Rational Assessment Research Articles

Study on the Enantioselective Separation, Dissipation, and Residue of Chiral Fenpropathrin in Vegetables by Supercritical Fluid Chromatography-Tandem Mass Spectrometry (SFC-MS/MS).

Chiral fenpropathrin is a widely used pyrethroid insecticide. A fast supercritical fluid chromatography-tandem mass spectrometry (SFC-MS/MS) method was developed for the enantioselective study of fenpropathrin in five kinds of vegetables. The enantioseparation could be completed in 2 min, and the recoveries were 75.5-107.7% with RSDs of ≤15.2%. The dissipation half-lives of R-fenpropathrin and S-fenpropathrin were 2.32-3.62 days and 1.89-3.56 days, respectively. The dissipation of fenpropathrin was the fastest in cucumbers and the slowest in celery cabbage. R-Fenpropathrin was preferentially dissipated in celery and cucumber, which was opposite in lettuce and pakchoi. The final residues of rac-fenpropathrin in celery cabbage, pakchoi, and lettuce were a little above the European Union's maximum residue limits (MRLs) but less than the China's MRLs. The acute and chronic dietary intake risks of rac-fenpropathrin for all age groups were acceptable. The results would provide a basis for the rational application and risk assessment of fenpropathrin.

Development and Application of a Tornado Database for the Chinese Mainland

This study develops a preliminary tornado database for the Chinese mainland using information provided by the Yearbook of Meteorological Disasters in China (YMDC) from 2003 to 2019 and other public media report data. This database includes tornado occurrence time, geographical location, intensity, and damage descriptions. A modified Enhanced Fujita (EF) scale criterion adapted to the description of the damage indicator (DI) and degree of damage (DOD) in the YMDC and media reports is presented to better estimate the tornado intensity. The spatial and temporal distribution characteristics of tornadoes in Chinese mainland are examined. A stochastic simulation algorithm is proposed to perform a risk assessment of tornado hazards. The occurrence of tornadoes is randomly sampled using negative binomial distribution. The kernel density estimation (KDE) method based on the Gaussian kernel is applied to estimate the probability density of geographically dependent tornado occurrence before randomly generating tornado occurrence locations. The probability and conditional probability of tornado occurrence with different intensity levels are calculated for different counties in Jiangsu and Guangdong Provinces using Bayes’ theorem. The database provides a forward step toward rational assessment of tornado-induced disasters in Chinese mainland. The database can be accessed at https://doi.org/10.4121/19801633.v1 (Zhang et al., 2022).

Aggregated economic damages and the role of dynamic carbon tax under climate change: A dynamic general equilibrium model

A rational assessment of the aggregated economic damages and a more effective carbon tax are crucial for climate governance. However, existing studies still under-recognize the utility damages and dynamic carbon taxes. In this study, we construct a multi-regional dynamic general equilibrium model considering both production and utility damages. Then, this paper re-estimates the regional aggregated economic damages reasonably (including utility damages) and compares in detail the role of static carbon tax, dynamic carbon tax and regionally optimal dynamic carbon tax (RCT). We found that: (1) As climate change intensifies, the world will suffer >8 % of production damage and 1 % of utility damage by the end of the 21st century. Of these, the US and China face more pronounced economic damage. (2) If the carbon tax is implemented only in some regions, other regions will have a “free rider” benefit. An environmentally friendly dynamic carbon tax (fast and then slow) would be more effective, giving economies room to adapt to the carbon tax. The risk of exacerbating global inequality needs to be guarded against when using carbon tax. (3) Based on the regional social cost of carbon (R-SCC), implementing RCT is most effective for curbing climate change and achieving warming targets. This paper contributes to improving the understanding of assessing regional economic damage from climate change and recognizing the role of dynamic carbon tax.

