Current Assessment Methods Research Articles

The inadequacy of current carbon storage assessment methods for rewilding: A Knepp Estate case study

Abstract In the context of global climate change mitigation, carbon storage in woody vegetation plays a crucial role. Recognising the value of the i‐Tree Eco model for carbon storage in urban and forestry settings, this study aimed to explore its applicability to rewilded landscapes. Using direct measurements from destructively sampled scrub from the Knepp Estate, our goal was to determine the model's suitability to this landscape. Our findings reveal that these methods are not appropriate for multi‐stemmed trees below browsing height, as we observed no significant relationship between stem basal diameter and height. The i‐Tree tool's assumption of below‐ground biomass being 26% of above‐ground biomass may not be applicable to herbivore‐influenced landscapes. Additionally, we found that, on average, scrub at Knepp had more biomass below the ground than above, with a root:shoot ratio of 1.07, which is more than four times the amount predicted by current models using the 0.26 estimate ratio. This study underscores the need for novel allometric approaches that consider species‐specific biomass and the impact of external factors, such as herbivory, on carbon storage. Accurate carbon accounting in future rewilding projects is essential for their contribution to both biodiversity enhancement and climate change mitigation. While the i‐Tree Eco model provides valuable insights for many ecosystems, our findings suggest that its applicability may be limited in scrubland ecosystems, especially in rewilded landscapes where natural processes create semi‐stable scrub and open wood pastures. Nonetheless, with suitable adjustments or when complemented with other methods, the i‐Tree Eco model could be a valuable tool for specific scrub or rewilding scenarios.

Target Damage Assessment Method Based on Interval‐Valued Intuitionistic Fuzzy Aggregation Operator under Dynamic Multi‐Expert

This study aims to solve the problem that the current target damage assessment method does not fully take into account the time series characteristics of the target and the dynamic changes in the battlefield situation, thus leading to insufficient accuracy of the assessment results. Target damage assessment method based on interval‐valued intuitionistic fuzzy aggregation operator (IVIFAO) under dynamic multi‐experts is a combination of interval‐valued intuitionistic fuzzy set (IVIFS) theory and dynamic multi‐attribute swarm decision‐making theory. Firstly, we extracted the effective factors that cause damage to the target and established the damage assessment index system. Secondly, in order to take into account the subjective weights and objective weights, we proposed a comprehensive weight model of damage factors based on interval‐valued intuitionistic fuzzy entropy (IVIFE) based on a cosine function (IVIFECF). The spatio‐temporal weights for each moment are determined using the Poisson distribution method. Then, given the inherent ambiguity and complexity of the decision‐making process, the decision‐making information is represented by interval‐valued intuitionistic fuzzy numbers (IVIFNs). We constructed a weighted interval‐valued intuitionistic fuzzy (WIVIF) decision matrix. Here, the interval‐valued intuitionistic fuzzy weighted geometric average (IVIFPWGA) operator is used to synthesize decision‐making information related to damage factors. We further introduce the dynamic interval‐valued intuitionistic fuzzy power‐weighted geometric average (DIVIFPWGA) operator, which combines instantaneous decision information with time series weights to generate a dynamic multi‐expert fusion WIVIF decision matrix. Finally, the improved TOPSIS method is used to rank the IVIFNs, resulting in a comprehensive damage assessment of the targets, which is analysed for superiority with the novel method, comparing both real‐time and scalability. The stability and validity of the method were verified.

Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection

Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.

ML-Pipeline for the Quality Assessment of Screwdriving Processes

Current quality assessment methods of screwdriving processes evaluate the final torque-angle combination only. Consequently, anomalies and errors during the screwdriving process may remain unnoticed. Furthermore, the cause of an error cannot always be determined. We present an ML-pipeline for the assessment of screwing operations based on time series data of the entire process. The analysis is performed using the publicly available AURSAD dataset. The transfer onto screwdriving processes of the commercial vehicle production is likewise discussed. We find that the ML-pipeline performance is highly dependent on the combined consideration of preprocessing methods and model training.

