Uncertain Process Research Articles

Alternative Careers toward Job Market Integration: Barriers Faced by International Medical Graduates in Canada.

International Medical Graduates (IMGs), who completed their medical degree and training outside Canada constitute a notable portion of the skilled migrants of the country. However, due to a long and uncertain licensure process and limited opportunities many IMGs look for alternative career pathways where they can utilize their learned skills. Alternative careers in the health and wellness sector may offer such opportunities; however, IMGs' success in these pathways were also less evident despite their high potential. In this study, we investigated the barriers that IMGs stated to face when attempting alternative jobs in Canada. Eight focus groups with 42 IMGs in Canada were conducted. Using a thematic analysis approach, we identified that IMGs encounter these barriers in different stages of their resettlement journey in Canada, including both the pre-migration and post-migration phases. In the pre-migration phase, IMGs were not aware of the success rates of the licensing pathways and did not have sufficient information regarding potential alternative careers. In the post-migration phase, the lack of information continues to affect IMGs where IMGs exhaust their resources pursuing alternative careers without proper guidance and support. Further, IMGs struggle with taking preparation for alternative careers by obtaining further certifications and completing other prerequisites for some barriers, such as financial constraints. While looking for jobs, some IMGs perceived systemic discrimination such as non-recognition of their credentials and experience. Furthermore, the mismatch of expectations and limited growth opportunities offered by potential careers serve to disincentivize IMGs from pursuing an alternative career. Addressing the current employment inequity experienced by IMGs in Canada warrants research collaborations between organizations supporting IMGs and policymakers that target known barriers to the pursuit of alternative careers by IMGs.

Inflow Scenario Generation for the Ethiopian Hydropower System

In a hydropower system, inflow is an uncertain stochastic process that depends on the meteorology of the reservoir’s location. To properly utilize the stored water in reservoirs, it is necessary to have a good forecast or a historical inflow record. In the absence of these two pieces of information, which is the case in Ethiopia and most African countries, the derivation of the synthetic historical inflow series with the appropriate time resolution will be a solution. This paper presents a method of developing synthetic historical inflow time series and techniques to identify the stochastic process that mimics the behavior of the time series and generates inflow scenarios. The methodology was applied to the Ethiopian power system. The time series were analyzed using statistical methods, and the stochastic process that mimics the inflow patterns in Ethiopia was identified. The Monte Carlo simulation was used to generate sample realizations of random scenarios from the identified stochastic process. Then, three cases of inflow scenarios were tested in a deterministic simulation model of the Ethiopian hydropower system and compared with the actual operation. The results show that the generated inflow scenarios give a realistic output of generation scheduling and reasonable reservoir content based on the actual operation.

Set-valued uncertain process: definition and some properties

Some phenomena develop over time, while they are uncertain sets at each moment. From an uncertain set, we mean an unsharp concept that is not clearly defined, even for an expert. The potential values of parameters describing this unsharp concept guide one to explain it quantitatively. For instance, in “recovering” from some disease, different levels of health might be defined, meaning that at each specific time, being healthy is measured by belonging some parameter values to a set with a specific belief degree. Clearly speaking, the set defining “recovery” at the beginning stage of a disease would be entirely or partially non-identical with it at other stages. These sets might be extracted using imprecisely observed data, or an expert opinion defines them by some belief degrees. An essential feature of these sets is their variation over time by considering a sequence of evolving sets over time. Such concepts would direct one to employ uncertainty theory as a solid axiomatic mathematical framework for modeling human reasoning. Analyzing the behavior of such a sequence motivated us to define the set-valued uncertain process. This concept combines uncertain set, uncertain process, and uncertain sequence to a new concept. Here, we introduce the main idea; some properties are extracted and clarified, along with some illustrative examples. We also put forward some potential state-of-the-art applications.

