Degree Of Uncertainty Research Articles

FEATURES OF THE CALCULATION OF REINFORCED CONCRETE ELEMENTS UNDER THE ACTION OF TRANSVERSE FORCE ACCORDING TO DIFFERENT STANDARDS

Shear force action is probably themost studied type of damage to reinforced concretestructures with conventional or pre-stressedreinforcement. However, some issues remainunclear or controversial. In contrast to the resistanceto bending, there is not even a universally acceptedcalculation model that would describe the behaviorof structures under the action of a transverse force.In beams, the transverse force mostly actstogether with the bending moment. The stressedstate at any point of the beam is characterized bytotal stresses - a set of normal and tangentialstresses.At points where the tangential stresses are equalto zero, the main tensile and main compressivestresses act.The main platforms are inclined at an angle of45 and 135 to the longitudinal axis of the beam.On the neutral axis of the beam, where the normalstresses are equal to zero, the principal stresses areequal in value to the tangential (rocking) stresses.The principal compressive stresses can mostlybe absorbed by the concrete and are not, as a rule,decisive. The main tensile stresses are the mostdangerous. Since concrete does not resist tensionwell, the principal tensile stresses cause obliquecracks to form. The criterion for their occurrence isthe excess of the tensile strength of concrete by themain tensile stresses. If inclined cracks are formed,it is necessary to place appropriate reinforcement inthe wall of the beam, which perceives tension. Suchreinforcement can be either vertical or inclined bars.One of the main questions in various methods ofcalculating elements for the action of transverseforce is the question of what perceives it - onlyconcrete, only reinforcement, or both concrete andreinforcement?Despite some degree of uncertainty, the designof inclined sections can be carried out withconfidence, since the accepted methods ofcalculation in various norms have been checked andadjusted on the basis of a large amount ofexperimental data.

Government intervention in green technology innovation: The carrot, the stick or both?

Green technologies are crucial for reducing carbon emissions; however, firms may maintain “dirty” technologies due to low returns on green alternatives. To promote the transition to a low-carbon society, the government can establish regulatory measures such as subsidizing green investments, imposing taxes on emissions, or employing a combination of the two instruments. However, there is ongoing debate regarding whether these measures incentivize green-technology innovation and abate carbon emissions. In this study, we examine the effectiveness of the carbon subsidy, carbon tax, and hybrid policy in terms of their impacts on society, customers, and the incentives for the green-technology innovation of a risk-averse firm under demand uncertainty. We develop models that incorporate each of these measures and the benchmark model with no intervention. We find that when the degree of demand uncertainty is low, the government will not employ any carbon regulation; when the degree rises, the hybrid policy generates the highest social welfare. In cases of a medium degree, there exist thresholds below which the government uses the carbon tax and above which it opts for the carbon subsidy. Furthermore, we examine the effects of environmental damage, risk aversion, and customers' environmental preferences on the optimal carbon regulatory policy.

Development of oil extraction screening methodology taking into account innovative methods using the example of the Ukrainian field

The object of research in the paper is the process of fluid transfer through the pore space of the reservoir rock. The traditional method of estimating oil recovery by flooding has a large number of uncertainties. In this study, to address limitations of the current approach to determining oil production, let’s introduce a systematic algorithm aimed at enhancing result precision. The methodology for calculating the oil recovery coefficient for determining the amount of oil that can be extracted by flooding is presented. In this work, the step-by-step process of determining the oil recovery coefficient was analytically established, which achieves a certain degree of accuracy due to the inclusion of a number of methods of calculation of scientists from different countries of the world. In particular, the lithofacies distribution of the reservoir using the kriging method, the use of a representative elementary volume (REV) to increase the accuracy of determining the irreducible water saturation of each facies, and the use of the Buckley-Leverett equation in the calculation of the oil recovery coefficient are proposed. The number of facies (sandstone, argillaceous sandstone, siltstone) was determined on the example of the B-16n horizon of the «Ukrainian deposit» and the oil recovery coefficients were calculated for each separately (0.53, 0.47, 0.29). Further determination of the average oil recovery coefficient is described in the researched and requires close integration of the obtained data in three-dimensional space, as it allows to calculate the fraction of facies content in the reservoir volume. The use of the proposed action algorithm will help to build a more reliable three-dimensional hydrodynamic model, will lead to a much lower degree of uncertainty of reservoir properties, and in particular irreducible water saturation, as well as more accurate distribution of lithological properties using kriging. Also, this methodology for calculating the oil recovery coefficient involves the use of the Buckley-Leverett equation and fractional flow curves, the data of which are based on relative permeabilities and depend on the irreducible water saturation determined in the laboratory for each lithofacies. These techniques justify the collection of additional core material, the importance of lithofacies dismemberment of the formation and are closely integrated in the three-dimensional space, which makes it possible to simulate the existing processes, reproduce the proposed methodology and perform the forecast.