Exploration of uncertainty of PRx time trends

IntroductionPRx can be used as surrogate measure of Cerebral Autoregulation (CA) in traumatic brain injury (TBI) patients. PRx can provide means for individualising cerebral perfusion pressure (CPP) targets, such as CPPopt. However, a recent Delphi consensus of clinicians concluded that consensus could not be reached on the accuracy, reliability, and validation of any current CA assessment method. Research questionWe aimed to quantify the short-term uncertainty of PRx time-trends and to relate this to other physiological measurements. Material and methodsIntracranial pressure (ICP), arterial blood pressure (ABP), end-tidal CO2 (EtCO2) high-resolution recordings of 911 TBI patients were processed with ICM + software. Hourly values of metrics that describe the variability within modalities derived from ABP, ICP and EtCO2, were calculated for the first 24h of neuromonitoring. Generalized additive models were used to describe the time trend of the variability in PRx. Linear correlations were studied for describing the relationship between PRx variability and the other physiological modalities. ResultsThe time profile of variability of PRx decreases over the first 12h and was higher for average PRx ∼0. Increased variability of PRx was not linearly linked with average ABP, ICP, or CPP. For coherence between slow waves of ABP and ICP >0.7, the variability in PRx decreased (R = −0.47, p < 0.001). Discussion and conclusionPRx is a highly variable parameter. PRx short-term dispersion was not related to average ICP, ABP or CPP. The determinants of uncertainty of PRx should be investigated to improve reliability of individualised CA assessment in TBI patients.

Research on the Application Effect of Metalearning Method Based on Prototype Network in Corrective Measures for Member State Audit

IMO Member State audits aim to identify non-compliant behavior with the requirements of relevant instruments, enabling the implementation of corrective measures to enhance performance. However, the complexity and diversity of IMO instruments’ requirements result in low evaluation effectiveness and efficiency in current assessment methods of implementation of IMO instruments. To address this challenge, this study proposes a meta-learning model based on prototype networks, focusing on the corrective measures outlined in consolidated audit summary reports approved and issued by the IMO Secretariat. The suggested model conducts meta-learning using small samples, offering a swift and straightforward assessment method. It facilitates the fine classification of corrective measures, providing a way for the consistent and effective assessment of various countries’ current implementation practices. Empirical results of two strategies demonstrate improved classification accuracy. In comparison with traditional manual evaluation, the proposed method achieves accuracy value 71.61% and 65.78% in two strategies respectively. Furthermore, the model exhibits varying prediction accuracy across different articles and demonstrates robust generalization capabilities, highlighting its practicality.

Unsupervised structural damage assessment method based on response correlations

Structural damage assessment for civil engineering structures has become an important research topic and has gained much attention for decades in the field of structural health monitoring (SHM). Current unsupervised structural damage assessment methods can achieve damage detection for practical civil engineering structures. However, these methods cannot fully mine the response correlations among sensors, thus are difficult to localize damage in practical engineering applications. Therefore, this study proposes an unsupervised structural damage assessment method with bidirectional long short-term memory (BiLSTM) networks to establish the response correlations among sensors. Numerical studies of a beam-like bridge model, an experimental study of Yellow Frame, and an in-field study of Yonghe Bridge were conducted to validate the accuracy and reliability of the proposed approach. The results show that unsupervised structural damage identification and localization can be achieved even in the case of sparse sensor deployment due to the fact that the response correlations among sensors can be well extracted. The proposed approach can be applied to damage identification of practical engineering structures under operational and ambient excitations and can automatically provide damage location warnings to ensure structural health and prevent catastrophic events.

Assessing Climate Disaster Vulnerability in Peru and Colombia Using Street View Imagery: A Pilot Study

Community and household vulnerability to natural hazards, e.g., earthquakes, hurricanes, and floods, is a concern that transcends geographic and economic boundaries. Despite the abundance of research in this field, most existing methods remain inefficient and face the challenge of data scarcity. By formulating and investigating the correlation between the household vulnerability and street view images of buildings, this research seeks to bridge the knowledge gap to enable an efficient assessment. Especially in developing countries, the widespread prevalence of outdated or inadequately enforced building codes poses a significant challenge. Consequently, a considerable portion of the housing stock in these regions fails to meet acceptable standards, rendering it highly vulnerable to natural hazards and climate-related events. Evaluating housing quality is crucial for informing public policies and private investments. However, current assessment methods are often time-consuming and costly. To address this issue, we propose the development of a rapid and reliable evaluation framework that is also cost-efficient. The framework employs a low-cost street view imagery procedure combined with deep learning to automatically extract building information to assist in identifying housing characteristics. We then test its potential for scalability and higher-level reliability. More importantly, we aim to quantify household vulnerability based on street view imagery. Household vulnerability is typically assessed through traditional means like surveys or census data; however, these sources can be costly and may not reflect the most current information. We have developed an index that effectively captures the most detailed data available at both the housing unit and household level. This index serves as a comprehensive representation, enabling us to evaluate the feasibility of utilizing our model’s predictions to estimate vulnerability conditions in specific areas while optimizing costs. Through latent class clustering and ANOVA analysis, we have discovered a strong correlation between the predictions derived from the images and the household vulnerability index. This correlation will potentially enable large-scale, cost-effective evaluation of household vulnerability using only street view images.

