Direct Loss Research Articles

Quantitative risk assessment of road exposed to landslide: A novel framework combining numerical modeling and complex network theory

The quantitative analysis of the landslide risk posed to road networks is a challenging task owing to the uncertainty involved both in the potential landslide hazard and the road value. To address this challenge, this paper proposes a novel framework to assess the road risk in quantitative terms. The landslide hazard is assessed using the depth-integrated method with consideration of the landslide size probability and initial conditions. The potential direct losses associated with road disruption are determined by exposure analysis in a geographical information system. The indirect losses of the road caused by the landslide were analyzed through complex network theory with consideration to regional socioeconomic development and the time required for road restoration. The proposed framework was used to assess the road risk posed by the Chunchangba landslide, Xiaojin County, China. The results show that the volume size probability of landslides in the Xiaojin area could be assessed using the function PVL=1/1+3.583×10−5VL2×0.391. The depth-integrated method based hazards assessment results show the maximum impact area of the Chunchangba landslide reached 0.383 km2, and the landslide has a high probability of damaging the lower road and forming a barrier lake. The losses associated with road disruption caused by the landslide were estimated. The maximum direct losses reached 293,100 USD, while indirect losses reached 423,800 USD, which has the same importance as direct losses. The risk curve reveals that the maximum probability of the road risk associated with the Chunchangba landslide is 0.0175 %, 0.0189 %, 0.0190 %, and 0.0191 % for the time interval of 5, 10, 20, and 50 years, respectively, with losses of 0.177 million USD/year. The regional disaster mitigation strategy is analyzed based on quantitative risk analysis. The results show that a new 2.7 km road on the mountain opposite the Chunchangba landslide can reduce indirect losses by approximately 300 times. The findings of this study contribute to sustainable development and landslide risk management in mountainous areas.

Estimation of Earthquake-Induced Direct Economic Losses of Portfolio Buildings Based on Seismic Fragility Surface

Regional seismic loss assessment is crucial for developing earthquake emergency plans, which can significantly reduce casualties and socioeconomic losses in urban communities. Due to the complex types of building structures in a region, it is not possible to accurately measure the damage state of building structures using a single scalar ground motion intensity measure (IM). To more accurately reflect the responses of structures under earthquake excitations, the seismic fragility surface models for 4 typical RC frame structures are developed based on the vector ground motion IMs. The results indicate that the fragility surface model offers a superior level of precision in capturing the inherent uncertainty of ground motion and subsequently determines a more precise probability of structural failure, outperforming traditional single scalar ground motion IM fragility curve models. This enhanced accuracy holds significant implications for regional loss assessment. To fully consider the spatial cross-correlation of vector ground motion IMs, based on the established ground motion database of 8778 records, principal component analysis (PCA) and geostatistical analysis methods are used to construct a spatial cross-correlation influence field that simultaneously considers multiple ground motion IMs to simulate more realistic earthquake scenarios. Considering the impact of uncertainty in loss ratio on earthquake-induced direct economic loss estimation results, the maximum entropy technique is used in conjunction with an improved Latin Hypercube Sampling (LHS) to achieve spatially uniform sampling of loss ratio (LR). This study proposes an assessment framework for regional seismic direct economic loss for portfolio buildings based on the seismic fragility surface model considering spatial cross-correlation and uncertainty of loss ratio. The proposed framework is verified by numerical examples, and it is proven that the regional portfolio buildings considering multiple ground motion IMs is more reliable and robust than those merely considering a single ground motion IM, and when spatial cross-correlation is not considered, the probability of serious economic losses will be underestimated.

