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Background: To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. Methods: We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. Results: Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. Conclusions: The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size. [ABSTRACT FROM AUTHOR]

Since capital may not be abundant for banks, banks have to assess the efficiency of small and medium enterprises (SMEs) to efficiently and profitably allocate available funds. This study aims to generate criteria priority series for the decision process of commercial credit applications (DPCCAs) to conduct statistical hypothesis testing and produce information for researchers, decision-makers/managers. Having lots of criteria in assessing SME performance, analytic hierarchy process (AHP) — a multi-criteria decision-making method — is applied according to experts' views. By determining SMEs' indicator-criteria priorities, this study's priorities give information on SME growth potential, managerial competence and institutionalization which are rarely used in SMEs' credit evaluation in academic studies. This study also evaluates tolerated consistency boundary approach (TCBA) defined in Pekkaya's studies and generates priority-series that allows for statistical testing. The findings reveal that "profitability and business volume" with its sub-criteria of "operating profitability" have the highest priority, and then "stability and intelligence" with "repayment practices of debts" and "total income and stability in profitability" have quite high priorities among seven main- and 26 sub-criteria. This study contains some criteria which are not previously observed in the related literature, incorporates considerably more expert views, and shows to conduct statistical hypothesis tests by using the generated priority series via AHP for each criterion. [ABSTRACT FROM AUTHOR]

Patient-physician communication has the potential to improve outcomes and satisfaction through the shared decision-making process (SDM). This study aims to assess the relationship between perception of SDM and demographic, clinical, and patient-reported outcomes in patients undergoing Hallux Valgus (HV) correction. A prospective analysis of 306 patients scheduled for HV surgery was completed. The CollaboRATE score was used to measure SDM. Multivariable linear regression model was used to assess whether SDM scores were associated with preoperative characteristics or postoperative outcome scores. The mean CollaboRATE score was 2.9 (SD 0.9) and did not differ by age, socioeconomic status, or sex. Lower CollaboRATE scores were associated with more symptoms of depression, lower socioeconomic status, and lower general health scores (p-value < 0.05). There was no association between SDM scores and postoperative outcome scores. In this study, patients with depressive symptoms and lower socioeconomic status had worse perceptions of SDM. There was no difference in postoperative outcomes among participants based on SDM scores. Level of Evidence: Level III, prospective observational study [ABSTRACT FROM AUTHOR]

Aim: To translate and culturally adapt the FRAIL scale into Spanish and perform a preliminary test of diagnostic accuracy in patients admitted to intensive care units. Design: Cross‐sectional diagnostic study. Methods: Five intensive care units (ICU) in Spain were participated. Stage 1: Three native Spanish‐speaking bilingual translators familiar with the field of critical care translated the scale from English into Spanish. Stage 2: Three native English‐speaking bilingual translators familiar with critical care medicine. Stage 3: Authors of the original scale compared the English original and back‐translated versions of the scale. Stage 4: Five nurses with more than 5 years of ICU experience and five critical care physicians assessed the comprehension and relevance of each of the items of the Spanish version in 30 patients of 3 different age ranges (<50, 50–65 and >65 years). Results: The FRAIL scale was translated and adapted cross‐culturally for patients admitted to intensive care units in Spain. The process consisted of four stages: translation, back translation, comparison and pilot test. There was good correspondence between the original scale and the Spanish version in 100% of the items. The participating patients assessed the relevance (content validity) and comprehensibility (face validity) of each of the items of the first Spanish version. The relevance of some of the items scored low when the scale was used in patients younger than 65 years. Conclusions: We have cross‐culturally adapted the FRAIL scale, originally in English, to Spanish for its use in the critical care medical setting in Spanish‐speaking countries. Implications for Professionals: Physicians and nurses can apply the new scale to all patients admitted to the intensive care units. Nursing care can be adapted according to frailty, trying to reduce the side effects of admission to these units for the most fragile patients. Reporting Method: The manuscript's authors have adhered to the EQUATOR guidelines, using the COSMIN reporting guideline for studies on the measurement properties of patient‐reported outcome measures. Patient or Public Contribution: In a pilot clinical study, we applied the first version of the FRAIL‐Spain scale to intensive care unit (ICU) patients. Five nurses with more than 5 years of ICU experience and five critical care physicians assessed the relevance (content validity) and comprehensibility (face validity) of the five items of the first Spanish version. Relevance was assessed using a 4‐point Likert scale ranging from 1 (no relevance) to 4 (high relevance), and comprehensibility was assessed as poor, acceptable or good. Each health professional applied the scale to three patients (total number of patients = 30) of three different age ranges (<50, 50–65 and >65 years) and recorded the time of application of the scale to each patient. Although the frailty scales were initially created by geriatricians to be applied to the elders, there is little experience with their application in critically ill patients of any age. Therefore, more information is needed to determine the relevance of using this scale in critical care patients. In this pilot study, we considered that nurses and critical care physicians should evaluate frailty using this adapted scale in adult patients admitted to the Intensive Care Units. [ABSTRACT FROM AUTHOR]

