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Landslide susceptibility maps are valuable sources for disaster mitigation works and future investments of local authorities in unstable hazard-prone areas. However, there are limitations and uncertainties inherent in landslide susceptibility assessment. For this purpose, many methods have been suggested and applied in the literature, which are generally categorized as bivariate and multivariate. Here, in this paper, the most popular and widely used multivariate [support vector regression (SVR), logistic regression (LR) and decision tree (DT)] and bivariate methods [frequency ratio (FR), weight of evidence (WOE) and statistical index (SI)] were compared with respect to their performances in landslide susceptibility modeling problem. Duzkoy district of Trabzon Province was selected due to its unique topographical and lithological characteristics, magnifying shallow landslide risk potential. Slope, lithology, land cover, aspect, normalized difference vegetation index, soil thickness, drainage density, topographical wetness index and elevation were employed as landslide occurrence factors. Accuracy measures based on confusion matrix (i.e., overall accuracy and Kappa coefficient) and receiver operating characteristic (ROC) curve were employed to compare the performances of the methods. Furthermore, McNemar’s test was employed to analyze the statistical significance of differences in method performances. The results indicated that multivariate approaches (i.e., SVR, LR and DT) outperformed the bivariate methods (i.e., FR, SI and WOE) by about 13 %. Within the multivariate approaches, SVR method performed the best with the highest accuracy, while FR method was the most effective and accurate bivariate method. Interpretation of AUC values and the McNemar’s statistical test results revealed that the SVR method was superior in modeling landslide susceptibility compared with the other multivariate and bivariate methods.

Functional magnetic resonance imaging (fMRI) based on the blood oxygenation level-dependent (BOLD) contrast has become an indispensable tool in neuroscience. However, the BOLD signal is nonlocal, lacking quantitative measurement of oxygenation fluctuation. This preclinical study aimed to introduced functional quantitative susceptibility mapping (fQSM) to complement BOLD-fMRI to quantitatively assess the local susceptibility and venous oxygen saturation (SvO2). Rats were subjected to a 5 Hz flashing light and the different inhaled oxygenation levels (30% and 100%) were used to observe the venous susceptibility to quantify SvO2. Phase information was extracted to produce QSM, and the activation responses of magnitude (conventional BOLD) and the QSM time-series were analyzed. During light stimulation, the susceptibility change of fQSM was four times larger than the BOLD signal change in both inhalation oxygenation conditions. Moreover, the responses in the fQSM map were more restricted to the visual pathway, such as the lateral geniculate nucleus and superior colliculus, compared with the relatively diffuse distributions in the BOLD map. Also, the calibrated SvO2 was approximately 84% (88%) when the task was on, 83% (87%) when the task was off during 30% (and during 100%) oxygen inhalation. This is the first fQSM study in a small animal model and increases our understanding of fQSM in the brains of small animals. This study demonstrated the feasibility, sensitivity, and specificity of fQSM using light stimulus, as fQSM provides quantitative clues as well as localized information, complementing the defects of BOLD-fMRI. In addition to neural activity, fQSM also assesses SvO2 as supplementary information while BOLD-fMRI dose not. Accordingly, the fQSM technique could be a useful quantitative tool for functional studies, such as longitudinal follow up of neurodegenerative diseases, functional recovery after brain surgery, and negative BOLD studies.

The mountainous region of the Hunza River watershed basin, especially along the Karakorum highway, and also known as a third pole for the high accumulation of glaciers, which leads to huge devastating landslides occurring every year. Landslide susceptibility mapping was carried out using two deep machine learning techniques (DeeplabV3+ & universal network U-Net) and two statistical models (Intuitionistic Fuzzy divergence IF-D & Frequency ratio FR). The landslide susceptibility mapping is conducted using landslide inventory data and twelve conditional factors. The landslide susceptibility maps obtained from the two statistical models were compared with those generated by two deep machine learning models based on prediction accuracy measures, such as the Area Under the Curve (AUC) and Seed Cell Area Index (SCAI). The Success Rate Curve (SRC) was obtained using the training dataset, and the AUC values for the four models were as follows: 76.9% for IF-D, 76.9% for FR, 80.4% for DeeplabV3+, and 76.3% for U-Net. In terms of the Prediction Rate Curve (PRC) obtained from the validation dataset, the AUC values were found to be 80.8% for IF-D, 80.8% for FR, 81% for DeeplabV3+, and 77.8% for U-Net. To assess the classification ability of the models, the Seed Cell Area Index (SCAI) test was conducted. The results indicated that the SCAI (D-value) was 7.3 for U-Net, 10 for DeeplabV3+, 7.6 for IF-D, and 9.1 for FR. Overall, the findings revealed that DeeplabV3+ exhibited the highest prediction accuracy and classification ability, making it the most suitable choice for landslide susceptibility mapping in the relevant study area.

