Real Preferences Research Articles

Comparison of Preimplantation Mouse Embryos with Different Genetic Backgrounds as Models for Evaluating Human Embryo Culture Media Composition.

Can the best culture medium be determined by mouse embryo testing? Zygotes/embryos of four different mouse strains: inbred Balb/c (n = 79) and C57Bl/6 (n = 95), F1 hybrid DBA/2*C57Bl/6 (n = 133), and outbred ICR (n = 69) were incubated in vitro in four culture media: 1-Step Culture Medium (VitaVitro), CSC medium (Irvine Scientific), Sage 1-Step medium (Origio), G-TL medium (Vitrolife). The embryos were cultured under standard conditions at atmospheric oxygen: 37℃; 6% CO2, 100% humidity. Standard MEA-test parameters (number of expanded blastocysts on E4.5), as well as additional characteristics (embryo development on E2.5-E4.5, number of hatching and hatched blastocysts on E4.5, embryo arrest and degeneration) were assessed for each of the strain-medium combinations. The results were compared across strains as well as across media. Embryo development depended on both the media and the mouse strain. Embryos from inbred and outbred mice were more sensitive to suboptimal culture conditions compared to the hybrid strain; this was reflected in reduced blastocyst and hatching rates, as well as an increased percentage of arrested and degraded embryos. The choice of optimal media depended strongly on the strain: CSC was better for Balb/c, Sage 1-step for C57Bl/6, while both were preferred for hybrids, and G-TL for outbreds. VitaVitro was quite good for hybrids and performed worse for other strains. Overall, the embryos of each strain behaved differently in different media, and it was not possible to make a real preference for any of them over human embryos based on the results of any single mouse strain. Only a pooled sample of different mouse strains can be used for comprehensive media MEA testing. MEA testing with any strain of mice is doomed to error due to their specific culture requirements. Hybrid mice are too reproductively efficient and cannot be used to distinguish media based on their subtle differences. Outbred and inbred mice are too sensitive and may be delayed or degraded in culture media that is well suited to the requirements of human embryos. Combined analysis of multiple mouse strains should be used to test media more fully and may serve as a model for the heterogeneity of human embryos in IVF clinics.

Personalised electric vehicle charging stop planning through online estimators

AbstractIn this paper, we address the problem of finding charging stops while travelling in electric vehicles (EVs) using artificial intelligence (AI). Choosing a charging station is challenging, because drivers have very heterogeneous preferences in terms of how they trade off the features of various alternatives (for example, regarding the time spent driving, charging costs, waiting times at charging stations, and the facilities provided at the charging stations). The key problem here is eliciting the diverse preferences of drivers, assuming that these preferences are typically not fully known a priori, and then planning stops based on each driver’s preferences. Our approach to solving this problem is to develop an intelligent personal agent that learns preferences gradually over multiple interactions. This study proposes a new technique that utilises a small-scale discrete choice experiment as a method of interacting with the driver in order to minimise the cognitive burden on the driver. Using this method, drivers are presented with a variety of routes with possible combinations of charging stops depending on the agent’s latest belief about their preferences. In subsequent iterations, the personal agent will continue to learn and refine its belief about the driver’s preferences, suggesting more personalised routes that are closer to the driver’s preferences. Based on real preference data from EV drivers, we evaluate our novel algorithm and show that, after only a few queries, our method quickly converges to the optimal routes for EV drivers [This paper is an extended version of an ECAI workshop short paper (Shafipour Yourdshahi et al., in: ECAI 2023 workshops, Kraków, Poland, 2023)].

A multiple criteria Bayesian hierarchical model for analyzing heterogeneous consumer preferences

We introduce a novel Bayesian hierarchical model for consumer preference analysis, addressing two significant challenges in this domain. First, it accommodates preference heterogeneity at both individual and segment levels. This enables actionable insights for targeting and pricing decisions while quantifying uncertainty. Second, it incorporates probabilistic value-based ranking to handle inconsistent and sparse preference data. This way, it mitigates the impact of cognitive biases and alleviates uncertainty in estimates. The proposed method performs robust inference of consumers’ preferences through hierarchical priors, allowing for flexible parameter learning and borrowing statistical strength from well-informed individuals. We demonstrate its practical usefulness by analyzing the real preferences of almost one hundred consumers considering mobile phone contracts. We also report the results of an extensive experimental study. The proposed method outperforms its counterpart, executing an independent estimation and the state-of-the-art approaches regarding predictive accuracy and preference similarity within identified customer groups. The performance improvements are more pronounced with larger sample sizes, smaller sets of items, and in contexts with reduced heterogeneity and increased consistency among consumers.