Basket trials have emerged as a new class of efficient approaches in oncology to evaluate a new treatment in several patient subgroups simultaneously. In this article, we extend the key ideas to disease areas outside of oncology, developing a robust Bayesian methodology for randomized, placebo-controlled basket trials with a continuous endpoint to enable borrowing of information across subtrials with similar treatment effects. After adjusting for covariates, information from a complementary subtrial can be represented into a commensurate prior for the parameter that underpins the subtrial under consideration. We propose using distributional discrepancy to characterize the commensurability between subtrials for appropriate borrowing of information through a spike-and-slab prior, which is placed on the prior precision factor. When the basket trial has at least three subtrials, commensurate priors for point-to-point borrowing are combined into a marginal predictive prior, according to the weights transformed from the pairwise discrepancy measures. In this way, only information from subtrial(s) with the most commensurate treatment effect is leveraged. The marginal predictive prior is updated to a robust posterior by the contemporary subtrial data to inform decision making. Operating characteristics of the proposed methodology are evaluated through simulations motivated by a real basket trial in chronic diseases. The proposed methodology has advantages compared to other selected Bayesian analysis models, for (i) identifying the most commensurate source of information and (ii) gauging the degree of borrowing from specific subtrials. Numerical results also suggest that our methodology can improve the precision of estimates and, potentially, the statistical power for hypothesis testing. [ABSTRACT FROM AUTHOR]

How should political decisions be made to ensure a high level of legitimacy in the eyes of ordinary citizens? In order to answer this question, we conducted six focus groups (N = 29) with adults (20-78 years old). We analyzed data using a thematic analysis, within the essentialist/realist framework and focused on the explicit meanings of the data. Two specific issues were explored: the adoption of the Euro and acceptance of Syrian orphan refugees. The bottom-up analysis revealed that participants considered two strategies of political decision-making (direct vote and representation based) and discussed the pros and cons of each process in detail. The results point out the importance of public deliberation, transparency, and the source of decision-making in evaluating the overall legitimacy of decisions-making process. Further, unlike popular belief that citizens are thirsty for direct democracy our results suggest that people are rather hesitant about placing big decisions into the hands of ordinary citizens, nor do they want to be burdened with making decisions about issues that might not affect them directly. Rather, people described representation-based decisions as legitimate if condition of transparency, deliberation, and trust in politicians is met. [ABSTRACT FROM AUTHOR]