Influence of retrofitting in the seismic behaviour of precast reinforced concrete industrial buildings

AbstractPrevious destructive earthquakes caused large economic losses as well as human losses. Seismic risk studies are of paramount importance to predict the impact of future earthquakes and support seismic risk mitigation policies. The present study presents a seismic risk assessment based on the cost and direct loss assessment of precast industrial buildings with different retrofitting levels in terms of horizontal strength. In this evaluation, a comparative analysis between the configurations of retrofitted buildings and the original building is performed. The effects of retrofitting solution strategies are first investigated in a single building. Then a study is carried out to assess which retrofit solutions level are most appropriate depending on the exposure and seismic risk. The results showed the use of bracing in the PRC building can improve seismic behaviour, with significant increases in horizontal strength. The cost of intervention is reduced, with a slight increase of less than 1% to almost 2% of the cost of the structure. From the derivation of seismic fragility functions for different retrofitting solutions level, the seismic risk analysis with dozens of existing PRC industrial buildings in Portugal points to a large reduction in economic losses as from retrofitted buildings, like an improvement of the seismic capacity of the buildings attributed to the retrofit systems considered. In general, among the different types of retrofit studied, there is an increase in the reduction of total economic losses, namely 49% when retrofitted with a 50% increase in horizontal strength, 58% when retrofitted with a 75% increase in horizontal strength and 64% for retrofitted with 100%.

Optimized Graph Sample and Aggregate‐Attention Network‐Based High Gain Meta Surface Antenna Design for IoT Application

ABSTRACTThis paper presents a High Gain Metasurface (HGM) antenna design optimized for IoT applications. The antenna operates at a 5.8 GHz frequency and utilizes a quarter‐wavelength unit cell structure. The design employs a Graph Sample and Aggregate‐Attention Network (GSAAN) to enhance the transmission characteristics of the metasurface. To optimize the performance of GSAAN, the Giza Pyramids Construction Algorithm (GPCN) is applied to adjust the weight parameters, leading to significant improvements in radiation efficiency, bandwidth, gain, directivity, and return loss. The proposed HGM antenna is simulated in MATLAB 2023b, where it demonstrates substantial performance gains over existing methods. Specifically, the proposed design achieves 28.34% higher gain compared to the MSA‐RHCOA method, 24.47% improvement over HGM‐AD‐MATCS, 26.34% better gain than BR‐HGA‐PGM, and a 32.12% gain increase relative to MSA‐Hyb‐ADSA‐HMSOA. These enhancements make the proposed antenna design a competitive solution for high‐gain applications in wireless communication, satellite communication, and radar systems. The integration of GSAAN with GPCN optimization in the HGM antenna design enables the creation of highly directive radiation patterns, making it well‐suited for IoT applications that require high performance and reliability. The results of this study demonstrate the effectiveness of the proposed approach in achieving superior antenna performance, positioning it as a strong alternative to existing metasurface antenna designs.

Improving Equipment Maintenance—Switching from Corrective to Preventative Maintenance Strategies

This paper explores different building maintenance strategies in commercial buildings in Sydney, Australia, focusing on corrective maintenance (CM) and preventive maintenance (PM). While CM involves rectifying issues after they occur, PM aims to enhance productivity by anticipating potential issues. Although PM seems more logical, the decision to implement this type of maintenance strategy are typically made based on item reliability, failure frequency, and downtime cost, commonly found in manufacturing facilities or critical environments. However, as found in the selected/surveyed commercial real estate buildings, CM was more frequently adopted in aged facilities with older infrastructure, and PM was favoured for buildings without structural deficiencies; however, operating equipment failures were common. However, in many cases, decision makers did not consider the broader effects of downtime beyond direct financial losses, costs associated with customer satisfaction, worker efficiency, rent abatements, and reputation damage. While each building is unique and may require a bespoke maintenance schedule, this study’s insights may help managers select the most appropriate maintenance strategy. Nonetheless, further research is needed to investigate the role of innovative technologies (such as machine learning and artificial intelligence) in enhancing maintenance efficacy and explore the influences of economic shifts, corporate and financial objectives, and the availability of technical resources.

Environmental Threat Assessment Framework for Mining Activities in Guinea: An Integrated Approach for Sustainable Development.