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h “golden window”. However, the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge. To address this gap, this work suggests an integrated method of Crossing Graph attention network and xgBoost (CGBoost). This method contains three branches, which extract the interrelations among pixels within a slope unit, the interrelations among various slope units, and the relevance between influencing factors and landslide probability, respectively, and obtain rich and discriminative features by an adaptive fusion mechanism. Thus, the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced. As a basic module of CGBoost, the proposed Crossing graph attention network (Crossgat) could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results. Moreover, the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance, and the prediction index set is established by terrain, geology, human activity, environment, meteorology, and earthquake factors. CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area. 3.43% of coseismic landslides are randomly selected, of which 70% are used for training, and the others for testing. In the testing set, the values of Overall Accuracy, Precision, Recall, F1-score, and Kappa coefficient of CGBoost attain 0.9800, 0.9577, 0.9999, 0.9784, and 0.9598, respectively. Validated by all the coseismic landslides, CGBoost outperforms the current major landslide susceptibility assessment methods. The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

Classification and Evaluation of Current Climate Vulnerability Assessment Methods

Classification and Evaluation of Current Climate Vulnerability Assessment Methods

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

Serviceability Limit State Assessment of Semi-Submersible Floating Wind Turbines

Abstract The design of a floating wind turbine (FWT) should satisfy the serviceability limit state (SLS) requirement for an efficient and safe operation throughout the entire work life. The SLS requirements are introduced by the owner/developer of the wind turbine facility to achieve serviceability (production of power) or an efficient operation of the facility or a “first step” towards ensuring safety. Currently, there is limited information about SLS requirements in design standards. This study deals with an assessment of current methods, criteria, and procedure for the SLS design check with an emphasis on tilt/pitch and nacelle accelerations in view of power production and its fluctuations. Moreover, other criteria, on the borderline between serviceability and safety criteria, e.g., relating to clearance, are briefly discussed. The criteria relating to power production are illustrated in a case study with a 10-MW semi-submersible FWT considered for an offshore site in the Northern North Sea. Simplified static/dynamic analysis methods for use in the global design phase and high fidelity integrated, dynamic analysis methods for detailed design in terms of serviceability are presented, discussed, and applied in the case study. A good understanding of wind turbine dynamic performance associated with serviceability is essential to facilitate design decision-making. The relative contribution of wind and wave loads to the different SLS criteria is investigated. Finally, the main conclusions are summarized. In lieu of the current state of the art regarding SLS requirements for FWTs, we hope that this study provides a basis for improving design standards and guiding research and engineering practice for the semi-submersible floater design of FWTs.

Low Urinary Free Cortisol as a Risk Factor for Patients with Variceal Bleeding.

Background and Objectives: Specificity and reliability issues of the current cortisol assessment methods lead to limitations on the accurate assessment of relative adrenal insufficiency. Although free cortisol provides a more accurate evaluation of adrenal cortisol production, the expense and time-consuming nature of these assays make them impractical for routine use. Research has, thus, focused on alternative methods, such as indirectly measuring free cortisol using Coolens' equation or directly assessing salivary cortisol concentration, which is considered a more favorable approach despite associated challenges like sampling issues and infection risks. The aim of this study was to explore correlations between 24 h urinary free cortisol (UFC), free plasma cortisol, serum total cortisol, and salivary cortisol as potential reliable indices of free cortisol in the setting of variceal bleeding. Additionally, we assessed the predictive value of UFC for 6-week mortality and 5-day treatment failure in patients with liver cirrhosis and variceal bleeding. Materials and Methods: A total of 40 outpatients with liver cirrhosis and variceal bleeding were enrolled. Free cortisol levels in serum, saliva, and urine were assessed using the electrochemiluminescence immunoassay method. For the measurement of plasma-free cortisol, a single quadrupole mass spectrometer was employed. The quantification of free cortisol was fulfilled by analyzing the signal response in the negative ESI-MS mode. Results: UFC was significantly correlated to free plasma cortisol. Negative correlations were demonstrated between UFC, the Child-Pugh (CP) score, and C reactive protein (CRP) levels. In the multivariate analysis, CP stage C was associated with 6-week mortality risk and portal vein thrombosis with 5-day treatment failure using Cox regression and binary logistic regression analyses, respectively. Patients who experienced rebleeding, infection, or death (or any combination of these events) presented with lower levels of UFC. Conclusions: This study suggests that low levels of UFC may impose a risk factor for patients with liver cirrhosis and variceal bleeding. The use of UFC as an index of adrenal cortisol production in variceal bleeding warrants further investigation.