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Purpose: To examine the impact of in situ simulation (ISS) with scripting on nursing staff's knowledge and confidence to initiate rapid response teams (RRTs) immediately after identifying patient condition deterioration. Background/Significance: Failure to rescue (FTR) related to delays in activation of RRT is on the rise, leading to poor patient outcomes. Lack of confidence, knowledge, and empowerment are associated with delayed activation of RRTs. As such, the nursing staff's confidence is integral in activating RRTs and FTR prevention. In situ simulation may help nurses increase their confidence, thus empowering timely RRT initiation. Methods: This quantitative pretest-posttest study used a convenience sample of nurses and nursing assistants. First, participants completed the Rapid Response Team Survey (RRTS) pretest. Then, they participated in the ISS scenario. Lastly, they completed debriefing and the RRTS posttest. Results: Pearson's correlation results showed no significant relationships between the variables. Dependent t-test results showed statistically significant increases between the pretest and posttest means (Part l, t = -5.51, p < .001, MD = 1.32; Part 2, t = -1.04, p < .01, MD = 3.1). These results suggest that ISS with scripting increased participants' knowledge and confidence in early activation of RRTs. Additionally, staff reported feeling more confident and empowered regarding future RRT decision-making and communication with other healthcare colleagues. Discussion: Early activation of RRTs prevents FTR. The results of this study suggest ISS with scripting increases staff's knowledge, confidence, and empowerment to activate RRTs. We recommend that hospital organizations adopt ISS with scripting to empower nurses to activate RRTs to prevent FTR. [ABSTRACT FROM AUTHOR]

We investigate what fraction of all significant results in website A/B testing is actually null effects (i.e., the false discovery rate (FDR)). Our data consist of 4,964 effects from 2,766 experiments conducted on a commercial A/B testing platform. Using three different methods, we find that the FDR ranges between 28% and 37% for tests conducted at 10% significance and between 18% and 25% for tests at 5% significance (two sided). These high FDRs stem mostly from the high fraction of true null effects, about 70%, rather than from low power. Using our estimates, we also assess the potential of various A/B test designs to reduce the FDR. The two main implications are that decision makers should expect one in five interventions achieving significance at 5% confidence to be ineffective when deployed in the field and that analysts should consider using two-stage designs with multiple variations rather than basic A/B tests. This paper was accepted by Eric Anderson, marketing. [ABSTRACT FROM AUTHOR]

A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. However, it has a significant problem in its uses, i.e., considerable data loss. The probabilistic hesitant fuzzy set (PHFS) has been proposed to improve the HFS. It provides probability values to the HFS and has the ability to retain more information than the HFS. Previously, fuzzy regression models such as the fuzzy linear regression model (FLRM) and hesitant fuzzy linear regression model were used for decision making; however, these models do not provide information about the distribution. To address this issue, we proposed a probabilistic hesitant fuzzy linear regression model (PHFLRM) that incorporates distribution information to account for multi-criteria decision-making (MCDM) problems. The PHFLRM observes the input–output (IPOP) variables as probabilistic hesitant fuzzy elements (PHFEs) and uses a linear programming model (LPM) to estimate the parameters. A case study is used to illustrate the proposed methodology. Additionally, an MCDM technique called the technique for order preference by similarity to ideal solution (TOPSIS) is employed to compare the PHFLRM findings with those obtained using TOPSIS. Lastly, Spearman's rank correlation test assesses the statistical significance of two rankings sets. [ABSTRACT FROM AUTHOR]

In this paper, Kruskal-Wallis test is extended to deal with neutrosophic data in single valued form using score, accuracy and certainty functions to calculate ranks of SVNNs, also Mann-Whitney test is extended to deal with same data type which makes it possible to do a post-hoc test after rejecting null hypothesis using Neutrosophic Statistics Kruskal-Wallis test. Numerical examples were successfully solved showing the power of this new idea to deal with SVNNs and make statistical decisions on them. [ABSTRACT FROM AUTHOR]

