination rate was examined as a significant effect modifier. Our study provided the first evidence about the spatiotemporal pattern in PM10-COVID-19 associations and suggested that air pollution deserves more attention in the post-pandemic era and in the middle and northeastern regions in America for COVID-19 control and prevention.

Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms

Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources.

Evaluation and analysis of statistical and coupling models for highway landslide susceptibility

Landslides have a great impact on the normal traffic of highway, and maintaining the normal traffic of highway is the foundation of economic development, so landslide susceptibility mapping is very important. In this study, four counties, which locate in the central Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China, are taken as the research region. Based on the 190 historical landslide disaster points in the region, six factors-elevation, slope, aspect, plan curvature, profile curvature and TWI (Topographic Wetness Index) - are finally selected for calculation. A landslide disaster is evaluated by two single models of CF (Certainty Factors) and IV (Information Value) models and four coupling models of CF-AHP (Analytic Hierarchy Process), CF-LR (Logistic Regression), IV-AHP and IV-LR models. The accuracy of the six models is evaluated by the ROC (Receiver Operating Characteristic) curve and the Sridevi Jadi parameters. The IV-AHP model has the highest value of 0.9189, which indicates that the IV-AHP model is more appropriate for landslide disaster assessment in the whole region. In the Sridevi Jadi parameters, the IV model have the highest value of 0.8696, showing that the IV model have the highest accuracy in landslide susceptibility assessment in high- and very high-susceptibility regions.

Regionalization Research of Mountain-Hazards Developing Environments for the Eurasian Continent

Carrying out mountain-hazards developing environment research is helpful for understanding the spatial characteristics of the mountain hazards so as to contribute to mountain-hazards prevention and mitigation and the safety of infrastructures and major projects. In this study, the Eurasian continent was selected as the research area to conduct regionalization research on mountain-hazards developing environments. Using peak ground acceleration (PGA), the annual average precipitation and topographic relief as root factors of mountain-hazards developing environments (known as PPR factors) to represent the characteristics of geological structures, climatic impacts and geomorphology, the regionalization of mountain-hazards developing environments of the Eurasian continent was conducted through the combination of computer automatic classification and later artificial cartographic generalization. Finally, 15 subregions were obtained. A preliminary judgment of the mountain-hazards susceptibility for each region according to the characteristics of PPR factors was made, and nine subregions were identified as the overall high-susceptibility areas of mountain hazards. Based on the analysis of the characteristics of PPR factors and the mountain-hazards susceptibility characteristics in different mountain-hazards developing environment subregions, the high susceptibility regions of mountain hazards could be divided into three types: arid and active-geologic regions, humid and active-geologic regions, and humid and inactive-geologic regions. We hope that our research provides support for subsequent works of more specific and reasonable mountain-hazards susceptibility, hazard and risk models construction for different types of mountain-hazards developing environments.

Landslide Susceptibility Assessment Considering Spatial Agglomeration and Dispersion Characteristics: A Case Study of Bijie City in Guizhou Province, China

Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities.

Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flood are different for every watershed as they depend upon the characteristics of each watershed. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of ensemble model showed 99% accuracy in training and 98% accuracy in testing datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.

Comparison of Tree-Structured Parzen Estimator Optimization in Three Typical Neural Network Models for Landslide Susceptibility Assessment

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.

Drought detection and declaration in India

Drought is a complex, multifaceted, and one of the most widespread natural disasters causing an economic loss of billions of dollars. Drought declaration is crucial since it signifies the beginning of states's response to drought. India's current drought management policy is specified in the Manual for Drought Management 2016. While the manual provides comprehensive guidelines to states and regional Drought Monitoring Centres (DMC's) for monitoring and declaration of droughts, assessing the current drought detection framework and its suitability in different regions has been lacking. Using gridded products of precipitation, soil moisture, and remote sensing indicators, we examine the differences between detected and declared droughts for all the districts in India during the 2000–2017 period. Comparison of detected and declared droughts show a higher probability of detection (POD) in central India. In contrast, lower POD was found in regions of high drought susceptibility like Rajasthan, Andhra Pradesh and Karnataka. Large differences between the detected and declared droughts were identified in the irrigated regions of Punjab and Gangetic Plain. The use of Solar-induced chlorophyll Fluorescence (SIF) instead of Normalized Difference Vegetation Index (NDVI) as a remote sensing indicator improved drought detection (based on POD and false alarm ratio, FAR) in central and western parts of India. The framework specified in the drought manual detects most of the major droughts that affected a large part of the country. However, regional droughts in Rajasthan, Gujarat, and Karnataka are frequently missed. Finally, we highlight the limitations in the existing drought monitoring framework and opportunities for its enhancement.