Increasing Student Engagement and Interaction in the Online Classroom: A High Impact Mini-Teaching Approach

The Covid-19 pandemic dramatically transformed how and where students were taught. Universities had to suddenly switch to online learning with virtually no preparation. Students and teachers lacked experience in online learning. This sudden change generated a high degree of uncertainty and a notable fear in the decline in the perceived quality of higher education due to the absence of in-person classes. In this context, this study explains how an accounting instructor navigated the crisis by adopting a diverse high impact mini-teaching approach. The quantitative analyses of the online student surveys showed that this strategy was successful in sustaining students’ interest levels high during the Covid pandemic period. The teaching strategy increased student retention and progression rates and can help address the current drastic declining undergraduate accounting student enrolments. The findings of this study suggest the strategy is exportable to other courses and can provide practical guidance for instructors who would like to achieve high level student engagement and satisfaction in the online classroom using digital learning platforms in the future.

Information – analytical support and formalized administration of business processes in the operating systems of construction developer enterprises

The article highlights the evolution of scientific views regarding the methodological foundations of the formation of economic and management tools by the administration of a project-oriented enterprise in the new era of the fourth industrial revolution and analyzes the paradigms of the implementation of enterprise management processes on the basis of information systems, identifies the development trends of the implementation of enterprise management processes in the structure and means of intellectual information systems It has been proven that the dynamism and high degree of uncertainty of the external environment have a negative effect on the efficiency of domestic construction enterprises, forcing modern organizations to transform into more and more complex systems. The basis of their adaptive diffusion into a competitive environment is the competition not only of goods or services, but also of innovative management technologies. The leading and systematic way out of crisis and destructive situations for construction enterprises, as well as for other industrial enterprises, is an innovative activity strategy, a radical or partial transformation (modernization) of the enterprise's operating system and the system and structure of the administration of its activities based on the latest information technologies. architectural environment including integrated project delivery (IPD, Integrated Project Delivery), virtual design and construction (VDC, Virtual Design Construction) and integrated digital delivery (IDD Integrated Digital Delivery), building information modeling (BIM, Building Information Modeling) are used in the construction and implementation of models of management processes in information systems, the use of which simplifies the complexity of modernizing the information system to solve the tasks of enterprise management processes. The main models of the organization of activities in the digital economy and the possibilities of improving the efficiency of the management processes of the construction enterprise using information systems as a digital way of presenting directive and economic-functional characteristics of the construction object are defined and systematized.

Uncertainty-driven generation of neutrosophic random variates from the Weibull distribution

ObjectiveThis paper aims to introduce an algorithm designed for generating random variates in situations characterized by uncertainty.MethodThe paper outlines the development of two distinct algorithms for producing both minimum and maximum neutrosophic data based on the Weibull distribution.ResultsThrough comprehensive simulations, the efficacy of these algorithms has been thoroughly assessed. The paper includes tables presenting neutrosophic random data and an in-depth analysis of how uncertainty impacts these values.ConclusionThe study's findings demonstrate a noteworthy correlation between the degree of uncertainty and the neutrosophic minimum and maximum data. As uncertainty intensifies, these values exhibit a tendency to decrease.