Biomedical research has come to rely on p-values as a deterministic measure for data-driven decision-making. In the largely extended null hypothesis significance testing for identifying statistically significant differences among groups of observations, a single p-value is computed from sample data. Then, it is routinely compared with a threshold, commonly set to 0.05, to assess the evidence against the hypothesis of having non-significant differences among groups, or the null hypothesis. Because the estimated p-value tends to decrease when the sample size is increased, applying this methodology to datasets with large sample sizes results in the rejection of the null hypothesis, making it not meaningful in this specific situation. We propose a new approach to detect differences based on the dependence of the p-value on the sample size. We introduce new descriptive parameters that overcome the effect of the size in the p-value interpretation in the framework of datasets with large sample sizes, reducing the uncertainty in the decision about the existence of biological differences between the compared experiments. The methodology enables the graphical and quantitative characterization of the differences between the compared experiments guiding the researchers in the decision process. An in-depth study of the methodology is carried out on simulated and experimental data. Code availability at https://github.com/BIIG-UC3M/pMoSS. [ABSTRACT FROM AUTHOR]

Introduction: In our research, we investigated the components, executive functions, and cognitive abilities underlying decision-making in sports performance using a computer-based test system. To define the athlete experience, we classified athletes according to an exact, unambiguous definition following international terminology, based on their performance, achievement, playing age, and sport's popularity. We hypothesize that team athlete with significant athletic experience will perform better on the executive function test. Methods: A cross-sectional study was conducted on team athletes N=52. The VTS-DT test was used to measure executive function. The classification of athlete experience was based on an internationally accepted taxonomy defined with scientific precision (SWANNN, 2015). Results: When classified by athlete experience, the athletes we studied fell into amateur and elite categories. The DT/S2 scores of the two groups were compared based on each factor. We obtained trend-like correlations for the factors 'number of reactions', 'number of stimulations,' and 'number of good responses. Correlation analysis was performed between the factors on a group-by-group basis. When examining the relationship between reaction time, we found that there is a strong relationship with the number of stimulations (amateurs: r = -0.80; elite athletes: r = -0.87) since the faster someone is (i.e., the less reaction time), the more times they can respond to stimulations. Looking at the results for elite athletes, we also observe a moderately strong relationship between reaction time and the number of good responses (r = -0.68). We may suggest that experienced athletes could produce better responses faster than less professional athletes. Conclusions: Our hypotheses were partially confirmed, as we found a trend-like correlation that elite athletes scored higher on the DT test, which examined athletes' executive functions in a complex, adaptive way. Our research demonstrates that, on the one hand, it is worthwhile for coaches to build on experienced athletes when assembling a team, in addition to the momentum of young athletes, and that the development of executive functions and cognitive skills can improve the performance of athletes. [ABSTRACT FROM AUTHOR]

Introduction/Objectives: Due to the risks associated with sedative hypnotic medications in older adults, our study evaluated the impact of a multidisciplinary approach to deprescribing in geriatric primary care clinics. Methods: The study was a retrospective review of older adults at 2 academic, geriatric primary care clinics who were prescribed sedativehypnotic medications. Patients were mailed an education packet of information that included working with the clinical pharmacy team, behavioral health team, or both in order to deprescribe their sedative-hypnotic medication. The study assessed the rate of discontinuation of sedative-hypnotic medications between the different intervention groups. Results: The study included 93 older adults with a mean age of 81.3 years and 39.8% discontinuation rate of their medication. The number of falls decreased in patients who discontinued use compared to when previously using a sedative hypnotic medication. Conclusion: Patients are more likely to discontinue their sedative hypnotic medication with a multidisciplinary approach, specifically with primary care provider support. [ABSTRACT FROM AUTHOR]

