High Probability Of Occurrence Research Articles

DTI and fMRI alterations in a surgically-treated patient with moyamoya disease suffering severe speech impairments

ABSTRACT Moyamoya is a rare chronic brain vascular disease with a set of potential life-threatening consequences due to a high probability of stroke occurrence. Here we present a case of a patient with moyamoya disease, who has been dynamically observed over more than 4 years after the first manifestation of her symptoms. In this study we report fMRI and DTI findings obtained at the final follow-up point, four years after two revascularization surgeries were successfully performed. Our findings lend support to the idea that revascularization surgery is effective in preventing future strokes, but may lead to only minor improvement in cognitive performance.

Impact of wildfires on the suitability habitat of the lowland tapir in a native savanna landscape of northern South America

Fire plays a crucial role in shaping multiple ecological processes in native savannas. Still, the natural fire regime in the Neotropical savanna of South America is being altered by climate variability, resulting in reduced precipitation and increased mean temperatures. In recent years, an increase in the duration, intensity, frequency, and extent of wildfires in South American savannas has been reported. However, knowledge of the ecology of wildfires in the savanna landscapes and their impact on the habitats of large mammals is limited or non-existent. This study investigates the impact of wildfires on the suitability habitat for Tapirus terrestris in a native high plains native savanna landscape in the department of Vichada in eastern Colombia. We used the MaxEnt algorithm to model the ecological niche of the lowland tapir and then classify land covers into low, medium, and high probability of species occurrence. We then assessed the representativeness of the occurrence of wildfires and the frequency of detection of burned areas within the land covers classified into the suitability model of lowland tapir. Our results indicate that wildfires significantly affect high-suitability habitats such as forest and shrubland covers. The effects of fire on these land cover types would reduce the resource supply for lowland tapir and cause the displacement of individuals to new areas which could increase the exposure to threats such as hunting and pathogens from domestic animals. This research underscores the need for targeted conservation strategies to mitigate wildfire impacts and preserve critical habitats for vulnerable species.

Breeding Season Habitat Selection of the Eurasian Collared Dove in a Dry Mediterranean Landscape

Birds select habitats to optimize resources and maximize fitness, with some species recently colonizing new areas, like the Eurasian collared dove (ECD) in the Iberian Peninsula. The ECD spread across Europe in the early 20th century from South Asia. This study reanalyzes data from the Atlas of Breeding Birds in the Province of Alicante (SE Spain) to identify macrohabitat-level environmental variables related to its occurrence and abundance in this semi-arid Mediterranean landscape during the breeding season. We performed Hierarchical Partitioning analyses to identify important environmental variables for the species associated with natural vegetation, farming, topography, hydrographical web, urbanization, and climate. Results show that ECD has a higher occurrence probability near anthropic areas (isolated buildings, suburban areas), water points (medium-sized ponds), larger crop surfaces (total cultivated area), and warmer localities (thermicity index). The species avoids natural habitats like pine forests and scrublands. Abundance is positively linked to anthropic features like larger suburban areas and urban-related land uses. These findings can help predict its expansion in regions with a Mediterranean climate in South America, North America, or Australia, and its continuous natural expansion and population increase within the Mediterranean basin and Europe.

Hector’s dolphin distribution at Kaikōura before and after a major earthquake

Catastrophic natural events can have major impacts on marine ecosystems, but effects on mobile predators, such as cetaceans, remain poorly understood. This study investigated whether the coastal distribution patterns of Hector’s dolphins Cephalorhynchus hectori hectori off Kaikōura, New Zealand, changed after the powerful Kaikōura earthquake in November 2016. Dolphin sightings from boat-based surveys conducted before (2013-2016) and after (2016-2020) the earthquake were binned into 18 sectors of 4 km length each. The dolphins’ occurrence across sectors was then compared during pre- and post-earthquake periods using generalised linear models. Areas of high and low occurrence probability were temporally stable for Hector’s dolphins from before to 1.5 yr post-quake. Historic sighting data (from the 1990s) matched with the observed high-occurrence areas, indicating decadal stability in distribution patterns. An increase in dolphin occurrence was noted in the final 2-4 yr post-quake period across most nearshore sectors. This was particularly noticeable in the historically low-use areas along the narrow shelf at the head of the deep Kaikōura Canyon. This observation could indicate increased population connectivity between dolphins using the northern and southern sections of the Kaikōura Peninsula. The dolphins’ occurrence patterns pre-quake at the sector level were broadly explained by water depth, distance to rivers, sea surface temperature and chlorophyll when explored with generalised additive models. We discuss scenarios that may explain the species’ persistent distribution patterns during natural perturbation events as well as synergies with other conservation measures.

