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This paper aims to introduce a novel bi-objective model for a Job Shop Scheduling Problem (JSSP) in order to minimize makespan and maximum tardiness simultaneously. Some realistic assumptions namely fuzzy processing times and due dates involving triangular possibility distributions, transportation times, availability constraints, modified position-based learning effects on processing times, and sum-of-processing-times based learning effects on duration of maintenance activities were considered to provide a more general and practical model for the JSSP. Based on the learning effects, processing times decrease as the machine performs an operation frequently and workers gain working skill and experience. In this paper, based on DeJong's learning effect, a novel and modified formulation is proposed for this effect. According to the above-mentioned assumptions, a novel Mixed-Integer Linear Programming (MILP) model for the JSSP is suggested. The proposed model is first converted into an auxiliary crisp model, given that the model is a possibilistic programming and is then solved by TH and "-constraint methods in the case of small-sized instances. Finally, the results are compared. For medium- and largesized instances, five metaheuristic algorithms including Non-dominated Sorting Genetic Algorithm (NSGA-III), Pareto Envelope-based Selection Algorithm (PESA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), NSGA-II, and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) are utilized, and the results are finally compared in terms of three performance metrics. [ABSTRACT FROM AUTHOR]

Objective - The present study investigates the impact of augmented reality (AR) on the learning abilities of primary and secondary students at the cognitive and affective levels. Methodology/Technique - The data of 59 relevant domestic and international studies between 2010 and 2021, including 83 studies and 4123 samples, were analyzed through CMA for meta-analysis. Finding - The overall effect size of AR technology on the teaching effectiveness of primary and secondary school students was 0.598, which had a positive contribution. The impact of AR technology on primary and secondary school students was stronger at the affective levels than at the cognitive levels. Novelty - In the process of learning with AR-assisted resources for primary and secondary school students, the school levels, teaching methods, and resource types did not differ significantly, and the differences in teaching outcomes were manifested in the different subject content. [ABSTRACT FROM AUTHOR]

Objectives: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects., Materials and Methods: A gradient-boosted decision tree ML method was used to analyze synthetic datasets that replicate the complexity of clinical scenarios involving high-risk medical devices. We designed this process to detect learning effects using a risk-adjusted cumulative sum method, quantify the excess adverse event rate attributable to operator inexperience, and adjust for these alongside patient factors in evaluating device safety signals. To maintain integrity, we employed blinding between data generation and analysis teams. Synthetic data used underlying distributions and patient feature correlations based on clinical data from the Department of Veterans Affairs between 2005 and 2012. We generated 2494 synthetic datasets with widely varying characteristics including number of patient features, operators and institutions, and the operator learning form. Each dataset contained a hypothetical study device, Device B, and a reference device, Device A. We evaluated accuracy in identifying learning effects and identifying and estimating the strength of the device safety signal. Our approach also evaluated different clinically relevant thresholds for safety signal detection., Results: Our framework accurately identified the presence or absence of learning effects in 93.6% of datasets and correctly determined device safety signals in 93.4% of cases. The estimated device odds ratios' 95% confidence intervals were accurately aligned with the specified ratios in 94.7% of datasets. In contrast, a comparative model excluding operator learning effects significantly underperformed in detecting device signals and in accuracy. Notably, our framework achieved 100% specificity for clinically relevant safety signal thresholds, although sensitivity varied with the threshold applied., Discussion: A machine learning framework, tailored for the complexities of post-market device evaluation, may provide superior performance compared to standard parametric techniques when operator learning is present., Conclusion: Demonstrating the capacity of ML to overcome complex evaluative challenges, our framework addresses the limitations of traditional statistical methods in current post-market surveillance processes. By offering a reliable means to detect and adjust for learning effects, it may significantly improve medical device safety evaluation., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Current challenges in patient care have increased research on technology use in nursing and health care. Digital assistive technologies (DATs) are one option that can be incorporated into care processes. However, how the application of DATs should be introduced to nurses and care professionals must be clarified. No structured and effective education concepts for the patient-oriented integration of DATs in the nursing sector are currently available., Objective: This study aims to examine how a structured and guided integration and education concept, herein termed the sensitization, evaluative introduction, qualification, and implementation (SEQI) education concept, can support the integration of DATs into nursing practices., Methods: This study used an explanatory, sequential study design with a mixed methods approach. The SEQI intervention was run in 26 long-term care facilities oriented toward older adults in Germany after a 5-day training course in each. The participating care professionals were asked to test 1 of 6 DATs in real-world practice over 3 days. Surveys (n=112) were then administered that recorded the intention to use DATs at 3 measurement points, and guided qualitative interviews with care professionals (n=12) were conducted to evaluate the learning concepts and effects of the intervention., Results: As this was a pilot study, no sample size calculation was carried out, and P values were not reported. The participating care professionals were generally willing to integrate DATs-as an additional resource-into nursing processes even before the 4-stage SEQI intervention was presented. However, the intervention provided additional background knowledge and sensitized care professionals to the digital transformation, enabling them to evaluate how DATs fit in the health care sector, what qualifies these technologies for correct application, and what promotes their use. The care professionals expressed specific ideas and requirements for both technology-related education concepts and nursing DATs., Conclusions: Actively matching technical support, physical limitations, and patients' needs is crucial when selecting DATs and integrating them into nursing processes. To this end, using a structured process such as SEQI that strengthens care professionals' ability to integrate DATs can help improve the benefits of such technology in the health care setting. Practical, application-oriented learning can promote the long-term implementation of DATs., (©Sebastian Hofstetter, Max Zilezinski, Dominik Behr, Bernhard Kraft, Christian Buhtz, Denny Paulicke, Anja Wolf, Christina Klus, Dietrich Stoevesandt, Karsten Schwarz, Patrick Jahn. Originally published in JMIR Medical Education (https://mededu.jmir.org), 09.10.2024.)