Claiming the Reality (of Loss): Some Remarks on the Screenplay, Novel and FilmKwiecień (April, 1961) by Witold Lesiewicz and Józef Hen

The article discusses the interrelationships between Józef Hen’s novel Kwiecień (April), his two unpublishedscreenplays based on it, and Witold Lesiewicz’s fi lm Kwiecień (April, 1961), a canonicalwork of the Polish film school. The key problem concerns the question of the sense of sendingsoldiers to fight in the final days of the war, when its final outcome is already determined. Whenwriting his novel and its screenplay versions, Józef Hen referred to his own experience in April1945, when he fought in the ranks of the Second Polish Army and personally witnessed the tragedyof the soldiers dying in the final hours of the war, when personal ambitions and attitudes of commanderstook precedence over a rational assessment of the situation at the front. Hen’s noveland its screenplay and fi lm versions can therefore be seen as attempts at claiming the realityof loss. This is one of the main themes of Józef Hen’s writing. In April it is reflected in the attitudesof the main characters, as shaped by various dimensions of loss.

Evolution of hydrochemical characteristics and the influence of environmental background in the Hailar River basin, China.

Understanding the evolution of hydrochemical characteristics in river systems is essential for environmental assessment and water resource management. This study explores the spatiotemporal distribution and the determinants of hydrochemical characteristics in the Hailar River basin, China, over an extensive period. Our results revealed that CODMn and CODCr were the primary concerns for long-term river management, with exceedance rates of 42.92% and 50.62%, respectively. These exceedances were predominantly driven by interactions between riparian soils and surface water, rather than anthropogenic pollution, as suggested by the strong correlations between dissolved organic carbon and soil water-extractable organic carbon, and the limited human footprint in this region. Piper trilinear and Gibbs diagram analysis further revealed that long-term rack weathering shaped the basin's hydrochemical characteristics, resulting in distinct HCO3--Ca2+ and HCO3--Ca2+-Na+ signatures. In addition, APCS-MLR analysis identified that elevated of CODMn and CODCr levels were mainly attributed to the interactions with adjacent soils, which are extensively covered by forests and grasslands. In contrast, leaching and migration processes contributed significantly on total dissolved solids and total phosphorus. The study also found that environmental self-purification processes played a key role in regulating Fe concentrations. This investigation provides a nuanced understanding of the environmental background's influence on hydrochemistry and dissolved organic matter (DOM) in the Hailar River basin, which offers valuable insights and methodologies for the rational assessment of water quality and aquatic ecosystem health in similar riverine systems.

An algorithm for building contour inference fitting based on multiple contour point classification processes

Extracting buildings from True Digital Ortho Maps often encounters occlusions and misidentifications, making it challenging to obtain complete, regular, and accurate building contours. To address this issue, we developed a building recognition process based on the Segment Anything Model, and proposed a novel regularization algorithm for building contour inference and fitting, which quantifies the confidence levels of contour points to accurately fit building contours from data containing substantial noise, and reformulates the fitting problem as progressive node classification tasks consisting of contour simplification, iterative regularization, and rationality assessment. In experimental evaluations, the proposed contour fitting algorithm achieved 97.99 % Intersection over Union (IoU), 95.39 % consistency with the standard contour edge count, and 88.06 % of cases with Hausdorff distances less than or equal to 15 pixels (30 cm), significantly outperforming comparative methods. Notably, it was the only contour regularization algorithm that improved IoU (1.03 %) compared to the original contours. The experimental results demonstrate that the proposed algorithm effectively suppresses noise and infers incomplete building contours, producing accurate and regular contours comparable to manual delineation. It is particularly suitable for buildings with near-orthogonal structures, exhibiting significant practical applicability and generalization potential.

Insights into effects of conventional and biodegradable microplastics on organic carbon decomposition in different soil aggregates

The impacts of microplastics on soil ecological functions such as carbon recycling and soil structure maintenance have been extensively focused. However, the mechanisms underlying the impacts of microplastics on soil carbon transformation and soil microbial community at soil aggregate scale have not been clarified yet. In this work, the effects and action mechanisms of traditional microplastic polypropylene (PP) and degradable microplastic polylactic acid (PLA) on carbon transformation in three sizes of soil aggregates were investigated. The results showed that both PP and PLA promoted CO2 emission, and the effect depended on the type and content of microplastics, and the size of soil aggregates. Changes in soil carbon stocks were mainly driven by changes in organic carbon associated with macroaggregates. For macroaggregates, PP microplastics decreased soil organic carbon (SOC) as well as dissolved organic carbon (DOC). These changes were reversed in microaggregates and silt and clay. Interestingly, PLA increased the SOC, DOC and CO2 emissions in bulk soil and all three aggregates with a dose-effect response. These changes were associated with soil microbes, functional genes and enzymes associated with the degradation of labile and recalcitrant carbon fractions. Furthermore, PP and PLA reduced bacterial community diversities and shifted bacterial community structures in both the three aggregates and in bulk soil. Alterations of functional genes induced by microplastics were the key driving factors of their impacts on carbon transformation in soil aggregates. This research opened up a new insight into the mechanisms underlying the impacts of microplastics on soil carbon transformation, and helped us make rational assessments of the risks and the disturbances of microplastics on soil carbon cycling.