Composition and micromechanical properties of the femoral neck compact bone in relation to patient age, sex and hip fracture occurrence.

Current clinical methods of bone health assessment depend to a great extent on bone mineral density (BMD) measurements. However, these methods only act as a proxy for bone strength and are often only carried out after the fracture occurs. Besides BMD, composition and tissue-level mechanical properties are expected to affect the whole bone's strength and toughness. While the elastic properties of the bone extracellular matrix (ECM) have been extensively investigated over the past two decades, there is still limited knowledge of the yield properties and their relationship to composition and architecture. In the present study, morphological, compositional and micropillar compression bone data was collected from patients who underwent hip arthroplasty. Femoral neck samples from 42 patients were collected together with anonymous clinical information about age, sex and primary diagnosis (coxarthrosis or hip fracture). The femoral neck cortex from the inferomedial region was analyzed in a site-matched manner using a combination of micromechanical testing (nanoindentation, micropillar compression) together with micro-CT and quantitative polarized Raman spectroscopy for both morphological and compositional characterization. Mechanical properties, as well as the sample-level mineral density, were constant over age. Only compositional properties demonstrate weak dependence on patient age: decreasing mineral to matrix ratio (p = 0.02, R2 = 0.13, 2.6 % per decade) and increasing amide I sub-peak ratio I~1660/I~1683 (p = 0.04, R2 = 0.11, 1.5 % per decade). The patient's sex and diagnosis did not seem to influence investigated bone properties. A clear zonal dependence between interstitial and osteonal cortical zones was observed for compositional and elastic bone properties (p < 0.0001). Site-matched microscale analysis confirmed that all investigated mechanical properties except yield strain demonstrate a positive correlation with the mineral fraction of bone. The output database is the first to integrate the experimentally assessed microscale yield properties, local tissue composition and morphology with the available patient clinical information. The final dataset was used for bone fracture risk prediction in-silico through the principal component analysis and the Naïve Bayes classification algorithm. The analysis showed that the mineral to matrix ratio, indentation hardness and micropillar yield stress are the most relevant parameters for bone fracture risk prediction at 70 % model accuracy (0.71 AUC). Due to the low sample number, further studies to build a universal fracture prediction algorithm are anticipated with the higher number of patients (N > 200). The proposed classification algorithm together with the output database of bone tissue properties can be used for the future comparison of existing methods to evaluate bone quality as well as to form a better understanding of the mechanisms through which bone tissue is affected by aging or disease.

Augmented reality for surgical skills training, update on the topic

Augmented reality (AR) combines digital information with physical reality, allowing users to interact with virtual data in their real environment. In medical education, this technology is attractive because it allows access to information without taking your eyes off the surgical field. The integration of surgical simulators in resident training offers flexible practice without direct supervision, with benefits such as objective performance evaluation, practice of unusual procedures, and the development of non-technical skills. In addition, simulators are useful for teaching new techniques to experts. In summary, AR and simulators offer valuable opportunities to improve surgical training. The objective of this review is to update the current state of augmented reality in surgical training. The use of augmented reality as an assessment tool in surgical training presents interesting perspectives that deserve consideration. AR can provide objective performance metrics by measuring the technical competency of surgical trainees, whether in a simulated operating environment or in real-world situations. This offers the opportunity to address subjective variability and potential bias in current assessment methods, which often rely on supervisor observation and rating. However, despite these advantages, there has not yet been a comprehensive review to evaluate the use of AR in surgical training. The cost-benefit and implications for data management have not yet been addressed.

