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Alzheimer's disease (AD) is the most common type of dementia, characterized by early memory impairments and gradual worsening of daily functions. AD-related pathology, such as amyloid-beta (Aβ) plaques, begins to accumulate many years before the onset of clinical symptoms. Predicting risk for AD via related pathology is critical as the preclinical stage could serve as a therapeutic time window, allowing for early management of the disease and reducing health and economic costs. Current methods for detecting AD pathology, however, are often expensive and invasive, limiting wide and easy access to a clinical setting. A non-invasive, cost-efficient platform, such as computerized cognitive tests, could be potentially useful to identify at-risk individuals as early as possible. In this study, we examined the diagnostic value of an episodic memory task, the mnemonic discrimination task (MDT), for predicting risk of cognitive impairment or Aβ burden. We constructed a random forest classification algorithm, utilizing MDT performance metrics and various neuropsychological test scores as input features, and assessed model performance using area under the curve (AUC). Models based on MDT performance metrics achieved classification results with an AUC of 0.83 for cognitive status and an AUC of 0.64 for Aβ status. Our findings suggest that mnemonic discrimination function may be a useful predictor of progression to prodromal AD or increased risk of Aβ load, which could be a cost-efficient, noninvasive cognitive testing solution for potentially wide-scale assessment of AD pathological and cognitive risk.

Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at −52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies. [ABSTRACT FROM AUTHOR]

Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue. [ABSTRACT FROM AUTHOR]

Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km2 to 49.5 km2 in Umhlabuyalingana; 54 km2 to 1.6 km2 in Joe Morolong; and 254.6 km2 to 7.4 km2 in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas. [ABSTRACT FROM AUTHOR]

El Fierro intrusive body is one of the bodies that compose the La Jovita–Las Aguilas mafic–ultramafic belt, located in the Sierra Grande de San Luis, Argentina. The units of this belt carry a base metal sulfide (BMS) mineralization and platinum group minerals (PGM). The macroscopic description of mafic and ultramafic rocks, as is usually done by the mining exploration companies, leads to an imprecise modal classification of the rocks. In this study, we develop a random forest-based prediction model, which uses geochemical parameters to classify mafic and ultramafic rocks intercepted by drill cores. This model showed an accuracy of between 86% and 94%, and an f1_score of 96%. Random forest classification is a widely adopted Machine Learning approach to construct predictive models across various research domains. However, as models become more complex, their interpretation can be considerably difficult. To interpret the model results, we use both global and local perspectives, incorporating the SHAP (SHapley Additive exPlanations) method. The SHAP technique allows us to analyze individual samples using force plots, and provides a measure of the importance of each geochemical input attribute in the model output. As a result of analyzing the contribution of each input feature to the model, the three variables with the highest contributions were identified in the following order: Al2O3, MgO, and Sr.

Benggang (a fragmented erosion landform) have emerged as crucial factors restraining regional economic development, social stability, and ecological conservation due to their suddenness, explosiveness, and unpredictability. Monitoring the dynamic changes in distribution density, degree of development, and influencing factors of Benggang in large-scale natural environments can constitute preliminary references for studying the mechanisms, patterns, and influencing factors of Benggang in the natural environment, as well as providing insights for the coordination of regional socio-economic development and ecological environment governance. In this study, Sentinel-1/2 satellite constellation remote sensing images and other auxiliary data from 2016 to 2022 were combined with machine learning methods to identify Benggang erosion and measure the influencing factors of Benggang erosion in the middle and upper reaches of the Han River. The results indicate that: 1) The overall accuracy of the classification model in this study is 84%, with a Kappa coefficient of 0.8. The user's and producer's accuracy of Benggang erosion identification both exceed 95%, and their FScore is 0.97. However, smaller-scale Benggang erosion (< 100 m2) is easily confused with grassland and sparse vegetation, thereby reducing the Benggang identification accuracy. 2) Currently, the total area of Benggang erosion in the middle and upper reaches of the Han River is 435.5 km2. There are significant differences in Benggang erosion areas across counties, cities, and districts, with varying annual trends. In terms of spatial distribution, the Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 199.2, 70.1, 57.7, 50.1, 35.4, 14.9, 3.9, and 1.6 km2, respectively. In terms of time change trends, the average annual rates of change of Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 16.29, 6.51, 5.31, 4.94, 3.56, 1.39, 0.42, and 0.18 km2/a, respectively. 3) The occurrence probability of Benggang in the study area is significantly correlated (P < 0.001) with elevation, slope, vegetation coverage, geological type, population density, atmospheric pressure, rainfall, meridional wind speed, zonal wind speed, and wind speed. Within a certain range of variation in the influencing factors, elevation, slope, geological type, atmospheric pressure, meridional wind speed, zonal wind speed, and wind speed have a positive impact on Benggang occurrence probability in the study area, while vegetation coverage, population density, and rainfall have a negative impact on the occurrence of Benggang erosion. Furthermore, geological type, slope, atmospheric pressure, elevation, vegetation cover, population density and wind speed are the main influencing factors for the occurrence of Benggang in the study area. Therefore, strengthening the monitoring of these seven variables is necessary to enhance early warning capabilities for geological disasters due to Benggang in the study area.