Analyzing the preferences of flood victims on post flood public houses (PFPH): Application of a hybrid choice model to the floodplains of southern Pakistan

Climate change related natural hazards cause significant suffering and economic damage. The aftermath of losing one's home leaves people facing difficult decisions. Resettling those affected by flood disasters remains a complex and uncertain process. This study aims to shed light on the factors influencing the decision to choose post-flood public houses (PFPH) in flood-prone areas. Using a stated preference (SP) survey conducted in the floodplains of Southern Pakistan, this research analyzes the impact of policy variables in conjunction with latent psychometric factors on people's choices. A hybrid choice model (HCM) is developed to assess the influence of two latent variables: risk perception and place attachment. The findings indicate that individuals with higher risk perception are more inclined to choose PFPH, whereas those with stronger place attachment tend to opt for rebuilding their houses at the same location. Compensation for PFPH was observed to have a positive impact on the choice of PFPH, while the distance from the village center, reflecting how far they would be relocated from their current village center, had a negative impact on choosing PFPH. The calculation of willingness-to-accept measures confirmed that for every 1 KM increase in the distance from the village center, respondents would require additional compensation of PKR 24,558.45 (approximately $87.28). Furthermore, multiple policy scenario tests were conducted to economically evaluate the impact of latent variables and SP attributes on the predicted share of PFPH. The results underscore the significant influence of place attachment attitudes and the distance to the village center attribute on the share of PFPH.

Power-barrier option pricing formulas in uncertain financial market with floating interest rate

<abstract><p>Power-barrier option is a typical exotic option formed by attaching some restrictions to the power option, where the power option evolves from standard European option with the strike price and underlying good price attached to some power. Compared with the ordinary options, power-barrier option can provide investors with stable leverage and premium income. Therefore, power-barrier option is more favored by investors. This paper mainly discusses the pricing problems of power-barrier option in uncertain financial market. The fluctuation of stock price is regarded as an uncertain process and the interest rate is floating. The uncertain differential equation is invoked to simulate this fluctuation in an uncertain environment. Then, the clear pricing formulas of power-barrier option are given. Finally, the corresponding numerical examples and a real data example are put forward to illustrate the method.</p></abstract>

Genetic Recombination Sites Away from the Insertion/Deletion Hotspots in SARS-Related Coronaviruses.

The genome sequences of severe acute respiratory syndrome (SARS)-related coronaviruses (sarbecoviruses) have been reported to include many long and complex insertions/deletions (indels) in specific genomic regions, including open reading frame 1a (ORF1a), S1 domain of the spike, and ORF8 genes. These indel hotspots incorporate various non-classical, long, and complex indels with uncertain developmental processes. A possible explanation for these complex and diversified indels at the hotspots is genetic recombination. To determine the possible association between recombination events and development of indel hotspots, this study investigated the genome sequences of many sarbecoviruses from different countries and hosts and compared the distributions of the indel hotspots and recombination sites by performing multiple sequence alignments and recombination analyses. The genomes of 19 SARS-related coronaviruses (15 coronaviruses that infect bats, two that infect humans, one that infects pangolins, and one that infects civets), including human-infecting SARS-CoV and SARS-CoV-2, were evaluated. Hotspots of complex indels with diverse RNA sequences around gaps were clustered in non-structural protein 2 (Nsp2) and Nsp3 of ORF1a, S1, and ORF8. Phylogenetic reconstructions revealed different structures of the inferred phylogenetic trees between genomic regions, and recombination analyses identified multiple recombination sites across ORF1ab and S genes. However, the nucleotide positions of the indel hotspots were not identical with the identified recombination sites in the recombinant viruses, suggesting the involvement of different developmental processes of indel hotspots and genetic recombination. Further research is required to elucidate the developmental mechanisms underpinning clustered complex indels and recombination events in the evolutionary history of sarbecoviruses.

Italian and French Democracies’ Containment of Communist Unrest in the Early Cold War

After a brief interlude of legality ending in 1947, France and Italy faced violence fuelled by Communist organisations; the most important took place from the autumn of 1947 to the autumn of 1948 and greatly impressed governments and public opinion, sustaining fear of a Communist uprising. Facing this challenge to public order were resolute Ministers of the Interior Mario Scelba and Jules Moch. Their policy gained them the reputation of reso­lute anti-Communists going beyond the limits of democratic legality. This paper questions this simple picture. In fact, for centre-right and centre-left govern­ments ruling these two countries, emergency powers were linked to the state of (civil) war and both ministries’ policies relied mainly on the application of the existing penal code and the mobilisation of existing forces. Moves to strengthen repressive laws depended on a long and uncertain parliamentary process and, without wide political consensus and solid parliamentary majorities, they often proved to be too little or too late. Judiciary repression was sometimes inefficient, leaving the Ministries infuriated by impunity. With the fear of world war peaking in 1950, there were legal efforts to thwart possible Communist support for a Soviet invasion; but those did not appear any more fruitful than prior attempts, and faded when internal matters seemed more urgent than the declining “Red Threat” in the 1950s. This study also highlights the fact that the repression by the Italian state, relying on former Fascist laws and sometimes infringing on civil liberties, was more violent than in France. Casualties caused by law enforcement persisted even after the decrease of Communist activism, underlying the histor­ically higher level of social and political confrontation in Italy.

