Suitable Index Research Articles

The Cough Response to Inhaled Mannitol in Healthy Subjects

PurposeInhaled mannitol induces bronchoconstriction and cough. This study aimed to describe the cough response to mannitol among healthy adult subjects.Methods125 healthy subjects (aged 18–82 years, 52% females, 50% skin prick test positive) underwent a mannitol test. The coughs were recorded both simultaneously and afterwards from video recordings by two researchers. Three indices were evaluated: The cumulative number of coughs per cumulative dose of mannitol (CDR), cumulative provocative dose of mannitol to cause at least 5 coughs, and the maximal number of coughs provoked by any single mannitol dose. The test was repeated in 26 subjects after 3–7 days.ResultsCDR showed the best repeatability with an intraclass correlation coefficient of 0.829. Gender was the only characteristics that associated with the cough response: The median CDR was 2.53 (interquartile range 0.45–7.01) coughs/100 mg among females and 0.787 (0.0–3.29) coughs/100 mg among males (p = 0.002). The interquartile range upper limits were defined as the cut-off limits for a normal response. The threshold for a statistically significant change in CDR was 6.26 coughs/100 mg. There was a close correlation between simultaneous- and video-assessed CDR (intraclass correlation coefficient 0.985).ConclusionFemales cough more than males in response to mannitol. CDR is the most suitable index to describe the cough responsiveness. The repeatability of the response is good. Video recording of the coughs is not mandatory. The cut-off limits for a normal cough response to mannitol were provided.

Practical methodology for quantifying the structural robustness of RC building structures

Several structural robustness requirements have been included in building codes over the past two decades. These mainly include measures to prevent collapse after an initial component failure by ensuring a minimum level of continuity within the structural system so that loads supported by the failed component can be redistributed to the rest of the structural system. However, there is still a general lack of consensus on how to evaluate the effectiveness of these measures. The present study proposes a practical methodology to evaluate the structural robustness of RC building structures by combining an efficient modelling strategy and suitable robustness indexes. The proposed computational modelling strategy for RC buildings accounts for relevant dynamic and nonlinear phenomena, including compressive arching, catenary action, and membrane effects. This strategy can be implemented at a reasonable computational expense in widely used commercial structural analysis software and has been validated by comparisons to selected experimental tests from the literature. The modelling strategy is then employed to compute four different robustness indexes proposed in the literature for three hypothetical building designs, allowing the indexes to be systematically compared. It was found that evaluating a system using multiple robustness indexes can provide a more holistic view of system performance compared to relying solely on a single index. Ultimately, the potential usefulness of the proposed methodology is demonstrated through two practical applications involving the estimation of component damage levels and the evaluation of different design and retrofitting solutions. Such a methodology can be useful for practising engineers to optimise the design of new buildings or to support decisions on the assessment and retrofitting of existing ones.

Experimental assessment of freeze-thaw deterioration in compression-cast fiber-reinforced concrete

An increasing number of critical infrastructures are built in cold and perma-frost regions, resulting in higher susceptibility to damage due to freeze-thaw (F-T) cycles. This paper assesses the mechanical properties and F-T damage evolution of compression-cast fiber-reinforced concrete (CCFRC) through rapid F-T cycling tests. Nine types of fiber-reinforced concrete (FRC) are experimentally investigated through 144 cubic and 39 prismatic specimens. The main parameters considered in the tests include the casting pressure applied during the compression casting procedure (between 0 and 15 MPa), type of the internal added fibers (polypropylene, end-hooked steel, or their combination), fiber content, and number of F-T cycles. The effects of these parameters on the failure modes, strength degradation rates, deterioration evolution, frost resistance durability, microscopic behavior, and F-T resistance of FRC specimens, are examined in detail. The microscopic performance of different specimens is further investigated using the X-ray computed tomography (X-CT), scanning electron microscopy (SEM), and backscattered scanning electron (BSE) tests. Based on the test results, it is shown that: (i) compared with the mass loss rate, the relative dynamic elastic modulus can be a more suitable index to assess the F-T resistance of FRC; (ii) compression casting and addition of fibers can effectively enhance the F-T denudation resistance of CCFRC; (iii) compression casting reduces the compressive and flexural strength degradation rates of CCFRC, but at the expense of more brittle behavior; and (iv) compression casting and use of the mixed polypropylene and end-hooked steel fibers can effectively delay the F-T cycling induced damage, reduce porosity and internal defects, and enhance the micro-structural behavior and F-T resistance of CCFRC. F-T deterioration prediction models are also proposed and shown to provide accurate and effective representation of the behavior of CCFRC materials.