Obraz nienawiści w polszczyźnie (w świetle danych leksykograficznych)

The article is an attempt to construct a partial image of hate in Polish, following the principles of Lublin cognitive ethnolinguistics. The data come from general and specialized dictionaries of Polish, specifically concerning the words nienawidzić ‘to hate’ and nienawiść ‘hatred, hate’: their contemporary and earlier forms and meanings, typical uses, relationships to other lexemes (synonyms, antonyms), collocations and other multi-word units. The analysis has revealed the most entrenched (in Polish and the minds of its speakers) descriptive and evaluative aspects of the concept of hate. It has been found that hate is a feeling that arises primarily in relation to other people, and secondarily to reality at large. It is caused by a negative attitude to and evaluation of the object(s) of hate, triggering unpleasent experiences in the person who feels it. This, in turn, leads to an emotional, intellectual, and volitional stance, characterized by a profound dislike towards the object of hate, and even a will to cause harm. Hate is a strong and violent feeling (similar to fire), uncontrollable and depriving people who feel it of a rational assessment of the situation. It is long-lasting but also internally dynamic: its intensity may rise or fall, subject to change depending on the action taken.

Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours

The scientific and rational assessment of the evolution of node importance in rail transit line networks is important for the sustainability of transportation systems. Based on the complex network theory, this study develops a weighted network model using the Space L method. It first considers the network topology, the mutual influence of neighboring nodes of the transportation system, and the land use intensity in the station influence domain to construct a comprehensive index evaluation system of node importance. It then uses the covariance-weighted principal component analysis algorithm to more comprehensively evaluate the node importance evolution mechanism and analyzes the similarity and difference of the sorting set by adopting three different methods. The interaction mechanism between the distribution of important nodes and the evolution of land use intensity is explored in detail based on the fractal dimension theory. The Xi’an rail transit network is considered an example of qualitative and quantitative analysis. The obtained results show that the importance of nodes varies at different times of the day and the complexity of the morning peak is more prominent. Over time, articulated fragments with significance values greater than 0.5 are formed around the station, which are aligned with the direction of urban development, creating a sustainable mechanism of interaction. As the network’s crucial nodes in the center of gravity increase and the southern network expands, along with the increased intensity of the city’s land utilization, the degree of alignment in evolution becomes increasingly substantial. Different strategies for attaching the network, organized based on the size of Si can lead to the rapid damage of the network (reducing it to 0.2). The identification of crucial nodes highlighted in this paper serves as an effective representation of the functional characteristics of the nodes in transportation networks. The results obtained can provide a reference for the operation and management of metro systems and further promote the sustainable development of transportation networks.

ON SUBSTANTIATING THE NECESSITY OF CREATING AND USE OF CONSENSUS MANAGEMENT TECHNOLOGY FOR SUSTAINABLE DEVELOPMENT OF TERRITORIAL COMMUNITIES IN UKRAINE

Based on the analysis of the concept, essence and content of the term «consensus», the article suggests that it is quite appropriate to use it in the processes of practical management of territorial communities of Ukraine, which today urgently need to use innovative principles of public consensus formation in relation to solving the problems of their development. It is demonstrated that the issue of managing the processes of reaching consensus at the basic level of territorial communities of Ukrainian society has not been studied or researched at all. It is proved that the technology of consensus building requires consolidation of intellectual resources of the population of the territorial community, which, in turn, requires professional management based on the intellectualization of personnel and leadership in local self-government bodies. At the same time, the main tool for achieving consensus is the use by leaders and functionaries of the governing body of a constructive dialogue with the population, based on logic and rational assessments of realities, when the parties resolve all issues by coming to a common opinion and common agreement, which raises the problem of increasing the professionalism of governance on the basis of intellectual interaction, analytical thinking and creative approach to the adoption and implementation of management decisions based on the «dictatorship of reason» and the intellectual consensus of the leadership and the population. For this purpose, territorial communities should «...be governed not by the strongest, but by the smartest».