A Study of Customer Preference Transmission for New Energy Vehicles Based on a Signaling Game and Separating Equilibrium

Although there are many methods that can be used to obtain customer preferences for new energy vehicles, most studies generally overlook the fact that customer preferences are private information. The purpose of this study is to investigate the transmission mechanism of customer preferences by taking into account situations in which customers lie. Through a signaling game model, this study analyzed the transmission mechanism of customer preference information for the center control touch screen of new energy vehicles based on separation equilibrium. The results show that when inequality (1) remains, such an equilibrium forms: the customers send the real preference signal, the manufacturer then adopts a new sample consistent with the received signal and prices the product accordingly, and, finally, the customers pay for the new NEV. When inequality (2) remains, the following equilibrium forms: customers signal the opposite of their private preference, the manufacturer then adopts a new sample opposite to the received signal, and, finally, customers pay for the new NEV.

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences.

To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver's preference is consistent with this driver's naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers' real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers' naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers' real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers.

An Integrated Entropy-TOPSIS Approach for Electric Vehicle Selection

Electric vehicles (EVs) have gained a significant role in reducing emissions of harmful greenhouse gases, which can result in serious effects like global warming, deteriorating air quality, etc. Thus, it helps tackle concerns related to the environment and health of the general public. Many nations are switching towards this new era of electric mobility, thus promoting and contributing to the goal of sustainable development. Due to the rapid evolution and expanding scope of the electric vehicle market, selecting the best alternative between various existing EV models was tedious. In this paper, the electric vehicle in the Indian market context was considered along with the TOPSIS approach as a multi-criteria decision-making tool (MCDM) for choosing and ranking the best available choice for the electric vehicle present in the Indian market for the customer. The entropy method is used to obtain the weights associated with the criteria. For this study, thirteen electric vehicles have been selected as alternatives. This study contributes by giving a real preference order, considering a broad set of selection criteria, and evaluating the best alternative from the existing EVs.

Assessing the validity and feasibility of using a best-worst scaling measure for values elicitation among older adults with newly-diagnosed blood cancers to inform treatment decisions.

344 Background: Multiple stakeholders have called for the development of tools to facilitate values elicitation with patients to inform treatment decision making about chemotherapy. We previously developed a best-worst scaling (BWS) measure, a theory-driven values elicitation tool, for older adults (≥ 60 years) with blood cancers through patient and expert engagement. We sought to evaluate the feasibility and validity of using this measure to inform initial treatment decisions for newly-diagnosed patients. Methods: We completed a convergent mixed-methods study to test feasibility and validity of remote delivery of the measure following guidance for choice experiments (Janssen, Exp Rev Pharm Out Res, 2017). Convergent validity was evaluated by comparing preferences with a ranking exercise. Content validity was tested in cognitive interviews. Results: 178 patients at a public safety net hospital were eligible, 157 (88%) were approached, and 48 (31%) consented (48% lymphoma, 40% leukemia/myelodysplastic syndrome, 12% other). Mean age was 73.9 years (range 60 - 95). 59% were male; 96% were white; 67% had a bachelor’s degree or higher. 52% completed the BWS measure prior to their initial treatment visit. Patients agreed or strongly agreed that the instrument was understandable (96%), relevant to them (58%), and showed their real preferences (75%). 50% felt it was easy to answer. 54% of responses from the BWS measure were concordant with ranking on their top priority. Cognitive interviews (n=7) demonstrated content validity: all patients were able to accurately describe the instructions of the BWS instrument and correctly define each included outcome; most patients reported that the BWS instrument helped solidify their preferences; all patients reported it was beneficial; no patients reported missing domains of outcomes; all reported that the time and emotional burden to complete the measure were appropriate. Conclusions: Using this BWS measure to inform treatment decisions of older adults with newly-diagnosed blood cancers is feasible and appears valid for clinical use. Future work is needed to optimize implementation and validate the measure in different settings and with a larger and more diverse population.