Computational hemodynamic indices to identify Transcatheter Aortic Valve Implantation degeneration

Background and Objectives:Structural Valve Deterioration (SVD) is the main limiting factor to the long-term durability of the bioprosthetic valves used for Transcatheter Aortic Valve Implantation (TAVI), a minimally invasive technique for the treatment of severe aortic stenosis. The aim of this retrospective study is to perform patient-specific computational analyses of blood dynamics shortly after TAVI to identify hemodynamic indices that correlate with a premature onset of SVD which is detected at 5-10 years long-term follow-up exam after TAVI. Methods:The study population comprises fourteen patients: seven cases with SVD at long-term follow-up were identified and seven cases without SVD were randomly extracted from the same cohort. Starting from pre-operative CT images, we created trustworthy post-TAVI scenarios by virtually inserting the bioprosthetic valve (stent and leaflets) and we qualitatively validated such virtual scenarios against post-TAVI CT scans, when available. We then performed numerical simulations imposing personalized inlet conditions based on patient-specific Echo Doppler cardiac output measurements and the numerical results were post-processed to identify suitable hemodynamics indices with the aim of discriminating between the SVD and non-SVD groups of patients. In particular, differences in terms of each individual index were evaluated using a Wilcoxon rank-sum test. Moreover, we defined three synthetic scores, based on suitably scaled hemodynamic indices of stress and vorticity, evaluated in different contexts: on the leaflets, in the ascending aorta, and in the whole domain. Results:We found that the hemodynamic index related to leaflets’ OSI individually shows statistically significant differences (p=0.007) between the SVD and non-SVD groups. Moreover, our proposed synthetic scores are able to clearly isolate the SVD group both in a two-dimensional space given by the aorta and leaflets scores and by only considering the global synthetic score. Conclusion:The results of this computational study suggest that blood dynamics may play an important role in creating the conditions that lead to SVD. Moreover, the proposed synthetic scores could provide further indications for clinicians in assessing and predicting TAVI valves’ long-term performance.

The best indices of anesthesia depth monitored by electroencephalogram in different age groups

Introduction Current electroencephalogram (EEG) indices for monitoring the depth of anesthesia have not been adjusted for age. This study aims to identify the most suitable EEG indices for monitoring anesthesia depth across different age groups. Method In here, we conducted a prospective observational trial to observe whether different EEG features in different age groups can detect anesthesia depth and compare their performance. Results In Group 1, absolute θ had the highest prediction probability (Pk) value (0.78 ± 0.07), while relative γ had the lowest relative coefficient of variation (CV) value in states 1 (0.160), 2 (0.153), 3 (0.214), and 4 (0.144). In Group 2, absolute θ had the highest Pk value (0.81 ± 0.09), and relative γ had the lowest CV value in states 1 (0.159), 2 (0.178), 3 (0.140), and 4 (0.095). In Group 3, absolute δ had the highest Pk value (0.79 ± 0.08), with absolute slow wave having the lowest CV value in states 1 (0.196), 2 (0.258), 3 (0.295), and 4 (0.181). In Group 4, absolute δ had the highest Pk value (0.75 ± 0.10), and absolute slow wave had the lowest CV value in states 1 (0.176), 2 (0.192), 3 (0.185), and 4 (0.144). Conclusions In this study, we found that absolute θ and relative γ are the most suitable choices for young and middle-aged patients, while absolute δ and absolute slow wave are the most suitable choices for young elderly and elderly patients.

Spatiotemporal analysis of phenological metrics on the Qinghai-Tibet Plateau based on multiple vegetation indices

As global climate change intensifies, understanding the response mechanisms and adaptive capacities of ecosystems to climate change has become a core focus in environmental science. The Qinghai-Tibet Plateau (QTP), a region highly sensitive to global climate change, shows vegetation phenological shifts that reflect the ecosystem’s response to climate fluctuations. However, how phenological metrics extracted from different vegetation indices affect our understanding of these shifts in the region remains unclear. This study analyzes the start (SOS) and end (EOS) of the growing season on the QTP from 1982 to 2015 using GIMMS NDVI3g data. These metrics were compared with phenological data derived from GIMMS LAI3g and MODIS EVI2 data. The results indicate that phenological metrics derived from different vegetation indices (NDVI, LAI, and EVI2) are generally consistent in their spatiotemporal distribution and show significant correlations. However, regional differences and temporal inconsistencies were observed. This comparative analysis reveals the strengths and limitations of various vegetation indices in phenological metric extraction. The results offer crucial insights for enhancing the precision of phenological modeling and highlight the significance of choosing suitable vegetation indices in future studies on phenology.