Rail crack identification of in-service turnout through Bayesian inference

In-service railway turnout, suffering from multi-factor coupling of operational and environmental loadings and special wheel–rail interaction, is prone to structural damage. The paper aims to develop a Bayesian damage identification method for crack-alike damage of the turnout rail under uncertainties. It consists of three core parts, including a damage index (DI) constructed by the transformation of time–frequency components of responses for generating damage-sensitive relationships, Bayesian models describing the crack-sensitive relationships hidden in the members of the damage index, and a mechanism synthesizing individual assessment results associated with the different reference models for providing one quantitative solution. The abnormal change in the stable relationships derived from the index can reflect damage occurrence and severity. The Bayesian approach is adopted to model the relationships under uncertainties of in-service railway turnout. The models trained by using monitoring data of the turnout rail before being damaged serve as a reference for healthy state, and deviations of actual observations from model predictions may indicate the existence of damage. The synthesizing process helps to offer a more rational assessment result through the weighted summation of individual quantitative assessment results. Rail monitoring data of a railway turnout are acquired to examine the damage detection performance of the proposed method. By exempting loadings measurement and physical model derivation, this data-driven methodology is potentially capable of supporting the damage identification and quantitative assessment of other structures in railway engineering.

Rock strength prediction based on machine learning: A study from prediction model to mechanism explanation

Rational and effective assessment of rock strength, especially uniaxial compressive strength (UCS), is the crucial basis of rock engineering practices. As computer technology advances, the application of machine learning techniques in predicting rock strength has become increasingly prevalent. However, due to the black-box effect of machine learning algorithms, the prediction process is always indescribable. Therefore, an innovative approach for the interpretable predicting uniaxial compressive strength (UCS) is initially introduced by using Explainable AI to crack the black-box effect of machine learning algorithms and explain the prediction mechanism. The findings reveal that the Xgboost model, optimized with interpretable hyper-parameters, achieves notable success in estimating the uniaxial compressive strength, with an RMSE of 14.69 and an R2 that can reach 0.9117. Additionally, multi-angle comparisons have verified that the robustness of the AOA-Xgboost model surpasses that of the other four hybrid models. The model employs SHAP analysis to measure how much each input factor contributes to the outcomes of the predictions. This work has potential for the prediction of crucial parameters and analysis of internal mechanisms of rock mass in geotechnical engineering.

Impacts of frost heave susceptibility and temperature-changing conditions on freeze-thaw deterioration of welded tuffs

Damage development in rocks by freeze-thaw cycles, a phenomenon that is typical to cold regions, is known to reduce the strength and durability of structures. In this study, a series of freeze-thaw tests were performed on frost and non-frost heave rocks to examine the impact of frost heave susceptibility and temperature-changing conditions on rock behaviors. The freeze-thaw life and uniaxial compressive strength (UCS) of rocks subjected to freeze-thaw cycles were estimated using a model that was based on fatigue damage mechanisms. Furthermore, we proposed a method for estimating the freeze-thaw life of rocks, using expansive strain as an important factor. The results indicated that the Shikotsu welded tuff (a non-frost heave rock) exhibited higher durability than the Noboribetsu welded tuff (a frost heave rock); this may be because the Shikotsu welded tuff had a larger pore radius and more unsaturated pores. The one-dimensional (1D) cooling-heating conditions induced significantly less damage than the three-dimensional (3D) conditions, owing to the continuous migration of free water into the unfrozen zone. The damage model estimated that for the Noboribetsu welded tuff, the freeze-thaw life in the 1D conditions was approximately eight times longer than that in the 3D conditions. Notably, with respect to the Shikotsu welded tuff, a critical freeze-thaw period induced significant expansion, resulting in damage development in the tuff. The freeze-thaw life of each rock sample was estimated based on the magnitude of the volumetric expansive strain. This study contributes to the rational assessment of the stability and durability of rock structures in cold regions.

