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The present pre-registration seeks to further elaborate on certain hypotheses and analyses related to tolerance of uncertainty (TOU) and perceived social support (PSS) that have been described, but without details, in the Tong et al. (2021; https://osf.io/6rc9h) and Melani et al. (2022; https://osf.io/ay9gr) pre-registrations. Both these projects include an investigation of how the relationship between structure learning and cognitive flexibility is independently moderated by TOU and PSS and includes self-report questionnaires and tasks to measure these variables (see the variable list furnished in the present pre-registration for the details of the questionnaires and tasks used to measure the TOU and PSS variables). Tolerance of uncertainty (TOU) is a cognitive tendency characterized by the willingness to engage in unconventional and novel ways of thinking and doing. The measures we use to assess tolerance of uncertainty include decision-making tasks such as Ambiguity Aversion and questionnaires such as the Need for Closure Scale and Multicultural Experiences Questionnaire (see attached variable list for the full list of measures). Ambiguity aversion is measured with the multiple price list paradigm (Holt & Laury, 2002) in which a participant makes nine binary choices between an ambiguous option and a risky option and choosing more ambiguous options indicates a higher tolerance of uncertainty. Need for closure is defined as a “desire for a firm answer to a question, any firm answer as compared to confusion and/or ambiguity” (Kruglanski, 2004, p. 6) and has been acknowledged by researchers to have conceptual overlap with tolerance of uncertainty (Rosen et al., 2014). Individuals who have had multicultural experiences are also likely to be more tolerant of uncertainty due to the unfamiliarity of foreign cultures and it was found that multicultural experiences reduce need for closure (Tadmor et al., 2018), suggesting that a latent construct underlies these measures. Hence, we aim to extract latent constructs from these measures. The questionnaires/tasks used to measure TOU are listed in the variable list furnished in the present pre-registration. Perceived social support (PSS) is measured by questionnaires including the Provisions for Social Relationships and Adaptations to Stress (PSRAS) scale and the Multidimensional Scale of Perceived Social Support (MSPSS; see attached variable list for the full list of measures). The PSRAS was designed to assess the degree of social support that an individual has (Cutrona & Russell, 1988) and the MSPSS was designed to assess perceptions of social support adequacy (Zimet et al., 1988), suggesting that a latent construct of perceived social support may underlie these measures, which we aim to extract. Therefore, the present pre-registration spells out our aims involving the analysis of the (i) TOU and (ii) PSS variables. Specifically, for TOU and PSS independently: (1) we will generate a score for each associated questionnaire/task (or each of its subscales), either by (a) following established methods as described in the literature (for instance, calculating the score of a questionnaire/task based on the recommendation of a published paper) or by (b) running new analyses (such as factor analysis) if the psychometric properties of some questionnaires/tasks are not good enough for our dataset; (2) we will examine the underlying structure of TOU and PSS variables, which includes (a) whether there is a (set of) latent construct(s)/factor(s) that can sufficiently account for the total variance of all the TOU/PSS variables - thus a (set of) composite score(s) can be derived, and (b) whether there is a hierarchical model of TOU/PSS (Hubbard et al., 2016), in which a general latent construct/factor underlies several specific latent constructs/factors, and each specific latent construct underlies the TOU/PSS variables. Subsequently, depending on the analyses described in aim (2), we will accordingly perform the tests described in Tong et al. (2021) and Melani et al. (2022). If aim (2) is not supported (i.e., there is no evidence for an underlying structure that can reduce the dataset), we will test (3) the association between cognitive flexibility and each of the TOU/PSS variables and related questionnaires (Melani et al., 2022) and (4) how the association between cognitive flexibility and structure learning will change at different levels of each of the TOU/PSS variables and related questionnaires (Tong et al., 2021). If aim (2) is supported and we can indeed extract a (set of) latent construct(s)/factor(s), we will additionally test (5) the association between cognitive flexibility and the latent TOU and PSS construct(s)/factor(s) and (6) how the association between structure learning and cognitive flexibility will change at different levels of the TOU and PSS construct(s)/factor(s). For reasons of completeness and baseline comparisons, we will naturally still conduct the analyses described in (3) and (4) and compare their predictive power with (5) and (6). In summary, the present pre-registration plans to generate specific scores for TOU and PSS, examine possible latent construct(s) for these variables, evaluate their associations with cognitive flexibility, and assess their interactions with the association between cognitive flexibility and structure learning. These analyses will help to identify potential factors that may influence the effectiveness of cognitive flexibility training.