Landslide susceptibility evolution in an area affected by the 2015 Nepal earthquakes, derived from multitemporal landslide inventories

The Mw 7.8 and Mw 7.3 Nepal earthquakes in 2015 triggered >47,000 landslides and significantly enhanced landslide activity in the affected region in the following years, likely due to a drop in rock strength of the slopes there. This work analyzed the evolution of landslide susceptibility of an area close to the Zhangmu border crossing on the Araniko Highway in Nepal, which was seriously affected by the mainshock, aftershock, and monsoon rainfalls. Using eight landslide inventories we constructed to register landslides triggered by monsoon rainfalls before 2015, the mainshock, the aftershock, and the monsoon rainfalls in 2015–2019, eight susceptibility assessment models were built using the logistic regression method considering 11 control factors of landslides. The changes in the high-susceptibility zones before, during, and after the earthquakes were examined. The backward prediction abilities of the models were checked by taking landsliding and nonlandsliding data in the following years. The results show that the best susceptibility mapping results are obtained based on landslides triggered by the mainshock and the aftershock, which both have successful prediction rates >87%. The high-susceptibility regions have moved from the slope surfaces to channels since 2015, suggesting that transportation of residual debris induced by the strong ground motion resulted from erosion related to the strong post-seismic rainfall and the rivers. A series of attempts using susceptibility models to backward predict landslides in the following years demonstrate that the combination of the susceptibility results of pre-seismic and mainshock-triggered landslides, which have a higher prediction accuracy, has prediction rates >70% until 2018. This study not only will further the understanding of post-seismic landsliding effects, but also will aid in efforts toward future seismic landslide mitigation and post-seismic reconstruction.

Spatial dependency and the role of local susceptibility for velocity selective arterial spin labeling (VS‐ASL) relative tagging efficiency using accelerated 3D radial sampling with a BIR‐8 preparation

Velocity selective arterial spin labeling (VS-ASL) is a promising approach for non-contrast perfusion imaging that provides robustness to vascular geometry and transit times; however, VS-ASL assumes spatially uniform tagging efficiency. This work presents a mapping approach to investigate VS-ASL relative tagging efficiency including the impact of local susceptibility effects on a BIR-8 preparation. Numerical simulations of tagging efficiency were performed to evaluate sensitivity to regionally varying local susceptibility gradients and blood velocity. Tagging efficiency mapping was performed in susceptibility phantoms and healthy human subjects (N = 7) using a VS-ASL preparation module followed by a short, high spatial resolution 3D radial-based image acquisition. Tagging efficiency maps were compared to 4D-flow, B1 , and B0 maps acquired in the same imaging session for six of the seven subjects. Numerical simulations were found to predict reduced tagging efficiency with the combination of high blood velocity and local gradient fields. Phantom experiments corroborated numerical results. Relative efficiency mapping in normal volunteers showed unique efficiency patterns depending on individual subject anatomy and physiology. Uniform tagging efficiency was generally observed in vivo, but reduced efficiency was noted in regions of high blood velocity and local susceptibility gradients. We demonstrate an approach to map the relative tagging efficiency and show application of this methodology to a novel BIR-8 preparation recently proposed in the literature. We present results showing rapid flow in the presence of local susceptibility gradients can lead to complicated signal modulations in both tag and control images and reduced tagging efficiency.

Land Prioritization: An Approach to the Effective Environmental Planning of Hydrographic Basins.