Abstract Background Balint groups aim to reflect doctor-patient relationships on the basis of personal cases. This study reports the validation of a questionnaire aimed at the identification of learning processes among Balint group participants in China. Methods This multicenter cross-sectional study was conducted during Balint group sessions in Beijing, Guangzhou and Shanghai. A heterogeneous sample of different professional groups was intended to adequately capture the reality of Balint work in China. After a Balint group session, the participants were asked to complete the Mandarin version of the Balint group session questionnaire (BGQ-C) and the group questionnaire (GQ), an internationally validated instrument to assess central dimensions of therapeutic relationships during group processes. Results Questionnaires from n = 806 participants from 55 Chinese Balint groups, predominantly comprising individuals with a medical background, were analyzed. Most participants were female (74.6%), and the average age was 34.2 years old (SD = 9.4). The results indicated good to very good reliability (Cronbach’s α = .70 to .86; retest rs = .430 to .697). The verification of the construct validity of the BGQ-C showed satisfying convergent (rs = .465 to .574) and discriminant validity (rs = -.117 to -.209). The model was tested with a confirmatory factor analysis of a three-factor model (standardized root mean square residual = .025; comparative fit index = .977; Tucker-Lewis index = .971). The 3 empirically identified scales resulted in good model fit with the theoretical dimensions of Balint work postulated in the literature: “reflection of transference dynamics in the doctor-patient relationship”, “emotional and cognitive learning” and “case mirroring in the dynamic of the group”. Due to the high correlations between the factors, a single-factor model was possible. A group comparison between the German and Chinese samples showed different loadings across cultures. Conclusions The BGQ-C is a quick-to-complete, item-based measuring instrument that allows the relevant dimensions of Balint group work to be recorded. This study suggests good psychometric properties of the Chinese version. Nevertheless, it must be assumed that the composition of constructs in the two countries is different.