Objective: To develop a classification framework based on random forest (RF) modeling to outline the declarative memory profile of patients with panic disorder (PD) compared to a healthy control sample. Methods: We developed RF models to classify the declarative memory profile of PD patients in comparison to a healthy control sample using the Rey Auditory Verbal Learning Test (RAVLT). For this study, a total of 299 patients with PD living in the city of Rio de Janeiro (70.9% females, age 39.9 ± 7.3 years old) were recruited through clinician referrals or self/family referrals. Results: Our RF models successfully predicted declarative memory profiles in patients with PD based on RAVLT scores (lowest area under the curve [AUC] of 0.979, for classification; highest root mean squared percentage [RMSPE] of 17.2%, for regression) using relatively bias-free clinical data, such as sex, age, and body mass index (BMI). Conclusions: Our findings also suggested that BMI, used as a proxy for diet and exercises habits, plays an important role in declarative memory. Our framework can be extended and used as a prospective tool to classify and examine associations between clinical features and declarative memory in PD patients.

In recent years, the movement analysis is commonly used to track the risk of injury and strengthen the efficiency of athlete performance. However, most of these devices are costly, found mainly in experimental settings, which analyze a few samples of each movement. In this paper, a new ambulatory movement analysis system with wearable sensors for the precise measurement of all athleteꞌs movements in an actual training scenario is introduced. Initially, an adaptive method categorizes a broad variety of training behaviors by the Differential Finite element Transformation method (DFET) along with a Random Forest Classification (DFET- RF) method. Secondly, the measurement of the absolute identities of the wearable sensor devices placed on the knee bone and pelvic bone is performed with a discrete gradient descent (DGD) algorithm, which calculates a range of motion-extension between the knee and hip angle. Finally, the edge computing is used to process data in real-time and reduce the latency of the system. The next version of wearable technology will know the person's identity, individually - not just physically and actively in a much more significant way; a wearable device that tells the world about the identity of the person and the connected devices. The knee flexion is greater at the terminal swing period (85%) and hip flexion (68%). The development of future device capabilities is based on verification. Once a wearable can validate the wearer's identity, several other things about their activities can be regulated. Such angles are automatically extracted for each movement during jogging at the acceleration of the sacrum effect. Besides, standard data has developed and is used to decide whether the movement methodology for a person varied from the standard data to classify potential instances due to injury. This is done by a gradient-shift recording technique for the joint-related angle details. For precise and automated assessment of athletic movements, effective activity measurement in various uncontrolled conditions for both injuries processing performance progress, the suggested system has been discussed in this paper.

Forest fires are one of the most common natural disasters and public crisis events, presenting a serious threat to the ecosystem and human security. The traditional forest fire monitoring is time-consuming and inaccurate, but nighttime light remote sensing is enhancing the efficiency and speed of forest fire detection. Thus, this study focused on forest fire detection in Southwest China that was affected by fires in 2021 and 2022 using daily remote sensing nighttime light data from the black marble product VNP46A2 of the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite. Multiple features were developed based on the temporal features of day-level nighttime light radiances, which were divided into black pixels, stable light pixels, and forest fire pixels using the random forest classification model. To verify the accuracy, a confusion matrix was constructed to calculate the accuracy of the forest fire identification results by combining multisource data, and the results were compared with the results of different machine learning models and fire products. Moreover, the spatiotemporal distribution of forest fires was analyzed using trend analysis and nearest-neighbor analysis. Results show that the overall accuracy of pixel classification was over 92% in both years, with forest fire pixels classified with user's accuracy exceeding 99%. The forest fires were mainly dispersed and concentrated around the stable light pixels 400–700 m, and the distribution in different regions showed obvious spatial heterogeneity. Compared with 2021, the distribution of forest fires decreased significantly in 2022. This study can support the future management and protection of forest resources and the prediction of fire disasters. The dataset (2021 and 2022) of forest fires in Southwest China is available free of charge.1

Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area. HIGHLIGHTS The combination of Sentinel-1 dual-polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests is shown to provide better classification results of mangroves.; Machine learning algorithms such as random forest and support vector machine are used for classification, with a comparison of their performance.; This study emphasizes the importance of feature selection for the accurate classification of mangrove forests.;

Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management. [ABSTRACT FROM AUTHOR]

Benggang (a fragmented erosion landform) have emerged as crucial factors restraining regional economic development, social stability, and ecological conservation due to their suddenness, explosiveness, and unpredictability. Monitoring the dynamic changes in distribution density, degree of development, and influencing factors of Benggang in large-scale natural environments can constitute preliminary references for studying the mechanisms, patterns, and influencing factors of Benggang in the natural environment, as well as providing insights for the coordination of regional socio-economic development and ecological environment governance. In this study, Sentinel-1/2 satellite constellation remote sensing images and other auxiliary data from 2016 to 2022 were combined with machine learning methods to identify Benggang erosion and measure the influencing factors of Benggang erosion in the middle and upper reaches of the Han River. The results indicate that: 1) The overall accuracy of the classification model in this study is 84%, with a Kappa coefficient of 0.8. The user's and producer's accuracy of Benggang erosion identification both exceed 95%, and their FScore is 0.97. However, smaller-scale Benggang erosion (< 100 m2) is easily confused with grassland and sparse vegetation, thereby reducing the Benggang identification accuracy. 2) Currently, the total area of Benggang erosion in the middle and upper reaches of the Han River is 435.5 km2. There are significant differences in Benggang erosion areas across counties, cities, and districts, with varying annual trends. In terms of spatial distribution, the Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 199.2, 70.1, 57.7, 50.1, 35.4, 14.9, 3.9, and 1.6 km2, respectively. In terms of time change trends, the average annual rates of change of Benggang erosion areas in Wuhua County, Xingning City, Dapu County, Fengshun County, Meixian District, Pingyuan County, Jiaoling County, and Meijiang District are 16.29, 6.51, 5.31, 4.94, 3.56, 1.39, 0.42, and 0.18 km2/a, respectively. 3) The occurrence probability of Benggang in the study area is significantly correlated (P < 0.001) with elevation, slope, vegetation coverage, geological type, population density, atmospheric pressure, rainfall, meridional wind speed, zonal wind speed, and wind speed. Within a certain range of variation in the influencing factors, elevation, slope, geological type, atmospheric pressure, meridional wind speed, zonal wind speed, and wind speed have a positive impact on Benggang occurrence probability in the study area, while vegetation coverage, population density, and rainfall have a negative impact on the occurrence of Benggang erosion. Furthermore, geological type, slope, atmospheric pressure, elevation, vegetation cover, population density and wind speed are the main influencing factors for the occurrence of Benggang in the study area. Therefore, strengthening the monitoring of these seven variables is necessary to enhance early warning capabilities for geological disasters due to Benggang in the study area. [ABSTRACT FROM AUTHOR]

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In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water. [ABSTRACT FROM AUTHOR]

Questions: Previous studies have shown that mire vegetation responds to fine‐scale differences in topography; however, very few studies have examined the relationship between multiple topographic conditions and vegetation distribution patterns by using topographic information with a resolution finer than microtopographic patches of sub‐meter scale in mires. Is it possible to predict vegetation distribution patterns in a mire using topographic information derived from drone imagery? How do topographic conditions affect the emergence of vegetation types? Location: 3 km2 of sloping mire in eastern Hokkaido, northern Japan. Methods: Digital surface models (DSMs) and orthomosaics were created using an affordable drone (Phantom3 advanced) and photogrammetry. We calculated 23 variables reflecting topographic conditions from DSMs. The locations of four vegetation types (Moliniopsis japonica–Eriophorum vaginatum, ME; Moliniopsis japonica–Carex lasiocarpa, MC; Sphagnum fuscum–Ledum palustre, SL; and Carex lyngbyei–Thelypteris palustris, CT) were recorded by field survey and orthomosaics. Random forest models that predicted vegetation types from topographic variables were created and we evaluated the prediction accuracy, importance of variables and effect of variables on the occurrence probability of the vegetation types. Results: All predictive indicators (recall, precision, overall accuracy, kappa) were very high (>0.9). Important variables for modeling were altitude, relative height to the surroundings, slopes at a scale of several 10 m, soil wetness index and terrain ruggedness. Each vegetation type appeared under different topographic conditions; altitude was a key factor in ME and MC, and slope in CT. SL showed patchy distribution predictions on hummocks. Conclusions: Vegetation distribution in the mire was strongly related to topographic information based on the DSMs. Analysis of the variable effects indicates that the emergence of the vegetation types was explained by a combination of multiple topographic conditions. This approach has the outstanding advantages of obtaining many survey points quickly and retrieving topographic conditions difficult to measure using on‐ground field surveys. [ABSTRACT FROM AUTHOR]

