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In the ever-evolving world of veterinary care, the occurrence of bone fractures in canines poses a common and complex problem, especially in extra-small breeds and dogs that are less than 1 year old. The objective of this research is to fill a gap in predicting the risk of canine bone fractures. A machine learning method using a random forest classifier was constructed. The algorithm was trained on a dataset consisting of 2261 cases that included several factors, such as canine age, gender, breed, and weight. The performance of the algorithm was assessed by examining its capacity to forecast the probability of fractures occurring. The findings of our study indicate that the tool has the capability to provide dependable predictions of fracture risk, consistent with our extensive dataset on fractures in canines. However, these results should be considered preliminary due to the limited sample size. This discovery is a crucial tool for veterinary practitioners, allowing them to take preventive measures to manage and prevent fractures. In conclusion, the implementation of this prediction tool has the potential to significantly transform the quality of care in the field of veterinary medicine by enabling the detection of patients at high risk, hence enabling the implementation of timely and customized preventive measures. [ABSTRACT FROM AUTHOR]

Gold prices have been of major interest for a lot of investors, analysts, and economists. Accordingly, a number of different modeling approaches have been used to forecast gold prices. In this manuscript, the geometric Brownian motion approach, used in the pricing of numerous types of assets, is used to forecast the prices of gold at yearly, monthly, and quarterly frequencies. This approach allows for simulating one-period-ahead prices and the associated probabilities. The expected prices obtained from the simulated prices and probabilities are found to provide reliable forecasts when compared with the observed yearly, monthly, and quarterly prices. [ABSTRACT FROM AUTHOR]

We identify and analyze three caveats that may arise when analyzing the linguistic abilities of Large Language Models. The problem of unlicensed generalizations refers to the danger of interpreting performance in one task as predictive of the models’ overall capabilities, based on the assumption that because a specific task performance is indicative of certain underlying capabilities in humans, the same association holds for models. The human-like paradox refers to the problem of lacking human comparisons, while at the same time attributing human-like abilities to the models. Last, the problem of double standards refers to the use of tasks and methodologies that either cannot be applied to humans or they are evaluated differently in models vs. humans. While we recognize the impressive linguistic abilities of LLMs, we conclude that specific claims about the models’ human-likeness in the grammatical domain are premature.

In the ever-evolving world of veterinary care, the occurrence of bone fractures in canines poses a common and complex problem, especially in extra-small breeds and dogs that are less than 1 year old. The objective of this research is to fill a gap in predicting the risk of canine bone fractures. A machine learning method using a random forest classifier was constructed. The algorithm was trained on a dataset consisting of 2261 cases that included several factors, such as canine age, gender, breed, and weight. The performance of the algorithm was assessed by examining its capacity to forecast the probability of fractures occurring. The findings of our study indicate that the tool has the capability to provide dependable predictions of fracture risk, consistent with our extensive dataset on fractures in canines. However, these results should be considered preliminary due to the limited sample size. This discovery is a crucial tool for veterinary practitioners, allowing them to take preventive measures to manage and prevent fractures. In conclusion, the implementation of this prediction tool has the potential to significantly transform the quality of care in the field of veterinary medicine by enabling the detection of patients at high risk, hence enabling the implementation of timely and customized preventive measures.

Mathematical education in Greece is constantly evolving in the pursuit of optimal learning outcomes for students despite their cognitive differences. This study seeks to gain insight into the use of new technologies in teaching probability theory and the gender differences in the comprehension of probability theory. To this end, a survey was conducted involving 500 students of the Department of Statistics and Insurance Science at the University of Piraeus. The respective questionnaire involves questions of self-reported results and employs the Likert scale to obtain the students' perceptions. Our data demonstrate no difference among the genders regarding the use of new technologies or their performance (i.e., the test scores) in the relevant courses. [ABSTRACT FROM AUTHOR]

The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall. [ABSTRACT FROM AUTHOR]

Copyright of Quetes Litteraires is the property of Katolicki Uniwersityt Lubelski Jana Pawla II, Wydzial Nauk Humanistycznych, Institut Filologii Romaj and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Entropy is the concepts from the science of information must be used in the situation where undefined behaviors of the parameters are unknown. The behavior of the casual parameters representing the processes under investigation is a problem that the essay explores from many angles. The provided uniformity criterion, which was developed utilizing the maximum entropy of the metric, has high efficiency and straightforward implementation in manual computation, computer software and hardware, and a variety of similarity, recognition, and classification indicators. The tools required to automate the decision-making process in real-world applications, such as the automatic classification of acoustic events or the fault-detection via vibroacoustic methods, are provided by statistical decision theory to the noise and vibration engineer. Other statistical analysis issues can also be resolved using the provided uniformity criterion.