Hydrographic basins are the fundamental unit for the effective territorial planning. However, areas of higher and lesser susceptibility to degrading processes can occur in the same region, where different actions are required to preserve natural resources, as soil and water. In this context, this study aimed to propose a prioritization model to guide the territorial management in hydrographic basins. The study was conducted in the Tarumã River basin, embedded in the southwest region of the São Paulo State, Brazil. First, the basin was compartmentalized into planning units, based on the delimitation and grouping of small watersheds. In each unit, seven morphometric parameters were calculated. The relationships among the parameters were evaluated by principal component analysis, from which the parameters were weighted. It resulted in an index expressing the environmental fragility of the planning units. Among the planning units, 20% present very-high priority, only 5% very-low priority, and 75% low, moderate, and high priority. Units of higher priority are commonly at the basins' headwaters, where linear channels and steeper slopes are concentrated. Lower priority units present low relief and hydrography conditions that do not favor high-intensity erosive processes. The proposed method is effective in identifying regions of high susceptibility to degradation according to morphometric parameters, which should be prioritized on the basins' territorial management. Since these parameters can be easily calculated, it can be presumed that the results could assist the environmental planning and the optimization of natural resources management within different hydrographic basins.

QSMART: Quantitative susceptibility mapping artifact reduction technique

Purpose: Quantitative susceptibility mapping (QSM) is a novel MR technique that allows mapping of tissue susceptibility values from MR phase images. QSM is an ill-conditioned inverse problem, and although several methods have been proposed in the field, in the presence of a wide range of susceptibility sources, streaking artifacts appear around high susceptibility regions and contaminate the whole QSM map. QSMART is a post-processing pipeline that uses two-stage parallel inversion to reduce the streaking artifacts and remove banding artifact at the cortical surface and around the vasculature. Method: Tissue and vein susceptibility values were separately estimated by generating a mask of vasculature driven from the magnitude data using a Frangi filter. Spatially dependent filtering was used for the background field removal step and the two susceptibility estimates were combined in the final QSM map. QSMART was compared to RESHARP/iLSQR and V-SHARP/iLSQR inversion in a numerical phantom, 7T in vivo single and multiple-orientation scans, 9.4T ex vivo mouse data, and 4.7T in vivo rat brain with induced focal ischemia. Results: Spatially dependent filtering showed better suppression of phase artifacts near cortex compared to RESHARP and V-SHARP, while preserving voxels located within regions of interest without brain edge erosion. QSMART showed successful reduction of streaking artifacts as well as improved contrast between different brain tissues compared to the QSM maps obtained by RESHARP/iLSQR and V-SHARP/iLSQR. Conclusion: QSMART can reduce QSM artifacts to enable more robust estimation of susceptibility values in vivo and ex vivo.

Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging.

PurposeTo develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging.MethodsThe proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects.ResultsThe unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI’s performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(−1%); MEDI + 2%(−11%) and then iSWIM −5%(−10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature.ConclusionThis study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts.

Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping.

SummaryAdaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously proposed regularized total field inversion methods, improvements were demonstrated in phantoms and subjects without and with hemorrhages. This algorithm, named TFIR, demonstrates the lowest error in numerical and gadolinium phantom datasets. In COSMOS data, TFIR performs well in matching ground truth in high-susceptibility regions. For patient data, TFIR comes close to meeting the quality of the reference local field method and outperforms other total field techniques in both clinical scores and shadow reduction.

Magnetic prospecting and geochemical analysis for the mineral exploration of Mali-Dera deposits, Kohistan Island Arc-Pakistan

Chromite deposits have long been reported and exploited in Northern Pakistan as a result of the ongoing convergence of Indian and Asian plates. This research work is a study of one such prospect. We used geophysical and geochemical tools such as magnetic susceptibility at seventy-seven observation stations along with fifteen rock samples collected for geochemical studies. Our geochemical analysis reveals a very high value of Fe, Mn, Cu, and Ni in regions of high magnetic susceptibility indicating the possibility of the presence of Magnetite, Chondrite, Copper-pyrites, and Pentlandite Ores. In addition, we also conducted the correlations analysis to investigate the potential association between various geochemical variables including trace elements to evaluate the potential of the ores for economic minerals others than chromite. Our PCs analysis result presents a clear picture that most of the minerals such as Fe, Ni, Co, Cd, Mg, Mn, and Pd are produced by the same source. Our various analyses are in perfect agreement with previous literature indicating the presence of economical mining of Chromite.