The body of knowledge surrounding infrared spectral analysis of drug mixtures continues to grow alongside the physical expansion of drug checking services. Technicians trained in the analysis of spectroscopic data are essential for reasons that go beyond the accuracy of the analytical results. Significant barriers faced by people who use drugs in engaging with drug checking services include the speed and accuracy of the results, and the availability and accessibility of the service. These barriers can be overcome by the automation of interpretations. A random forest model for the detection of two compounds, MDA and fluorofentanyl, was trained and optimized with drug samples acquired at a community drug checking site. This resulted in a 79% true positive and 100% true negative rate for MDA, and 61% true positive and 97% true negative rate for fluorofentanyl. The trained models were applied to selected drug samples to demonstrate a proposed workflow for interpreting and validating model predictions. The detection of MDA was demonstrated on three mixtures: (1) MDMA and MDA, (2) MDA and dimethylsulfone, and (3) fentanyl, etizolam, and benzocaine. The classification of fluorofentanyl was applied to a drug mixture containing fentanyl, fluorofentanyl, 4‐anilino‐N‐phenethylpiperidine, caffeine, and mannitol. Feature importance was calculated using shapely additive explanations to better explain the model predictions and k‐nearest neighbors was used for visual comparison to labelled training data. This is a step toward building appropriate trust in computer‐assisted interpretations in order to promote their use in a harm reduction context. [ABSTRACT FROM AUTHOR]

Intercropping – the planting of more than one crop in the same plot of land – is a prevalent agricultural management practice which can be used for risk reduction. Despite its widespread use, intercropping is not commonly reported in agricultural statistics, resulting to very limited spatially disaggregated information about its prevalence. Remote sensing-based approaches to detect and estimate the area of cropping patterns like intercropping require good understanding of the spectral response of (intercropped) crops at different crop growth phases. This study integrates field surveys, farmer interviews and temporal Sentinel-2 data from four crop growth phases and the post-harvest period of maize and intercropped maize (imaize). The goal is to identify the optimal crop growth phases, spectral regions and vegetation indices (VIs) that can accurately discriminate the two cropping patterns. We computed p-values for the spectral bands using Mann-Whitney U test and identified critical crop growth phases. Classification of maize and imaize cropping patterns was performed using random forest classifier. Our spectral analysis revealed effective discrimination between maize and imaize cropping patterns during the vegetative (in all spectral bands) and flowering-yield phases (in Blue, Green, Red, RE704, RE783, NIR833, NIR865). The most suitable VIs contained red-edge and near-infrared spectal bands. Utilizing spectral data and VIs from vegetative and flowering-yield phases, we achieved optimal discrimination during the vegetative phase (user’s accuracy of 100 % and producer’s accuracy of 100 %). However, accuracy decreased during the flowering yield phase (overall accuracy of 87 % for all spectral bands). The highest classification results using all spectral bands at the flowering yield phase resulted in 80 % producer’s accuracy for maize and 100 % for imaize. This study illustrates the utility of temporal Sentinel-2 spectral data for identifying the critical crop growth phase, spectral regions and VIs for cropping patterns classification, particularly for intercropping.

Accurate and effective access to agricultural and pastoral information is of great practical importance for scientific planning in agriculture and pastoral husbandry and food security reserves. This paper utilised intra-annual monthly synthetic data from Sentinel-1 and Sentinel-2, combined with band information and index information to simultaneously obtain intra-annual phenological change information and extracted different types of information for random forest classification, thus obtaining the fine-grained agricultural and pastoral map of Jingyang District. On this basis, the effects of different data sources and the importance of different features on the classification results were further analysed. The results show that in the classification results of the Sentinel-1 data method, March feature is the most important and May feature has the least influence on the classification results. In the classification results of the Sentinel-2 data method and the combined Sentinel-1 data and Sentinel-2 data, the December feature has the greatest importance and the February feature has the least impact on the classification results. The NDVI index has the highest importance and the EVI index has the lowest importance score. The importance of band features varies in the classification results of different data sources. The accuracy of the agricultural and pastoral information in Jingyang District is 83.31% and the kappa is 0.76. Rice is the largest crop with an area of 239,830,800 m2, mainly located in the northwest and mountainous areas of Jingyang District. It is followed by green manure with an area of 60,274,000 m2, mainly in the mountainous part. This study demonstrates the feasibility of extracting agricultural and pastoral information from remote sensing images, and also provides basic data for future agricultural and pastoral planning in Jingyang District. [ABSTRACT FROM AUTHOR]