This article presents a study based on evaluating different techniques to automatically recognize the basic emotions of people with Down syndrome, such as anger, happiness, sadness, surprise, and neutrality, as well as the statistical analysis of the Facial Action Coding System, determine the symmetry of the Action Units present in each emotion, identify the facial features that represent this group of people. First, a dataset of images of faces of people with Down syndrome classified according to their emotions is built. Then, the characteristics of facial micro-expressions (Action Units) present in the feelings of the target group through statistical analysis are evaluated. This analysis uses the intensity values of the most representative exclusive action units to classify people's emotions. Subsequently, the collected dataset was evaluated using machine learning and deep learning techniques to recognize emotions. In the beginning, different supervised learning techniques were used, with the Support Vector Machine technique obtaining the best precision with a value of 66.20%. In the case of deep learning methods, the mini-Xception convolutional neural network was used to recognize people's emotions with typical development, obtaining an accuracy of 74.8%. [ABSTRACT FROM AUTHOR]

Demographers and other social scientists often study effect heterogeneity (defined here as differences in outcome-predictor associations across groups defined by the values of a third variable) to understand how inequalities evolve between groups or how groups differentially benefit from treatments. Yet answering the question "Is the effect larger in group A or group B?" is surprisingly difficult. In fact, the answer sometimes reverses across scales. For example, researchers might conclude that the effect of education on mortality is larger among women than among men if they quantify education's effect on an odds-ratio scale, but their conclusion might flip (to indicate a larger effect among men) if they instead quantify education's effect on a percentage-point scale. We illuminate this flipped-signs phenomenon in the context of nonlinear probability models, which were used in about one third of articles published in Demography in 2018-2019. Although methodologists are aware that flipped signs can occur, applied researchers have not integrated this insight into their work. We provide formal inequalities that researchers can use to easily determine if flipped signs are a problem in their own applications. We also share practical tips to help researchers handle flipped signs and, thus, generate clear and substantively correct descriptions of effect heterogeneity. Our findings advance researchers' ability to accurately characterize population variation. [ABSTRACT FROM AUTHOR]

A strong foundation in early number concepts is crucial for students' future success in statistics. Despite its importance in statistics, many first-year students struggle to comprehend the normal distribution due to a lack of basic number sense. Students get confused about the order and magnitude of negative z-scores on a standard normal curve or when problems about normally distributed random variables are presented in word questions which involve phrases that indicate inequalities. As a result, students shade wrong areas on the bell-shaped curve when they have to calculate probabilities for normally distributed variables. Visual representations such as the number line can support students' development of quantitative literacy or number sense by helping them create a mental representation of the order and magnitude of numbers as well as inequalities. Based on a comprehensive investigation of evidence demonstrating this weakness, this experienced-based perspective proposes a framework that demonstrates how the number line can be used as a powerful teaching tool to promote students' conceptual understanding about the normal distribution. The framework illustrates with authentic examples how the number line relates to the horizontal axis of the normal and standard normal curve and how it can be used to address erroneous quantitative reasoning when students are required to calculate probabilities for normally distributed variables. To determine if the number line teaching intervention affects student performance, the researcher adopted a non-equivalent pretest-posttest design with two intact classes. The results of the analysis show that students who received the intervention performed significantly better in the posttest. [ABSTRACT FROM AUTHOR]

