Preference Function Research Articles

<b><i>The effect of applying Six Sigma methodology in improving quality and reducing internal failure costs: a case study</i></b>

The use of the hexagonal diffraction methodology helps in improving processes by using effective analytical and statistical tools and methods. This research sought to apply the hexagonal diffraction methodology to improve the quality characteristics of the yoghurt production process and reduce the costs of internal failure in the Rafidain Factory / Dairy Department. For the purpose of applying the hexagonal diffraction methodology, the following stages were carried out: Definition: The critical quality characteristics of the yoghurt production process were described and the acidity characteristic was selected. Measurement: Five potential factors affecting the acidity of yoghurt have been identified. Analysis: The experimental design method was used to discover the important factors that affect the acidity values ​​of yoghurt. Optimization: The optimal process parameter settings were determined by using the preference function method. Control: The settings of the optimal process factors are attached to the checklist for verification operations within the audits. The results of the research showed that skim milk powder and incubation time were significant factors on the acidity values ​​of yoghurt, and the optimal settings for these factors were 11,1287 g/100 g for skim milk powder and 2,5538 hours for incubation time.

Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss

Abstract Training a neural network for picking seismic phase arrivals has been commonly posed as a segmentation problem. It is a highly imbalanced segmentation problem in the sense that the background vastly dominates the foreground because we are trying to pick the optimal single sample point that represents the arrival of a seismic phase in a many seconds long time window. Here, we test the Dice loss, which is a preferred loss function for highly imbalanced image segmentation problems. We show that phase-picking neural networks trained on the Dice loss behave in a binary fashion for which the prediction output is almost always either nearly 1 or nearly 0. This feature removes the strong dependence of data processing workflows on the prediction score threshold, which is an otherwise critical parameter to determine when using neural networks trained on the cross-entropy loss. When strategically used, models trained on the Dice loss can reduce the parameter dependency of machine learning-based seismic monitoring.

State of charge estimation method for lithium-ion batteries based on adaptive central difference particle filter with weight reconstruction

The particle filter (PF) has been widely used for state of charge (SOC) estimation. However, the particle degradation phenomenon will affect the estimation accuracy. In response to this issue, a state of charge estimation method for lithium-ion batteries based on adaptive central difference particle filter with weight reconstruction (ACDPF-WR) is designed in this paper. This method combines the preferred importance density function and the optimized resampling strategy. Firstly, the central difference Kalman filter (CDKF) is used to update the sampled particles to reduce the influence of particle degradation. Secondly, the Gaussian process regression (GPR) model of particles and weights is constructed by combining the offline learning experimental battery data set, and the GPR is used to generate the weight distribution of the particle filter. Then, an adaptive step size mechanism is introduced, which determines the optimal step size by calculating the mean square error of the weight distribution under different steps. Finally, the weight distribution generated by the central difference particle filter (CDPF) based on the optimal step size is combined with the typical resampling algorithm to select high-quality particles to achieve the optimal estimation. The adaptability and robustness of the algorithm are verified under Beijing Dynamic Stress Test (BJDST), US06 Highway Driving Schedule (US06), and Dynamic Stress Test (DST) conditions, and evaluated by mean absolute error (MAE) and root mean square error (RMSE) indicators. The average comparison results of the three working conditions show that the MAE of ACDPF-WR is 54.1 % higher than that of EPF and 24.3 % higher than that of CDPF, and the RMSE of ACDPF-WR is 64.6 % higher than that of EPF and 24.5 % higher than that of CDPF. The proposed algorithm achieves better performance and provides new insights and methods for the optimization and improvement of the battery management system.