Agential cuts of regulatory science practices – the case of microplastics

Over the past decades, a new field of research related to microplastics has emerged which still faces a high degree of uncertainty and conflicting views regarding the risks posed by microplastics. To date, social research has not addressed the regulatory science practices necessary for assessing the risks created by microplastics and related ethical questions. Therefore, the objective of this article is to analyse the role of central regulatory science practices, that is, risk assessments as they relate to microplastics. I draw on the work of Karen Barad to conceptualise these regulatory science practices as boundary-drawing practices which produce agential cuts. I will show that scientific and regulatory boundary-drawing practices draw agential cuts determining the properties of microplastics and regulatory actions that have ‘real’ consequences for human and environmental health. My empirical case demonstrates that different versions of risk assessment exist – one based on thresholds and the other on persistence – each of which have different regulatory consequences. Threshold risk assessment does not legitimise action to regulate microplastics, because the threshold at which microplastics have toxic effects requires such high concentrations that industry could continue to emit microplastics for decades. Therefore, only risk assessments that relate to the materiality of microplastics in terms of persistence and accumulation legitimise regulatory action.

Differences in Organizational Behavior amongst Startup and Established Company: A Literature Review

Two business models differ significantly between an established company and a startup in the industrial world. There are notable distinctions between the two business models in several areas that, upon closer examination, can offer entrepreneurs, practitioners, researchers, and the government guidance in devising the most appropriate business model approach. The subsequent goal of this research is to conduct a more thorough analysis of the organizational behavior variations between startups and larger businesses. Because numerous research sources have been completed but have yet to be more thoroughly consolidated, this study uses a literature review approach to address the research issues posed. This approach is also capable of identifying variations from the given context. According to the study, established businesses and startups differ in three ways. The study's findings, to be more precise, revealed variations in the two business models' definitions and life cycles. Second, the social relations system and structure derived from these two company models differ. Finally, these two categories of businesses must deal with varying degrees of uncertainty in the workplace. This research suggests that by examining the distinctions between large and small companies which are just starting, business actors can benefit from this research and use it as a guide to improve organizational performance.

The effects of climate change on the natural rate of interest: A critical survey

AbstractThis survey reviews the literature about the possible impacts of climate change on the natural rate of interest (r*), an important yardstick for monetary policy. Prima facie, economic, and financial developments can lower r* in scenarios with increasing climate‐related damages and uncertainty that reduce productivity growth and raise precautionary savings. Orderly climate policies have a pivotal role in facilitating the transition to a carbon‐neutral economy and supporting a steady investment flow. We discuss the main models used to simulate the effects of climate change on r* and summarize the outcomes. However, in scenarios that assume innovations and investments induced by transition policies, r* could be affected positively. Overall, the downward effects of climate change on r* can be substantial, even considering the high degree of uncertainty about the outcomes, with tipping points and nonlinear effects aggravating the economic impacts. The downward pressure on r* will further challenge monetary policy in the long run, by limiting its policy space.This article is categorized under: Climate Economics > Economics and Climate Change Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change

The Moderating Role of Uncertainty Reduction on the Relationship between Leadership Styles and Organizational Identification within Saudi Hospitals

This study embarked on an exploration into the dynamics of various leadership styles and their influence on organizational identification, placing a spotlight on the potential moderating effect of uncertainty reduction within the healthcare sector of Saudi Arabia. Adopting a hypothesis testing approach, the research employed a correlation investigation design, ensuring minimal interference from the researcher. The backdrop of this study was set in five hospitals across Riyadh and Makkah, targeting the entire population of permanent nurses. To derive a representative sample, a probability sampling technique was used, more specifically, stratified sampling, which culminated in a sample size of 375 participants. The unit of analysis was set at an individual level, with a cross-sectional study time horizon. Data collection involved a 7-point Likert scale, which underwent a comprehensive descriptive analysis via SPSS, followed by a Partial Least Square assessment using Smart-PLS. The findings highlighted the nuanced interplay between leadership strategies and organizational contexts, emphasizing the significance of adaptability in leadership in alignment with varying degrees of environmental uncertainty.