The rising cases of floods and the onset of drought in different parts of Nigeria require urgent attention particularly because Nigeria accommodates the largest population in Africa, hence any negative climate impact on it can easily ripple into other African regions. To understand the risk factors that drive these extreme events, we study the bivariate extreme cases of monthly precipitation and temperature observations over a period of 116 years (1901–2016). This is the first paper providing bivariate extreme value analysis of data in Nigeria. The mean rainfall and temperature variables exhibit interrelationships such as dry-cold and wet-cold associations. We further investigate whether these relationships are present at the tails by making use of the annual minimum rainfall-annual minimum temperature and annual maximum rainfall-annual minimum temperature pairs. Their extreme dependence structures are also quantified by applying the parametric bivariate extreme value models. Our results show that the compound extremes of dry-cold and wet-cold conditions exhibit a zero to weak extreme dependence at varying quantile levels. A much stronger dependence structure is present between the annual maximum rain and the total volume of rainfall. By considering both independent and dependent probability assumptions, we show that the former may lead to an underestimation of the risks associated with existing climatic hazards. The implications of these results are highlighted throughout the paper. [ABSTRACT FROM AUTHOR]

Data analysis is one of the most popular fields of mathematics and includes statistics and probability. These two mathematical domains are some of the most well-known, influencing everyday life and the various sciences. Their teaching lays the foundation for primary education and culminates in secondary education. Probability and statistics are necessary for today and the future of several professions. This research attempts to highlight the multidisciplinary character of these two disciplines through the textbooks of primary education in Greece and Singapore. It aims to highlight the dependence of mathematics teaching on interdisciplinarity through textbooks. The textbook analysis was chosen because books offer varied learning opportunities. The researchers selected the books, partaking in the comparative analysis. After defining the basic principles dividing lines for the differentiation of the exercises, the analysis was conducted. It included two stages. In the first stage, the activities of the books were examined in their framework application. Then, their interdisciplinary character was accentuated in the scientific field. The results reveal a substantial dependence of data analysis on interdisciplinarity. More interesting is that the distribution of interdisciplinary exercises is prevalent in the scientific milieus.

Project schedule monitoring and controlling are critical challenges of construction project management that are not adequately implemented, likely due to the predominance of earned value management and the lack of utilizing technology, such as BIM tools. Unlike earned value, earned duration management (EDM) was developed, which includes several indices to track schedule progress and measure the performance of a schedule. The goal of this research was to establish a decision support system to track and monitor construction project activities during construction, with better performance and accuracy. A survey was conducted and distributed among ten site engineers, selected from different construction sites. The survey asked the site engineers about the possible durations of certain activities; based on their answers, the authors started the proposed system. In this study, we aimed to develop a decision support system (DSS), which combines BIM with EDM to help calculate probabilistic total project duration, visually detecting critical activities, monitoring visually risky activities subjected to delay and visually categorizing the accuracy of estimated duration for delayed activities. [ABSTRACT FROM AUTHOR]

Bayesian learning has been successfully used in many fields to make decision or sense the outcome of causes or influences of events. However, the relations between causes and observed events are more complicated than the Bayesian inference can learn, like prediction in some psychology experiments, which should consider human experience and the interdependence of the events. This paper raises Bayesian learning to intelligent learning by putting events in a network to analysis the complicated criterions and interdependences among events with the individual probability judgments. Finally, the results of experiment used by intelligent learning illustrate more complex relations than Bayesian learning. [ABSTRACT FROM AUTHOR]

Data analysis is one of the most popular fields of mathematics and includes statistics and probability. These two mathematical domains are some of the most well-known, influencing everyday life and the various sciences. Their teaching lays the foundation for primary education and culminates in secondary education. Probability and statistics are necessary for today and the future of several professions. This research attempts to highlight the multidisciplinary character of these two disciplines through the textbooks of primary education in Greece and Singapore. It aims to highlight the dependence of mathematics teaching on interdisciplinarity through textbooks. The textbook analysis was chosen because books offer varied learning opportunities. The researchers selected the books, partaking in the comparative analysis. After defining the basic principles dividing lines for the differentiation of the exercises, the analysis was conducted. It included two stages. In the first stage, the activities of the books were examined in their framework application. Then, their interdisciplinary character was accentuated in the scientific field. The results reveal a substantial dependence of data analysis on interdisciplinarity. More interesting is that the distribution of interdisciplinary exercises is prevalent in the scientific milieus. [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) equations have important applications in several engineering areas such as urban drainage designs, hydrological modeling, and soil conservation projects. This study analyzes the annual maximum series and fits IDF equations for 44 rainfall stations in Alagoas State, Brazil. We adjusted parameters of the Gumbel distribution (GD) and the Generalized Extreme Value (GEV) distribution. The fitting of the observed data to the probability distributions, as well as the selection of the best distribution, were based on the Kolmogorov-Smirnov and Anderson-Darling tests at a 5% significance level. The GEV distribution with parameters obtained by the L-moments method was considered the best in 73% of rainfall stations. The estimated IDF equations showed a good fit, with determination coefficients above 0.991. The maximum rainfall intensities have spatial variation following the climatic zones of the state. The fitted equations allow estimating rainfall intensities from 5 minutes to 24 hours with a return period of 2 to 100 years, and standard error of less than 6.83 mm h-1.