Stochastic Uncertain Degradation Modeling and Remaining Useful Life Prediction Considering Aleatory and Epistemic Uncertainty

Remaining useful life (RUL) prediction based on the degradation model is vital for the maintenance and management of complex equipment. It is necessary to describe the aleatory uncertainty in internal changes during equipment degradation and quantify the epistemic uncertainty caused by a lack of sufficient knowledge simultaneously. This article proposes a novel framework for the degradation model and RUL prediction with aleatory and epistemic uncertainties. First, the probability theory is combined with the uncertainty theory to establish a stochastic uncertain degradation model. Following that, a new stochastic uncertain maximum likelihood estimation (SUMLE) method is proposed based on probability function and uncertainty distribution to identify model parameters. The Bayesian inference is adopted to update the model parameters in the current time. Afterward, the above dual-source uncertainty is incorporated into RUL prediction. The distribution function of the RUL is derived, which can be updated in real-time according to the arrival of new degradation observations. Finally, the experimental studies on gallium arsenide (GaAs) laser and gyroscope degradation data are conducted to interpret the superiority of the proposed method over based on the stochastic process or uncertain process separately for predicting the RUL.

Maintenance Policy with Lifetime Reduction Based on Uncertain Renewal Process

This paper focus on a (N, T) preventive maintenance (PM) policy with lifetime reduction discount rate. In our consideration, the lifetime of component is assumed to be an uncertain variable due to the absence of historical operational data, an uncertain (N, T) PM model with the lifetime reduction in a certain proportion is proposed based on uncertain renewal process. Accordingly, the uncertain model, which minimizes the expected maintenance cost rate, is formulated to find the optimal replacement cycle length T∗ and the number N∗ of preventive maintenance. Finally, a numerical example with sensitivity analysis of parameters is provided to illustrate the proposed model, the results imply that the parameters of cost and lifetime can significantly affect the optimal solutions N∗ and T∗, which can provide a useful reference and guidance for aircraft maintenance decision.

The COVID-19 pandemic restrictions affect the healthcare and health status of paediatric patients with neuromuscular diseases: a developing country perspective

Purpose The COVID-19 pandemic has forced the rapid and unprecedented reorganisation of current practices in the treatment of neuromuscular disorders (NMD). Cessation of care and treatments can worsen the underlying condition, exacerbate symptoms, and increase anxiety, leading to a vicious circle and increased management concerns. This study aims to determine the changes in healthcare and health status of children with NMD from a developing country perspective. Materials and Methods Forty-seven children with NMD were included in this cross-sectional study. The participants were contacted via phone call. The survey conducted for the study was included demographic data, changes and problems in healthcare, perceived health status change, and satisfaction with the services provided. Descriptive statistics were used to characterise the sample. Results The mean age of the children was 7.86 ± 3.45 years. The participants encountered 24.83 ± 26.54% of difficulties in getting medication care, and there was 69.95 ± 24.47% disruption in accessing routine medical care. The participants’ rehabilitation sessions were disrupted in the 78.54 ± 14.93%, and there were 95.83 ± 10.03% deficiencies in therapists’ informing. Children with NMD indicated that their perceived health status decreased compared to before pandemic in all parameters. Conclusion This study highlights the unfavourable indirect effect of the COVID-19 pandemic restrictions on healthcare and health status of paediatric patients with NMD. Since the COVID-19 pandemic is an uncertain process, the solutions or modifications should be promptly put into effect to improve the healthcare and health status of children with NMD.