Effect of the social environment on olfaction and social skills in wild-type and a mouse model of autism

Autism spectrum disorders (ASD) are complex, polygenic and heterogenous neurodevelopmental conditions. The severity of autism-associated variants is influenced by environmental factors, particularly social experiences during the critical neurodevelopmental period. While early behavioral interventions have shown efficacy in some children with autism, pharmacological support for core features — impairments in social interaction and communication, and stereotyped or restricted behaviors — is currently lacking. In this study, we examined how the social environment influences both wild-type (WT) and Shank3 knockout (KO) mice, a model reflecting core autism-like traits. Our findings revealed that chronic social isolation enhanced social interaction and olfactory neuron responses in WT animals. Furthermore, it restored impairments in social novelty preference and olfactory function, as well as self-grooming in Shank3 KO mice. Conversely, an enriched social environment heightened social interest toward novel conspecifics in WT mice, but elicited the opposite effect in Shank3 KO mice. Notably, Shank3 KO mice displayed distinct social responses when exposed to WT or Shank3 KO mice. These results offer novel insights that could favor the implementation of behavioral interventions and inclusive classroom programs for children with ASD.

Dietary protein intake interacts with weak handgrip strength and cognitive impairment.

Handgrip strength correlates with cognitive function, but how gender and dietary protein interact with it is unclear. To investigate the relationship between handgrip strength and cognitive function among non-stroke elders, and potential interaction effects of gender and dietary protein. Non-stroke older adults with handgrip strength and cognitive tests available were included from National Health and Nutrition Examination Survey (NHANES) database. Multivariable linear regression analyses were used to explore the association between grip strength and cognitive performance. Multivariable logistic regression analyses were to investigate the effect of weak handgrip strength on cognitive impairment. Subgroup analyses were conducted to explore differences among sub-populations. Interaction effects of gender and protein intake were investigated by interaction analyses. Larger handgrip strength was associated with preferable memory function in men (CERAD Word Learning sub-test: p = 0.005; CERAD Delayed Recall: p = 0.009), better verbal fluency (Animal Fluency test: p = 0.005) and executive function in women (Digit Symbol Substitution Test: p = 0.017). Weak handgrip strength was associated with cognitive impairment, especially in older, female, obese participants or elders with low protein consumption. Participants with weak grip strength, compared to normal strength, were twice as likely to perform poorly in complex cognition evaluations (odds ratio = 2.01, p = 0.028). Interaction effect of protein intake was observed (p-interaction = 0.022). Compared to conditions of low protein intake, high protein intake significantly offset the impact of weak handgrip on cognitive impairment. The association between grip strength and cognitive performance varies by gender. Non-stroke elders with weak handgrip strength are vulnerable to cognitive impairment. Improving muscle strength and increasing protein intake may be effective to mitigate decline in executive function.

Selective Functional Connectivity between Ocular Dominance Columns in the Primary Visual Cortex.

The primary visual cortex (V1) in humans and many animals is comprised of fine-scale neuronal ensembles that respond preferentially to the stimulation of one eye over the other, also known as the ocular dominance columns (ODCs). Despite its importance in shaping our perception, to date, the nature of the functional interactions between ODCs has remained poorly understood. In this work, we aimed to improve our understanding of the interaction mechanisms between fine-scale neuronal structures distributed within V1. To that end, we applied high-resolution functional MRI to study mechanisms of functional connectivity between ODCs. Using this technique, we quantified the level of functional connectivity between ODCs as a function of the ocular preference of ODCs, showing that alike ODCs are functionally more connected compared to unalike ones. Through these experiments, we aspired to contribute to filling the gap in our knowledge of the functional connectivity of ODCs in humans as compared to animals.

Robust multilinear target-based decision analysis considering high-dimensional interactions

The Multilinear Target-based Preference Functions (MTPFs) support multi-attribute decision problems characterized by attribute interactions and targets. However, existing research falls short in flexibly modeling high-dimensional interactions and lacks robustness in decision-making recommendations when faced with uncertain parameters and targets. The paper proposes a robust multilinear target-based decision analysis framework considering high-dimensional interactions, along with uncertainties in parameters and targets. First, the necessity of high-dimensional interactions and the limitations of available MTPFs in modeling high-dimensional interactions are demonstrated. Second, the MTPFs based on the 2-interactive fuzzy measure and the Nonmodularity index are proposed to model the high-dimensional interactions and simultaneously reduce the computational challenges of parameter identification. Third, new descriptive measures are proposed based on the Stochastic Multicriteria Acceptability Analysis to evaluate the robustness of decision recommendations subject to uncertain targets and parameters. The validation and advantages of the framework are illustrated with simulation studies and an application in customer competitive evaluation of smart thermometer patches.

