Optimistic Predictions Research Articles

A Longitudinal Study of the Impact of COVID-19 on Optimism Prediction

The purpose of this longitudinal study was to investigate how optimistic predictions, hopelessness, and depressive symptoms changed as a result of the COVID-19 pandemic, and the causal relationships between these variables. To achieve this purpose, we used data from online surveys conducted in 2019 and 2021 among men and women aged 20–79. Based on item response theory, we developed a future prediction task for the assessment of optimistic predictions. Our comparison of online survey responses found a decline in optimistic predictions before and after the pandemic. More specifically, there were no change in predictions of negative future events, but there was the decrease in predictions of positive future events. Furthermore, we found that those who were more stressed by COVID-19 were less likely to have an optimistic view of the near future. We also found a relationship between optimistic predictions and hopelessness and depressive symptoms with lower optimism predicting more hopelessness and more depression predicting lower optimism. To prevent feelings of hopelessness, it is important to help people develop positive expectations about the future.

Excessive discounting, longevity expectations, and retirement saving: An online survey

I report results of a major online experiment focused on two behavioural mechanisms that might affect long-term saving: impatience (excessive discounting) and distorted beliefs about own longevity. I observe the longevity expectations to be generally reasonable, both in terms of their mean values and their determinants, although the estimates show large variance and, on balance, slight pessimism. In line with previous studies, I find excessive discounting and (self-reported) insufficient retirement saving to be prevalent. Both expectations and excessive discounting affect retirement saving in the natural direction. However, there is no link between these two determinants: neither overly optimistic nor pessimistic predictions concerning own longevity seem to be systematically linked to excessive discounting. Thus, these two behavioural effects neither strengthen nor cancel each other out.

Calling “Gevald”: on the emergence of negative election forecasts in partisan communications

AbstractIndividuals were found to anonymously predict positive election outcomes for their preferred candidate. Yet, there is little scientific knowledge about election predictions made in the context of same-camp political communications (i.e., partisan communications) that are presumably meant to encourage other supporters. In five studies of low-information elections and a study of hypothetical U.S. elections (n = 1889), we found that people tended to communicate favorable forecasts to others sharing their view, compared to the neutral point and to the actual election outcomes. On the other hand, negative framing reduced the positivity of forecasts in these communications to the extent that it led most participants to predict an election loss. This occurred in response to a single addressee acting discordantly and even more strongly when the election results were phrased as a drop. When both positive and negative framing options were available, this still negativity affected participants’ predictions even though only a minority selected the negative framing option. Thus, people tend to make optimistic election predictions in partisan communications, but this pattern is easily manipulable given subtle changes in the forecasting prompt, either by negative framing or selectable positive and negative framing.

Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective.

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.

A Study on Sugar Content Improvement and Distribution Flow Response through Citrus Sugar Content Prediction Based on the PyCaret Library

Despite the increasing attention on smart farms as a solution to rural issues such as aging agricultural populations, a shortage of young farmers, decreased production area, and reduced investment leading to stagnant income, exports, and growth rates, many farms still rely on traditional methods like cultivating tangerines in open fields. Despite this, increasing farm income requires producing high-quality tangerines and selling them at premium prices, with fruit sweetness being a crucial factor. Therefore, there is a need to examine the close correlation between tangerine quality and sweetness. In this paper, we use deep learning with the PyCaret library to predict and analyze tangerine sweetness using data from seven regions in Jeju and 13 comprehensive factors influencing sweetness, including terrain, temperature, humidity, precipitation, sunlight, wind speed, acidity, sugar-acid ratio, and others. Although applying all 13 factors could achieve over 90% accuracy, our study, limited to seven factors, still achieves a respectable 82.4% prediction accuracy, demonstrating the significant impact of weather data on sweetness. Moreover, these optimistic predictions enable the estimation of tangerine quality and price formation in the market for the coming year, allowing tangerine farmers and related agencies to respond to market conditions proactively. Furthermore, by applying these data to smart farms to control factors influencing tangerine sweetness, it is anticipated that high-quality tangerine production and increased farm income can be achieved.

Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets

ABSTRACT This study investigates the performance of six machine learning (ML) models – Random Forest (RF), Adaptive Boosting (ADA), CatBoost (CAT), Support Vector Machine (SVM), Lasso Regression (LAS), and Artificial Neural Network (ANN) – against traditional empirical formulas for estimating maximum scour depth after sluice gates. Our findings indicate that ML models generally outperform empirical formulas, with correlation coefficients (CORR) ranging from 0.882 to 0.944 for ML models compared with 0.835–0.847 for empirical methods. Notably, ANN exhibited the highest performance, followed closely by CAT, with a CORR of 0.936. RF, ADA, and SVM performed competitive metrics around 0.928. Variable importance assessments highlighted the dimensionless densimetric Froude number (Fd) as significantly influential, particularly in RF, CAT, and LAS models. Furthermore, SHAP value analysis provided insights into each predictor's impact on model outputs. Uncertainty assessment through Monte Carlo (MC) and Bootstrap (BS) methods, with 1,000 iterations, indicated ML's capability to produce reliable uncertainty maps. ANN leads in performance with higher mean values and lower standard deviations, followed by CAT. MC results trend towards optimistic predictions compared with BS, as reflected in median values and interquartile ranges. This analysis underscores the efficacy of ML models in providing precise and reliable scour depth predictions.

Non-professional versus professional investors’ trust in financial analysts’ recommendations and influences on investments

PurposeThe paper aims to investigate differences in non-professional and professional stock investors’ trust in and tendency to follow financial analysts’ buy and sell recommendations.Design/methodology/approachOnline experiment conducted in Sweden in March 2022 comparing non-professional private investors (n = 80), professional investors (n = 33), and master students in finance (n = 28). Information was presented about four company stocks listed on the New York stock exchange. Two stocks were buy-recommended and two stocks sell-recommended by financial analysts. For one stock of each type, the recommendation was presented to participants. Dependent variables were predictions of the stock price after three months, ratings of confidence in the predictions and choices of holding, buying or selling the stock. Ratings were also made of the importance of presented stock-related information as well as trust in analysts’ skill and integrity.FindingsMore positive return predictions were made of buy-recommended than sell-recommended stocks. Non-professionals and to some degree finance students tended to trust financial analysts more than professional investors did and they were more influenced by the presentation of the buy recommendations. All groups made too optimistic return predictions, but the professionals were less confident in their predictions, more likely to sell the stocks and lost less on their investments.Originality/valueA new finding is that non-professional stock investors are more likely than professional stock investors to trust financial analysts and follow their recommendations. It suggests that financial analysts’ recommendations influence non-professional investors to take unmotivated investment risks. Non-professionals in the stock market should hence be advised to exercise more caution in following analysts’ recommendations.

Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area

Correlative species distribution models (SDMs) are important tools to estimate species’ geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species’ physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a naïve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models’ sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with naïve models, the physiologically informed models successfully captured the negative influence of high temperature on A. japonicus and were less sensitive to the choice of calibration area. The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available.

The Role of Wall Mechanics in the Hemodynamics of a Realistic Abdominal Aortic Aneurysm: A Fluid-Structure Interaction Study

Abdominal aortic aneurysm (AAA) can lead to high mortality rates and further complications such as stroke or heart attack due to the risk of rupture and thrombosis. Wall mechanics play a crucial role in the development and progression of aneurysms. This study investigated the effects of wall mechanics on hemodynamic parameters in AAA to understand the risk of rupture and thrombosis. The impact of three aortic wall models (rigid, linear elastic, and hyperelastic) on structural and hemodynamic parameters was examined using CFD and FSI techniques. The blood was modeled using the Carreau non-Newtonian model, and the flow was simulated using the k-ω model. Physiological pulses were used for the velocity at the inlet and the pressure at the outlet. The results demonstrated close similarity between the predictions of the linear elastic and hyperelastic models, in contrast to the somewhat different results of the rigid model. The hyperelastic model predicted higher deformation and von Mises stress levels than the elastic model, although the difference in stress predictions was smaller than the difference in deformation predictions. The rigid model evaluated the time-averaged wall shear stress and oscillatory shear index higher than the other two models in the aneurysmal area but with a lower relative residence time. In general, the hyperelastic model predicted a higher risk of rupture than linear elastic models and a higher risk of thrombus formation than the other two models. The rigid model had the most optimistic prediction.