Public enemies? The differential effects of reputation and celebrity on corporate misconduct scandalization

AbstractResearch SummaryWe explore misconduct scandalization's antecedents by focusing on the rational and emotional bases underlying reputation and celebrity, and considering how they can enhance or reduce the likelihood misconduct is scandalized as a function of the misconduct's objective and perceived severity. Specifically, we argue the quantifiable nature of objective misconduct severity enhances reputation's rational influence, but attenuates celebrity's emotion‐based appeal. Conversely, perceived misconduct severity reduces reputation's influence, while enhancing the media‐driven interest in celebrity firms' behaviors. Our findings based on corporate data breaches confirm that objective severity amplifies reputation's effect and attenuates celebrity's effect, while perceived severity amplifies celebrity's effect and attenuates reputation's effect. Our findings highlight the importance of social evaluations' sociocognitive content in understanding why only some misconduct becomes scandalized.Managerial SummaryCommitting misconduct is costly; having it scandalized is devastating. Yet little is known about how social evaluations influence why only some firms' misconduct is scandalized, beyond the vague notion that prominent firms' misconduct attracts media attention. We find that the rational and emotional bases of firms' evaluations matter. High reputation, based on the rational assessment of firms' capabilities, increases the likelihood of scandalization for objectively severe misconduct, and the influence of celebrity—originating from audiences' emotional resonance with firms' unconventional traits and behaviors—weakens as objective severity increases. Conversely, reputation's influence weakens, and celebrity's influence strengthens, as media “availability cascades” grow and increase perceived severity. In addition to providing a more realistic portrayal of media behavior, we offer insights into post‐misconduct communications and remedial actions.

Conspiracy beliefs in the context of a comprehensive rationality assessment

The recent intense interest in conspiratorial thinking is fuelled by the perception that belief in conspiracies is highly irrational. However, there have been few studies that have examined the associations of conspiracy belief with a comprehensive battery of rational thinking tasks that tap both epistemic and instrumental rationality. The Comprehensive Assessment of Rational Thinking (CART) provides an opportunity to do just that because one of the subtests on the CART assesses the tendency to believe false conspiracies. That subtest is in the part of the CART that measures the presence of contaminated mindware—stored declarative knowledge that embodies poorly justified beliefs. Converging analyses (N = 747) using the 18 subtests and four thinking dispositions measured on the CART indicated that three variables were key predictors of conspiratorial thinking: superstitious thinking, actively open-minded thinking, and probabilistic reasoning. Theoretical consideration of these best predictors, and of the variables that predict the endorsement of true conspiracies, led us to rethink the classification of conspiracy belief as contaminated mindware and move instead towards a conception of conspiratorial thinking as a cognitive style.

The “must” of laparoscopic evaluation in ovarian cancer. A brief review

Ovarian cancer (OC) is the fifth major factor in female mortality worldwide, for which consistent and precise assessment of its anatomical extent is recognized as a fundamental determinant in therapeutic planning as its multimodal management has evolved. Although complete surgery without residual disease is often the mainstay of OC treatment, radiological underestimation of stage can lead to futile laparotomies in presumed early-stage cases that are surgically restaged as more advanced. Despite the continuous improvement in the performance of medical imaging, its precise recognition during the classification assessment remains a challenge. With more regularity and success in the use of minimally invasive surgery, laparoscopic surgery further paves the way for a safe, feasible and more rational assessment strategy. The modality allows for recommended staging procedures to be performed while avoiding the morbidity associated with unnecessary laparotomies. The aim of this review is to provide an overview on laparoscopy assessment for ovarian cancer and the impact of its addition in therapeutic decision making.

Research on the Application of Kohonen Neural Networks in Football Clustering Analysis

This study applies Kohonen neural networks for clustering analysis in sports. By iteratively optimizing the objective function, it effectively avoids numerous subjective factors, providing a novel and efficient approach for obtaining objective clustering results. The results demonstrate that clustering analysis using Kohonen neural networks offers a clear practical value in evaluating the comprehensive strength of soccer teams. It serves as an effective method for rational, effective, objective, and quantifiable assessment of team tactics and strategies. Furthermore, this method is readily applicable to other comprehensive evaluations in competitive sports.