Enhancing decision credibility in transport safety through a modified IPSI–EXPROM Ⅱ–PAM model with kernel density estimation

Effective management of decision problems with high stability and credibility is critical for conducting multi-criteria decision-making (MCDM) activities, particularly when formulating safety actions. Therefore, this study introduces a novel hybrid MCDM model that integrates the Improved Preference Selection Index (IPSI), EXtended PROMethee Ⅱ (EXPROM Ⅱ), and Partitioning Around Medoids (PAM), named the IPSI–EXPROM Ⅱ–PAM model. Specifically, the IPSI aggregation method was converted into a weighting method that assigned weights based on convergence. Moreover, a reformative nonparametric preference function was incorporated into EXPROM Ⅱ, which substantially reduced the workload of decision-makers. Furthermore, Kernel Density Estimation (KDE) was integrated into the model to estimate the distribution of the dimension-reduced scores. By identifying potential cluster structures within the data, KDE facilitates the selection of cluster number k in PAM. The performance of the model was evaluated by applying it to a real case of formulating a transport safety action for Asia-Pacific Economic Cooperation (APEC) member countries. Intensive comparisons of the empirical results demonstrated the superiority of the model in defending policy conclusions and confirmed its validity in conveying the associated policy message. In summary, this study provides government officials, decision-makers, and policymakers with a reliable decision-making tool for handling real-life MCDM activities with considerable applicability, credibility, and efficiency.

DEFINING THE ROLE OF EDUCATIONAL PLATFORMS IN MATHEMATICS IN THE EFFECTIVE TRAINING OF FUTURE MATHEMATICS TEACHERS

The present study analyzes the results of a survey of teachers and students designed to identify the preferences and needs of universities in using educational platforms in mathematics to study the subject «Elementary Mathematics». The aim of the study was to determine the preferred characteristics and functionality of mathematics learning platforms that can contribute to effectivelearning and increase interest in mathematics for future mathematics teachers. To achieve this goal, questionnaires were developed, which were distributed among mathematics teachers and first-year students in the specialty «Mathematics» when teaching the subject «Elementary Mathematics» and third and fourth-year students when teaching the subject «Methods of teaching mathematics». The questionnaires included questions about preferences in using educational platforms in mathematics, the level of comfort andperception of educational material when working with technologies, preferred learning formats and expected learning outcomes. The analysis of the obtained results allowed us to identify the key principles that should be taken into account when using educational platforms in mathematics to study the subject «Elementary Mathematics» at the university. Following these principles contributes to the use of educational platforms in mathematics, and as a result, to improving the quality of mathematical education at the university and improving the learning ability of future mathematics teachers.

Within-population variation in preference functions reveals substantial among-female disagreement in mate assessment.

The mate choice behaviours of females can greatly affect patterns of reproductive success in males and influence the evolution of sexually selected male traits. Population-level estimates of display preferences may provide an accurate estimate of the strength and direction of selection by female choice if all females in the population show homogeneous preferences. However, population-level estimates may yield misleading estimates if there is within-population variation in mate preferences. While it is increasingly clear that the latter situation is common in nature, empirical data on the magnitude of variation in female preferences are required to improve our current understanding of its potential evolutionary consequences. We explored variations in female preference functions for 3 male call properties in a treefrog. We document substantial within-population variation not only in peak preferences but also in preference function shape (open, closed, flat), with at best 62% of females sharing a preference function shape with the respective population curve. Our findings suggest that population curves may accurately capture the direction of sexual selection, but depending on the properties of the constituting individual functions they may over- or underestimate the strength of selection. Particularly population estimates suggesting weak selection may in fact hide the presence of individual females with strong but opposing preferences. Moreover, due to the high within-population variation in both peak preferences and preference function shapes, the population functions drastically underestimate the predicted variation in male mating success in the population.