When the in/consistency in Pairwise Comparisons (PCs) is taken into consideration as the subarea of the Multi Attribute Decision Making (MADM) scientific field, it has many repercussions in various types of research areas including different modelling scenarios e.g. reduction of inconsistency during PCs, deriving appropriate consistency thresholds for inconsistent Pairwise Comparison Matrices (PCMs), completing of incomplete PCMs, aggregating of individual PCMs in relation to Group Decision Making (GDM) aspects, and PCMs in/consistency relation to credibility of Priority Vectors (PV) derived from PCMs with the application of various Priorities Deriving Methods (PDMs). The examination objective in the latter area of research is the uncertainty related to the inexactitude of prioritization based on derived PVs. However, only few research studies examine this problem from the perspective of PCM applicability for credible designation of decision maker's (DM) priorities in the way that leads to minimization of the prioritization uncertainty related to possible, and sometimes very probable, ranking fluctuations. This problem constitutes the primary area of interest for this research paper as no research study was thus far identified that examines this problem from the perspective of consistent PCMs. Hence, a research gap was identified. Thus, the objective of this research paper is to fill in this scientific gap. The research findings have serious repercussions in relation to prioritization quality with the application of PCs methodology, mostly in relation to the interpretation and reliability evaluation of prioritization results. Firstly, the research study outcome changes the perspective of the rank reversal phenomenon, which shed new light on many research studies that have been presented in the subject's literature for many decades. Secondly, the research study results throw new light on the discussion concerning the fuzziness of AHP's results. Last but not least, the effect of the research opens the unique opportunity to evaluate the prioritization outcome obtained within the process of consistent PCs from the well-known perspective of statistical hypothesis testing i.e. the probability designation of the chance that accepted ranking results which were considered as correct due to low probability of change may be incorrect, hence they should be rejected, and the probability designation of the chance that rejected ranking results which were considered as incorrect due to high probability of change may be correct and should be accepted. The paramount finding of the research is the fact that consistent PCMs provide PVs, which elements cannot be considered as established, but only approximated within certain confidence intervals estimated with a certain level of probability. As problems related to heuristics can be analyzed only via a computer simulation process, because they cannot be mathematically determined, the problem examined in this research paper is examined via Monte Carlo simulations, appropriately coded and executed with the application of Wolfram's Mathematica Software. It is believed that this research findings should be very important and useful for all decision makers and researchers during their problems' examinations that relate to prioritization processes with the application of PCs methodology., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Pawel Tadeusz Kazibudzki. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Research purpose. Currently, the Internet is used by almost 5 billion users worldwide, of which 80% actively use at least one of the social networks. And marketing managers are well aware of this fact, as they had to switch to a new way of attracting potential customers. One of these methods is also influencer marketing as a form of marketing communication. In influencer marketing, the emphasis is placed on influencers with a significant impact on the community on which marketing activities and campaigns can be oriented. It is a method of non-violent and natural promotion of a product or brand, which acts on the consumer as a natural part of the space in which it is located. Especially one generation of consumers, Generation Z, is considered to be the target segment for influencer marketing due to their digital skills and inclination to social networks. The main aim of the article is to determine the impact of influencer marketing on the decision-making process of Generation Z. This also includes providing the theoretical background and analysis of influencer marketing from the viewpoint of Slovak and foreign authors. Design / Methodology / Approach. General scientific methods were applied for the processing of the data as well as mathematical-statistical methods to evaluate the data collated from the results of the questionnaire survey and to statistical hypothesis testing. The important source for secondary data was scientific research, annual company reports, statistical databases, and published professional publications. In order to find out the impact of influencer marketing on the decision-making process of Generation Z, a questionnaire survey was conducted. Findings. A successful marketing campaign on social media in cooperation with influencers requires the correct selection of the influencer according to their follower base, profile focus, credibility or reach, which is measured by Engagement rate. The number of followers is not as significant as the relationship that the creator has with their fans. When choosing an influencer, it is also necessary to take into account the way they promote products, ask them for originality and creativity, and prefer long-term collaborations to one-time contributions. These steps can bring a lot of benefits to the company, from which it will subsequently profit. Based on the analysis and results of the questionnaire survey, it is thus clear that the implementation of influencer marketing in marketing communication of Slovak companies targeted at Generation Z brings many benefits, including building relationships with Generation Z, increasing their loyalty and improving the company image. Originality / Value / Practical implications. Finally, measures for effective influencer marketing usage targeted at Generation Z are proposed, and its benefits are highlighted. [ABSTRACT FROM AUTHOR]