Objective: To develop a classification framework based on random forest (RF) modeling to outline the declarative memory profile of patients with panic disorder (PD) compared to a healthy control sample. Methods: We developed RF models to classify the declarative memory profile of PD patients in comparison to a healthy control sample using the Rey Auditory Verbal Learning Test (RAVLT). For this study, a total of 299 patients with PD living in the city of Rio de Janeiro (70.9% females, age 39.9 6 7.3 years old) were recruited through clinician referrals or self/family referrals. Results: Our RF models successfully predicted declarative memory profiles in patients with PD based on RAVLT scores (lowest area under the curve [AUC] of 0.979, for classification; highest root mean squared percentage [RMSPE] of 17.2%, for regression) using relatively bias-free clinical data, such as sex, age, and body mass index (BMI). Conclusions: Our findings also suggested that BMI, used as a proxy for diet and exercises habits, plays an important role in declarative memory. Our framework can be extended and used as a prospective tool to classify and examine associations between clinical features and declarative memory in PD patients. [ABSTRACT FROM AUTHOR]

Deforestation has become a critical concern that brings about changes in the global ecological environment. The forest inventory is the first step toward the adoption of the necessary decisions at the national and regional level for the protection of the ecological environment and the sustainable development and use of forest resources. The present study analyzed the forest cover change dynamics in the mid-upstream area of the Taedong River in the Democratic People's Republic of Korea during 1990–2020 by applying remote sensing technology. For the analysis, annual image datasets layered with spectral index bands fitted to vertices of the normalized burned ratio segment curve were created by implementing LandTrendr algorithm in the Google Earth Engine platform. The sample dataset used to conduct random forest classification was collected by sampling from pixels in which ecological change events were not observed during the study period. The overall accuracy was 95.12 ± 0.14% in all years. The results showed that over the past 20 years (1990–2010), the forest area of the study area decreased by 652.5 km2, accounting for 13.6% of the total forest area in the starting year (1990). The annual rate of deforestation in the study area between 1990 and 2000 was 1.043% year−1, which was much higher than the global deforestation rate (0.184% year−1) during the same period. For the period from 2000 to 2010, the deforestation rate was 0.423% year−1, 3.4 times the global deforestation rate (0.125% year−1). During these two time periods, deforestation in the study area was substantial. However, from 2010 to 2020, the forest area showed a tendency to increase, especially in the midstream region where it increased significantly. The average growth rate of forest area in the midstream region was much higher than that in the upstream region, where there was no significant change in forest area between 2010 and 2020. The current forest area of the research area was estimated at 92% of the forest area in 1990 (4783.9 km2). [ABSTRACT FROM AUTHOR]

This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship between the crashworthiness parameters of the hexagonal composite ring specimens under lateral compressive, energy absorption, and failure modes. These algorithms include random forest (RF) classification and artificial neural networks (ANN). The ultimate target is to develop a robust multi-modal machine learning method to predict the optimum geometry (i.e., hexagonal ring angle) and suitable in-plane crushing arrangements of the hexagonal ring system for targeted crashworthiness parameters. The results demonstrate that the suggested RF-ANN-based technique can predict the optimal composite design with high accuracy (precision, recall, and f1-score for test and train dataset were 1). Furthermore, the confusion matrix validates the random forest classification model's accuracy. At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10−7 and 6.40 × 10−7, respectively, for X1 and X2 loading conditions at 45° angle). Furthermore, the developed models can predict crashworthiness parameters for any hexagonal ring angle within the range of the trained dataset, requiring no additional experimental effort.