Substantial seagrass blue carbon pools in the southwestern Baltic Sea include relics of terrestrial peatlands

Seagrass meadows have a disproportionally high organic carbon (Corg) storage potential within their sediments and thus can play a role in climate change mitigation via their conservation and restoration. However, high spatial heterogeneity is observed in Corg, with wide differences seen globally, regionally, and even locally (within a seagrass meadow). Consequently, it is difficult to determine their contributions to the national remaining carbon dioxide (CO2) budget without introducing a large degree of uncertainty. To address this spatial heterogeneity, we sampled 20 locations across the German Baltic Sea to quantify Corg stocks and sources in Zostera marina seagrass-vegetated and adjacent unvegetated sediments. To predict and integrate the Corg inventory in space, we measured the physical (seawater depth, sediment grain size, current velocity at the seafloor, anthropogenic inputs) and biological (seagrass complexity) environment to determine regional and local drivers of Corg variation. Here we show that seagrass meadows in Germany constitute a significant Corg stock, storing on average 1,920 g C/m2, three times greater than meadows from other parts of the Baltic Sea, and three-fold richer than adjacent unvegetated sediments. Stocks were highly heterogenous; they differed widely between (by 22-fold) and even within (by 1.5 to 31-fold) sites. Regionally, Corg was controlled by seagrass complexity, fine sediment fraction, and seawater depth. Autochthonous material contributed to 12% of the total Corg in seagrass-vegetated sediments and the remaining 88% originated from allochthonous sources (phytoplankton and macroalgae). However, relics of terrestrial peatland material, deposited approximately 6,000 years BP during the last deglaciation, was an unexpected and significant source of Corg. Collectively, German seagrasses in the Baltic Sea are preventing 2.01 Mt of future CO2 emissions. Because Corg is dependent on high seagrass complexity, the richness of this pool may be contingent on seagrass habitat health. Disturbance of this Corg stock could act as a source of CO2 emissions. However, the high spatial heterogeneity warrant site-specific investigations to obtain accurate estimates of blue carbon, and a need to consider millennial timescale deposits of Corg beneath seagrass meadows in Germany and potentially other parts of the southwestern Baltic Sea.

Linearity in Deng entropy

Linearity is considered as a fundamental property of significant importance in numerous domains. A linear mathematical expression is clear, concise, and convenient for solving problems, making it a valuable tool for approximating complex issues. In recent years, Deng entropy has been proposed as a generalization of Shannon entropy, applied to measure the uncertainty degree of the mass function in the power set. There are two main contributions in this paper. One is that the linearity can be observed in Deng entropy. We present a set of specific mass functions named power assigned mass function (PAMF), which could generate linear type Deng entropy (LTDE). The other is that we find the slope is nothing else but the information fractal dimension of mass function. Moreover, we discover that for any given slope within the range of 0 to ln3ln2, at least one mass function that yields Deng entropy corresponding to the given slope can be derived through the mass function generator (MFG), in a strict linear way. Some proofs, numerical examples, discussions and an error analysis are provided to validate the effectiveness of our findings.

Gut Microbiota Composition of Insectivorous Synanthropic and Fructivorous Zoo Bats: A Direct Metagenomic Comparison.

Bats are natural reservoirs for many emerging viral diseases. That is why their virome is widely studied. But at the same time, studies of their bacterial gut microbiota are limited, creating a degree of uncertainty about the role of bats in global microbial ecology. In this study, we analyzed gut microbiota of insectivorous Nyctalus noctula and Vespertilio murinus from rehabilitation centers from Rostov-on-Don and Moscow, respectively, and fructivorous Carollia perspicillata from the Moscow Zoo based on V3-V4 16S rRNA metagenomic sequencing. We revealed that microbial diversity significantly differs between the insectivorous and fructivorous species studied, while the differences between N. noctula and V. murinus are less pronounced, which shows that bats' gut microbiota is not strictly species-specific and depends more on diet type. In the gut microbiota of synanthropic bats, we observed bacteria that are important for public health and animal welfare such as Bacteroides, Enterobacter, Clostridiaceae, Enterococcus, Ureaplasma, Faecalibacterium, and Helicobacter, as well as some lactic acid bacteria such as Pediococcus, Lactobacillus, Lactococcus, and Weisella. All these bacteria, except for Bacteroides and Weisella, were significantly less abundant in C. perspicillata. This study provides a direct metagenomic comparison of synanthropic insectivorous and zoo fructivorous bats, suggesting future directions for studying these animals' role in microbial ecology.