In open and shared workplaces, like co-working spaces or educational studios, thermal preferences vary widely among occupants. With the rise of flexible organizational workplace strategies, the challenge lies in balancing optimum, efficient temperature setpoints with maintaining occupants' comfort. A potential solution involves a deeper understanding of variations in indoor climate and building occupants' decision-making and preferences. This paper explores how an Occupant-Centric Digital Twin (OCDT) might address this by mapping indoor microclimates through a grid of IoT temperature sensors in real time. A large-screen display is utilized to present and visualize these data in an open workplace. The goal is to enhance awareness and provide agency for occupants to identify zones that align with their individual thermal preferences. A mixed-method occupant study (N = 27) was conducted to validate the approach. Exposure to the OCDT display resulted in higher thermal satisfaction among participants (p-value = 1.269 × 10−5, 0.05 significance level). The novelty of OCDT lies in extending the use of digital twin technology from facility managers to occupants, by granting them the agency to address comfort issues even in buildings where they lack direct control over the thermostat. This approach paves the way for a future where occupants in open workspaces can make informed decisions about where to work and how to achieve thermal comfort in those choices. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making. Author summary: As SARS-CoV-2 began to spread throughout the world in early 2020, modelers played a critical role in predicting how the epidemic could take shape. Short-term forecasts of epidemic outcomes (for example, infections, cases, hospitalizations, or deaths) provided useful information to support pandemic planning, resource allocation, and intervention. Yet, infectious disease forecasting is still a nascent science, and the reliability of different types of forecasts is unclear. We retrospectively evaluated COVID-19 case forecasts, which were often unreliable. For example, forecasts did not anticipate the speed of increase in cases in early winter 2020. This analysis provides insights on specific problems that could be addressed in future research to improve forecasts and their use. Identifying the strengths and weaknesses of forecasts is critical to improving forecasting for current and future public health responses. [ABSTRACT FROM AUTHOR]

Hydro power has provided significant economic benefits to society due to its cleanliness and convenience. As the number of hydropower stations has increased, many serious ecological issues have also emerged. This study uses Wan'an Reservoir as its research object and investigates single–multi-objective optimal operation and decision-making regarding reservoirs for ecology-oriented operation, to meet ecological water demand and seek the optimal operation schemes for energy generation and ecological benefits. The full-process research is conducted based on the "objective-modeling constraint optimization scheme decision-making" framework. The Mann–Kendall test and ordered clustering method were used to diagnose the hydrological variation in the basin. Based on this, a hierarchical and phased ecological flow process was derived. The objectives were defined according to the flow process, and optimal operation models were constructed. The differential evolution algorithm (DE) and improved non-dominated sorting genetic algorithm-II (NSGA-II) were used to solve the models. A non-fitting curve method was used to determine the approximate inflection point of the Pareto front curve, and the curve was fitted linearly according to the approximate inflection point to obtain the conversion formula between the objectives. Based on the coefficient of variation and Mahalanobis distance, a new multi-attribute decision-making method for reservoir operation, CV-ITOPSIS, was constructed by improving the traditional TOPSIS. The results show that: (1) There is a piecewise linear contradiction between energy generation and ecological objectives, and the contradiction intensifies with an increase in incoming water frequency. (2) Before the approximate inflection point, the head significantly influences the conversion rate from the energy generation to ecology, while the discharge flow is the major influencing factor after the inflection point. The inflection point and the formula for the piecewise straight line can reveal the conversion law between the two objectives. (3) CV-ITOPSIS considers the degree of differentiation of index data and fully considers the correlation between indicators while retaining the good evaluation performance of the traditional method. It recommends the optimal benefit scheme for a multi-objective non-inferior solution set. The research results provide a theoretical foundation and decision support for the optimal ecological operation of the Ganjiang River Basin. [ABSTRACT FROM AUTHOR]