Modelling of a falling-film evaporator for adsorption chillers

The objective of the present study was to develop a dynamic model to simulate a prototype falling-film evaporator that is part of a single-bed adsorption chiller test bench located at the Department of Energy of the Politecnico di Milano. The model is based on the evaporator energy and mass balances and was calibrated and validated using experimental data coming from realistic operating conditions in a range of inlet chilled water temperatures (Tin,chw) from 15 to 25°C. From the experimental data, it was obtained that the average overall heat transfer conductance (UA) was approximately 530 W/K for all temperatures during the quasi steady-state section of the process. A correlation to calculate the wetted surface through a variable called wettability factor (fwet) was developed from experimental data. The fwet factors were identified using the model and were in the range of 0.80 – 0.20 (Tin,chw= 15°C) and 0.60 – 0.20 (Tin,chw= 25°C). It was seen that, the higher the Tin,chw, the lower the fwet values. The UA and saturation temperature (Tref,sat) values from the model were in good accordance with experimental data during the quasi steady-state section of the process. Nevertheless, the final transition stage (i.e., a situation in which the evaporator's refrigerant pool is empty) required an additional hypothesis due to the uncertain process’ dynamics. The mass and energy balances that are part of the hydrodynamics and heat transfer sections of the model use Nusselt's classic theory for falling-film.

Data-Driven and Model-Based Control Techniques for a Wind Turbine Benchmark Model

Abstract: Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high–fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchhmark model and the Monte–Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model–reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

Through an Ice Sheet, Darkly

Through an Ice Sheet, Darkly

No Peaks without Valleys: The Stable Mass Transfer Channel for Gravitational-wave Sources in Light of the Neutron Star–Black Hole Mass Gap

Gravitational-wave (GW) detections are starting to reveal features in the mass distribution of double compact objects. The lower end of the black hole (BH) mass distribution is especially interesting as few formation channels contribute here and because it is more robust against variations in the cosmic star formation than the high-mass end. In this work we explore the stable mass transfer channel for the formation of GW sources with a focus on the low-mass end of the mass distribution. We conduct an extensive exploration of the uncertain physical processes that impact this channel. We note that, for fiducial assumptions, this channel reproduces the peak at ∼9 M ☉ in the GW-observed binary BH mass distribution remarkably well and predicts a cutoff mass that coincides with the upper edge of the purported neutron star–black hole (NS–BH) mass gap. The peak and cutoff mass are a consequence of the unique properties of this channel; namely (1) the requirement of stability during the mass transfer phases, and (2) the complex way in which the final compact object masses scale with the initial mass. We provide an analytical expression for the cutoff in the primary component mass and show that this adequately matches our numerical results. Our results imply that selection effects resulting from the formation channel alone can provide an explanation for the purported NS–BH mass gap in GW detections. This provides an alternative to the commonly adopted view that the gap emerges during BH formation.

Reliability modelling considering multi-dimensional cognitive uncertainties based on uncertainty theory

Aiming at the multi-dimensional cognitive uncertainties in performance degradation modelling for small samples, a reliability assessment method is proposed based on uncertainty theory. First, a degradation model is established based on the uncertain process, which considers cognitive uncertainties from the time fluctuation, the measurement error, and the individual differences in initial value and degradation rate, and the reliability function is derived. Then, a two-step parameter estimation method for the degradation model is proposed based on the α-path and the least square, which is verified by numerical simulation. Finally, the effectiveness and superiority of the proposed method are verified by the cases of GaAs lasers and magneto-optic data storage disks, and parameter sensitivity analysis is conducted. The results indicate that uncertainty affects product reliability greatly. For small samples, thedegradation models considering multi-dimensional uncertainties based on the Liu process has smaller fitting errors than that based on the Wiener process.

Patient views on asthma diagnosis and how a clinical decision support system could help: A qualitative study.

Making a diagnosis of asthma can be challenging for clinicians and patients. A clinical decision support system (CDSS) for use in primary care including a patient-facing mode, could change how information is shared between patients and healthcare professionalsand improve the diagnostic process. Participants diagnosed with asthma within the last 5 years were recruited from general practices across four UK regions. In-depth interviews were used to explore patient experiences relating to their asthma diagnosis and to understand how a CDSS could be used to improve the diagnostic process for patients. Interviews were audio recorded, transcribed verbatim and analysed using a thematic approach. Seventeenparticipants (12 female) undertook interviews, including 14 individuals and 3 parents of children with asthma. Being diagnosed with asthma was generally considered an uncertain process. Participants felt a lack of consultation time and poor communication affected their understanding of asthma and what to expect. Had the nature of asthma and the steps required to make a diagnosis been explained more clearly, patients felt their understanding and engagement in asthma self-management could have been improved. Participants considered that a CDSS could provide resources to support the diagnostic process, prompt dialogue, aid understanding and support shared decision-making. Undergoing an asthma diagnosis was uncertain for patients if their ideas and concerns were not addressed by clinicians and were influenced by a lack of consultation time and limitations in communication. An asthma diagnosis CDSS could provide structure and an interface to prompt dialogue, provide visuals about asthma to aid understandingand encourage patient involvement. Prespecified semistructured interview topic guides (young person and adult versions) were developed by the research team and piloted with members of the Asthma UK Centre for Applied Research Patient and Public Involvement (PPI) group. Findings were regularly discussed within the research group and with PPI colleagues to aid the interpretation of data.