Copolymeric hydrogels with high capacities of hydration and methylene blue adsorption in water

Copolymers based on monomethyl hydrogen itaconate (βHI) with 2-hydroxyethyl methacrylate (HEMA) and βHI with 2-hydroxypropyl methacrylate (HPMA) were prepared in different monomer molar ratios. The reactivity parameters of copolymerization (r1 and r2) were determined. The values obtained for r1 and r2 indicate that P(βHI-co-HEMA) exhibits a high tendency towards alternate copolymerization. Structural characterization of the copolymeric hydrogels was carried out using Fourier transform infrared (FTIR) and 1H NMR spectroscopies, where the characteristic signals of the constituent functional groups were observed. The typical thermal stability of the polymeric systems was observed by thermogravimetric analysis (TGA). In addition, the swelling capacity in water was evaluated, and a swelling percentage over 655 % and 1249 % presented P(βHI-co-HEMA) and P(βHI-co-HPMA) respectively, in a period of 15 days at pH 10, and presence of 0.05 M NaNO3.Additionally, methylene blue (MB) adsorption capacity was studied in batch mode by varying different experimental parameters. MB adsorption capacity of 122.79 mg g−1 and 112.24 mg g−1 were achieved for P(βHI-co-HEMA) and P(βHI-co-HPMA), respectively. The data obtained were fitted to the Elovich kinetic model and to a Redlich-Peterson isotherm. The thermodynamic parameters indicated that the adsorption phenomenon is spontaneous, endothermic and with a high probability of occurrence. The adsorption efficiency decreases by approximately 60 % in the presence of interfering ions (Na+, K+, Ca2+ and Cl−). The adsorbents are reusable and achieve high MB adsorption efficiency (90 % and 80 % for P(βHI-co-HEMA) and P(βHI-co-HPMA) respectively) in a textile-simulated wastewater. Based on the results obtained, it is concluded that P(βHI-co-HEMA) and P(βHI-co-HPMA) show great potential for use in adsorption of water molecules, for MB retention and purification of simulated effluents from the textile industry.

A Novel Landmark-based Semi-supervised Deep Learning Method for Cerebral Aneurysm Detection Using TOF-MRA

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence.Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network.Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%.Conclusions: This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

СИСТЕМАТИЗАЦИЯ СОВРЕМЕННОГО МЯСОРЕЗАТЕЛЬНОГО ОБОРУДОВАНИЯ И ОЦЕНКА НЕИСПРАВНОСТЕЙ ПРОМЫШЛЕННЫХ ВОЛЧКОВ

Based on the analysis of the designs of modern meat cutting equipment used by processing plants, their great diversity has been established, justified by the design and technological requirements for this type of machines. (Objective of the study) To establish the features characterizing industrial grinders, compile their classification, determine the main faults and the degree of their influence on the operability of industrial grinders. (Materials and methods) Design and technological requirements for industrial grinders have been determined. An analysis has been carried out and the main causes of malfunctions of industrial grinders have been identified. Structural parameters characterizing the technical condition of industrial grinders have been established. A priori ranking technique has been selected based on an expert assessment by specialists of the main faults of industrial grinder units. (Results and discussion) The features characterizing industrial grinders have been established, based on this, their classification has been compiled. A structural diagram of the "Industrial grinder" technical system is presented. The main faults and the degree of their influence on the loss of operability of the devices under consideration during operation have been determined. The entire set of industrial grinding machine malfunctions is grouped relative to three subsystems: cutting, feeding and driving. During the malfunction analysis, 10 most important of them were identified, affecting the grinder operability and its technological process performance. (Conclusions) The entire set of presented industrial grinding machine malfunctions was ranked by weight. A 10-point system was used for their assessment and points were assigned in accordance with complexity within the range from 1 to 10. The first group included malfunctions with ranks higher than 238 (malfunctions with a higher probability of occurrence). The second group included malfunctions with a rank value lower than 150 (malfunctions with a lower probability of occurrence). It was found that the maximum weight has malfunctions designated as n6, n10, n9. They characterize the technical condition of the cutting and feeding subsystems. Priority for maintenance and control should be given to these subsystems