Correction on stress intensity factor of root within pipeline girth weld considering various geometric parameters

The study focuses on the stress intensity correction factor (Mk) for internal surface cracks located at the girth pipe root toe. To assess the impact of various parameters on Mk, three-dimensional finite element models are developed. These models take different types of misalignment, a range of variable-wall-thickness ratios, and initial crack depths into account. The findings reveal that the stress intensity correction factor Mk tends to increase with the rise in misalignment and variable-wall-thickness ratio. Conversely, an increase in the initial crack depth leads to a decrease in Mk. The study demonstrates that the Mk values, when accounting for different geometric factors, are significantly higher than those calculated using BS 7910. This suggests that the BS 7910 standard may provide overly optimistic predictions. As a result, a revised Mk formula is proposed, which generally maintains an error margin of less than 10 %. This modified formula offers a significant enhancement to prevailing codes and standards, furnishing a more dependable benchmark for the assessment of structural integrity in pipeline laying procedures.

Explainable AI in Credit Card Fraud Detection: Interpretable Models and Transparent Decision-making for Enhanced Trust and Compliance in the USA

Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce. The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions.

Оцінювання тенденцій фінансового результату збутової діяльності виробничого підприємства

The article discusses the main principles of forming the financial result of the sales activity of the business entity. At the same time, the main attention is focused on the evaluation of the trends of changes in the gross profit of the manufacturing enterprise. The main factors influencing its change under martial law are outlined. It has been proven that timely monitoring of the results of sales activity requires the use of economic and mathematical methods and models for estimating gross profit. The means of econometric modeling provide for the selection of the most significant factors of its change, assessment of the density and closeness of the connection, the share of influence of the selected factors on the performance indicator, modeling and further forecasting of the gross profit. The trends of changes in the result of the sales activity of the manufacturing enterprise over the last fifteen years were analyzed. The main problems have been identified and the ways to overcome problematic areas of sales activities have been outlined. It is substantiated that in order to overcome the mentioned troubles, it is necessary to apply the correlation-regression analysis of the gross profit of the manufacturing enterprise. At the same time, the most influential factors of its change were chosen: the volume of sales of the main type of products, the cost of goods and retained earnings (uncovered loss). The result of the conducted research is the construction of multifactorial econometric models of the formation of the gross profit of the manufacturing enterprise. It was established that all these models are adequate according to the coefficient of determination and Fisher's test. Therefore, gross profit forecasting for the next two years was carried out based on them. The forecasting process was carried out on the basis of the assumption of a one percent change in the selected factors and on the basis of the trends existing at the production enterprise. The forecasting results are of a recommendatory nature and can be applied in accordance with changing business conditions. Forecasting changes in factors, using the TREND function, leads to a rapid increase in gross profit. Applying an expert assessment of changes in factors proves a more optimistic prediction of the size of the gross profit of the manufacturing enterprise. The work offers several possible options for changing the result of sales activities in accordance with the dynamics of changes in the selected factors. The relevance of the provided information will allow not only to evaluate the existing trends regarding changes in gross profit, but also to choose the best result depending on the dynamism of the internal and external environment of the production enterprise.

Utilizing cost-effective portable equipment to enhance COVID-19 variant tracking both on-site and at a large scale.

Despite optimistic predictions on the eventual end of COVID-19 (Coronavirus Disease 2019), caution is necessary regarding the emergence of new variants to sustain a positive outlook and effectively address any potential future outbreaks. However, ongoing efforts to track COVID-19 variants are concentrated in developed countries and unique social practices and remote habitats of indigenous peoples present additional challenges. By combining small-sized equipment that is easily accessible and inexpensive, we performed SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) whole genome sequencing and measured the sample-to-answer time and accuracy of this portable variant tracking tool. Our portable design determined the variant of SARS-CoV-2 in an infected individual within 9 hours and 15 minutes without external power or internet connection, surpassing the speed of previous portable tools. It took only 16 minutes to complete sequencing run, whole genome assembly, and lineage determination using a single standalone laptop. We then demonstrated the capability to produce 289 SARS-CoV-2 whole genome sequences in a single portable sequencing run, representing a significant improvement over an existing throughput of 96 sequences per run. We verified the accuracy of portable sequencing by comparison with two other independent sequencing methods. We showed that our high-throughput data consistently represented the circulating variants in Los Angeles, United States, when compared with publicly available sequences. Our scheme is designed to be flexible, rapid, and accurate, making it a valuable tool for large-scale surveillance operations in low- and middle-income countries as well as targeted surveys for vulnerable populations in remote locations.IMPORTANCEThere have been significant efforts to track COVID-19 (Coronavirus Disease 2019) variants, accumulating over 15 million SARS-CoV-2 sequences as of 2023. However, the distribution of global survey data is highly skewed, with nearly half of all countries having inadequate or low levels of genomic surveillance. In addition, indigenous peoples face more severe threats from COVID-19, due to their generally remote residence and unique social practices. Cost-effective portable sequencing tools have been used to investigate Ebola and Zika outbreaks. However, these tools have a sample-to-answer time of around 24 hours and require an internet connection for data transfer to an off-site cloud server. In our study, we rapidly determined COVID-19 variants using only small and inexpensive equipment, with a completion time of 9 hours and 15 minutes. Furthermore, we produced 289 near-full-length SARS-CoV-2 genome sequences from a single portable Nanopore sequencing run, representing a threefold increase in throughput compared with existing Nanopore sequencing methods.