This validation study on the crosswise model (CM) examines five survey experiments that were implemented in a general population survey. Our first crucial result is that in none of these experiments was the crosswise model able to verifiably reduce social desirability bias. In contrast to most previous CM applications, we use an experimental design that allows us to distinguish a reduction in social desirability bias from heuristic response behaviour, such as random ticking, leading to false positive or false negative answers. In addition, we provide insights on two potential explanatory mechanisms that have not yet received attention in empirical studies: primacy effects and panel conditioning. We do not find consistent primacy effects, nor does response quality improve due to learning when respondents have had experiences with crosswise models in past survey waves. We interpret our results as evidence that the crosswise model does not work in general population surveys and speculate that the question format causes mistrust in participants.

Learnability in Automated Driving (LiAD) is a neglected research topic, especially when considering the unpredictable and intricate ways humans learn to interact and use automated driving systems (ADS) over the sequence of time. Moreover, there is a scarcity of publications dedicated to LiAD (specifically extended learnability methods) to guide the scientific paradigm. As a result, this generates scientific discord and, thus, leaves many facets of long-term learning effects associated with automated driving in dire need of significant research courtesy. This, we believe, is a constraint to knowledge discovery on quality interaction design phenomena. In a sense, it is imperative to abstract knowledge on how long-term effects and learning effects may affect (negatively and positively) users’ learning and mental models. As well as induce changeable behavioural configurations and performances. In view of that, it may be imperative to examine operational concepts that may help researchers envision future scenarios with automation by assessing users’ learning ability, how they learn and what they learn over the sequence of time. As well as constructing a theory of effects (from micro, meso and macro perspectives), which may help profile ergonomic quality design aspects that stand the test of time. As a result, we reviewed the literature on learnability, which we mined for LiAD knowledge discovery from the experience perspective of long-term learning effects. Therefore, the paper offers the reader the resulting discussion points formulated under the Learnability Engineering Life Cycle. For instance, firstly, contextualisation of LiAD with emphasis on extended LiAD. Secondly, conceptualisation and operationalisation of the operational mechanics of LiAD as a concept in ergonomic quality engineering (with an introduction of Concepts for Applying Learnability Engineering (CALE) research based on LiAD knowledge discovery). Thirdly, the systemisation of implementable long-term research strategies towards comprehending behaviour modification associated with extended LiAD. As the vehicle industry revolutionises at a rapid pace towards automation and artificially intelligent (AI) systems, this knowledge is useful for illuminating and instructing quality interaction strategies and Quality Automated Driving (QAD).

This study investigates the optimization of an integrated production–inventory system that consists of an original equipment manufacturer (OEM) supplier and an OEM brand company. The cognitive and skill learning effect, imperfect quality, and fuzzy random demand are incorporated into the integrated two-echelon supply chain model to minimize the total cost. We contribute to dividing the learning effect into cognitive learning and skill learning, we build a new learning curve to resemble the real complexity more closely and avoid the problem that production time tends towards zero after production is stable. In total, five production–inventory models are constructed. Furthermore, a solution procedure is designed to solve the model to obtain the optimal order quantities, and the optimal shipment size. Additionally, the symbolic distance method is used to deal with the inverse fuzzification. Then numerical analysis shows that the increase of the cognitive learning coefficient and skill learning coefficient will reduce the total cost of the production–inventory system. With the increase of the cognitive learning coefficient, the gap between the total cost of cognitive learning and skill learning, and that of Wright learning, correspondingly decreases consistently. However, with the increase of the skill learning coefficient, there is a consistent corresponding increase. The total cost of cognitive learning and skill learning shows hyperbolic characteristics. The important insights of this study for managers are that employees' knowledge plays an important role in reducing costs in the early learning stage and humanistic management measures should be taken to reduce employees' turnover. Compared with the skill learning training for production technicians, we should pay more attention to the training of cognitive learning. [ABSTRACT FROM AUTHOR]