Improved decision-making through life event prediction: A case study in the financial services industry

Life event prediction is an important tool for customer relationship management (CRM), because life events shift customers’ preferences towards different products and services. Existing life event research mainly uses cross-sectional data, whereas in the CRM field, incorporating longitudinal data is increasingly common. Because longitudinal data can capture the dynamics of customer behavior, opportunities arise to benchmark the power of longitudinal customer data for predictions of cross-sectional versus longitudinal life events. Therefore, this study compares statistical and machine learning (SaML) classifiers, such as logistic regression, random forest, and XGBoost, with long- and short-term memory networks (LSTM), using data represented in both cross-sectional and longitudinal setups for life event prediction. Through a real-life longitudinal customer data set from a European bank, the authors represent the longitudinal data in a cross-sectional data format, using featurization in the form of aggregation. The available data cover 42 end-of-month snapshots for 760,438 unique customers. For marketing decision-making literature, this article (1) introduces three novel life events (i.e., primary, secondary, and rental residence purchases) to life event predictions; (2) offers guidance for how to leverage longitudinal customer data, according to the comparison of various featurization approaches and benchmarking SaML classifiers against LSTM; and (3) clarifies the importance of features and timing for improving marketing decision-making dynamically. The results show that aggregating features over time is preferable as a featurization approach for cross-sectional modeling using SaML classifiers. Furthermore, LSTM can capture behavioral changes over time, unlike SaML classifiers. It also performs significantly better than SaML classifiers on the area under curve and F1 metrics. Insights into the uses of integrated gradients reveal that feature importance changes over time. An integrated gradients method can assist decision-makers in their efforts to plan effective communication with customers in advance, such as by allocating more resources to customers who exhibit high probabilities of a particular life event occurrence.

Simulation Analysis of Loss of Coolant Accidents Based on CNIFA Program

Abstract The loss of coolant accident in the primary pipeline of a reactor is a typical accident in the safety analysis of nuclear power plants, and it is one of the accidents with a high probability of occurrence. Due to the extremely complex structure of the lower space of the containment vessel, it increases the complexity of fluid flow during a loss of coolant accident. This article is based on the internal water flooding analysis simulation software CNIFA of a nuclear power plant to simulate the water flooding of a rupture in the main pipeline heat pipe section of a power plant, and to conduct a comprehensive water flooding scenario simulation for this working condition. The quantitative information collected during the simulation process, including dynamically displayed water volume, water level, and their changes over time, facilitate emergency response personnel to choose the correct response plan in advance, it can also provide more accurate basis for various specialties in design analysis.

Long-Term Outcomes of Traditional Chinese Medicine in the Treatment of Granulomatous Lobular Mastitis: A Two-Year Follow-Up Study on Recurrence and New Occurrence Rates with Analysis of Risk Factors.