Qualified placebo for trials of herbal medicine treatment in rare diseases? A cross-sectional analysis

BackgroundWhile substantial placebos have been used in herbal medicine (HM) clinical trials for rare diseases, the use and quality of reporting of HM-placebo remain unclear. We aim to describe the use of HM-placebo in clinical trials for rare diseases and determine the quality of reporting in these trials.MethodsThis is a cross-sectional study. We searched PubMed, Embase, Web of Science, SinoMed, China National Knowledge Infrastructure, WanFang database, China Science and Technology Journal Database, National Institute of Informatics Support Academic Information Services, ClinicalTrials.gov and Chinese Clinical Trials Registry from their inception date to 14 February 2023 to identify registered and published trials that use placebos as a comparator in rare diseases. We collected data on placebo use reporting and the efficacy and safety of placebo. Descriptive statistics, the Chi-square test, and Binary multivariable logistic regression analysis were used to determine the placebo characteristics of the HM trial and its effect on reporting.ResultsAmong the 55 studies, we included that with a median administration time of placebo of 84 days (IQR 42–180) and a median placebo sample size of 30 (IQR 24–54). About half of the trials (27, 49.1%) did not provide their ethical approvals, and one trial had details of informed consent. None of the studies were fully reported and more than half of the items reported less than 50%. A total of 10 trials (18.2%) of placebo has active ingredients even though none of them performed pharmacological inert tests. Of the 29 studies with available data on adverse events, 5 (17.2%) trials did not show a better safety profile in the placebo group. Under the context that a relatively high-quality report is defined as a report with more than 9 items, there was a statistically significant difference between the two groups in the rate of relatively high-quality reports of the administration time (p = 0.047, OR 0.10, 95% CI 0.01 to 0.90), but the results are not representative.ConclusionThe overall situation of HM-placebo in the field of rare diseases was poor. In particular, the placebo is tied to the quality of trials, and poor placebo hinders the generation of high-quality evidence for herbal clinical trials in the field of rare diseases. We summarize the current methods of assessment involved in the use of placebos and propose various considerations for placebos in different contexts. Our study can greatly promote rare disease researchers to review the quality of their placebo and clinical trials. It is imperative to guarantee that meticulously conducted research generates clinical evidence of the highest caliber. We also expect that in the future, more rigorous relevant standards about the reporting and design of HM-placebo will be developed. High-quality clinical trials are the prerequisite for the wide clinical application of herbal medicines for rare diseases.

Assessing flexibility by ramping factor in power systems with high renewable energy proportion

Current flexibility assessment methods (FAMs) have limitations, such as difficulty in evaluating entire grids with static FAMs and challenges in grid planning with interval FAMs. While probability FAMs are commonly used for grid planning, they are unable to provide an analytical mathematical model, leading to slow and complex solutions. To address these issues, this paper proposes a ramping factor-based FAM. This method not only guides flexible planning of the entire grid but also offers simplicity and speed in its application. As the flexibility metric, the ramping factor can evaluate the flexibility of a node or a grid. The ramping factor of a local grid is defined by that of the generalized node in the higher-level grid, and the concept of the generalized node is from electrical grid theory. Building upon this, a mathematical model is developed to optimize the allocation of flexible resources, providing guidance for the planning of these resources in the local grid. The proposed method’s feasibility and effectiveness are verified through a case analysis on an improved IEEE 30-Bus System with high renewable energy proportion, demonstrating its advantages over traditional probability assessment methods.© 2017 Elsevier Inc. All rights reserved.

Dynamic Changes in Lymph Node Size and Skin Scoring Predict Response to Treatment in CTCL: An Interim Analysis