The present study aimed to investigate the environmental consequences of mining activities in boke bauxite mining areas and the Kerouane iron mining project in Guinea using a mixed-methods approach that combines quantitative and qualitative data. A reference matrix was used to evaluate the impact of the mining activities, classifying them as negligible, moderate, or significant. Data were collected from October 2022 to January 2023 by assessing their impact on water pollution, soil, noise, air quality, vegetation, fauna, and flora. These findings indicate concerns regarding the water pH, electrical conductivity, and turbidity in both the Boke and Kerouane regions. The soil composition analysis revealed the presence of Cr, Cu, Ni, and Zn in both the Boke and Kerouane project areas. Noise levels exceeded the guideline levels and substantial amounts of particulate matter (PM) were detected, with high levels of PM10 in mining operations. The Kerouane Iron Project resulted in the direct loss of 2929 ha of natural habitats and 466 ha of modified habitats, with the extraction of approximately 1.291 billion tons of ore anticipated over a 22-year mine lifespan. This study proposes an environmental threat assessment framework that integrates technical and human activity data to evaluate the environmental impacts of mining activities comprehensively. To promote sustainable development and minimize the negative impacts of mining, an integrated index of economic and environmental performance in the mining sector is recommended, along with collaboration between researchers and policymakers to develop effective climate-change mitigation strategies.

CRITICAL PEDAGOGICS IN THE VIEW OF H.A.R TILAAR AND ITS RELEVANCE FOR NATIONAL EDUCATION

The research was conducted to find out how the critical pedagogic concept in the view of Prof. H.A.R. Tilaar and its relevance to national education. The purpose of conducting this research is because nowadays, on this day of a very fast globalization, besides its usefulness, it also causes several problems, including the loss of direction from national diversity and dehumanization. It is challenging to require a form of critical reflective attitude that is presented to the community. This effort can very effectively occur if there is a transformation in education. The change from traditional pedagogy to critical pedagogy is an alternative way of dealing with globalization. The methodology used is descriptive qualitative methodology which makes literature study and document collection the primary thing in data collection. The findings of this critical pedagogic research are in fact still very much needed and further encouraged in its concrete application to answer the challenges of globalization and to meet the golden Indonesia 2045.

Studies on the Presence of Different Mating Types of<em> Phytophthora infestans</em> in Nuwara Eliya Sri Lanka

Late blight, caused by Phytophthora infestans is one of the most devastating diseases in potato cultivation. It results in direct crop losses but also causes a considerable amount of cost for disease management. The primary objective of this study was to investigate P. infestans mating types in selected areas in Sri Lanka and evaluate the risks associated with mating types. In this experiment potato leaves from a range of late blight severity levels were sampled from infected farmer fields. The pathogen was isolated by leaf culture method and confirmed by microscopic observations. The mating types of each isolate were studied by a Metalaxyl sensitivity test. Mating type A1 has been previously reported in Sri Lanka, but mating type A2 was not reported since 2013. Results of this study reveal that mating type A1 was more prominent than A2 (75% and 50% respectively). A2 was reported only from two fields. These observations prove the presence and emergence of mating type A2 in Sri Lankan potato fields. Gradual increase of detectable A2 mating types suggests a high possibility of pathogen inoculum movement between the locations over time. The presence of both mating types raises the possibility of sexual reproduction and generation of oospores. The ratio of A1: A2: (A1+A2) in the current study was 2:1:1. This could permit genetic recombination with the biological evolution of fungicideresistant types of P. infestans species. This study emphasizes the importance of investigations on the mating types to manage late blight disease in Sri Lankan potato fields.

Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN

The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF4-based plasma. To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. This configuration enables accurate mapping of data across domains, effectively compensating for the blind spots of individual diagnostic techniques. The model incorporates self-attention mechanisms to address temporal misalignments and a direct loss function to preserve fine-grained features, further enhancing data accuracy. Experimental results show that the Tri-CycleGAN model achieves high consistency in reconstructing plasma measurement data under various conditions. The model’s ability to fuse multi-domain diagnostic data offers a robust solution for plasma monitoring, potentially improving precision, yield, and process control in semiconductor manufacturing. This work lays a foundation for future applications of machine learning-based diagnostic integration in complex plasma environments.