Travelling away from home? Joining global change and recovery scenarios to anticipate the marine distribution of diadromous fish

Species Distribution Models (SDM) are useful tools providing results that can be extrapolated to anticipate species range shifts, under climate change scenarios. SDM studies integrating spatial constraints are significantly lacking in the marine environment, leading to optimistic predictions. This is particularly true for anadromous species in which marine distributions can be driven by their affinity to their natal rivers. Acipenser sturio is a critically endangered anadromous fish for which two stocked populations are currently maintained in the Gironde-Garonne-Dordogne (France) and Elbe (Germany) river systems. Benefiting from bycatch reports of A. sturio, we applied a SDM process that explicitly considers distance to home when evaluating habitat suitability. More precisely, we included the variable ‘distance to mouth of the natal river system’ into SDM inputs to test and characterize its influence on the marine distribution of A. sturio. We used this model to obtain the marine distribution under current climatic conditions with the two source populations and under population recovery scenarios (functional populations hypothesized to exist in ten currently unoccupied river systems). We projected the model under future conditions with two climatic scenarios (RCP 4.5 and 8.5) and three time slices over the 2023–2099 period. Constrained-ranges of both existing and hypothetical populations are projected to expand in the future. We observed an overall increase of habitat suitability, with new suitable sectors localized further from natal river mouths. By informing on the suitable marine surface that each hypothetical population holds and adds to the existing ones, our approach aims at informing about the feasibility of species recovery and marine habitats protection strategies. Our findings highlight the need for including dispersal information in marine SDM. The application of our dispersal-constrained approach may be considered for other less-well-known species for which dispersal point sources are identifiable, such as other diadromous species in different study areas.

Grade prediction in the middle east: a post-pandemic case study of the optimism bias.

Evidence exists that the pandemic has brought about stress, and altered study habits and academic performance. No evidence exists regarding whether metacognition has also been altered. The present field study examined the accuracy and confidence with which college students make grade predictions in a general education course after the pandemic. It tested whether one of three types of biases affected students' predictions as a way to cope with the uncertainty of a final exam's outcome: illusion-of-knowing, optimism, and pessimistic bracing. Students made predictions both before and after completing the final exam (summative assessment) to determine the impact of each of the hypothesized biases on estimates made in a context of varying uncertainty. Accuracy was computed as the difference between expected and actual grades on the final exam. Confidence in the predictions made was measured on a Likert scale. Exam performance was categorized as good, poor, or inadequate. In this study, less-than-desirable performance was accompanied by overestimations. However, overestimations were made with little confidence and benefited from the information acquired from completing the exam. This pattern of results suggests that students who are not doing well are not under the spell of the illusion-of-knowing phenomenon. Indeed, their optimistic predictions are punctured by the awareness of a likely undesirable outcome (as indicated by their weak confidence in the predictions made). Implications and applications of these findings are discussed.

Linking climate stressors to ecological processes in ecosystem models, with a case study from the Gulf of Alaska

Abstract As climate stressors are impacting marine ecosystems and fisheries across the world, ecosystem models that incorporate environmental variables are increasingly used to inform ecosystem-based fisheries management. The assumptions around the mechanistic links between climate stressors and the biological processes in these models are important, but the implications for model outcomes of which stressors are captured and how they affect modeled biological processes are seldom explored. Using a whole-ecosystem model (Atlantis) for the Gulf of Alaska, we explore the effects of capturing physical (increased temperature) and biogeochemical (decreased low trophic level productivity) climate stressors, and disentangle the effects of each stressor on the productivity of forage fish, groundfish, and fish-eating seabirds. We then test the effects of alternative model specifications of temperature-driven habitat determination and bioenergetics. Increased temperature resulted in increased weight-at-age and higher natural mortality, while decreased productivity resulted in decreased weight-at-age and higher natural mortality. Model specification of temperature dependence of movement and spawning influenced model outcomes, and decoupling these processes from temperature led to overly optimistic biomass predictions. As the use of ecosystem models to inform fisheries management becomes more operational, we illustrate that the assumptions around the links between climate stressors and ecological processes influence model outcomes.