Understanding consumer behavior is crucial for increasing the likelihood of product success. Virtual Reality head-mounted displays incorporating physiological techniques such as eye-tracking offer novel opportunities to study user behavior in decision-making tasks. These methods reveal unconscious or undisclosed consumer responses. Yet, research into gaze patterns during virtual product evaluations remains scarce. In this context, an experiment was conducted to investigate users' gaze behavior when evaluating their preferences for 64 virtual prototypes of a bedside table. Here, 24 participants evaluated and selected their preferred design through eight repeated tasks of an 8-AFC, with individual evaluations conducted for each design to ensure the reliability of the findings. Several eye-tracking metrics were computed (i.e., gaze time, visits, and time to first gaze), statistical tests were applied, and a Long Short-Term Memory model was created to recognize decisions based on attentional patterns. Our results revealed that the Gaze Cascade Model was replicated in virtual environments and that a correlation between product liking and eye-tracking metrics exists. We recognize subjects' decisions with a 90% accuracy, based on their eye patterns during the three seconds before their decision. The results suggest that eye-tracking can be an effective tool for decision-making prediction during product assessment in virtual environments. [ABSTRACT FROM AUTHOR]

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives. [ABSTRACT FROM AUTHOR]

The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI systems explainable to human decision makers. Rather than always making black boxes transparent, these approaches are at risk of painting the black boxes white, thus failing to provide a level of transparency that would increase the system's usability and comprehensibility, or even at risk of generating new errors (i.e., white-box paradox). To address these usability-related issues, in this work we focus on the cognitive dimension of users' perception of explanations and XAI systems. We investigated these perceptions in light of their relationship with users' characteristics (e.g., expertise) through a questionnaire-based user study involved 44 cardiology residents and specialists in an AI-supported ECG reading task. Our results point to the relevance and correlation of the dimensions of trust, perceived quality of explanations, and tendency to defer the decision process to automation (i.e., technology dominance). This contribution calls for the evaluation of AI-based support systems from a human–AI interaction-oriented perspective, laying the ground for further investigation of XAI and its effects on decision making and user experience. [ABSTRACT FROM AUTHOR]

OnkoNetwork is a patient navigation program established in the Moritz Kaposi General Hospital to improve the timeliness and completeness of cancer investigations and treatment. The H2020 SELFIE consortium selected OnkoNetwork as a promising integrated care initiative in Hungary and conducted a multicriteria decision analysis based on health, patient experience, and cost outcomes. In this paper, a more detailed analysis of clinical impacts is provided in the largest subgroup, non-small cell lung cancer (NSCLC) patients. A retrospective cohort study was conducted, enrolling new cancer suspect patients with subsequently confirmed NSCLC in two annual periods, before and after OnkoNetwork implementation (control and intervention cohorts, respectively). To control for selection bias and confounding, baseline balance was improved via propensity score weighting. Overall survival was analyzed in univariate and multivariate weighted Cox regression models and the effect was further characterized in a counterfactual analysis. Our analysis included 123 intervention and 173 control NSCLC patients from early to advanced stage, with significant between-cohort baseline differences. The propensity score-based weighting resulted in good baseline balance. A large survival benefit was observed in the intervention cohort, and intervention was an independent predictor of longer survival in a multivariate analysis when all baseline characteristics were included (HR = 0.63, p = 0.039). When post-baseline variables were included in the model, belonging to the intervention cohort was not an independent predictor of survival, but the survival benefit was explained by slightly better stage distribution and ECOG status at treatment initiation, together with trends for broader use of PET-CT and higher resectability rate. In conclusion, patient navigation is a valuable tool to improve cancer outcomes by facilitating more timely and complete cancer diagnostics. Contradictory evidence in the literature may be explained by common sources of bias, including the wait-time paradox and adjustment to intermediate outcomes. [ABSTRACT FROM AUTHOR]