The present pre-registration seeks to further elaborate on certain hypotheses and analyses related to tolerance of uncertainty (TOU) and perceived social support (PSS) that have been described, but without details, in the Tong et al. (2021; https://osf.io/6rc9h) and Melani et al. (2022; https://osf.io/ay9gr) pre-registrations. Both these projects include an investigation of how the relationship between structure learning and cognitive flexibility is independently moderated by TOU and PSS and includes self-report questionnaires and tasks to measure these variables (see the variable list furnished in the present pre-registration for the details of the questionnaires and tasks used to measure the TOU and PSS variables). Tolerance of uncertainty (TOU) is a cognitive tendency characterized by the willingness to engage in unconventional and novel ways of thinking and doing. The measures we use to assess tolerance of uncertainty include decision-making tasks such as Ambiguity Aversion and questionnaires such as the Need for Closure Scale and Multicultural Experiences Questionnaire (see attached variable list for the full list of measures). Ambiguity aversion is measured with the multiple price list paradigm (Holt & Laury, 2002) in which a participant makes nine binary choices between an ambiguous option and a risky option and choosing more ambiguous options indicates a higher tolerance of uncertainty. Need for closure is defined as a “desire for a firm answer to a question, any firm answer as compared to confusion and/or ambiguity” (Kruglanski, 2004, p. 6) and has been acknowledged by researchers to have conceptual overlap with tolerance of uncertainty (Rosen et al., 2014). Individuals who have had multicultural experiences are also likely to be more tolerant of uncertainty due to the unfamiliarity of foreign cultures and it was found that multicultural experiences reduce need for closure (Tadmor et al., 2018), suggesting that a latent construct underlies these measures. Hence, we aim to extract latent constructs from these measures. The questionnaires/tasks used to measure TOU are listed in the variable list furnished in the present pre-registration. Perceived social support (PSS) is measured by questionnaires including the Provisions for Social Relationships and Adaptations to Stress (PSRAS) scale and the Multidimensional Scale of Perceived Social Support (MSPSS; see attached variable list for the full list of measures). The PSRAS was designed to assess the degree of social support that an individual has (Cutrona & Russell, 1988) and the MSPSS was designed to assess perceptions of social support adequacy (Zimet et al., 1988), suggesting that a latent construct of perceived social support may underlie these measures, which we aim to extract. Therefore, the present pre-registration spells out our aims involving the analysis of the (i) TOU and (ii) PSS variables. Specifically, for TOU and PSS independently: (1) we will generate a score for each associated questionnaire/task (or each of its subscales), either by (a) following established methods as described in the literature (for instance, calculating the score of a questionnaire/task based on the recommendation of a published paper) or by (b) running new analyses (such as factor analysis) if the psychometric properties of some questionnaires/tasks are not good enough for our dataset; (2) we will examine the underlying structure of TOU and PSS variables, which includes (a) whether there is a (set of) latent construct(s)/factor(s) that can sufficiently account for the total variance of all the TOU/PSS variables - thus a (set of) composite score(s) can be derived, and (b) whether there is a hierarchical model of TOU/PSS (Hubbard et al., 2016), in which a general latent construct/factor underlies several specific latent constructs/factors, and each specific latent construct underlies the TOU/PSS variables. Subsequently, depending on the analyses described in aim (2), we will accordingly perform the tests described in Tong et al. (2021) and Melani et al. (2022). If aim (2) is not supported (i.e., there is no evidence for an underlying structure that can reduce the dataset), we will test (3) the association between cognitive flexibility and each of the TOU/PSS variables and related questionnaires (Melani et al., 2022) and (4) how the association between cognitive flexibility and structure learning will change at different levels of each of the TOU/PSS variables and related questionnaires (Tong et al., 2021). If aim (2) is supported and we can indeed extract a (set of) latent construct(s)/factor(s), we will additionally test (5) the association between cognitive flexibility and the latent TOU and PSS construct(s)/factor(s) and (6) how the association between structure learning and cognitive flexibility will change at different levels of the TOU and PSS construct(s)/factor(s). For reasons of completeness and baseline comparisons, we will naturally still conduct the analyses described in (3) and (4) and compare their predictive power with (5) and (6). In summary, the present pre-registration plans to generate specific scores for TOU and PSS, examine possible latent construct(s) for these variables, evaluate their associations with cognitive flexibility, and assess their interactions with the association between cognitive flexibility and structure learning. These analyses will help to identify potential factors that may influence the effectiveness of cognitive flexibility training.

Purpose:  One of the topics for manufacturers today is to discuss the diversity of customer tastes, which to manage this situation with the least change in products, requires multiple lines that have the necessary flexibility to produce these products. On the other hand, many products require assembly operations. The main purpose of this article is to balance these issues according to the conditions of the workforce and different products. Methodology:  This paper presents two mathematical models to minimize the number of workstations per given cycle time. In the first model, all parameters are definite. Since customer demand may not be constant and this factor can affect the cycle time, the second model uses a robust approach to this issue. Findings:  Analysis of various issues shows that a robust modeling approach provides a more reliable design and allows decision makers to have better assembly based on a better understanding of short-term and long-term conditions under conditions of demand uncertainty. Originality/Value:  In this paper, two new mathematical models for assembly line balance are presented. Multi-models in which assembly operations are performed manually by workers and for more accurate planning, the differences that workers have in terms of learning and forgetfulness effect on assembly line balance are considered.