Cooking readiness in stressful times: Navigating food choices for a healthier future

Food choice is a complex function of preferences and combines a myriad of food-related factors, such as biological, economic, physical, social, and psychological determinants. People's food choices directly affect their health and can have both positive and negative outcomes. For instance, the pandemic may have presented an opportunity to cook more at home, eventually resulting in dietary benefits, health improvements, and reduced stress. Conversely, adverse behavioral, psychological, and physical outcomes, such as overeating, have also been associated with stressful situations. Therefore, the aim of this study is twofold: first, to propose “cooking readiness” as a higher-order construct, in which ‘food literacy’ and ‘cooking skills’ correspond to the capability, ‘cooking attitudes’ reflect the motivation, and the variable ‘COVID-19 restrictions’ represent the opportunity for consumers to cook and consume healthier food at home. Furthermore, as the pandemic scenario was imposed and caused changes in mental health and emotional mood, the second objective is to explore the relationship between cooking readiness and stress levels in consumers' choices for nutritionally recommended (or non-recommended) food. Cross-country data (Brazil, Denmark) from 1074 consumers was analyzed using structural equation modeling. This study confirms cooking readiness as a higher-order construct, integrating food literacy, cooking skills, and cooking attitudes with pandemic-induced opportunities. Cooking readiness is crucial in promoting healthier eating habits, particularly in Brazil. The stronger tradition of healthier eating in Denmark may mitigate the impact of cooking readiness on reducing non-recommended food consumption. The varying effects of stress on food choices between the two countries underscore the need for tailored public health strategies that consider cultural differences and existing dietary habits. Promoting cooking skills and food literacy, particularly in times of crisis, can be instrumental in supporting healthier food choices and improving public health.

A Study on the Direction of Self-Diagnosis Device for Breast Cancer in MZ Generation Women - focusing on type and preferred functions of femtech technology and services

A Study on the Direction of Self-Diagnosis Device for Breast Cancer in MZ Generation Women - focusing on type and preferred functions of femtech technology and services

Multicomponent Support Program for Secondary Prevention of Stroke Using Digital Health Technology: Co-Design Study With People Living With Stroke or Transient Ischemic Attack.

Few individuals (<2%) who experience a stroke or transient ischemic attack (TIA) participate in secondary prevention lifestyle programs. Novel approaches that leverage digital health technology may provide a viable alternative to traditional interventions that support secondary prevention in people living with stroke or TIA. To be successful, these strategies should focus on user needs and preferences and be acceptable to clinicians and people living with stroke or TIA. This study aims to co-design, with people with lived experience of stroke or TIA (referred to as consumers) and clinicians, a multicomponent digital technology support program for secondary prevention of stroke. A consumer user needs survey (108 items) was distributed through the Australian Stroke Clinical Registry and the Stroke Association of Victoria. An invitation to a user needs survey (135 items) for clinicians was circulated via web-based professional forums and national organizations (eg, the Stroke Telehealth Community of Practice Microsoft Teams Channel) and the authors' research networks using Twitter (subsequently rebranded X, X Corp) and LinkedIn (LinkedIn Corp). Following the surveys, 2 rounds of user experience workshops (design and usability testing workshops) were completed with representatives from each end user group (consumers and clinicians). Feedback gathered after each round informed the final design of the digital health program. Overall, 112 consumers (male individuals: n=63, 56.3%) and 54 clinicians (female individuals: n=43, 80%) responded to the survey; all items were completed by 75.8% (n=85) of consumers and 78% (n=42) of clinicians. Most clinicians (46/49, 94%) indicated the importance of monitoring health and lifestyle measures more frequently than current practice, particularly physical activity, weight, and sleep. Most consumers (87/96, 90%) and clinicians (41/49, 84%) agreed that providing alerts about potential deterioration in an individual's condition were important functions for a digital program. Intention to use a digital program for stroke prevention and discussing the data collected during face-to-face consultations was high (consumers: 79/99, 80%; clinicians 36/42, 86%). In addition, 7 consumers (male individuals: n=5, 71%) and 9 clinicians (female individuals: n=6, 67%) took part in the user experience workshops. Participants endorsed using a digital health program to help consumers manage stroke or TIA and discussed preferred functions and health measures in a digital solution for secondary prevention of stroke. They also noted the need for a mobile app that is easy to use. Clinician feedback highlighted the need for a customizable clinician portal that captures individual consumer goals. Following an iterative co-design process, supported by evidence from user needs surveys and user experience workshops, a consumer-facing app that integrates wearable activity trackers and a clinician web portal were designed and developed to support secondary prevention of stroke. Feasibility testing is currently in progress to assess acceptability and use.