Background: Palliative sedation is used to relieve refractory symptoms and is part of clinical practice in Sweden. Yet we do not know how frequently this practice occurs, how decision-making takes place, or even which medications are preferentially used. Objectives: To understand the current practice of palliative sedation in Sweden. Methods: We conducted a retrospective cross-sectional medical record-based study. For 690 consecutive deceased patients from 11 of 12 specialized palliative care units in the southernmost region of Sweden who underwent palliative sedation during 2016, we collected data on whether the patient died during sedation and, for sedated patients, the decision-making process, medication used, and depth of sedation. Results: Eight percent of patients were sedated. Almost all (94%) were given midazolam, sometimes in combination with propofol. The proportions of sedation were similar in the patient groups with and without cancer. The largest proportion of the sedated patients died in inpatient care, but 23% died at home, with specialized palliative home care. Among the patients with a decision to sedate, 42% died deeply unconscious, while for those without such a decision the corresponding figure was 16%. In only one case was there more than one physician involved in the decision to use palliative sedation. Conclusion: 8% of patients in specialized palliative care received palliative sedation, which is lower than international measures but much increased compared to an earlier Swedish assessment. The level of consciousness achieved often did not correspond to the planned level; this, together with indications of a scattered decision process, shows a need for clear guidelines. [ABSTRACT FROM AUTHOR]

Facial expressions of emotion can convey information about the world and disambiguate elements of the environment, thus providing direction to other people's behavior. However, the functions of facial expressions from the perspective of learning patterns over time remain elusive. This study investigated how the feedback of facial expressions influences learning tasks in a context of ambiguity using the Iowa Gambling Task. The results revealed that the learning rate for facial expression feedback was slower in the middle of the learning period than it was for symbolic feedback. No difference was observed in deck selection or computational model parameters between the conditions, and no correlation was observed between task indicators and the results of depressive questionnaires. [ABSTRACT FROM AUTHOR]

In recent decades, the agricultural sector has witnessed rapid technological interventions from field to the production stage. Thus, the importance of these technological interventions must be strictly evaluated. The traditional statistical method often deems low statistical differences as a significant one, which cannot be considered effective from different perspectives. In this sense, the aim of this research was to develop a new statistical method for evaluating agricultural experiments based on different criteria; hence, the significant importance of the technological interventions can be clearly determined. Data were collected from of a long-term (13-year) crop production experiment (Central Europe, Hungary), which involved five different fertilization levels, along with non-fertilized treatment (control), two irrigation treatments (irrigated and non-irrigated), and 15–20 genotypes of maize. The output of this research showed that the classic statistical approach for testing the significant differences among treatments should be accompanied with our new suggested approach (i.e., professional test), which reflect whether treatments were professionally effective or not. Also, results showed that good statistical background is not enough for interoperating the analysis of agricultural experiments. This research suggested that erroneous conclusions can be avoided by merging classical and professional statistical tests, and correct recommendations could be provided to decision makers and farmers based on their financial resources. [ABSTRACT FROM AUTHOR]

Futures markets offer investors many attractive advantages, including high leverage, high liquidity, fair, and fast returns. Highly leveraged positions and big contract sizes, on the other hand, expose investors to the risk of massive losses from even minor market changes. Among the numerous stock market forecasting tools, deep learning has recently emerged as a favorite tool in the research community. This study presents an approach for applying deep learning models to predict the monthly average of the Taiwan Capitalization Weighted Stock Index (TAIEX) to support decision-making in trading Mini-TAIEX futures (MTX). We inspected many global financial and economic factors to find the most valuable predictor variables for the TAIEX, and we examined three different deep learning architectures for building prediction models. A simulation on trading MTX was then performed with a simple trading strategy and two different stop-loss strategies to show the effectiveness of the models. We found that the Temporal Convolutional Network (TCN) performed better than other models, including the two baselines, i.e., linear regression and extreme gradient boosting. Moreover, stop-loss strategies are necessary, and a simple one could be sufficient to reduce a severe loss effectively. [ABSTRACT FROM AUTHOR]