Some new discoveries of the uncertainty, deficiency, and corruptions of human knowledge and learning. With particular instances in grammar and the tongues, poetry, criticism, history and antiquity, ... and more especially in the study of theology ... By John Edwards, D.D. 1714.. Digitized from IA40313617-03 . Previous issue: bim_eighteenth-century_a-treatise-against-the-n_rye-george_1719_2 . Next issue: bim_eighteenth-century_pluralities-indefensible_newton-richard_1743 .

Rochelle Humes is a television presenter, entrepreneur behind innovative hair and skincare brand My Little Coco, and a former singer. Rochelle has been performing professionally and presenting television since she was in school, and in this episode she opens up on a lifetime chasing the highest goals like never before. It hasn't always been easy for Rochelle. Growing up in a single parent household, nothing was handed to her on a plate. When she tried to launch a hair and skincare business which had some products marketed at people of colour, stores didn't want to stock it. But in all these parts of her life Rochelle has found success by staying true to herself. Hers is a story of real perseverance. The new collection from My Little Coco is available now in Boots. Topics: Your early years Your Dad & your family The Saturdays Starting your business - My Little Coco Black maternity scandal Relationships Fiver year plan and deciding to manage yourself Making tough decisions The last guests question Rochelle: https://www.instagram.com/rochellehumes My Little Coco (Rochelle's Business): https://mylittlecoco.co.uk Watch the episodes on Youtube: https://www.youtube.com/c/TheDiaryOfACEO/videos Sponsor: Huel - https://uk.huel.com/

Includes bibliographical references (p. 23-25)

When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy. We present the FLOLA-Voronoi method introduced previously for deterministic responses, and theoretically derive the impact of output uncertainty. The algorithm automatically puts more emphasis on exploration to provide more information to the models.

Project Based Learning (PBL) is a pedagogical approach that seeks to provide Year 7 pupils with independent and group learning skills to meet both the needs of the Year 7 curriculum as well as support their learning in future stages of their education. It particularly aims to improve their engagement in learning as well as practical literacy skills. This trial evaluates a specific type of PBL known as "Learning through REAL Projects", developed by the Innovation Unit--an independent social enterprise that aims to improve public sector services. A year long pilot project in 2013/2014 with eight schools (763 pupils) established the feasibility of a main trial. It provided the opportunity to develop the intervention and test the research procedures. The results contained in this report relate to a randomised controlled trial that took place between September 2014 and April 2016. Twelve intervention schools (2,101 pupils) and 12 control schools (1,973 pupils) were involved in the trial. REAL projects was delivered by teachers, supported in many instances by teaching assistants (TAs), with further support from senior leadership colleagues. It was delivered for a year in the intervention schools with a relatively large proportion of timetabled teaching (varying between 20% and 50%). In almost all cases it was delivered to mixed-ability Year 7 classes. The intervention used structured cross-subject "REAL Projects" planned by the delivery teachers who were supported by Classroom Coaches from the Innovation Unit. Schools were also supported by Leadership Coaches. REAL Projects are driven by an "essential question" which has significant educational content. The projects encouraged pupils to create an "excellent" product through drafting and redrafting and then to exhibit their work to an "authentic" audience. Visits were made to intervention schools at two time points and consisted of lesson observations, interviews, and focus groups with school leadership, project leads, class teachers, and pupils. Two case studies were also conducted in schools that it was agreed delivered the intervention well. Key conclusions are as follows: (1) Adopting PBL had no clear impact on either literacy (as measured by the Progress in English assessment) or student engagement with school and learning; (2) The impact evaluation indicated that PBL may have had a negative impact on the literacy attainment of pupils entitled to free school meals. However, as no negative impact was found for low-attaining pupils, considerable caution should be applied to this finding; (3) The amount of data lost from the project (schools dropping out and lost to follow-up) particularly from the intervention schools, as well as the adoption of PBL or similar approaches by a number of control group schools, further limits the strength of any impact finding; (4) From our observations and feedback from schools, we found that PBL was considered to be worthwhile and may enhance pupils' skills including oracy, communication, teamwork, and self-directed study skills; and (5) PBL was generally delivered with fidelity but requires substantial management support and organisational change. The Innovation Unit training and support programme for teachers and school leadership was found to be effective in supporting this intervention. Overall, the findings have low security. The trial was designed as a two-armed randomised controlled trial with schools being allocated to intervention or control groups. It was set up as an efficacy trial which aimed to test if the intervention can succeed under ideal conditions. However, 47% of the pupils in the intervention and 16% in the control group were not included in the final analysis. Therefore there were some potentially important differences in characteristics between the intervention and control groups. This undermines the security of the result. The trial results did not find that the PBL programme had an impact either on the pupils' literacy performance (as measured by Progress in English 12 tests), engagement, or attendance. The analysis did find a statistically significant negative impact on students eligible for free school meals (FSM), however no negative impact was found for lower-attaining pupils more generally, which makes it difficult to hypothesise why PBL might negatively impact FSM pupils specifically. This adds to the uncertainty of the finding.