A Tropical Cyclone Risk Prediction Framework using Flood Susceptibility and Tree-based Machine Learning Models: County-level Direct Economic Loss Prediction in Guangdong Province

Tropical cyclones (TCs), characterized by strong winds, heavy rainfall, storm surges, and flooding, have caused significant economic losses and fatalities in coastal regions globally. However, existing TC risk prediction frameworks often fail to adequately account for the direct impacts of flooding. In this study, we propose integrating flood susceptibility, a critical component of flood early warning systems, into TC risk prediction frameworks. Focusing on Guangdong Province, we employ four tree-based machine learning (ML) models (random forest, extreme gradient boosting, light gradient boosting machine, and categorical boosting) to predict county-level direct economic losses (DELs) based on flood susceptibility, oceanographic-meteorological data, and vulnerability data. These ML models are trained and tested on a dataset of 896 samples, achieving high prediction accuracies, with Pearson correlation coefficients exceeding 0.81 between the predicted and observed DEL values. Among the four models, the light gradient boosting machine demonstrates the best performance, achieving the highest values of R and R2, and the lowest values of MSE, MAE, and MedAE. The integration of flood susceptibility is validated by comparing it with traditional methods that directly incorporate environmental factors. Furthermore, the proposed TC risk prediction framework is applied to forecast the impacts of Super Typhoon Mangkhut in 2018, illustrating its potential ability for “real-time” TC risk assessments. These “real-time” DEL predictions not only estimate potential losses but also facilitate timely interventions, thereby enhancing the practical value of the model for disaster prevention and response.

Research on identification and quantification methods for loss sources of compressors

Abstract A deep understanding of the internal loss mechanisms and specific values of turbomachinery is crucial for its design and analysis. In this paper, based on the theory of entropy production, the loss sources in the NASA Rotor67 transonic fan and Radiver centrifugal compressor were quantified by using numerical simulation methods and physical flow characteristic description methods. The proportion of losses in various regions was presented. The results show that in the transonic fan, boundary layer losses and wake losses account for more than 70%, while direct shock losses account for less than 2%. Additionally, as the mass flow increases, the losses gradually move downstream, with the proportion of wake losses increasing. Within the wake losses, the influence of tip leakage flow and corner separation flow exceeds 70%. In the centrifugal compressor, as the mass flow decreases, the proportion of impeller losses to the total stage losses increases significantly, from 21.7% at the choking condition to 63.5% near the design condition. Most of the losses within the impeller occur near the casing area, accounting for about 47% of the impeller losses. The areas near the blades and the main flow area are the other two major sources of impeller losses, with both contributing to 36% of the losses.

A Concentrated Analysis of the Situation of Flood Control Management: A Review

Floods are happening more often and with greater severity due to socioeconomic development and climate change. Flood control is widely acknowledged as an effective means of reducing the negative effects, and recent research has aimed to develop a more robust and sustainable approach to flood management. A thorough bibliometric examination of terms, keywords, and dates in the flood research field was employed in this work. It provides new insight into the flood research trends, by examining the research frontiers from 2000 to 2021. We conclude that there has been a shift in the focus of flood research from flood management to flood resilience. Flood resilience management offers a more resilient and sustainable plan to deal with flood disasters, while flood risk management offers an adaptation approach by adjusting mitigation measures. The review demonstrates how flood research has progressed from traditional flood management, which provides mitigation strategies, to flood resilience management. Along with reviewing the definition of risk, risk analysis techniques, flood resilience, flood management, and flood risk management, we also provide a thorough introduction of the field of flood research. We come to the conclusion that a preferable strategy for future flood control directions is to incorporate the idea of resilience into the risk management framework. Consequently, employing the theories of risk, resilience, and sustainability, sensible choices and actions made before, during, and after a disaster will successfully minimize the negative effects. Devastating floods brought on by swift urbanization and severe weather patterns have claimed millions of lives and continue to inflict direct economic losses estimated at tens of billions of dollars annually. Furthermore, as extreme precipitation events intensify (Tabari, 2020) and the population at risk of water-related disasters rises (Jongman et al., 2012; Paudel et al., 2014; Tellman et al., 2021), these losses will only rise in the future due to global warming (Bloeschl et al., 2019; CRED and UNISDR, 2020; Hallegatte et al., 2013). Even worse, severe compound flooding from excessive river flow, intense rainfall, and storm surges might arise from the simultaneous occurrence of flash floods, river flooding, urban flooding, and coastal flooding (Ming et al., 2022). Given that the causes of coastal, urban, and river flooding vary, determining the locations most vulnerable to these types of floods is a challenging. While flood risk is known to rise in response to population growth, climate change, and the accumulation of economic assets, it is also known that this risk is dynamic and ever-changing due to changes in underlying surface conditions (Hallegatte et al., 2013; Lai et al., 2020). Thus, it is critical to manage flooding in order to address the rising risk of flooding. Prior studies have demonstrated the pressing need to address flood events (da Silva et al., 2020), and future flood mitigation measures must be developed in order to lessen the negative effects and handle increasingly complicated flood kinds.