Summary: Background: Social and material deprivation accelerate the development of multimorbidity, yet the mechanisms which drive multimorbidity pathways and trajectories remain unclear. We aimed to examine the association between health inequality, risk factors and accumulation or resolution of LTCs, taking disease sequences into consideration. Methods: We conducted a retrospective cohort of adults aged 18 years and over, registered between April 2005 and May 2020 in general practices in one inner London borough (n = 826,936). Thirty-two long term conditions (LTCs) were selected using a consensus process, based on a definition adapted to the demographic characteristics of the local population. sThe development and resolution of these LTCs were examined according to sociodemographic and clinical risk factors (hypertension; moderate obesity (BMI 30·0–39·9 kg/m2), high cholesterol (total cholesterol > 5 mmol/L), smoking, high alcohol consumption (>14 units per week), and psychoactive substance use), through the application of multistate Markov chain models. Findings: Participants were followed up for a median of 4.2 years (IQR = 1·8 - 8·4); 631,760 (76%) entered the study with no LTCs, 121,424 (15%) with 1 LTC, 41,720 (5%) with 2 LTCs, and 31,966 (4%) with three or more LTCs. At the end of follow-up, 194,777 (24%) gained one or more LTCs, while 45,017 (5%) had resolved LTCs and 27,021 (3%) died. In multistate models, deprivation (hazard ratio [HR] between 1·30 to 1·64), female sex (HR 1·13 to 1·20), and Black ethnicity (HR 1·20 to 1·30; vs White) were independently associated with increased risk of transition from one to two LTCs, and shorter time spent in a healthy state. Substance use was the strongest risk factor for multimorbidity with an 85% probability of gaining LTCs over the next year. First order Markov chains identified consistent disease sequences including: chronic pain or osteoarthritis followed by anxiety and depression; alcohol and substance dependency followed by HIV, viral hepatitis, and liver disease; and morbid obesity followed by diabetes, hypertension, and chronic pain. Interpretation: We examined the relations among 32 LTCs, taking the order of disease occurrence into consideration. Distinctive patterns for the development and accumulation of multimorbidity have emerged, with increased risk of transitioning from no conditions to multimorbidity and mortality related to ethnicity, deprivation and gender. Musculoskeletal disorders, morbid obesity and substance abuse represent common entry points to multimorbidity trajectories.

In recent years, there have been efforts to promote probabilistic reporting and the use of computational algorithms across several forensic science disciplines. Reactions to these efforts have been mixed—some stakeholders argue they promote greater scientific rigor whereas others argue that the opacity of algorithmic tools makes it challenging to meaningfully scrutinize the evidence presented against a defendant resulting from these systems. Consequently, the forensic community has been left with no clear path to navigate these concerns as each proposed approach has countervailing benefits and risks. To explore these issues further and provide a foundation for a path forward, this study draws on semi-structured interviews with fifteen participants to elicit the perspectives of key criminal justice stakeholders, including laboratory managers, prosecutors, defense attorneys, judges, and other academic scholars, on issues related to interpretation and reporting practices and the use of computational algorithms in forensic science within the American legal system.

The notion focus affinity refers to the likelihood that a grammatical role (e.g., subject, object) is the narrow focus of a sentence. There is evidence that such differences exist at the level of distributional data. For example, subjects are less often focal and more often topical as compared to direct objects. However, it is not obvious whether focus affinity is genuinely related to grammatical roles. Focus affinity could be an epiphenomenon of various other linguistic and pragmatic processes or their interactions (e.g., direct objects are more often indefinite, less often animate, more often new than subjects). In our controlled experimental study, we investigate the focus affinity of three adjunct types (instruments, locatives, depictive secondary predicates) in Spanish and provide evidence that these grammatical roles indeed differ with respect to focus affinity. Depictives show the highest degree of focus affinity, followed by instruments and finally locatives. These effects are robust and stable even if we account for a number of possible alternative explanations. Thus, we suggest that focus affinity is a property sufficiently closely associated with grammatical roles to justify more attention both in theoretical linguistic and psycholinguistic literature. • Focus affinity is an understudied information structural concept. • Focus affinities of three adjunct types have been experimentally investigated. • Focus hierarchy: Depictives > Instruments > Locatives. • Differences between grammatical roles are robust even if we consider alternative explanations (e.g., probabilities). [ABSTRACT FROM AUTHOR]