Copyright of Journal for Religion in Education / Religionspädagogische Beiträge is the property of Arbeitsgemeinschaft Katholische Religionspadagogik & Katechetik (AKRK) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Assembly lines with non-constant task time attribute are widely studied in the literature. For the SALBP-II assembly line balancing problem, we take account of stochastic task time changes, which is more practical than the deterministic times often assumed in industrial application. An algorithm - ENCORE, which leverages the traditional algorithm SALOME2, is proposed to address the assembly line balancing problem with stochastic task time attribute. Computational and statistical experiments are conducted to show the efficiency of proposed algorithms over traditional methods with regards to the improvement of total production times. [ABSTRACT FROM AUTHOR]

Copyright of Psychotherapie Forum is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although high and advanced technologies are used to produce high quality items, some defective items are produced due to an error in technical operation or in maintenance. The defective cost is the expense involving rework, repair and replacement of defective items, and also the cost incurred due to loss of goods quality. The learning function acts as a substantial function for cost diminution. Meanwhile, the impact of learning is an incident which occurs approximately everywhere and enables the workers to carry out new work with better performance after flowing repetition over a course of time. Further, a retailer offers buyers an allowable setback time to arrange the money payable to him and no extra fine if money is paid within the allowable financing time period. On the other hand, if the cash is not paid on trade-credit financing period of time, the retailer will charge on remaining cash provided by the buyer after the allowed period. Keeping these facts, we developed an inventory model for imperfect quality items with a learning effect, in which demand rate is assumed as an exponential function of the trade credit period. The expected total profit function is maximized with respect to trade credit financing period under learning effect. A numerical example is illustrated, and a comprehensive sensitivity analysis is depicted to understand the robustness of the model.

Whether political communication research is media-centric or politics-centric is an interesting question. In this article, I argue however that it is important is to clarify whether the answer depends on the extent to which the focus is on media or on politics, or the extent to which the role of the media in political processes is viewed positively or negatively. With respect to the assessment of the role of media in political processes, it is also important to distinguish between assessments based on a priori normative assumptions and based on empirical results. In conclusion, it is argued that the strength of political communication research lies in its interdisciplinarity. From that perspective, it is imperative that political communication scholars include relevant theories regardless of their disciplinary origin. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to determine whether peer‐assisted learning (PAL) is a more effective learning and test method in terms of oral‐ and maxillofacial surgery. Material and methods: In July 2020, a total of 267 students took a PAL‐based exam on fictional patients with surgical issues, in which they had to evaluate two fellow students and were themselves evaluated by two fellow students. The students evaluated their experience with the PAL‐based exam through a questionnaire which consisted of five given statements (answer possibilities: agree, disagree, neutral) and two questions (answer possibilities: better, equal, worse) to rate. Results: In the survey, 77.9% of the students rated PAL as a better learning method and 21% rated it as at least equally effective to the known multiple‐choice (MC) test. A total of 74.9% of the students indicated that they learned more content with PAL and 20.2% said they learned the same amount; 83.7% said that their "clinical thinking has improved" through PAL. In the comments, 73% of the students noted that they think PAL is a good learning method, and at least 22% rated it as useful but in need of improvements. Only 5% did not see PAL as an acceptable learning method. In contrast to this, 1.3% saw PAL as a "bad alternative to MC tests." Conclusion: PAL, especially peer assessment, might represent a better learning method as it might encourage students to deal more intensively with the learning content and to improve clinical thinking. [ABSTRACT FROM AUTHOR]

In this paper, the seller offers a credit period to his buyer for more sales and the buyer accepts the seller’s policy to gain more profit, and it is assumed that the seller has defective and non-defective items. When the seller provides lots for sale to his buyer then, the buyer separates the whole lots with the help of inspection process into defective and perfect quality items. Further, in this scenario, the percentage of defective items present in the lot follows the S-shape learning curve and it is also considered that the demand rate is imprecise in nature. Here, the demand rate assumes a triangular fuzzy number due to the imprecise nature and it is the model assumption. Based on this assumption, we developed an inventory model with the effect of learning and trade credit strategy under a fuzzy environment for the buyer. The buyer’s total profit has been optimized concerning the order quantity in the fuzzy environment where order quantity has been assumed as a decision variable. The results of this model were verified with the help of numerical examples and sensitivity analysis. We compared the buyer’s total profit in a crisp and fuzzy environment and the buyer gained more profit in a fuzzy environment compared to the crisp environment. Moreover, we compared the results with and without the effect of learning and trade credit on the buyer’s ordering policy and obtained a positive effect on the ordering policy in the numerical section. We determined positive results from the sensitivity analysis, which proved that the trade credit policy will be beneficial for both partners of the supply chain.