China’s Economy as Xi’s Means of Power Or Why the Chinese Economy is Not yet Bigger than That of the USA

Abstract Contrary to optimistic predictions from the financial industry in 2011, China’s economy will not have overtaken that of the USA by 2025. Since 2012, Xi Jinping has largely brought the market, private property and the capitalist mode of production back under the control of the Chinese Communist Party. Xi has curtailed the success factor of decentralization and continued to expand the failure factors of interventionism and state control. Capital is now flowing out of China, putting the renminbi under devaluation pressure. Even a relocation of production capacities from China primarily to other Asian countries can already be observed. In its policy of hegemony, China is steering the economy to achieve political and geopolitical ends and is practising geoeconomics. What remains unclear is how the geoeconomics pursued by Xi is intended to enable long-term growth in China.

The study of shadowing effect for LTE and 5G networks in suburban environment

Abstract The presence of obstacles in the propagation path is a critical factor in air-to-ground (AG) communication. The behavior of wireless signal propagation depends on several variables, such as frequency, building height, elevation angle, and street design. This paper aims to compare the three established line of sight (LOS) probability model based on actual site data, including the building geometry in suburban environment. The comparison between these three models using the site data provide a guideline for selecting the LOS probability model based on the optimistic and pessimistic predictions. The shadowing loss was evaluated at frequencies 2 and 3.5 GHz with an elevation angle of 20° in two suburban locations at Universiti Tun Hussein Onn Malaysia. Three prediction models, ITU-R P.1410-5, Holis and Pechac, and Pang et al., available in the literature were used to identify and compare the line-of-sight probability. By focusing on the shadowing model in suburban area, the guideline for optimizing LOS communications or navigation in these challenging environments can be developed. The finding highlights the importance of considering building height in AG communication for network performance evaluation and design.

Induced optimism to lessen hopelessness-related cognitions among young adults at risk for suicidal thoughts and behavior

BackgroundHopelessness is one of the best-studied cognitive predictors of depression and suicide. Previous research suggests that hopelessness may develop through repetitive thinking about the occurrence of positive and negative future outcomes. The present study sought to investigate whether mental rehearsal in making optimistic future-event predictions, or induced optimism, would lead to reductions in hopelessness, particularly among individuals with a history of suicide ideation or suicide attempts. MethodsParticipants with (n = 58) and without (n = 76) a history of suicide ideation or attempts were randomly assigned to either practice making optimistic future-event predictions or to a control condition in which they practiced making a lexical decision (using the same stimuli) over three study sessions, each separated by one week. ResultsFindings offered modest support for the hypothesis that induced optimism would decrease hopelessness but not improve mood; this was regardless of history of suicide ideation or attempts. LimitationsThe sample was predominantly female, and assessment of suicide ideation and attempt history was not confirmed by clinical interview, which may limit generalizability. ConclusionPractice in making optimistic future-event predictions over time may be one way to reduce the hopelessness-related cognitions that confer vulnerability to suicide ideation and behavior.

Size effect and electrical ageing of PDMS dielectric elastomer with competing failure modes

Large-scale dielectric elastomer generators dielectric elastomer generators (DEGs) such as those employed in wave energy converter projects require a significant volume of electrically stressed materials. Meanwhile, predictions of energy output from such systems are generally extrapolated from electrical and mechanical breakdown measurements performed on small scale samples, where the presence of small defects can be extremely small. This can lead to overly optimistic upscaled predictions for the performance and reliability of full-scale devices. In this study, multilayer DEGs were prepared to evaluate the dielectric breakdown strength of thin polydimethylsiloxane PDMS elastomer at different values of active areas. The results indicated the presence of two separate breakdown mechanisms resulting in an enhanced size effect and a reduced reliability for the larger samples. Electrical ageing tests were performed on three different sample geometries and the dielectric breakdown strength was found to be marginally affected by the time under stress. A Weibull competing failure model was applied to the distribution of experimental breakdowns and electrical reliability was accurately modeled over more than four decades of variation in the electrode area.