In the process of modernization and development, intelligent technology empowers rural tourism, which in turn enables the effective implementation of the rural revitalization strategy. In this paper, a rural tourism recommendation model combined with artificial intelligence technology is constructed, which effectively improves the accuracy and personalization level of tourist attraction recommendation by mining and analyzing tourism data and extracting tourism characteristic factors. Secondly, the random forest classification algorithm is used to establish a random forest preference attraction prediction model, and several recommendation algorithms are combined to design a personalized rural tourism recommendation model to improve the accuracy of tourists’ tourism recommendations. Finally, the countryside around a city is being investigated to explore the modern development of rural tourism and help revitalize rural areas. The results show that the number of people received by rural tourism in City A and the number of tourists received by the province in 2022 is 972 million and 1,424 million more than that of 2014, and the total income from rural tourism in City A and the total income from tourism in the province in 2022 is 724.8 billion and 1,202.02 billion yuan more than that of 2014, and it can be seen that the high level of tourism income maintains a positive correlation with the number of tourists received. It shows that rural tourism improves the overall economic level of the countryside, accelerates the process of social progress in rural areas, and strongly contributes to the modernization and development of rural revitalization.

Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at −52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.

Abstract There is strong evidence to support that mutations and dysregulation of miRNAs are associated with a variety of diseases, including cancer. However, the experimental methods used to identify disease-related miRNAs are expensive and time-consuming. Effective computational approaches to identify disease-related miRNAs are in high demand and would aid in the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In this study, we develop an ensemble learning framework to reveal the potential associations between miRNAs and diseases (ELMDA). The ELMDA framework does not rely on the known associations when calculating miRNA and disease similarities and uses multi-classifiers voting to predict disease-related miRNAs. As a result, the average AUC of the ELMDA framework was 0.9229 for the HMDD v2.0 database in a fivefold cross-validation. All potential associations in the HMDD V2.0 database were predicted, and 90% of the top 50 results were verified with the updated HMDD V3.2 database. The ELMDA framework was implemented to investigate gastric neoplasms, prostate neoplasms and colon neoplasms, and 100%, 94%, and 90%, respectively, of the top 50 potential miRNAs were validated by the HMDD V3.2 database. Moreover, the ELMDA framework can predict isolated disease-related miRNAs. In conclusion, ELMDA appears to be a reliable method to uncover disease-associated miRNAs.

With growing global concern for food and water insecurity, an efficient method to monitor irrigation projects is essential, especially in the developing world where irrigation performance is often suboptimal. In Nepal, the irrigated area has not been objectively recorded, although their assessment has substantial implications for national policy, project's annual budgets, and donor funding. Here, we present the application of Landsat images to measure irrigated areas in Nepal for the past 17 years to contribute to the assessment of the irrigation performance. Landsat 5 TM (2006–2011) and Landsat 8 OLI (2013–2022) images were used to develop a machine learning model, which classifies irrigated and non-irrigated areas in the study areas. The random forest classification achieved an overall accuracy of 82.2% and kappa statistics of 0.72. For the class of irrigation areas, the producer's accuracy and consumer's accuracy were 79% and 96%, respectively. Our regionally trained machine learning model outperforms the existing global cropland map, highlighting the need for such models for local irrigation project evaluations. We assess irrigation project performance and its drivers by combining long-term changes in satellite-derived irrigated areas with local data related to irrigation performance, such as annual budget, irrigation service fee, crop yield, precipitation, and main canal discharge. [ABSTRACT FROM AUTHOR]

Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. This approach can be applied to both the initial establishment and on-going maintenance of the system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers' land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions were utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of "boundary" and "non-boundary" outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) were undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file were used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artifacts were also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadastres. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4 m and 0.6 m, respectively, for the interactively delineated and reference boundaries. The study proposed publicly available software solutions and outlined a workflow to (semi-) automatically extract cadastral boundaries from aerial/satellite images. It further demonstrated the potentially significant role AFE could play in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting. [ABSTRACT FROM AUTHOR]

Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to obtain the classification prediction performance of TBM oil temperature, a camel walking resistance and walking endurance strategy based on natural weather phenomena was proposed to improve the camel algorithm to optimize the prediction model of random forest. Firstly, the traditional camel algorithm was improved by using the proposed strategies. The results showed that the improved camel algorithm has good convergence speed and convergence accuracy. Secondly, the improved camel algorithm was used to optimize the parameters of the TBM oil temperature prediction model established by the random forest to obtain the best model. Finally, on this basis, the classification prediction research and analywis of the test data set was carried out. The experimental results show that the prediction accuracy of the proposed model reaches 97.71%, compared with the traditional random forest model, the accuracy is mproved 6.38%, which can achieve the purpose of avoiding the failure of the shield machine caused by high oil temperature. It provides the basis for the whoce machine material-structure-control future multi-disciplinary cooperative optimization design and performance prediction.

Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km2 to 49.5 km2 in Umhlabuyalingana; 54 km2 to 1.6 km2 in Joe Morolong; and 254.6 km2 to 7.4 km2 in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas.

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Summary: Background: Socioeconomic pressures, sex, and physical health status strongly influence the development of major depressive disorder (MDD) and mask other contributing factors in small cohorts. Resilient individuals overcome adversity without the onset of psychological symptoms, but resilience, as for susceptibility, has a complex and multifaceted molecular basis. The scale and depth of the UK Biobank affords an opportunity to identify resilience biomarkers in rigorously matched, at-risk individuals. Here, we evaluated whether blood metabolites could prospectively classify and indicate a biological basis for susceptibility or resilience to MDD. Methods: Using the UK Biobank, we employed random forests, a supervised, interpretable machine learning statistical method to determine the relative importance of sociodemographic, psychosocial, anthropometric, and physiological factors that govern the risk of prospective MDD onset (total n = 15,710). We then used propensity scores to rigorously match individuals with a history of MDD (n = 491) against a resilient subset of individuals without an MDD diagnosis (retrospectively or during follow-up; n = 491) using an array of key social, demographic, and disease-associated drivers of depression risk. 381 blood metabolites and clinical chemistry variables and 4 urine metabolites were integrated to generate a multivariate random forest-based algorithm using 10-fold cross-validation to predict prospective MDD risk and resilience. Outcomes: In unmatched individuals, a first case of MDD, with a median time-to-diagnosis of 72 years, can be predicted using random forest classification probabilities with an area under the receiver operator characteristic curve (ROC AUC) of 0.89. Prospective resilience/susceptibility to MDD was then predicted with a ROC AUC of 0.72 (x˜ = 3.2 years follow-up) and 0.68 (x˜ = 7.2 years follow-up). Increased pyruvate was identified as a key biomarker of resilience to MDD and was validated retrospectively in the TwinsUK cohort. Interpretation: Blood metabolites prospectively associate with substantially reduced MDD risk. Therapeutic targeting of these metabolites may provide a framework for MDD risk stratification and reduction. Funding: New York Academy of Sciences’ Interstellar Programme Award; Novo Fonden; Lincoln Kingsgate award; Clarendon Fund; Newton-Abraham studentship (University of Oxford). The funders had no role in the development of the present study.

In recent years, significant technological advances have emerged in renewable power generation systems (RPGS), making them more economical and competitive. On the other hand, for the RPGS to achieve the highest level of performance possible, it is important to ensure the healthy operation of their main building blocks. Power electronic converters (PEC), which are one of the main building blocks of RPGS, have some vulnerable components, such as capacitors, which are responsible for more than a quarter of the failures in these converters. Therefore, it is of paramount importance that the design of fault diagnosis techniques (FDT) assess the capacitor's state of health so that it is possible to implement predictive and preventive maintenance plans in order to reduce unexpected stoppage of these systems. One of the most commonly used capacitors in power converters is the aluminum electrolytic capacitor (AEC) whose aging manifests itself through an increase in its equivalent series resistance (ESR). Several advanced intelligent techniques have been proposed for assessing AEC health status, many of which require the use of a current sensor in the capacitor branch. However, the introduction of a current sensor in the capacitor branch imposes practical restrictions; in addition, it introduces unwanted resistive and inductive effects. This paper presents an FDT based on the random forest classifier (RFC), which triggers an alert mechanism when the DC-link AEC reaches its ESR threshold value. The great advantage of the proposed solution is that it is non-invasive; therefore, it is not necessary to introduce any sensor inside the converter. The validation of the proposed FDT will be carried out using several computer simulations carried out in Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Background: Dysfunctional processes in Alzheimer's disease and other neurodegenerative diseases lead to neural degeneration in the central and peripheral nervous system. Research demonstrates that neurodegeneration of any kind is a systemic disease that may even begin outside of the region vulnerable to the disease. Neurodegenerative diseases are defined by the vulnerabilities and pathology occurring in the regions affected. Method: A random forest machine learning analysis on whole blood transcriptomes from six neurodegenerative diseases generated unbiased disease‐classifying RNA transcripts subsequently subjected to pathway analysis. Results: We report that transcripts of the blood transcriptome selected for each of the neurodegenerative diseases represent fundamental biological cell processes including transcription regulation, degranulation, immune response, protein synthesis, apoptosis, cytoskeletal components, ubiquitylation/proteasome, and mitochondrial complexes that are also affected in the brain and reveal common themes across six neurodegenerative diseases. Conclusion: Neurodegenerative diseases share common dysfunctions in fundamental cellular processes. Identifying regional vulnerabilities will reveal unique disease mechanisms. Highlights: Transcriptomics offer information about dysfunctional processes.Comparing multiple diseases will expose unique malfunctions within diseases.Blood RNA can be used ante mortem to track expression changes in neurodegenerative diseases.Protocol standardization will make public datasets compatible. [ABSTRACT FROM AUTHOR]