Evaluating the role of prosumers in providing energy systems with flexibility products

AbstractIncreasing the penetration of renewables on the demand side causes operational challenges in distribution grids due to their variable and uncertain behaviour. These resources have increased distribution grid net load fluctuation during recent years, which needs to be covered by demand side flexibility (DSF). DSF should be quantified and measured by a suitable index to show the level of a system DSF in different situations. In this paper, the flexibility area index of distribution systems as a suitable metric for prosumers’ DSF evaluation, especially in the presence of renewable sources, is calculated. This index is defined first for one prosumer and then extended to a distribution system by a combination of prosumer indices. The mentioned index is decomposed into two components, one for ramp‐up (negative flexibility) and another for ramp‐down (positive flexibility). To calculate and manage prosumers’ flexibility, a novel flexibility‐oriented stochastic framework for prosumers is suggested. The amount of load shedding is under control by the risk‐management strategy in the proposed framework, which can facilitate distribution system operator operation. The results obtained from the implementation of the case study validate the major features of the proposed framework more efficiently.

Burden of disease and socioeconomic deprivation in Germany – regional patterns and variations

Abstract Within the scope of the BURDEN 2020 project (Burden of disease in Germany at the National and Regional Level) carried out by Robert Koch Institute (RKI) in collaboration with the German Environmental Agency and AOK Scientific Institute, burden of disease estimates for Germany and its regions become available for the first time. The national estimates of YLL, YLD and DALY are provided not only stratified by age and sex, but also by detailed regional level (96 spatial planning regions). The regional estimates provide valuable information and support the evidence-based decisions in health policies aiming at reducing the burden of disease in the country. To facilitate further the interpretation and usability of these estimates, the provided indicators were linked to the regional information on deprivation. For this purpose, the German index of socioeconomic deprivation (GISD) was used. The index was developed at the RKI and the last revised version was published in 2022. It is based on nine indicators which reflect the three subdimensions of deprivation: education, employment, and income. That is why GISD is a very suitable index for evaluating regional health disparities. Connecting both informations on BoD and deprivation, gives deeper insides in the regional differences within the country. Some diseases exhibit east/west differences, while others show north/south patterns or rural/urban distinctions. These differences reflect the variation in the prevention and health care needs for the different diseases in Germany. The results underline the necessity to study the regional inequalities separately for each disease. Different health conditions are connected to variations in settings and outline different needs in intervention programs and allocation of means.

Numerical study of phase space analysis for the development of subsurface crack detection method using pulsed eddy current

Root-deck cracks, a prominent form of subsurface damage observed in orthotropic steel decks (OSDs), pose significant challenges to maintaining structural integrity. Early detection of these cracks is crucial to prevent further deterioration. This paper introduces novel and robust techniques for processing response signals obtained from the Pulsed Eddy Current Technique (PECT) using phase space analysis. The proposed methods aim to improve sensitivity and noise handling capabilities by employing an appropriate damage index derived from comprehensive phase space analysis, enabling effective detection of subsurface cracks in OSDs. A numerical study demonstrates the effectiveness of the proposed methods in detecting crack length change as small as 1 mm from the bottom of a 12 mm steel plate, with the Change in Phase Space Topology (CPST) identified as a suitable damage index. Furthermore, the newly proposed signal processing techniques allow for a more comprehensive interpretation of response signals for subsurface damage detection, encapsulating the extent of damage within a single value, even in the presence of small crack lengths near the bottom of the steel plate and noise. As a result, the proposed methods overcome the limitations of conventional approaches and offer promising prospects for enhanced subsurface crack detection in OSDs.

Deformation Characteristics and Influence Factors of Shear Force Lateral Stiffness Matching Index for Non-Rigid Plate Bent Frame Structures

The period ratio and the drift ratio are commonly used as plane regularity control indices for multi-story buildings. However, they fail to reasonably reflect the regularity of lateral force-resisting component configuration and deformation characteristics in non-rigid plate bent frame structures. This study focuses on the analysis of non-rigid single-span bent frames, examining the variation patterns of a suitable regularity index for non-rigid plate bent frame structures, referred to as the shear force lateral stiffness matching index, under various parameters. Additionally, it introduces indices to quantify the deformation response of non-rigid plate bent frame structures, providing a detailed analysis of the impact of factors such as eccentricity, torsional stiffness, and roof slab stiffness on the deformation characteristics of non-rigid plate bent frame structures and the shear force lateral stiffness matching index. The results show that the shear force lateral stiffness matching index can reflect the inconsistency in the horizontal displacement response of lateral force-resisting components caused by deformations in the roof slab. The proposed indices for torsional and bending deformations accurately quantify the roof slab’s deformation response, revealing the horizontal deformation characteristics of lateral force-resisting components in non-rigid frames. When eccentricity is present, the stiffness of the roof slab has a non-monotonic effect on the torsional component of the structural seismic response.

Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra-Class Correlation Coefficient in Inter-Rater Reliability Studies.

The agreement intra-class correlation coefficient (ICCa) is a suitable statistical index for inter-rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter-rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior-power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter-rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter-rater reliability studies.

A framework for drought monitoring and assessment from a drought propagation perspective under non-stationary environments

According to the coupled influence of climate variation and anthropogenic activities, hydro-meteorological variables are hard to keep stationary in a changing environment. Consequently, the efficacy of traditional standardized drought indices, predicated upon the assumption of stationarity, has been called into question. In China, the challenge of drought monitoring and declaration is exacerbated by the need for multiple drought indices covering meteorological, agricultural, hydrological, and groundwater aspects, often lacking real-time availability. To address these challenges, we developed a framework for drought monitoring and assessment from a drought propagation perspective. Central to this is the Nonstationary Integrated Drought Index (NIDI), which integrates responses from meteorological, agricultural, hydrological, and groundwater droughts, accounting for climate change and anthropogenic influences. First, we analyse the process of drought propagation to select the suitable time scale standardized drought index. Subsequently, significant large-scale climatic indices are selected through linear and nonlinear correlation analyses to identify climate anomalies. Anthropogenic influences are assessed using indicators such as the Normalized Difference Vegetation Index (NDVI), Impervious Surface Ratio (ISR), and population density (POP). Nonstationary probability models are then developed for precipitation, soil moisture, runoff, and groundwater series, incorporating climatic and human-induced factors. Finally, the NIDI is calculated using a D-vine copula model, with parameter estimation and updating facilitated by a genetic algorithm, representing the temporal dependence structure among the variables. A case study in the Hulu River Basin of western China validated the NIDI. Results showed that the NIDI effectively accounts for nonstationary hydro-meteorological variables due to climate change and human activities, accurately reproducing their time-dependent structure. Compared to conventional indices like SPI, SSI, SRI, and SGI, the NIDI identifies more extreme drought events. In conclusion, the presented NIDI offers a more comprehensive approach to drought identification, providing valuable insights for accurate drought detection and effective drought-related policy-making.

Reliability and validity assessment of instrument to measure sustainability practices at shipping ports in India

Sustainability has emerged as one of the most critical factors influencing the competitiveness of maritime shipping ports. This emergence has led to a surge in research publications on port sustainability-related topics. However, despite the increasing awareness and adoption of sustainability practices, documented literature on empirical studies with survey and interview data is very limited. Moreover, the existence of validated instruments to objectively assess sustainability through sustainability practices for shipping ports in India needs to be traced. This study contributes by validating an instrument to evaluate objectively sustainability practices in shipping ports by adopting a four-stage process, starting with item identification based on an extensive literature review, instrument evaluation by subject matter experts, assessing of the instrument with suitable content validation indices, and finally evaluating the validity and reliability of the hypothesized theoretical model. For content validation, Content Validity Index, Cohens Kappa coefficient, and Lawshe’s Content Validity Ratio were computed with the assessment by a subject matter expert panel comprising six members from the port industry as well as academicians cum researchers in the field of shipping ports. The content-validated instrument was administered to 200 samples comprising officer category port employees. The measurement model was evaluated and validated using the Confirmatory Factor Analysis to assess the extent to which the measured variables represent the theoretical construct of the study and ascertain the factor structure. The empirically validated instrument met the required guidelines of model fit, reliability, and construct validity measures and was found to be a confirmed model for measuring sustainability practices in shipping ports. Structural Equation Modeling methodology was adopted to explain the variance and the path relationship between the higher-order and lower-order constructs of sustainability. The results indicate that the economic dimensions are the major contributors to the overall sustainability of the port as they drive investments in environmental and social dimensions, leading to overall sustainable development. The study’s findings will be helpful for researchers, academicians, policymakers, and industry practitioners working towards sustainability practices that contribute to sustainable growth and development in the shipping industry.