Employing behavioural portfolio theory for sustainable investment: Examining drawdown risks and ESG factors

ABSTRACT This study uses behavioural portfolio theory (BPT) within the Markowitz Portfolio Theory framework to enhance portfolio management by focusing on sustainability and risk mitigation during market downturns. It selects portfolios to hedge against market lows using Conditional Drawdown at Risk (CDaR) and Expected Regret of Drawdown (ERoD). These measures help choose securities that perform well during a market decline. This study applies drawdown-based risk metrics to assist institutional investors and fiduciaries in making informed investment and fund management decisions. By merging BPT with Markowitz’s mean–variance framework, selected investments are maintained above a safety threshold, contributing to the portfolio’s overall quality and sustainability. Additionally, by incorporating an Environmental, Social, and Governance (ESG) preference function, the findings suggest that BPT built portfolios meet traditional performance standards and align with socially responsible investment principles, thereby offering higher utility and alignment with investor values focused on sustainable investing.

Symmetry groups for social preference functions

We introduce the anonymity group, the neutrality group and the symmetry group of a social preference function. Inspired by an unsolved problem posed by Kelly in 1991, we investigate the problem of recognizing which permutation groups may arise as anonymity, neutrality and symmetry groups of a social preference function. A complete description is provided for neutrality groups. In the case of anonymity groups, we derive a sufficient condition, which largely captures the desired class of objects. Our approach also is of relevance for the notion of representability by Boolean functions and, therefore, the results of this paper also shed some light on this field of study.

Oral aripiprazole in the treatment of tic disorders in China: a cost-effectiveness analysis based on a mapping algorithm derived from a Chinese children and adolescents population

BackgroundOral aripiprazole exhibits favorable clinical efficacy and safety in the suppression of tics in children and adolescents with tic disorders. This study aims to evaluate and compare the cost-effectiveness of high-dose and low-dose aripiprazole in children and adolescents with tic disorders from the perspective of the Chinese healthcare system.MethodsA questionnaire survey was conducted on 146 patients with tic disorders, of whom 144 completed EQ-5D-Y and YGTSS. Four models were built to convert YGTSS onto EQ-5D-Y utility using two mapping algorithms. We constructed a decision tree model containing efficacy and safety to compare the cost-effectiveness of high-dose and low-dose aripiprazole based on our mapping function.ResultsThe GLM with model 1 (YGTSS total tic scores) was selected as the preferred function in our decision tree model. The base case cost-effectiveness analysis showed that compared to low-dose aripiprazole, high-dose aripiprazole improves effectiveness by 0.001QALYs and increases the overall cost by $197.99, resulting in an ICER of $174339.22 per QALY, which exceeds three times the gross domestic product per capita. Hence, high-dose aripiprazole is not likely to be a cost-effective option for child patients with tic disorders. One-way sensitivity analysis and probabilistic sensitivity analysis showed that these results is robust.ConclusionOn the basis of currently available data, low-dose aripiprazole may be a safe, effective, and economical dosage for children and adolescents with tic disorders.LimitationsThe main limitation of our study is the lack of utility directly used for cost-effectiveness analysis. We obtained the utility of patients with tic disorders indirectly by the mapping function. This may introduce some bias and uncertainty. And it is a limitation to use the direct medical costs of Germany in our model. Although we converted it to the equivalent value of China using purchasing power parities, caution should be exercised when interpreting the results of this study.