Analysing the indoor temperature ranges for an efficient control of HVAC systems

The selection of a suitable indoor temperature is a challenge to society, since its relationship with thermal comfort and energy consumption. The adaptive thermal comfort theory addresses this problem relating indoor with outdoor temperatures and based on that, defines temperature ranges. This paper defines a fuzzy logic-based methodology to assess the suitability of indoor adaptive temperature ranges proposed by existing regulations of the real thermal sensation of the occupants. For this purpose, the study suggests considering four indicators to evaluate the representativeness of different comfort ranges on the real preferences of the occupants. Based on that, four metrics are proposed for interpreting the needs of the occupants of a building and the changes required to the temperature ranges to satisfy such needs. To validate the proposed methodology, questionnaires on the thermal sensation of the occupants of office buildings considering three case studies (Mediterranean, Europe, ASHRAE) are used, showing the results obtained the representativeness of adaptive comfort ranges for such databases. The feasible introduction of this methodology in a conditioning systems would assist in decision making regarding energy efficiency, thermal comfort and a balance between them.

Enhanced Social Recommendation Method Integrating Rating Bias Offsets

Current social recommendations based on Graph Neural Networks (GNNs) often neglect to extract rating bias from user and item statistics, leading to misinterpreting real user preferences. For example, a high rating from a user with lenient rating standards and a high average rating does not always indicate a real preference for the item. This situation highlights inherent flaws in existing recommendation algorithms that do not adequately account for bias in user and item ratings and rating trends. To address this problem, this paper proposes an enhanced social recommendation method based on GNNs with integrated rating bias offsets (SR-BS). Firstly, we obtain rating bias from users and items by subtracting their average rating value from the historical rating value for each user/item. To enhance the model’s learning capability, we transform the rating biases into vector representations. Secondly, in the model learning, diverse meta-paths are predefined for modeling interaction relations between graph nodes (e.g., user–item–user, user–user). The aggregation of semantic information from these relational paths is achieved by stacking multiple GNN layers, enabling the fusion of higher-order information. Finally, the experimental results on four datasets—Ciao, Epinions, Douban, and FilmTrust—show that our method outperforms other state-of-the-art methods in social recommendation tasks, exhibiting high stability and personalization.

Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.

INVESTMENT OF BIOMEDICAL APPLICATIONS IN MARKETING: ELECTROENCEPHALOGRAM-BASED CONSUMER DECISION PREDICTION

The neuroscience field provides extensive knowledge regarding cerebral activity principles. Therefore, it enables congregating consumer information and anticipating its preferences. Unlike classical marketing techniques, for instance, interviews with consumers, in which they usually do not communicate their real preferences, biomedical methodologies provide more powerful tools such as electroencephalogram signals and brain imaging, to explore the activity within the brain and examine its miscellaneous responses, which contribute efficiently to understanding human behavior related to its purchasing decision-making. Aiming to highlight the impact of neuroscience on marketing advancement, we first present in this paper a thoughtful background based on state-of-the-art studies to investigate the rate of several neurology techniques’ contribution to the advancement of the marketing field and their effect on purchasing decision-making. Second, we propose a predictive modeling framework based on the analysis of EEG signals recorded during decision-making in terms of “like” or “dislike” of specific consumer products. The discrete wavelet transform (DWT) and kNN classifier were combined to develop such an automated model. For evaluation purposes, the developed model was performed on a well-known and public EEG dataset collected for marketing studies. Achieving promising results confirms that the developed framework can be used as a reliable tool for market strategy development.

Price-aware matrix factorization model for personalized recommendations

Recommendation methods aim to assist consumers in their decision-making process to find products that they are quite likely interested in. Current recommendation methods generally use online consumer reviews or ratings to predict consumers’ preferences for products. In e-commerce transactions, price plays a significant role in consumers’ purchase decisions. And each consumer's preference for product prices is specific. In this paper, we propose a novel price-aware recommendation method based on the matrix factorization model which fully considers the effect of price. We distill the price preferences of consumers from ratings and discover consumers’ real preferences for products. We further calculate the price sensitivities of consumers, not only considering the difference in price preference between a consumer and others but also focusing on the consumers’ price preferences in a specific price range. The final predicted ratings are calculated by adding the term of price effect into the matrix factorization framework. The results show that the proposed method has achieved high accuracy and performed better than several existing methods in both rating prediction and Top-N recommendation. And the products we recommend for consumers are more in line with their price preferences. Moreover, we find that, to some extent, the proposed method can solve the long-tail product recommendation problem with the consideration of the price effect.

Multicriteria decision model for electoral preferences

In this article, a multicriteria model for the assessment of electoral preferences is built. The main hypothesis underlying the model is that the population as a whole evaluates each party by considering its position with respect to a set of issues. An analysis is developed of the case of parties’ positions and voters’ preferences set on the basis of government spending distribution. A principle of preference concentration is applied to explain criteria importance. The influence of group interests, though not assumed to be determinant, is not denied. An expected use of the model is for parties to evaluate alliance strategies, since the probabilistic modeling of the preferences and of the direct treatment of the possible interactions among criteria opens a new perspective for the analysis of electoral allegiances. A theoretical example is analyzed. An application involving real preferences in a Brazilian election is discussed.