Re-ordered fuzzy conformance checking for uncertain clinical records

Modern hospitals implement clinical pathways to standardize patients’ treatments. Conformance checking techniques provide an automated tool to assess whether the actual executions of clinical processes comply with the corresponding clinical pathways. However, clinical processes are typically characterized by a high degree of uncertainty, both in their execution and recording. This paper focuses on uncertainty related to logging clinical processes. The logging of the activities executed during a clinical process in the hospital information system is often performed manually by the involved actors (e.g., the nurses). However, such logging can occur at a different time than the actual execution time, which hampers the reliability of the diagnostics provided by conformance checking techniques. To address this issue, we propose a novel conformance checking algorithm that leverages principles of fuzzy set theory to incorporate experts’ knowledge when generating conformance diagnostics. We exploit this knowledge to define a fuzzy tolerance in a time window, which is then used to assess the magnitude of timestamp violations of the recorded activities when evaluating the overall process execution compliance. Experiments conducted on a real-life case study in a Dutch hospital show that the proposed method obtains more accurate diagnostics than the state-of-the-art approaches. We also consider how our diagnostics can be used to stimulate discussion with domain experts on possible strategies to mitigate logging uncertainty in the clinical practice.

THE COMPLEX PROBLEM OF EXPANDING THE CAPABILITIES OF CADASTRAL SYSTEMS AND WAYS TO SOLVE IT

The development of cadastral systems in various countries, including Ukraine, has gone through creating information systems that serve users (stakeholders) in one of the subject areas, such as land administration, real estate construction, water resources, and others. Modern cadastral systems have no capacity to solve complex problems, and, at the same time, their structure does not contain specialised mathematical support in the form of models capable of simulating complex processes. Intelligent cadastres will be able to solve complex problems. Along with the existing types of cadastre support, intelligent cadastres should have well-structured mathematical and heuristic support. It should allow for solving private computing and service tasks, as well as loosely structured ones with a high degree of uncertainty, and provide good visualisation and detailing of objects, processes, and phenomena based on cloud and other modern information technologies. We propose introducing an intelligent cadastral system, presented as a geoinformation system with components of both a decision support system and a system for manipulating thematic layers. The decision support system should contain typical models used during the formalisation of one or another subject area or as a variant of the formal model of the subject areas’ description. The thematic layers manipulation system should allow the integration of attributive data belonging to different subject areas. In this case, ensuring prompt and fruitful work of cognitologists with experts is essential. The shortage of time and the variety of problems to be solved require the development of technology for formalising the knowledge of experts or specific subject areas. Implementing this technology and ensuring its implementation will require the creation of banks of typical knowledge models, which should store standardised procedures for formal representations. The bank of formal representations of heuristic knowledge includes procedures for forming production rules, building semantic and other networks, building frame representations and genetic algorithms, and so on. The bank of formal logical procedures contains procedures for creating logical rules based on the logic of statements, rules of logical inference based on the predicate logic, procedures of other modal logics, in particular, the presence logic, and procedures for building logical constructions based on the theory of categories and functors. Keywords: cadastral system, geoinformation system, decision support system, thematic layer manipulation system, formalisation.