Building a network with assortative mixing starting from preference functions, with application to the spread of epidemics

Compartmental models of disease spread have been well studied on networks built according to the Configuration Model, i.e., where the degree distribution of individual nodes is specified, but where connections are made randomly. Dynamics of spread on such “first order” networks were shown to be profoundly different compared to epidemics under the traditional mass action assumption. Assortativity, i.e., the preferential mixing of nodes according to degree, is a second order property that is thought to impact epidemic trajectory. We first show how assortative mixing can come about from individual preferences to connect with others of lower or higher degree, and propose an algorithm for constructing such a network. We then investigate via simulation how this network structure favors or inhibits diffusion processes, such as the spread of an infectious disease.

Towards Building an E-Librarian System: Exploring Recommendation System Preferences Among Ovarian Cancer Patients and Caregivers.

Ovarian cancer (OvCa) patients encounter complex treatment decisions, and often have difficulties in searching and integrating online health information to guide their treatment decision-making. The objective of this study was to explore the preference of online health information among OvCa patients and caregivers, by exploring their preferred content, format, and function features for the design of a personalized recommender system. This study used qualitative research methods to collect data through in-depth interviews with 18 OvCa patients and 2 caregivers. A total of (N=20) face-to-face interviews were conducted, and subsequently analyzed by audio recordings, verbatim transcription, and theory-driven approach with thematic analysis. A total of 5 themes were identified for content-related design, 4 themes identified for system function and one theme identified for frequency format. The results of this study inform the preference and therefore OvCa specific features can be tailor-made in a recommendation system.

Selective Functional Connectivity between Ocular Dominance Columns in the Primary Visual Cortex.

The primary visual cortex (V1) in humans and many animals is comprised of fine-scale neuronal ensembles that respond preferentially to the stimulation of one eye over the other, also known as the ocular dominance columns (ODCs). Despite its importance in shaping our perception, to date, the nature of the functional interactions between ODCs has remained poorly understood. In this work, we aimed to improve our understanding of the interaction mechanisms between fine-scale neuronal structures distributed within V1. To that end, we applied high-resolution functional MRI to study mechanisms of functional connectivity between ODCs. Using this technique, we quantified the level of functional connectivity between ODCs as a function of the ocular preference of ODCs, showing that alike ODCs are functionally more connected compared to unalike ones. Through these experiments, we aspired to contribute to filling the gap in our knowledge of the functional connectivity of ODCs in humans as compared to animals.

Мобильные приложения для психологического благополучия: отношение пользователей и определение требований.

In the paper, an analysis of the opinion of Tomsk students on the use of software in psychological care, defines the requirements and recommendations for the development of such mobile applications. Among 82 respondents, the survey revealed: the level of awareness and experience in the field of psychology, attitude to the use of software for psychological well-being and their preferred functionality. Considering the results of the analysis, it has been concluded the respondents are ready to use applications in various contexts, but they see barriers for themselves. It has been identified an application should include psychoeducation, scientifically proven testing methods, and practical exercises. The structured exercises of the cognitive behavioral therapy technique have been regarded as the most suitable for implementing in software applications. It has been also concluded the requirement to provide an access to statistics on the work performed and its analysis in applications. The results of the study may be useful to developers of psychological digital tools for improving and promoting existing developments, or creating new ones.