Copyright of Revista Habanera de Ciencias Médicas is the property of Universidad de Ciencias Medicas de La Habana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is a first-order interdisciplinary research, in an interstice defined by linguistic statistics and it appeared by extending the statistical way of thinking in the universe of language. The research structure includes an introductory section that states the specific paradigm or the common statistical and linguistic concepts of the investigation, followed by a brief methodological section, simultaneously of a descriptive and applied type, with reference to laws, methods and statistical models possible in the initially quantitative universe and finally qualitative universe of language, to eventually reach the final section dedicated to an attempt to assess entropy and information energy, based on the results of research in its whole. From an evolutionary point of view, after the philologist initially investigates in an exemplary way and with philological accuracy, and the statistician approaches selectively and inferentially, but also with specific statistical rigor, the interdisciplinary point of view of linguistic statistics allows a rapid confrontation and a multiple evaluation of entropy through the information energy method, but also through other equally interesting methods. The justified hope of interdisciplinary thinking of linguistic statistics to optimally capitalize on simultaneously philological and methodological reasoning to validate or invalidate preliminary hypotheses and to allow the expression of conclusions, follow-up analysis, outlines if not an answer, certainly at least several useful questions, which confirms practically and theoretically both the necessity and the veracity of the research in this article, according to the logic of a successful research in a Veblenian way. [ABSTRACT FROM AUTHOR]

This article presents a study based on evaluating different techniques to automatically recognize the basic emotions of people with Down syndrome, such as anger, happiness, sadness, surprise, and neutrality, as well as the statistical analysis of the Facial Action Coding System, determine the symmetry of the Action Units present in each emotion, identify the facial features that represent this group of people. First, a dataset of images of faces of people with Down syndrome classified according to their emotions is built. Then, the characteristics of facial micro-expressions (Action Units) present in the feelings of the target group through statistical analysis are evaluated. This analysis uses the intensity values of the most representative exclusive action units to classify people’s emotions. Subsequently, the collected dataset was evaluated using machine learning and deep learning techniques to recognize emotions. In the beginning, different supervised learning techniques were used, with the Support Vector Machine technique obtaining the best precision with a value of 66.20%. In the case of deep learning methods, the mini-Xception convolutional neural network was used to recognize people’s emotions with typical development, obtaining an accuracy of 74.8%.

The usage and adoption of electric vehicles (EVs) have increased rapidly in the 21st century due to the shifting of the global energy demand away from fossil fuels. The market penetration of EVs brings new challenges to the usual operations of the power system. Uncontrolled EV charging impacts the local distribution grid in terms of its voltage profile, power loss, grid unbalance, and reduction of transformer life, as well as harmonic distortion. Multiple research studies have addressed these problems by proposing various EV charging control methods. This manuscript comprehensively reviews EV control charging strategies using real-world data. This review classifies the EV control charging strategies into scheduling, clustering, and forecasting strategies. The models of EV control charging strategies are highlighted to compare and evaluate the techniques used in EV charging, enabling the identification of the advantages and disadvantages of the different methods applied. A summary of the methods and techniques for these EV charging strategies is presented based on machine learning and probabilities approaches. This research paper indicates many factors and challenges in the development of EV charging control in next-generation smart grid applications and provides potential recommendations. A report on the guidelines for future studies on this research topic is provided to enhance the comparability of the various results and findings. Accordingly, all the highlighted insights of this paper serve to further the increasing effort towards the development of advanced EV charging methods and demand-side management (DSM) for future smart grid applications.