How to infuse mobile technologies in frontline service encounters? An experimental analysis of customer perceptions of service competence

ABSTRACT In technology-infused frontline service encounters, human-based service represents a high degree of uncertainty. However, understanding how technology can meet customer demands without undermining the importance of human interaction is crucial for retailers to remain competitive. However, empirical evidence comparing different types of service encounters (technology-facilitated vs. technology-assisted vs. technology-free) is lacking. Building on existing approaches to technology-enabled services, this study aims to extend the understanding of the optimal type of technology infusion and human interaction in the context of customer trust and willingness to pay. Drawing on social interaction theory, emphasizing that technology can influence the nature and quality of interactions between customers and frontline employees, this paper empirically examines different types of service encounters. A quasi-experimental online study (N = 944) was conducted.General results show that technology infusion within the service encounter has a negative impact on customers’ trust in the frontline employee, but a positive impact on customers’ willingness to pay. Furthermore, a mediating influence of customers’ perceptions of the competence of frontline employees can be demonstrated. The results show that customers place more value on a technology-facilitated encounter than on a technology-assisted service. Based on the findings, this paper derives implications for retail managers at various levels. Since the customer’s perception of the competence of front-line employees in using a technology is of high relevance to the customer, the retailer should pay more attention to training employees in the use of these technologies in their interactions with customers because of the perception of service competence and other relationship-building criteria such as trust. In addition, the competent use of these technologies and the customer’s perception of them can also affect the customer’s willingness to pay and thus the success of the business.

Mapping Seismic Hazard for Canadian Sites Using Spatially Smoothed Seismicity Model

The estimated seismic hazard based on the delineated seismic source model is used as the basis to assign the seismic design loads in Canadian structural design codes. An alternative for the estimation is based on a spatially smoothed source model. However, a quantification of differences in the Canadian seismic hazard maps (CanSHMs) obtained based on the delineated seismic source model and spatially smoothed model is unavailable. The quantification is valuable to identify epistemic uncertainty in the estimated seismic hazard and the degree of uncertainty in the CanSHMs. In the present study, we developed seismic source models using spatial smoothing and historical earthquake catalogue. We quantified the differences in the estimated Canadian seismic hazard by considering the delineated source model and spatially smoothed source models. For the development of the spatially smoothed seismic source models, we considered spatial kernel smoothing techniques with or without adaptive bandwidth. The results indicate that the use of the delineated seismic source model could lead to under or over-estimation of the seismic hazard as compared to those estimated based on spatially smoothed seismic source models. This suggests that an epistemic uncertainty caused by the seismic source models should be considered to map the seismic hazard.

Cost-effectiveness of Spironolactone for Adult Female Acne (SAFA): economic evaluation alongside a randomised controlled trial

ObjectiveThis study aims to estimate the cost-effectiveness of oral spironolactone plus routine topical treatment compared with routine topical treatment alone for persistent acne in adult women from a British NHS...

Design and development of a fuzzy explainable expert system for a diagnostic robot of COVID-19

<p><span lang="EN-US">Expert systems have been widely used in medicine to diagnose different diseases. However, these rule-based systems only explain why and how their outcomes are reached. The rules leading to those outcomes are also expressed in a machine language and confronted with the familiar problems of coverage and specificity. This fact prevents procuring expert systems with fully human-understandable explanations. Furthermore, early diagnosis involves a high degree of uncertainty and vagueness which constitutes another challenge to overcome in this study. This paper aims to design and develop a fuzzy explainable expert system for coronavirus disease-2019 (COVID-19) diagnosis that could be incorporated into medical robots. The proposed medical robotic application deduces the likelihood level of contracting COVID-19 from the entered symptoms, the personal information, and the patient's activities. The proposal integrates fuzzy logic to deal with uncertainty and vagueness in diagnosis. Besides, it adopts a hybrid explainable artificial intelligence (XAI) technique to provide different explanation forms. In particular, the textual explanations are generated as rules expressed in a natural language while avoiding coverage and specificity problems. Therefore, the proposal could help overwhelmed hospitals during the epidemic propagation and avoid contamination using a solution with a high level of explicability.</span></p>

Intuitionistic fuzzy level operators related to degree of uncertainty

Two new intuitionistic fuzzy operators of level type are introduced and some of their properties are studied. They are related to the degree of uncertainty of a given intuitionistic fuzzy set. Geometrical interpretations onto the intuitionistic fuzzy interpretation triangle are provided.