Estimating the dynamic economic impacts of oil supply disruptions on China: A case study of Malacca Strait block

The intensification of geopolitical conflicts necessitates formulating economically effective coping strategies, which requires scientifically understanding the economic losses caused by oil supply disruptions and their evolutionary patterns. However, current assessment results are mostly static and cover direct losses, which are inadequate to fully support the development of disaster prevention measures. Therefore, considering the interdependence across different sectors, we employed a Dynamic Inoperability Input-Output Model to assess the economic impacts of a 20% disruption in oil imports through the Malacca Strait, lasting for 90 days. The main findings are as follows: (1) 2% of a sector's inoperability caused by oil supply disruption will, on average, be transmitted to other sectors due to their interdependencies. (2) The disruption will result in total economic losses of 62.7 billion yuan for China, and 70% of the total economic losses were incurred within the first 30 days. (3) To cope with the adverse economic impacts of oil transport disruption in the Malacca Strait, the optimal range of China's strategic petroleum reserve is 47.91–169.55 million barrels.

Multi-attribute-based procedure for seismic loss scenario in a historical area

AbstractSeismic events have shown to be exceedingly damaging to structures over time, with serious social and economic consequences. As a result, large-scale seismic risk assessments are essential for reducing the potential damage from future earthquakes. Therefore, the proposed study attempts to examine the vulnerability and risk of unreinforced masonry buildings (URM) placed in aggregate conditions in a historical area of the city centre of Lisbon. To this purpose, a comprehensive exposure model was developed combining satellite remote sensing, GIS software, and census data. Subsequently, seismic hazard was evaluated in the area, considering both Peak Ground Acceleration (PGA) and macro-seismic intensity for different return periods (i.e. 2-50-100-475-975-2500-5000 years). Vulnerability was assessed by introducing a novel approach to earthquake risk assessment using Multi-Criteria Decision-Making methodologies. Specifically, the method employs the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) computational methods to evaluate parameter weights and vulnerability index. Damage scenarios, fragility curves and mean damage ratios curves were provided to offer an overview of the vulnerability of the assets exposed to risk. Finally, the expected consequences were evaluated in terms of direct economic losses showing an economic loss of 595 M€ for a 475-years return period, while 1108 M€ for 5000-years. This study significantly enhances seismic analysis for urban areas by introducing a Multi-Criteria Approach. This method simplifies vulnerability assessment, ensuring ease of application and reproducibility. Its insights offer valuable support for disaster risk management decisions, facilitating the implementation of resilience and risk-reduction strategies.

Study of direct and indirect risk assessment of landslide impacts on ultrahigh-voltage electricity transmission lines