Quantitative evaluation of internal cluster validation indices using binary data sets

AbstractAimsDifferent clustering methods often classify the same data set differently. Selecting the “best” clustering solution from alternatives is possible with cluster validation indices. Because of the large variety of cluster validation indices (CVIs), choosing the most suitable index concerning the data set and clustering algorithms is challenging. We aim to assess different internal clustering validation indices.MethodsArtificial binary data sets with equal‐ and unequal‐sized well‐separated a priori clusters were simulated and three levels of noise were then added. Twenty replications of each of the six types of data sets (two group sizes × three levels of noise) were created and analyzed by three clustering algorithms with Jaccard dissimilarity. Twenty‐seven clustering validation indices are evaluated including both geometric and non‐geometric indices.ResultsAlthough, in theory, all CVIs could differentiate between good and wrong classifications, only a few perform as expected with noisy data. Tau and silhouette widths proved to be the best geometric CVIs both for equal and unequal cluster sizes. Among non‐geometric indices, crispness and OptimClass performed best.ConclusionWe recommend using these best‐performing CVIs. We suggest plotting the CVI value against the number of clusters because the lack of a sharp peak means that the position of the maximum is uncertain.

Double-Exposure Algorithm: A Powerful Approach to Address the Accuracy Issues of Fractional Vegetation Extraction under Shadow Conditions

When recording the vegetation distribution with a camera, shadows can form due to factors like camera angle and direct sunlight. These shadows result in the loss of pixel information and texture details, significantly reducing the accuracy of fractional vegetation coverage (FVC) extraction. To address this issue, this study proposes an efficient double-exposure algorithm. The method reconstructs the pixel information in shadow areas by fusing normal-exposure and overexposed images. This approach overcomes the limitations of the camera’s dynamic range in capturing pixel information in shadowed regions. The study evaluates images with five levels of overexposure combined with five vegetation extraction indices. The aim is to determine the best-performing double-exposure combination under shadow conditions and the most suitable vegetation index. Experimental results reveal that the R² value between the best vegetation index and the FVC calculated from the fused double-exposure images and the ground truth FVC increases from 0.750 to 0.969. The root mean square error (RMSE) reduces from 0.146 to 0.046, and the intersection over union (IOU) increases from 0.856 to 0.943. These results demonstrate the excellent vegetation extraction capability of the double-exposure algorithm under shadow conditions, offering a straightforward and effective solution to low accuracy of FVC in shadowed areas.

Artificial Neural Networks for Drought Forecasting in the Central Region of the State of Zacatecas, Mexico

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R2 (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better.

Somatic cell count as an indicator of subclinical mastitis and increased inflammatory response in asymptomatic lactating women.

This pilot study suggests that SCC at a level of (greater than or equal to) 250,000 cells mL-1, as used in the dairy industry, is a suitable index to identify asymptomatic subclinical mastitis in lactating women since it reflects a significant increase in the inflammatory response compared to milk samples from healthy women. Using this index should aid studies into the short- and long-term consequences of subclinical mastitis for mother and infant.

Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI.

Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.

Identifying regional hotspots of heatwaves, droughts, floods, and their co-occurrences

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{S}$$\\end{document}) and anomalous temperatures (HWIE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{E}$$\\end{document}), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{S}$$\\end{document}, SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.

Novel approach for forest road maintenance using smartphone sensor data and deep learning methods

ABSTRACT High costs primarily pose challenges to forest management in planning and executing the repair of forest roads. With budget limitations and inadequate oversight, it has become critically essential to monitor the state of these roads. Monitoring the condition of forest roads has become imperative, driven by budget constraints and a lack of effective supervision. While smartphones have proven effective in detecting road defects on public roads, their application on forest roads is hindered by the absence of suitable indices and software infrastructure. Addressing this gap, this research focuses on the development of the Forest Road Pavement Condition Index (FRPCI) to facilitate smartphone-based monitoring. We collected and compared data from 4 kilometers of forest roads, employing two traditional harvesting methods alongside smartphone sensor data. Utilizing deep learning methods, including Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and CNN-LSTM, we processed the collected data. Signal processing using GPS data, coupled with wavelet transformation, demonstrated promising results with an accuracy and recall exceeding 80%. The proposed system functions as a distributed information system, transitioning data from organizational mode to field mode. It measures damage, assesses forest road conditions, and leverages image processing and GPS technologies. This monitoring system technology offers capabilities for preparing, storing, updating, maintaining, and analyzing diverse information. Importantly, adopting this method can significantly reduce operating costs, making forest road monitoring for maintenance purposes more feasible.