Synchronization-Enhanced Deep Learning Early Flood Risk Predictions: The Core of Data-Driven City Digital Twins for Climate Resilience Planning

Floods have been among the costliest hydrometeorological hazards across the globe for decades, and are expected to become even more frequent and cause larger devastating impacts in cities due to climate change. Digital twin technologies can provide decisionmakers with effective tools to rapidly evaluate city resilience under projected floods. However, the development of city digital twins for flood predictions is challenging due to the time-consuming, uncertain processes of developing, calibrating, and coupling physics-based hydrologic and hydraulic models. In this study, a flood prediction methodology (FPM) that integrates synchronization analysis and deep-learning is developed to directly simulate the complex relationships between rainfall and flood characteristics, bypassing the computationally expensive hydrologic-hydraulic models, with the City of Calgary being used for demonstration. The developed FPM presents the core of data-driven digital twins that, with real-time sensor data, can rapidly provide early warnings before flood realization, as well as information about vulnerable areas—enabling city resilience planning considering different climate change scenarios.

Identifying patent classification codes associated with specific search keywords using machine learning

The purpose of this research is to retrieve relevant patent documents and identify classification codes and search keywords that best characterize a given technological domain found in patent literature. The World Intellectual Property Organization (WIPO) recorded a rising number of patent applications filed under the Patent Cooperation Treaty (PCT) which is becoming the norm for filing patents in multiple jurisdictions. As such, PCT documents are a valuable source of information related to innovation activities with some degree of entrepreneurial intention. However, searching for relevant patent documents can be a daunting and uncertain process. We constructed a high-dimensional matrix consisting of two data types: classification codes and search keywords known as the code-keyword matrix. In turn, two machine learning algorithms called principal components analysis (PCA) and k-means clustering were used to derive insights from the high-dimensional dataset. Consequently, a two-dimensional PCA biplot and clustering on an optimized PCA dataset called Eigen-PCA were obtained using our combined machine learning method. Using such algorithms, we were able to identify correlation relationships found between the two data types. We also clustered the classification codes by least-relevance, medium-relevance, and high-relevance for the domain of anti-corrosion technologies, an impactful area for steel infrastructure in maritime environments. Such patent data analytics can be adapted to other areas such as medical technologies, green energy transition towards Net Zero and conservation of biological diversity.

A Gaussian Type-2 Fuzzy Programming Approach for Multicrowd Congestion-Relieved Evacuation Planning

Large-scale emergencies occur frequently around the world, causing serious casualties. Emergency evacuation is one of the top priorities after a disaster. Due to the uncertain and complex evacuation processes, evacuation plans obtained in a deterministic context may not meet the requirements of practical engineering applications. This research considers a multi-crowd congestion-relieved evacuation problem in Gaussian Type-2 fuzzy environments. A path-based one destination network flow (P-ODNF) model is developed for the problem formulation. To relieve the traffic congestion, a multi-point diversion evacuation strategy is employed in the model’s construction. The uncertainties associated with the evacuation process are expressed as Gaussian Type-2 fuzzy variables. A critical value-based defuzzification technique is adopted to handle the Type-2 fuzziness. Based on the credibility measure, the uncertain P-ODNF model is transformed into its deterministic counterpart. An efficient adaptive chaos particle swarm optimization algorithm (A-CPSO) is designed for model solving. Several numerical experiments are performed to demonstrate the proposed methodology. A sensitivity analysis is performed to illustrate the logical correctness of the proposed defuzzification process. The computational results show that A-CPSO is competitive in both its effectiveness and efficiency. The proposed methodology can coordinate multiple simultaneous evacuation processes to relieve congestion and improves the overall evacuation efficiency.

Risk-Based Constraints for the Optimal Operation of an Energy Community

This paper formulates an energy community’s centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio’s aggregated flexibility. First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent. To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios’ temporal characteristics.