Concepts like the 100‐year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20‐, 50‐, and 100‐year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20‐, 50‐, and 100‐year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20‐/50‐year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100‐year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments. Plain Language Summary: Here, we provide the first global examination of recent changes in the size, frequency, and probability of extreme river floods using historical river records. Since the 1970s, the 20‐year and 50‐year extreme river floods have mostly increased in temperate zones but decreased in arid, tropical, polar, and cold zones. In contrast, the 100‐year floods have decreased in arid and temperate zones, and show mixed results in cold zones, but at a smaller sample of sites with long records. Descriptions of changes in extreme flooding depend largely on site selection, and are constrained by availability of long‐term data. Overall, our findings highlight the importance of regularly updating flood hazard assessments under nonstationarity. Key Points: We provide the first global nonstationary assessment of changes in the magnitude, return period, and probability of observed extreme floodsWe find increasing 20‐year and 50‐year floods in temperate climate zones but mostly decreases in arid, tropical, polar, and cold zonesFor the 100‐year floods, at a smaller sample of sites, we find decreases in arid and temperate zones and mixed results elsewhere [ABSTRACT FROM AUTHOR]

Project schedule monitoring and controlling are critical challenges of construction project management that are not adequately implemented, likely due to the predominance of earned value management and the lack of utilizing technology, such as BIM tools. Unlike earned value, earned duration management (EDM) was developed, which includes several indices to track schedule progress and measure the performance of a schedule. The goal of this research was to establish a decision support system to track and monitor construction project activities during construction, with better performance and accuracy. A survey was conducted and distributed among ten site engineers, selected from different construction sites. The survey asked the site engineers about the possible durations of certain activities; based on their answers, the authors started the proposed system. In this study, we aimed to develop a decision support system (DSS), which combines BIM with EDM to help calculate probabilistic total project duration, visually detecting critical activities, monitoring visually risky activities subjected to delay and visually categorizing the accuracy of estimated duration for delayed activities.

Different risks are associated with the operation and maintenance of wind farms in cold climate regions, mainly due to the harsh weather conditions that wind farms experience in that region such as the (i) increased stoppage rate of wind turbines due to harsh weather conditions, (ii) limited accessibility to wind farms due to snow cover on roads, and (iii) cold stress to workers at wind farms. In addition, there are risks that are caused by wind farms during their operation, which impact the surrounding environment and community such as the (iv) risk of ice throw from wind turbines, (v) environmental risks caused by the wind farms, and (vi) social opposition risk to installing wind farms in cold climate regions, such as the Arctic. The analysis of these six risks provides an overall view of the potential risks encountered by designers, operators, and decision makers at wind farms. This paper presents a methodology to quantify the aforementioned risks using fuzzy logic method. At first, two criteria were established for the probability and the consequences of each risk; with the use of experts’ judgments, membership functions were graphed to reflect the two established criteria, which represented the input to the risk analysis process. Furthermore, membership functions were created for the risk levels, which represented the output. To test the proposed methodology, a wind farm in Arctic Norway was selected as a case study to quantify its risks. Experts provided their assessments of the probability and consequences of each risk on a scale from 0–10, depending on the description of the wind farm provided to them. Risk levels were calculated using MATLAB fuzzy logic toolbox and ranked accordingly. Limited accessibility to the wind farm was ranked as the highest risk, while the social opposition to the wind farm was ranked as the lowest. In addition, to demonstrate the effects of the Arctic operating conditions on performance and safety of the wind farm, the same methodology was applied to a wind farm located in a non-cold-climate region, which showed that the risks ranked differently.

The number of electric vehicles (EVs) is expected to grow significantly, which calls for effective planning of charging infrastructures. While the planning of the charging infrastructure relies on an accurate charging demands, the behaviours of EVs charging are not always predictable and can be sensitive to many uncertain future environmental factors. Considering such uncertainties, this study aims to robustly and optimally determine the chargers and main switch board (MSB) capacities without violating queuing time constraints and load flow constraints. The non-parametric estimations of charging demands are derived with data-driven charging behaviour analysis considering diverse social factors, including travelling patterns, queuing, and changes of charging facilities. Then, the impacts of the EV integration are modeled by a stochastic load flow program. The samples of the stochastic load flow stipulate the conditional value-at-risk constraints for the planning of chargers and MSBs, which consider the probabilities and scenarios in a box of ambiguity with bounds. Afterwards, by limiting the frequency and severity of constraints violation, the total investment cost is minimized with a distributionally robust optimisation program. Simulation based on a real-world residential community in Singapore is carried out to testify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]