Comparing Two Methods of Clinical Supervision

Purpose: The purpose of this study was to compare two methods of clinical supervision, remote and in-person, in group speech-language therapy in a university-based outpatient clinic, in terms of student clinical behaviors that occur with each method. Clinical supervision is an essential part of graduate education programs in speech-language pathology and clinical supervisors in university-based speech and hearing clinics often employ remote supervision. The professional literature concerning the impact of remote supervision on student clinical behaviors is limited. The studies found advantages and disadvantages to in-person and remote supervision and no real preference for one method was noted by participants. Method: This study compared student clinical behaviors, specifically percentage of interactional time, in remote versus in-person supervision conditions. Results: No significant difference was found between the two conditions. Conclusions: Both in-person and remote are valuable methods for clinical supervision of graduate students. There are strengths and weaknesses to both methods and graduate clinicians tend to present similar behaviors when being supervised via either method.

An intelligent film recommender system based on emotional analysis.

The existing personalized film recommendation methods take the user's historical rating as an important basis for recommendation. However, the user's rating standards are different, so it is difficult to mine the user's real preferences and form accurate push. Therefore, to achieve high-quality personalized recommendation of films, it is particularly important to mine the emotion of user reviews. In this article, a personalized recommendation method based on sentiment analysis of film reviews is proposed, where natural language processing technology is used to mine the emotional tendency of user reviews. The multi-modal emotional features are weighted and the weighted fusion feature vector after PSO is taken as the overall emotion vector, then the emotional similarity of weighted fusion is calculated by considering the time factor of content publishing and the average emotional tendency of users. By calculating the matching degree of emotional value between users and films, the top-N film recommendation for target users is given. The test results show that the effect of the personalized film recommendation system based on multimodality is superior to that of the comparison method, which effectively solves the problem of different user rating scales, and really increases users' interest in watching films.

Детская игра как территория свободы

Background. Xe issue of children’s role-playing game belongs to the category of eternal in developmental psychology, since it strongly inYuences not only the theory of child development, but also the practice of preschool education, aZects the interests of the family, and, most importantly, to a large extent determines the direction and quality of the child’s development in preschool age. However, we have seen numerous shi[s in interest to this topic as societal priorities in child development have changed over the past century. In the 21st century, the discussion around the possibilities and conditions for the emergence of pretend play is of great interest on the part of both parents and the professional community. Play represents a speci\c resource for the development of a child in a world of uncertainty and an unpredictable future. Objectives. Xe study aims to analyze the current socio-cultural situation in Russian preschool education from th point of view of the role-playing game development in children, to identify the main reasons for the lack of play in kindergartens, and also to present possible parental and pedagogical educational strategies related to the place and possibilities of play in the development of preschool children. Sample. Xe study involved parents of children attending kindergartens from three regions of the Russian Federation. Xe sample was structured according to the factor of socio-economic situation in the region, as well as the factors “center-periphery”, “city-village”, “type of preschool institution”. Xe number of respondents varied from 1000 to 1200 each year. Method. A number of methods were used in the study. Xese included literature analysis on the research topic, expert evaluation, written survey (questionnaire, with the method of ranking the alternatives by respondents), statistical data analysis (with the method of variance analysis). Results. Based on the analysis of the obtained data, the following results were obtained. Xe rating of parental educational preferences shows that the conYict between learning activities and children’s play, which is discussed in the literature and is actually present in kindergartens, is not based on the real preferences of parents. Xe study revealed the phenomenon of imitation play, which is o[en presented as a real children’s game. An analysis of trends associated with the denial of the role of play in the development of modern children is presented. Conclusion. Ways to overcome the conYict between learning and play are discussed. Xe discrepancies between the real preferences of the family in the \eld of preschool education and their interpretation by preschool teachers are shown. Xe actual preferences of parents provide a picture that can be used to establish productive communication between preschool teachers and parents of children attending kindergarten. Practical application of the results. All the results obtained in the study can be applied in the practice of preschool education, at the level of preschool organizations. Xe areas of application of the results obtained include organization of a real, non-imitation, game of preschool children, building up productive communication with the parents of children attending a preschool organization.