Ultrahigh-voltage electricity systems are a vital part of energy networks, often across wide regions passing through mountainous terrains. The instability of slopes forming tower foundations along transmission lines poses a large risk to the entire energy network. Moreover, the tower foundations along extra-high-voltage electricity transmission lines are a very special type of element that are also highly vulnerable to landslides. In this study, an integrative risk assessment framework, including a hazard assessment, vulnerability analysis for the specified element at risk, and consequence estimation, is proposed to quantify the direct and indirect risks of landslides to power transmission lines. The Yanzi landslide, where a tower of the major ultrahigh-voltage transmission line from Sichuan (western China) to Shanghai (eastern China) has just been constructed, is taken as a case study. The correlation between landslide displacement and tower structure tilting is studied. In terms of the precipitation return period, factors of safety, failure probability, and deformation are simulated. A physical vulnerability curve is fitted by assuming a Weibull relationship between the horizontal deformation (hazard intensity) of the landslide and the tower inclination ratio. The total risk is calculated as the sum of the direct and indirect losses, among which the potential indirect risk due to tower damage is much greater than the direct risk. This work demonstrates the importance of landslide treatment strategies along electricity transmission lines by considering the potential indirect risks. The risk assessment model of landslides affecting ultrahigh-voltage electricity transmission lines proposed in this study could be useful for other types of indirect landslide risk evaluations.

Left ventricular absolute myocardial work measured by cardiovascular magnetic resonance: a proof-of-concept

Abstract Background Myocardial Work Index (MWI) is a load-independent and non-invasive method for assessing cardiac systolic function [1]. However, MWI does not directly measure absolute myocardial work (AMW), since it does not consider the effect of variations in left ventricular (LV) wall thickness and regional wall curvature. Therefore, a direct comparison of MWI results may be difficult. Cardiovascular magnetic resonance (CMR) is the gold standard for assessing the LV geometry and therefore can be used to directly measure the AMW by calculating the area of the stress-strain curve. Purpose This study aims to measure and compare CMR-based AMW in three groups with different work profiles: healthy subjects, patients with ST-elevation myocardial infarction (STEMI), and patients with severe aortic valve stenosis (AS). Additionally, it aims to compare AMW with strain to provide additional insights into remodelling. Methods A total of 78 participants were included in our study; 38 healthy subjects from the CENS study [2], 19 STEMI patients 3-7 days post-PCI from the ASSAIL study [3], and 21 patients with AS scheduled for operation. Collected data were not adjusted for age or sex. Stress was estimated using Laplace’s equation [4]. Intra-ventricular pressure was calculated from brachial cuff pressure as previously described [5]. Mid-ventricular circumferential strain was calculated using CMR-based tissue phase mapping [4]. AMW was calculated both as Myocardial Work per unit endocardial Surface (MWS, in J/m^2) and per unit Mass (MWM, in mJ/g), both serving as measurements of global circumferential systolic function. Results Mean circumferential strain did not differ significantly between the AS and the healthy groups (p=0.37), but it was significantly lower in the STEMI group compared to the healthy group (p<0.001). MWS was significantly higher in the AS group (p<0.001) and lower in the STEMI group (p<0.001) compared the healthy group. This suggests that each unit of endocardial surface in the myocardium performed, on average, more work in the AS group and less work in the STEMI group relative to the healthy group. MWM was not significantly different between the AS and the healthy group (p=0.74), while in the STEMI group, it was significantly lower (p=0.001) than in healthy subjects. Conclusion Our results indicate that in patients with AS, the myocardium did more work per unit surface, while each gram of the myocardium did the same amount of work. This is likely due to concentric hypertrophy and the increase of afterload. In contrast, in patients with STEMI, the myocardium did, on average, less work per unit surface and per mass. This is expected due to the direct loss of mechanical function in the infarcted myocardium. Our results indicate that AMW enhances traditional strain analysis, improves myocardial function evaluation, and may provide deeper insight into myocardial remodeling in diverse cardiac conditions.

ApCarE4 and ApPOD3 participate in the adaptation of pea aphids to different alfalfa varieties