Project Based Learning (PBL) is a pedagogical approach that seeks to provide Year 7 pupils with independent and group learning skills to meet both the needs of the Year 7 curriculum as well as support their learning in future stages of their education. It particularly aims to improve their engagement in learning as well as practical literacy skills. This trial evaluates a specific type of PBL known as "Learning through REAL Projects", developed by the Innovation Unit--an independent social enterprise that aims to improve public sector services. A year long pilot project in 2013/2014 with eight schools (763 pupils) established the feasibility of a main trial. It provided the opportunity to develop the intervention and test the research procedures. The results contained in this report relate to a randomised controlled trial that took place between September 2014 and April 2016. Twelve intervention schools (2,101 pupils) and 12 control schools (1,973 pupils) were involved in the trial. REAL projects was delivered by teachers, supported in many instances by teaching assistants (TAs), with further support from senior leadership colleagues. It was delivered for a year in the intervention schools with a relatively large proportion of timetabled teaching (varying between 20% and 50%). In almost all cases it was delivered to mixed-ability Year 7 classes. The intervention used structured cross-subject "REAL Projects" planned by the delivery teachers who were supported by Classroom Coaches from the Innovation Unit. Schools were also supported by Leadership Coaches. REAL Projects are driven by an "essential question" which has significant educational content. The projects encouraged pupils to create an "excellent" product through drafting and redrafting and then to exhibit their work to an "authentic" audience. Visits were made to intervention schools at two time points and consisted of lesson observations, interviews, and focus groups with school leadership, project leads, class teachers, and pupils. Two case studies were also conducted in schools that it was agreed delivered the intervention well. Key conclusions are as follows: (1) Adopting PBL had no clear impact on either literacy (as measured by the Progress in English assessment) or student engagement with school and learning; (2) The impact evaluation indicated that PBL may have had a negative impact on the literacy attainment of pupils entitled to free school meals. However, as no negative impact was found for low-attaining pupils, considerable caution should be applied to this finding; (3) The amount of data lost from the project (schools dropping out and lost to follow-up) particularly from the intervention schools, as well as the adoption of PBL or similar approaches by a number of control group schools, further limits the strength of any impact finding; (4) From our observations and feedback from schools, we found that PBL was considered to be worthwhile and may enhance pupils' skills including oracy, communication, teamwork, and self-directed study skills; and (5) PBL was generally delivered with fidelity but requires substantial management support and organisational change. The Innovation Unit training and support programme for teachers and school leadership was found to be effective in supporting this intervention. Overall, the findings have low security. The trial was designed as a two-armed randomised controlled trial with schools being allocated to intervention or control groups. It was set up as an efficacy trial which aimed to test if the intervention can succeed under ideal conditions. However, 47% of the pupils in the intervention and 16% in the control group were not included in the final analysis. Therefore there were some potentially important differences in characteristics between the intervention and control groups. This undermines the security of the result. The trial results did not find that the PBL programme had an impact either on the pupils' literacy performance (as measured by Progress in English 12 tests), engagement, or attendance. The analysis did find a statistically significant negative impact on students eligible for free school meals (FSM), however no negative impact was found for lower-attaining pupils more generally, which makes it difficult to hypothesise why PBL might negatively impact FSM pupils specifically. This adds to the uncertainty of the finding.

This paper proposes a novel deep ensemble learning framework designed for the efficient detection and classification of cervical cancer from Pap smear images. The proposed study implements three advanced learning models namely DenseNet201, Xception, and a classical convolutional neural network (CNN) customized with optimal hyperparameters to automate feature extraction and cervical cancer detection process. The proposed study also introduces a novel ensemble learning to enhance the classification of cervical cancer. The proposed ensemble mechanism is based on the confidence aggregation followed by uncertainty quantification and prediction ranking scheme, thus ensuring that more reliable predictions have a proportionally greater influence on the final outcome. The primary goal is to leverage the collective intelligence of the ensemble in a manner that prioritizes reliability and minimizes the impact of less certain predictions. The experimental analysis is carried out on two dataset one with whole slide images (WSI) and another on cropped images. The proposed ensemble model achieves an accuracy rate 100 and 97% for dataset with WSI and with cropped images respectively.