Rationalizing systematic discrepancies between election outcomes and opinion polls

The Bradley effect concerns the discrepancy between opinion polls and actual election outcomes that emerges when candidates do not exhibit mainstream ideological, sexual or racial features. This effect was first observed during the 1982 election for the Governor of California that resulted in a significant loss for the black Democratic candidate, Tom Bradley, despite him being ahead in polls. It has been argued that poll respondents tend to mask their true political preference and favour of what is generally considered more socially acceptable. We propose an exactly solvable statistical mechanical model, which allows for a quantitative exploration of this phenomenon. The model includes three main ingredients: (i) the tendency of individuals to align their real preference to the declared (public) opinions of others, (ii) a term accounting for an individual integrity factor, which induces voters to be consistent between their public and private opinions, and (iii) a self-monitoring component, which tunes the strength by which an individual is affected by and wishes to publicly conform to social norms. We observe the emergence of order in a wide range of parameters of the model and discuss the effect of noise on the correlation between hidden and public preference. Finally, we present an application of this model to the prediction of election outcomes.

Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model for guaranteeing data dissemination reliability in Internet of Things

SummaryInternet of Things (IoT) is the rapidly growing domain that facilitates seamless connectivity to physical objects that makes them suitable and ideal entity of smart environment. The enforcement of trust between the physical objects is essential for completely utilizing the significant potentiality of these connected IoT objects. The traditional security strategies are not potent enough in provisioning comprehensive protection to the smart world. In this paper, Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model (BFPSMTM) is proposed for determining the outranking class that aids in superior trust estimation of interconnected IoT objects to guarantee reliable data dissemination. This BFPSMTM adopted different preference functions depending on the nature and type of criteria or attributes considered for evaluating the trust of the IoT objects during data dissemination. It utilized bipolar fuzzy number for assessing trust that represents an asymmetrical associated between the different judgmental factors considered for decision making. It represented the bipolar linguistic terms into trapezoidal bipolar fuzzy numbers to handle the issue of imprecise and vague knowledge during trust computation. It utilized a trapezoidal bipolar fuzzy number‐based ranking function and identified the crisp real preferences of trusted IoT objects during data dissemination. It incorporated an entropy weighting information and computed the attribute weights based on the normality condition to prevent bias during trust estimation. The results of BFPSMTM confirmed improved throughput of 22.18%, reduced delay of 20.86%, minimized energy consumption of 19.32%, and better packet forwarding rate of 19.84%, with different number of IoT objects in the environment.

Cross-domain decision making with parameter transfer based on value function

Decision models based on value function can help learn the preferences of decision makers using historical data. However, inconsistency of the preferences learned from data with the real preferences of decision makers may occur. Suppose decision data or scenarios in the target domain are insufficient to guarantee the consistency of learning preferences, and there exists one similar source domain with sufficient effective data. Then, knowledge from the source domain can help learn preferences in the target domain. Following this idea, this paper proposes a cross-domain decision making method using a parameter transfer strategy with homogeneous and heterogeneous criteria. A decision model based on the value function is constructed using a generalized additive model to ensure the interpretability of the model. The marginal value functions are adjusted using Sturm’s theorem to keep the monotonicity of the learned marginal value functions on the criteria. Then, the parameter transfer strategy is adopted to transfer the obtained value functions to help learn the real preferences of the decision maker for the decision problem where preferences in historical data may not be consistent. The effectiveness of the proposed method is validated by solving the problem of diagnosing breast lesions in a hospital.

경영전략과 경영자의 과신성향 및 적자회피를 위한 실제이익조정

The purpose of this study is to examine the relationship between management strategy, manager's overconfidence and real earnings management decision-making under the earning management motive to avoid deficit, which Burgstahler and Dichev (1997) mentioned as an important profit reference point. The analysis results are as follows. First, it is verified that companies pursuing a leading management strategy are more actively carrying out real earnings management to avoid losses than analytical or defensive companies. This can be interpreted as the fact that companies pursuing a leading management strategy with a large amount of resources available for real earnings management, such as investment scale, are performing real earnings management in a situation to avoid losses than those that do not. Second, if the managers of companies implementing leading management strategies have a tendency to overconfidence, it appears that they are more actively carrying out real earnings management to avoid losses than those that do not. This means that when managers of companies adopting leading management strategies are overconfident, they underestimate the negative effects of real earnings management, so they are actively using real earnings management. It is expected that this study will be able to suggest to researchers that differentiated real earnings management preference can occur depending on business strategy and earnings management incentives. In addition, it is expected that it can be a basis for the management strategy to have a dominant effect on corporate decision-making rather than the manager's tendency.