This study aimed to evaluate an intracerebral hemorrhage (ICH) model using quantitative susceptibility mapping (QSM) and chemical exchange saturation transfer (CEST) with preclinical 7T-magnetic resonance imaging (MRI) and determine the potential of amide proton transfer-CEST (APT-CEST) for use as a biomarker for the early detection of ICH. Six Wistar male rats underwent MRI, and another six underwent histopathological examinations on postoperative days 0, 3, and 7. The ICH model was created by injecting bacterial collagenase into the right hemisphere of the brain. QSM and APT-CEST MRI were performed using horizontal 7T-MRI. Histological studies were performed to observe ICH and detect iron deposition at the ICH site. T2-weighted images (T2WI) revealed signal changes associated with hemoglobin degeneration in red blood cells, indicating acute-phase hemorrhage on day 0, late-subacute-phase hemorrhage on day 3, and chronic-phase hemorrhage on day 7. The susceptibility alterations in each phase were detected using QSM. QSM and Berlin blue staining revealed hemosiderin deposition in the chronic phase. APT-CEST revealed high magnetization transfer ratios in the acute phase. Abundant mobile proteins and peptides were observed in early ICH, which were subsequently diluted. APT-CEST imaging may be a reliable noninvasive biomarker for the early diagnosis of ICH.

Flood risk modeling is essential for effective disaster mitigation, particularly in flood-prone areas such as Hulu Sungai Utara Regency, Indonesia. This study leverages Google Earth Engine (GEE) to integrate multi-source satellite data and machine learning techniques for flood susceptibility mapping. Key geospatial variables, including the Normalized Difference Vegetation Index (NDVI), elevation, distance from rivers, and the Topographic Position Index (TPI), were analyzed using a weighted overlay method within GEE. A supervised classification approach was employed to enhance accuracy, and validation was performed using historical flood event data. The results indicate that 51.66% (47,875.86 ha) of the study area falls into the low-risk category, 42.90% (39,763.08 ha) is at moderate risk, and 5.44% (5,040.36 ha) is highly susceptible to flooding. This study highlights the advantages of GEE in large-scale flood risk assessments by enabling real-time processing, high computational efficiency, and seamless integration of geospatial datasets. The findings provide critical insights for local governments and disaster management agencies to develop proactive flood mitigation strategies.

Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions. Plain Language Summary Floods have the potential to negatively affect human wellbeing, infrastructures and the natural environment. Identifying regions prone to flooding is essential in avoiding such catastrophes. In the current study, standard and optimized convolutional neural network models are used to generate maps identifying the regions of Sweden with highest probability of flooding. Various topographic, hydrological, and anthropogenic factors are taken into account for the modeling. The analysis reveals numerous areas in Sweden prone to flooding, especially in the northern, central, and southeastern parts of the country. Malmo (the third largest city in Sweden) and some areas of Stockholm (capital) are the cities most susceptible to flooding. Additionally, considerable extensions of Sweden's roadways and railways might be impacted by floods. Accurate flood susceptibility mapping is required to assist policymakers and urban planners in implementing measures aimed at mitigating flood vulnerability. Furthermore, the findings show the ability of deep learning models for detecting flood‐susceptible areas, which can be implemented worldwide to improve flood management strategies and protect lives. Key Points Deep learning model of convolutional neural network (CNN) was optimized and improved with gray wolf optimizer (GWO) and imperialist competitive algorithm (ICA) to detect flood‐prone areas During both the training and testing phases, the CNN‐GWO model demonstrated superior performance compared to CNN‐ICA and the standalone CNN model The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra—tissue with low perfusion yet intact cellular integrity—making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders.

Quantitative susceptibility mapping (QSM) is used to obtain quantitative magnetic susceptibility maps of materials from magnitude and phase images acquired by three-dimensional gradient-echo using inverse problem-solving. Few preclinical studies have evaluated the intracerebral hemorrhage (ICH) model and asymmetric iron deposition. We created a rat model of ICH and compared QSM and conventional magnetic resonance imaging (MRI) during the longitudinal evaluation of ICH. Collagenase was injected in the right striatum of 12-week-old Wistar rats. QSM and conventional MRI were performed on days 0, 1, 7, and 28 after surgery using 7-Tesla MRI. Susceptibility, normalized signal value, and area of the hemorrhage site were statistically compared during image analysis. Susceptibility decreased monotonically up to day 7 but increased on day 28. Other imaging methods showed a significant increase in signal from day 0 to day 1 but a decreasing trend after day 1. During the area evaluation, conventional MRI methods showed an increase from day 0 to day 1; however, decreases were observed thereafter. QSM showed a significant increase from day 0 to day 1. The temporal evaluation of ICH by QSM suggested the possibility of detecting of asymmetric iron deposition for normal brain site.

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain. [Display omitted]