The adaptability of insects to hosts has long been a focal point in the study of insect-plant interactions. The pea aphid (Acythosiphon pisum), a significant pest of numerous leguminous crops, not only inflicts direct economic losses but also disseminates various plant viruses. To understand how pea aphids adapt to diverse alfalfa varieties. We analyzed the differentially expressed genes (DEGs) of pea aphids in distinct alfalfa varieties using transcriptome sequencing, and subsequently conducted functional validation of these genes. Comparative analysis between pea aphids feeding on susceptible and resistant strains revealed that DEGs in aphids feeding on resistant strains were primarily associated with transcriptional enrichment in the sugar, amino acid, protein, and lipid metabolism pathways. Fourteen DEGs related to adaptation of the pea aphid to alfalfa were chosen, including five carboxylesterases (CarE), four cytochrome P450s, three glutathione S-transferases, and two peroxidases (POD). RT-qPCR results indicated significant up-regulation of two carboxylesterase genes and two peroxidase genes after 24 h of feeding resistant alfalfa (Gannong 5, GN5) compared to the susceptible varieties (Hunter River, LRH), particularly highlighting the high expression levels of ApCarE4 and ApPOD3. Simultaneously, RNAi-induced knockdown of ApCarE4 and ApPOD3 led to a higher mortality of pea aphids in the alfalfa Hunter River. These results indicate that ApPOD3 and ApCarE4 are involved in the detoxification of metabolic functions in the adaptation of pea aphids to host switching. These findings contribute to the understanding of pea aphid adaptation to host plants and lay a foundation for further exploration of the physiological roles of carboxylesterase and peroxidase genes in pea aphids.

Towards a Better Uncertainty Quantification in Automated Valuation Models

AbstractThis study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.

Демографічні втрати балканських країн унаслідок воєн

Looking at the current state in which Ukraine found itself due to the Russian-Ukrainian war, many questions arise regarding the possibility of estimating demographic losses resulting from this war. A study of the demographic losses of the Western Balkan countries during the most recent wars in this region is a useful preparation for approbation of a methodological basis for future calculations. Therefore, it is currently relevant to study the specifics of the use of general methodological approaches for determining losses under the conditions of a specific social disaster; to assess the demographic losses of the republics of the former Yugoslavia resulting from the Balkan wars, taking into account the specifics of the course of the crisis and the available information base; to determine the possibility of applying the used approaches to the assessment of demographic losses resulting from the Russian-Ukrainian war. The purpose of the article is an attempt to estimate the demographic losses of the republics of the former Yugoslavia resulting from the Balkan wars. The tasks are the analysis of available data and research on demographic losses and obtaining authors’ own assessment of them. The scientific novelty is the generalized information from various sources and the study of the specifics of its formation, the authors’ assessment of the excess mortality and the birth deficit in some republics of the former Yugoslavia. For the purposes of the analysis, the authors provided a chronology of events in the Western Balkans of the late 20th and early 21st centuries and identified sources of information on losses for each former Yugoslav republic. However, despite the fact that many sources can be found in open access, they differ in the diversity of data, calculation methodology, and completeness of presentation. The article presents generalized information on the number of dead as a result of military conflicts based on several analytical sites. The authors defined two approaches, according to which demographic losses can be calculated: the first is the assessment of direct and indirect losses; the second is the use of the demographic balance equation, when excess mortality and birth deficit are estimated. In all the resources cited in the article, the determination of losses is carried out according to the first approach, the authors of the article made an attempt to estimate demographic losses according to the second approach. Using this approach, based on Eurostat data, it was possible to determine losses for only three countries: Serbia, Slovenia, and Croatia. For two regions, Eurostat does not provide demographic data for the years of active military operations: for Bosnia and Herzegovina for 1992—1995, for Kosovo for 1998—2001. In Macedonia, the number of casualties was small, which makes it impractical to build a hypothetical model, while Montenegro managed to avoid war altogether. So, both approaches have one common problem — the lack of a reliable information base. However, differences in loss estimates between the two approaches are natural due to the use of different methodological tools. Nevertheless, the magnitude and causes of large differences require additional research. It should be noted that there are quite a lot of estimates of direct losses, but at the time of the study, the authors were not aware of attempts to estimate excess mortality in individual republics of the former Yugoslavia. The obtained estimates require critical analysis and, if necessary, adjustments. The direction is the reconstruction (or clarification) of demographic dynamics (especially for Bosnia and Herzegovina) taking into account long-term demographic trends.

Modelling of directional power loss of electrical sheets at axial magnetisation

Modelling of directional power loss of electrical sheets at axial magnetisation