Patients with clinically cured granulomatous lobular mastitis (GLM) still face a high probability of recurrence and new occurrence. To evaluate the long-term efficacy of traditional Chinese medicine (TCM) in treating GLM and to hypothesize potential risk factors for recurrence or new occurrence. A retrospective analysis was conducted on GLM patients treated with TCM at Longhua Hospital affiliated with Shanghai University of Traditional Chinese Medicine from January 2016 to July 2021. We analyzed general data, two-year recurrence and new occurrence rates, and 12 risk factors associated with recurrence or new occurrence. This cross-sectional study included 261 GLM patients with an average age at onset of 31.95 years (primarily aged 31-40). The two-year recurrence rate for GLM was 1.53%, and the new occurrence rate was 4.21%. Univariate and multivariate conditional logistic regression analyses revealed that a history of inverted nipples was associated with the risk of both recurrence and new occurrence of GLM (HR = 8.672, 5.375, P < 0.05), and menstrual irregularity was related to a higher risk of recurrence (HR = 13.172, P < 0.001). A history of inverted nipples is identified as a potential risk factor associated with the long-term recurrence and new occurrence of GLM, while menstrual irregularity is associated with recurrence. Despite this, patients with GLM undergoing TCM demonstrate low rates of long-term recurrence and new occurrence after achieving clinical cure, underscoring the effectiveness of TCM. This study lays the groundwork for a long-term effectiveness strategy to guide future GLM treatments.

Trends of maximum annual sea surface temperature in the Eastern China Seas

The increasing ocean warming due to climate change significantly threatens regional marine ecosystems by raising the frequency and severity of extreme temperature events. This study examines patterns and trends of maximum annual sea surface temperature (Tmax) in the Eastern China Seas from 1985 to 2022. The results show a significant warming trend in Tmax, exceeding the global average, with notable differences between southern and northern regions. The northern Tmax warming rate is faster, with occurrence times significantly advancing, while the southern Tmax warming rate is slower, with occurrence times significantly delayed. The southern Tmax and its timing are closely correlated with the annual maximum air temperature and its timing. In the north, Tmax timing is influenced by latent heat flux (QLH); a significant increase in August QLH inhibits the continued rise of SST, causing Tmax to advance. The study also highlights a significant increase in marine heatwaves at Tmax timing, with higher Tmax indicating a higher occurrence probability. By elucidating these Tmax trends and dynamics, our study enhances understanding of regional climate impacts, supporting targeted conservation efforts and adaptive ecosystem management strategies in the Eastern China Seas.

Inflammatory Biomarkers in Patients With Type 2 Diabetes Mellitus and Heart Failure With Preserved Ejection Faction.

To verify the relationship between gene polymorphisms of tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) with inflammation markers and codependent metabolic variables in patients with type 2 diabetes mellitus and chronic heart failure (CHF). This study included 154 patients (mean age, 69.1±3.2 years). The control group consisted of 47 patients with metabolic syndrome (MS) without CHF; the 2nd group included 56 patients with CHF with preserved ejection fraction (CHFpEF); and the 3rd group consisted of 51 patients with CHF with reduced ejection fraction (CHFrEF). The rs1800629 polymorphism of the TNF-α gene (TNF-α: G308A) was studied in real time by the polymerase chain reaction (PCR) method and the rs1800795 polymorphism of the IL-6 gene (IL-6: 174 G&gt;C) was studied by PCR with the electrophoretic detection. The frequencies of polymorphic alleles were compared with the clinical blood test results, plasma concentrations of C-reactive protein (CRP), TNF-α, leptin, and fibrinogen. Differences between the groups were determined using the F test. Relationships between individual studied parameters were identified using the regression analysis. In most patients, the occurrence of gene polymorphisms was eident as increased plasma concentrations of biomarkers. An association was found between the TNF-α gene polymorphism (G308A) and an increase in plasma TNF-α and between the IL-6 gene polymorphism (174 C&gt;G) and an increase in plasma CRP. In the CHFpEF group, the rs1800629 gene polymorphism was observed in 55% of patients, among whom 93% had increased TNF-α. The rs1800795 gene polymorphism was observed in 82% of CHFpEF patients, among whom 21% had increased CRP. In the CHFrEF group, the G308A transition in the TNF-α gene was observed in 53% of patients; an increase in the respective cytokine was noted in 67% of patients; the IL-6 gene polymorphism 174 C&gt;G was found in 78%, however, only 14% of patients with this polymorphism had also increased CRP. In the control group, the TNF-α G308A gene polymorphism was found in 30% of patients, while an increase in free TNF-α was associated with this polymorphism in 50% of patients; the IL-6 174 C&gt;G gene polymorphism was detected in 78%, while no increase in the CRP level was observed in this group. This demonstrates a high probability of the TNF-α G308A gene polymorphism occurrence in patients with CHF. Inflammatory markers are important predictors of CHF. The most significant predictor was the TNF-α G308A gene polymorphism, which was observed in more than 50% of patients, the majority of whom had an increase in plasma TNF-α.