Purpose: To determine the reliability of the 505 change-of-direction (COD) test performed with both a stationary and a flying start. Methods: Fifty-two female netball players (age 23.9 ± 5.4 y, height 169.9 ± 3.3 cm, body mass 65.2 ± 4.6 kg) performed 6 trials of the 505 COD test, 3 with a flying start and 3 with a stationary start, once per week over a 4-wk period to determine within- and between-sessions reliability. Results: Testing revealed high within-session reliability for the stationary start (ICC = .96-.97) and for the flying start (ICC = .90-.97). Similarly, both the stationary start (ICC = .965) and the flying start (ICC = .951) demonstrated high reliability between sessions, although repeated-measures analysis of variance (P < .001) revealed learning effects between sessions for both tests. Performances stabilized on day 2 for the static start and on day 3 for the flying start. Conclusions: The 505 COD test is a reliable test in female netball players, with either a stationary or flying start. Smallest detectable differences of 3.91 % and 3.97% for the stationary start and the flying start, respectively, allow practitioners to interpret whether changes in time taken to complete the 505 COD test reflect genuine improvements in performance or are measurement errors. It is suggested that 1 d of familiarization testing be performed for the stationary start and 2 d of familiarization for the flying start, to minimize learning effects. [ABSTRACT FROM AUTHOR]

Background: Balint groups aim to reflect doctor-patient relationships on the basis of personal cases. This study reports the validation of a questionnaire aimed at the identification of learning processes among Balint group participants in China. Methods: This multicenter cross-sectional study was conducted during Balint group sessions in Beijing, Guangzhou and Shanghai. A heterogeneous sample of different professional groups was intended to adequately capture the reality of Balint work in China. After a Balint group session, the participants were asked to complete the Mandarin version of the Balint group session questionnaire (BGQ-C) and the group questionnaire (GQ), an internationally validated instrument to assess central dimensions of therapeutic relationships during group processes. Results: Questionnaires from n = 806 participants from 55 Chinese Balint groups, predominantly comprising individuals with a medical background, were analyzed. Most participants were female (74.6%), and the average age was 34.2 years old (SD = 9.4). The results indicated good to very good reliability (Cronbach’s α =.70 to.86; retest rs =.430 to.697). The verification of the construct validity of the BGQ-C showed satisfying convergent (rs =.465 to.574) and discriminant validity (rs = -.117 to -.209). The model was tested with a confirmatory factor analysis of a three-factor model (standardized root mean square residual =.025; comparative fit index =.977; Tucker-Lewis index =.971). The 3 empirically identified scales resulted in good model fit with the theoretical dimensions of Balint work postulated in the literature: “reflection of transference dynamics in the doctor-patient relationship”, “emotional and cognitive learning” and “case mirroring in the dynamic of the group”. Due to the high correlations between the factors, a single-factor model was possible. A group comparison between the German and Chinese samples showed different loadings across cultures. Conclusions: The BGQ-C is a quick-to-complete, item-based measuring instrument that allows the relevant dimensions of Balint group work to be recorded. This study suggests good psychometric properties of the Chinese version. Nevertheless, it must be assumed that the composition of constructs in the two countries is different. [ABSTRACT FROM AUTHOR]