Influence of pore size distribution and applied cross-flow on ion rejection and separation

To improve the understanding of the separation mechanism of ions through membrane filtration, ion transport through a membrane with non-uniform pore sizes is investigated theoretically by solving the coupled Poisson-Nernst-Planck and Navier-Stokes equations under various conditions. The model focuses on a membrane with pore sizes following a log-normal distribution and considers the flow interaction between pores. The rejection performance for different pore arrangements is addressed for a rational assessment of the best ion rejection and selectivity in membrane filtration processes. In addition to the dead-end setting, a cross-flow is applied on the feed side to alleviate the concentration polarization effect. In the presence of positively charged nanopore surfaces, both the cases of a single salt (NaCl) and mixed salts (NaCl, MgCl2) present in the liquid are considered. We show that the uniform pore system outperforms non-uniform ones in terms of the overall rejection and selectivity between salts. When pore size polydispersity is present, the arrangement among the larger and smaller pores impacts the local rejection and volumetric flux of pores. Moreover, the competitive transport between cationic co-ions makes Mg2+ demonstrate generally higher rejection in all the pore arrangements considered. As uniform pores show lower flow rates, the separation efficiency of ions may be improved by pore size polydispersity at the same concentration and pressure difference.

Numerical simulation on natural gas hydrate depressurization production considering sediment compression effects

Sediment compression during submarine hydrate depressurization production causes changes in physical and mechanical characteristics, which in turn affects production results. In this paper, based on geological conditions of SHSC-4 well in Shenhu area, a theoretical model considering sediment compression effects is established by COMSOL, and the effects on the evolution of reservoir physical and mechanical characteristics within 60 days during depressurization production is simulated. The results show that model dimension effects can be ignored when the size l ≥ 100 m within 60 days. The effects of sediment compression on the physical characteristics of the reservoir are mainly realized by affecting the evolution of porosity, and the porosity reduction leads to permeability reduction. The larger the sediment compression coefficient, the higher the pore pressure in the reservoir. Sediment compression hinders the propagation of low pore pressure and heat transfer in the reservoir, which is unfavorable for hydrate decomposition favorable to hydrate reformation. A larger or smaller sediment compression coefficient selected in the simulation can lead to under- or over-estimate of reservoir gas production, so choosing an appropriate sediment compression coefficient when considering sediment compression is necessary for rational assessment of reservoir production behavior. In the production test site, when the production pressure is higher than 3 MPa, the pore pressure gradient is the key factor to promote gas production from the reservoir. When the production pressure is lower than 3 MPa, the sediment compression is the key factor to impede gas production from the reservoir.

How does evacuation risk change over time? Influences on evacuation strategies during accidental toxic gas releases

Evacuation may be the most necessary and effective method of saving human lives in an emergency where toxic gases are released. Therefore, there is a need for a rational assessment of the risk of evacuees from the affected area at different times. In this paper, a modeling framework is developed to assess and control the risk of the emergency evacuation process under toxic gas release. Data from geographic information systems, big data on the spatial aggregation of population activities, and multi-agent-based modeling are combined to formulate and simulate evacuation event scenarios under toxic gas release. The characteristics of evacuation message dissemination at different times were explored, while the risk of people evacuating at different times was assessed in terms of two dimensions: health and fear emotion. In this case study, the proposed modeling framework was used to assess the potential risks faced by people when following the original evacuation plan in the event of an accidental release of toxic gas. The results of the assessment show that the factors affecting the risk of the evacuation process include the geometric parameters of the sub-evacuation area (SEA; location of the SEA, delineation pattern of SEA, degree of crowding, complexity of the SEA environment, population density, and age structure of the population) and the rate of information communication. Therefore, a control strategy is proposed based on geometric optimization and emergency communication optimization. The results of evacuation risk control show that the latter has a wider range of applicability, while the former has a weaker impact on evacuation risk in densely populated areas. The study results can provide information for public emergency authorities to develop effective emergency evacuation plans.