Extremely high wall pressure events in shock wave and turbulent boundary layer interactions using DNS data

This study investigates high-amplitude Extreme Wall Pressure fluctuation Events (EWPEs) in Shock wave/Turbulent Boundary Layer Interactions (STBLIs) through the conditional sampling of direct numerical simulation databases. The aim is to evaluate the effect of STBLIs and their strength on the statistical properties and associated turbulent structures of EWPEs using the conditional-averaging and clustering method. The temporal statistical results show that the occurrence probability and contribution ratio of EWPEs decrease downstream of strong STBLI, but their duration and interval time increase. Regarding two-dimensional wall pressure structures, the large population of small-scale structures becomes more elongated, but strong interactions induce a greater number of large-scale structures. The pairing of wall pressure events with a higher occurrence probability is verified by the joint probability density functions. Conditional analysis reveals that, as the interaction strength increases, the ejection motion associated with positive events occurs farther downstream and the spanwise vortex core locating above negative events is lifted up along the wall-normal direction. Moreover, analysis associates the paired wall pressure events with the sweep, ejection, and swirl motions in STBLIs, where hairpin eddies play an important role in the formation of positive–negative paired wall pressure structures.

Analyzing ship collision accidents in China: A framework based on the N-K model and Bayesian networks

Maritime accidents are frequent in China, with collisions constituting a significant portion, leading to substantial casualties, financial losses, and environmental harm. Analyzing collision risk is crucial for safe shipping operations, as such accidents arise when multiple risk factors interact beyond certain thresholds. Thus, understanding the coupled relationships among these factors is essential for accident prevention. This study presents a quantitative coupling risk assessment approach for collisions using the N–K model and Bayesian networks (BNs). Through an analysis of 304 accident reports from the China Maritime Safety Administration (CMSA), four primary risk factors (human, ship, environment, and management) are identified. The N–K model quantifies the coupling degree between these factors, while a BN model further elucidates and quantifies the collision risk based on the calculations of N–K model. Validation of the BN model is followed by sensitivity analysis to assess the impact of risk factors on accident occurrence. Subsequently, the method is applied to a case study on collision risk assessment in Chinese waters. Findings indicate that multifactor coupling has a higher occurrence probability than two-factor coupling, with human-ship-environment-management coupling presenting the highest likelihood. Human and management factors emerge as pivotal in collision incidents, while the impact of ship and environmental factors on coupling becomes more pronounced as their probabilities vary. Leveraging the capability of BNs to handle uncertainties and logical relationships, combined with the data-driven N–K model, mitigates subjectivity. The results propose the formulation of risk management strategies and measures for practitioners to enhance maritime safety.

KANO 모델을 활용한연안 지역의CPTED 수요 분석

Although the overall crime rate in South Korea has been showing a declining trend recently, the total number of crimes remains at a high level. Excluding these metropolitan areas, Busan experiences the highest crime rate. This study focuses on Mipo Village in Haeundae-gu, Busan. Located in the scenic Haeundae area, Mipo Village has a relatively high influx of people from various regions and a correspondingly high probability of crime occurrence. This research aims to establish the KANO model through an investigation of the study area. By analyzing and statistically examining the collected data, the demand and design elements for CPTED(Crime Prevention Through Environmental Design) in Mipo Village will be assessed. Based on this analysis, we intend to derive the relationships within the demand hierarchy for CPTED in the area and propose effective measures to reduce the crime rate in this region.