Investigating the accuracy and precision of TE-dependent versus multi-echo QSM using Laplacian-based methods at 3 T.

Multi-echo gradient-recalled echo acquisitions for QSM enable optimizing the SNR for several tissue types through multi-echo (TE) combination or investigating temporal variations in the susceptibility (potentially reflecting tissue microstructure) by calculating one QSM image at each TE (TE-dependent QSM). In contrast with multi-echo QSM, applying Laplacian-based methods (LBMs) for phase unwrapping and background field removal to single TEs could introduce nonlinear temporal variations (independent of tissue microstructure) into the measured susceptibility. Here, we aimed to compare the effect of LBMs on the QSM susceptibilities in TE-dependent versus multi-echo QSM. TE-dependent recalled echo data simulated in a numerical head phantom and gradient-recalled echo images acquired at 3 T in 10 healthy volunteers. Several QSM pipelines were tested, including four distinct LBMs: sophisticated harmonic artifact reduction for phase data (SHARP), variable-radius sophisticated harmonic artifact reduction for phase data (V-SHARP), Laplacian boundary value background field removal (LBV), and one-step total generalized variation (TGV). Results from distinct pipelines were compared using visual inspection, summary statistics of susceptibility in deep gray matter/white matter/venous regions of interest, and, in the healthy volunteers, regional susceptibility bias analysis and nonparametric tests. Multi-echo versus TE-dependent QSM had higher regional accuracy, especially in high-susceptibility regions and at shorter TEs. Everywhere except in the veins, a processing pipeline incorporating TGV provided the most temporally stable TE-dependent QSM results with an accuracy similar to multi-echo QSM. For TE-dependent QSM, carefully choosing LBMs can minimize the introduction of LBM-related nonlinear temporal susceptibility variations.

A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm

The choice of model parameters in landslide susceptibility mapping makes a major determinant of model accuracy. The purpose of this study is to optimize the hyperparameters based on a Bayesian optimization algorithm, and to obtain a high accuracy random forest landslide susceptibility evaluation model. The research steps are detailed as follows. Firstly, taking a typical landslide prone mountainous area as an example, 16 conditioning factors, such as elevation, annual average rainfall, distance from roads, distance from buildings and so on, were preliminarily selected as the conditioning factors of landslide susceptibility. Combined with 1520 historical landslide events, a geospatial database was established with 30 m resolution. Secondly, the geospatial data sample set was constructed by random sampling according to ratio of historical landslides and non-landslides of 1:10. Based on the whole sample set, the random forest model adopted the Bayesian optimization algorithm to optimize the hyperparameters. Next, the optimal hyperparameters were selected to be trained to get the evaluation model of landslide susceptibility. In addition, they were carried out the analysis of landslide susceptibility mapping for the whole study area. After that, the recursive feature elimination method was used to screen out the dominant conditioning factors that can explain the degree of landslide susceptibility. The results indicated that the area under curve (AUC) values of receiver operating characteristic (ROC) curve in training data set, verification data set and regional simulation were 0.95, 0.87 and 0.93, respectively. 65% of the historical landslides fell between the high susceptibility and very high susceptibility regions, which made up <20% of the research area. The model was in good agreement to the distribution characteristics of historical landslides in the study area. We noted that all the three recent landslides with impact on the study area occurred at the locations predicted by the model to have high or very high susceptibility in terms of typical landslides in the near future. As for conditioning factors, the contribution related to human activities accounted for a large proportion. In conclusion, an evaluation model with high precision for random forest landslide susceptibility can be built based on hyperparameter optimization with Bayesian optimization algorithm. Simultaneously, using recursive feature elimination method, a random forest landslide susceptibility model with fewer dominant conditioning factors and guaranteed evaluation accuracy can also be built to save the running time and input data resources of the model.

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility

Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.