There is strong evidence to support that mutations and dysregulation of miRNAs are associated with a variety of diseases, including cancer. However, the experimental methods used to identify disease-related miRNAs are expensive and time-consuming. Effective computational approaches to identify disease-related miRNAs are in high demand and would aid in the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In this study, we develop an ensemble learning framework to reveal the potential associations between miRNAs and diseases (ELMDA). The ELMDA framework does not rely on the known associations when calculating miRNA and disease similarities and uses multi-classifiers voting to predict disease-related miRNAs. As a result, the average AUC of the ELMDA framework was 0.9229 for the HMDD v2.0 database in a fivefold cross-validation. All potential associations in the HMDD V2.0 database were predicted, and 90% of the top 50 results were verified with the updated HMDD V3.2 database. The ELMDA framework was implemented to investigate gastric neoplasms, prostate neoplasms and colon neoplasms, and 100%, 94%, and 90%, respectively, of the top 50 potential miRNAs were validated by the HMDD V3.2 database. Moreover, the ELMDA framework can predict isolated disease-related miRNAs. In conclusion, ELMDA appears to be a reliable method to uncover disease-associated miRNAs. [ABSTRACT FROM AUTHOR]

As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the crowd of people masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BAs of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BAs footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. Based on the changes in spatial-temporal data, the direction of urban development has been determined. In addition, to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. As a result of the study, SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data. [ABSTRACT FROM AUTHOR]

With an increasing number of network devices, the need for a robust intrusion detection system is also increasing for ensuring ubiquitous and secure Internet of Things (IoT) network traffic flow. Most of the existing intrusion detection systems do not consider the dataset imbalance and model maintenance, subsequently this leads to high bias, high false positive and false negative rates leading to security breaches. To mitigate these shortcomings, an ensemble learning model is proposed to detect anomalous behaviour in IoT network flow. The proposed machine learning pipeline uses voting between a random forest classifier and an XGBoost classifier, thus combining the bagging and the boosting algorithms, to classify the network flow as normal or anomalous. The proposed model is trained on two standard benchmark datasets: UNSW-NB15 and BoT-IoT and it attained an accuracy of 99.7% and 99.66% respectively with false positive rates of 0.0027 and 0.0042 over the two datasets with 10 folds cross-validation. If the network flow is classified as anomalous, the category of anomaly is predicted for which accuracies of 99.53% and 99.65% are attained. With such high accuracies and low false positive rate, the proposed framework can be deployed to detect any malicious or anomalous behaviour in IoT networks in the real-world conditions like in smart cities. [ABSTRACT FROM AUTHOR]

Desertification has become a major problem in the field, affecting both the global ecological environment and economy. The effective monitoring of desertified land is an important prerequisite for land desertification protection and governance. With the aim of addressing the problems of spectral confusion as well as the salt and pepper phenomenon concerning the successful extraction of desertification information by utilizing the pixel-based method in the studies, Landsat remote sensing images obtained from the year 2001 to 2021 were selected in this study as the data source, and then, the object-oriented random forest classification method was improved by using different optimal segmentation scale selection techniques and combining multi-thematic index characteristics for measuring the extent of land desertification. Finally, the improved method was applied to study the dynamic changes in desertification in the Mu Us Sandy Land Ecological Function Reserve. The results show that the optimal scale determined by different optimal segmentation scale selection methods is not entirely consistent, and a minor scale should be selected as the optimal scale. Compared with the pixel-based classification method, the overall accuracy of object-oriented classification based on the optimal segmentation scale was improved by 8.06%, the Kappa coefficient increased by 0.1114, and the salt and pepper phenomenon was significantly reduced. From 2001 to 2021, the area of desertified land decreased by 587.12 km2 and the area of severely desertified land decreased by 4115.92 km2, indicating that the control effect was remarkable. This study can provide effective decision-making evidence and support for the successful governance of desertification. [ABSTRACT FROM AUTHOR]

PremiseX-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers.Methods and resultsWe present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single-leaf microCT scans into detailed segmentations. By training the model on each scan using six hand-segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation.ConclusionsOverall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.