Assessing Machine Learning and Statistical Methods for Rock Glacier‐Based Permafrost Distribution in Northern Kargil Region

ABSTRACTEstimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

Factors Affecting the Winter Distribution of Birds in Forests at the Eastern Edge of Europe

We show for the first time the results of a study into the spatial distribution of birds in forests at the eastern edge of Europe (Republic of Tatarstan, Russia) and changes from early to late winter. A transect method was used to census randomly selected plots spread over a large geographical area in the winters 2018/9–2022/3. We used regression and ordination methods to assess the influence of key environmental factors on species richness, total density of birds, biological diversity, and the probabilities of occurrence of individual species. The most abundant bird species in early winter was the Willow Tit, and in the late winter was the Common Redpoll. Compared with the end of the 20th century, the number of wintering bird species has increased in the study area, likely due to climate warming. Species richness, total density, and the Shannon Index of diversity were higher in early winter than in late winter. Species richness and the Shannon Index were also higher at low elevations and in the west of the study region. Our research shows strong ecological-geographical differences in the preferences of individual bird species in the studied forests. However, almost without exception, birds had a higher probability of occurrence at lower elevation and toward the west.

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

Compound droughts under climate change in Switzerland

Abstract. The co-occurrence of meteorological, agricultural, and hydrological droughts (multivariate compound droughts) in Switzerland during growing season is problematic due to limitations in water abstractions from rivers during low-flow periods, while at the same time the need for irrigation is high. We analyse compound droughts for 52 catchments in Switzerland during the extended summer season (May–October) using the transient climate and hydrological scenarios for Switzerland (CH2018 and Hydro-CH2018) for both a scenario with mitigation (representative concentration pathway 2.6 (RCP2.6), 8 model chains) and a scenario without mitigation (RCP8.5, 20 model chains). In the RCP8.5 scenario the number of compound drought days is projected to significantly increase by mid-century across all greater regions of Switzerland. The increased frequency is mainly a result of more frequent events (significant) rather than longer event durations (non-significant). Models generally agree on the sign of change. By 2085, compound drought events are projected to occur in median once per catchment per extended summer season north of the Alps and every 1–2 years south of the Alps. Further, the increases in compound drought days mainly occur between May–October, leading to a shift in the main agricultural production season and a more pronounced seasonality with the highest occurrence probabilities between mid-July and the beginning of October. Coupled to the increase in days and events, significantly more catchments are projected to be affected by compound droughts at the same time. In the RCP2.6 (mitigation) scenario, the increase in the number of compound drought days and events is not significant by the end of the 21st century. In comparison with RCP8.5, the number of compound drought days is reduced by 50 %–55 % north of the Alps and by up to 75 % south of the Alps by the end of the century. This emphasizes the need for coordinated adaptation in combination with mitigation measures taken at an early stage.

CONCEPTUAL FRAMEWORK FOR FORMING AN ENTERPRISE ANTI-CRISIS MANAGEMENT SYSTEM

The main reason for the crisis in domestic enterprises is the low level of stimulation for further development. Other reasons that contribute to the crisis include loss of efficiency of production facilities and passivity of counterparties, lack of prospects for stabilizing the economic situation of enterprises. Also among the factors noted is the inconsistency of the mechanism for managing the business processes of the enterprise with the requirements of today, the uncertainty and unpredictability of the economic environment in Ukraine during the war and post-war period leads to destabilization of the business activity of enterprises. Impact of the war on the majority of economic entities in Ukraine is a difficult test for the development of the domestic economy. This situation showed the incompetence of business owners in case of failure to fulfill contractual obligations, as well as the provision of unpaid deferments to employees for insignificant periods, reduction or liquidity of general purchases, purchases, etc., destabilization of logistics stability, insignificance of a specific situation. In the conditions of an unstable external environment, it is practically impossible to strategically plan and forecast the company's development scenarios. We are observing a situation where the incomes of enterprises have significantly decreased and expenses have increased. Considering the high probability of occurrence of crisis phenomena in the activities of enterprises, especially in modern conditions, this indicates the necessity of forming a system of anti-crisis management of the enterprise, countering their development and neutralization.