Cutaneous T-cell lymphoma (CTCL) is a heterogenous group of non-Hodgkin T-cell lymphomas with predominant skin involvement. Mycosis fungoides and Sézary syndrome are the most common subtypes and present with a wide spectrum of cutaneous and extra-cutaneous manifestations. Early-stage disease is often controlled with skin-directed therapies while advanced-stage disease can be treatment-refractory and often requires multiple lines of systemic therapy. It is therefore of great interest to identify early predictors of treatment response to tailor therapeutic management decisions. Modified Severity-Weighted Assessment Tool (mSWAT) scoring and lymph node evaluation are important components of treatment response assessment in clinical practice and in trials. A major limitation of current assessment methods is the use of relative changes in mSWAT and lymph node size which do not account for heterogeneous treatment effects. Novel assessment techniques that reflect the dynamic changes occurring within the tumor environment during treatment have the potential to offer more accurate insights into treatment response than current methods. Among these novel approaches is kinetic modeling of tumor growth rate ( g), which has been validated as a biomarker for treatment response and survival in other types of cancer but has never been used in CTCL. Kinetic modeling provides the benefit of evaluating the simultaneous rates of growth of treatment-resistant cells and regression of treatment-responsive cells. We hypothesized that dynamic changes in lymph node size and mSWAT scores over time could predict response to treatment in CTCL patients. We retrospectively analyzed clinical data and computed tomography scans from 119 patients enrolled in the MAVORIC trial. An open-source imaging platform with integrated semi-automated segmentation algorithms was used to quantify lymph node volumes for each patient. Kinetic modeling was applied to estimate the simultaneous rates of growth ( g) and regression ( d) of lymph node size and mSWAT scores during treatment. Patients were divided into two groups based on g and correlation with time to treatment failure (TTF) was evaluated. Tumor doubling times were estimated using the equation dt = 0.693/ g. For most patients, g and d could be estimated, and these values could be determined early in the treatment course. Volumetric lymph node growth rate had an inverse correlation with TTF (p &amp;lt; 0.0001). Patients with with faster rates of nodal growth (median g = 0.0077 d -1, doubling time = 90 days) had a median TTF of 2.9 months, whereas patients with slower rates of nodal growth (median g = 0.0013 d -1, doubling time = 533 days) had a median TTF of 8.8 months. Rate of growth of mSWAT scores also had an inverse correlation with TTF (p &amp;lt; 0.0001). Those with the fastest rate of increase in mSWAT scores (median g = 0.0057 d -1, doubling time = 122 days) had a median TTF of 2.9 months while those with the slowest rate of increase (median g = 0.0013 d -1, doubling time = 533 days) had a median TTF of 8.5 months. In this interim analysis, we use a novel kinetic model to demonstrate that g correlates with time to treatment failure, suggesting that lymph node and mSWAT growth kinetics may be early predictors of treatment response in CTCL. Our findings have potential implications for both clinical practice and trials. Lymph node and mSWAT growth rates may support clinicians in making early decisions about adapting treatment regimens for individual patients. In clinical trials and early drug development, the incorporation of g as an endpoint may accelerate the recognition of promising drug candidates. Our findings require validation in a larger cohort as well as prospective studies.

SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

The development of information and communication technology and its wide application in industrial control systems (ICSs) has brought a growing number of security risks to ICSs. Quantifying and dynamically assessing the security risks of ICSs is of great significance to protect ICSs from cyber attacks. Current risk assessment methods, however, do not take into account social engineering (SE) attacks and the potential cyber-to-physical risks associated with cyber attacks. To address these issues, we propose a novel method for the dynamic security risk assessment of ICSs, called SEAG. Specifically, the scheme first extends and modifies three metrics in the common vulnerability scoring system to four metrics for objectively calculating the exploit probability of SE. Then, we construct the attack graph by relying on the knowledge graph that integrates three kinds of knowledge including SE knowledge, common vulnerability knowledge, and control system knowledge. In addition, we combine real time attack data that causes system performance loss with the industrial protocol function code attack detected by the intrusion detection system to accurately quantify the potential cyber-to-physical risks associated with cyber attacks. This method allows us to dynamically assess the security risks of ICSs in real time. Finally, the method is verified by one simulation testbed, which shows the effectiveness and accuracy of the proposed method for dynamic quantitative evaluating security risks of ICSs.

Mapping urban green equity and analysing its impacted mechanisms: A novel approach

Current assessment methods of Urban green equity (UGE) ignore the social differentiation of urban space, which may lead to a misallocation of green space resources, exacerbating differentiation, and potentially giving rise to green gentrification (GG). This study employs a novel evaluation framework and approach for UGE, considering the impact of landscape and greening on Spatial Differentiation of Residential Areas (SDORA), to evaluate the UGE of the west bank core area in Changsha city, Hunan Province, China, and explores its influencing mechanisms. The results show that (1) The study area exhibits significant greening differentiation, and residential areas with high property prices have higher quality landscapes and greening than those with low property prices. (2) The region displays significant green equity disparity (GEI = 0.497) and GG. (3) The cumulative contribution of landscape and greening to SDORA reaches 39.04 %, with the features of water body and mountain proximity as well as accessibility having the most significant impact on urban green space differentiation. This study enhances the understanding of UGE assessment, emphasizing the importance of considering spatial differentiation during UGE evaluation, and provides technical support and scientific guidance for the planning and construction of urban green space systems.