Background: Smartphones and their built-in sensors allow for measuring functions in disease-related domains through mobile tests. This could improve disease characterization and monitoring, and could potentially support treatment decisions for multiple sclerosis (MS), a multifaceted chronic neurological disease with highly variable clinical manifestations. Practice effects can complicate the interpretation of both improvement over time by potentially exaggerating treatment effects and stability by masking deterioration.Objective: The aim of this study is to identify short-term learning and long-term practice effects in 6 active tests for cognition, dexterity, and mobility in user-scheduled, high-frequency smartphone-based testing.Methods: We analyzed data from 264 people with self-declared MS with a minimum of 5 weeks of follow-up and at least 5 repetitions per test in the Floodlight Open study, a self-enrollment study accessible by smartphone owners from 16 countries. The collected data are openly available to scientists. Using regression and bounded growth mixed models, we characterized practice effects for the following tests: electronic Symbol Digit Modalities Test (e-SDMT) for cognition; Finger Pinching and Draw a Shape for dexterity; and Two Minute Walk, U-Turn, and Static Balance for mobility.Results: Strong practice effects were found for e-SDMT (n=4824 trials), Finger Pinching (n=19,650), and Draw a Shape (n=19,019) with modeled boundary improvements of 40.8% (39.9%-41.6%), 86.2% (83.6%-88.7%), and 23.1% (20.9%-25.2%) over baseline, respectively. Half of the practice effect was reached after 11 repetitions for e-SDMT, 28 repetitions for Finger Pinching, and 17 repetitions for Draw a Shape; 90% was reached after 35, 94, and 56 repetitions, respectively. Although baseline performance levels were highly variable across participants, no significant differences between the short-term learning effects in low performers (5th and 25th percentile), median performers, and high performers (75th and 95th percentile) were found for e-SDMT up to the fifth trial (β=1.50-2.00). Only small differences were observed for Finger Pinching (β=1.25-2.5). For U-Turn (n=15,051) and Static Balance (n=16,797), only short-term learning effects could be observed, which ceased after a maximum of 5 trials. For Two Minute Walk (n=14,393), neither short-term learning nor long-term practice effects were observed.Conclusions: Smartphone-based tests are promising for monitoring the disease trajectories of MS and other chronic neurological diseases. Our findings suggest that strong long-term practice effects in cognitive and dexterity functions have to be accounted for to identify disease-related changes in these domains, especially in the context of personalized health and in studies without a comparator arm. In contrast, changes in mobility may be more easily interpreted because of the absence of long-term practice effects, even though short-term learning effects might have to be considered. [ABSTRACT FROM AUTHOR]

Background: Despite the frequent use of the Timed Up and Go (TUG) test in clinical trials, evaluation of longitudinal test-retest reliability is generally lacking and still inconclusive for patients with Parkinson's disease (PD). Objective: We aimed to further investigate long-term reliability and sensitivity of the TUG test among this population. Furthermore, we explored alternative assessment strategies of the test aimed at elucidating whether the inclusion or combination of timed trials may have potential implications on outcome measure. Methods: Relative and absolute reliability of the TUG performance were obtained in forty-three subjects with PD over three timed trials in two different testing sessions separated by a two-months period. Results: Our results reported excellent intra-session and moderate inter-session reliability coefficients. The use of different assessment strategies of the TUG was found to have an important impact on outcome measure, highlighting the averaging of several timed trials in each testing session as a recommended alternative to minimize measurement error and increase reliability in longitudinal assessments. Nevertheless, beyond acceptable reliability, poor trial-to-trial stability of the measure appears to exist, since the ranges of expected variability upon retesting were wide and the incidence of spurious statistical effects was not negligible, especially in longitudinal repeated testing. Conclusion: Limitations may exist in the interpretation of the TUG outputs as part of longitudinal assessments aimed at evaluating treatment effectiveness in PD population. Researchers and practitioners should be aware of these concerns to prevent possible misrepresentations of functional ability in patients for a particular intervention. [ABSTRACT FROM AUTHOR]

Groups of people offer abundant opportunities for social interactions. We used a two-phase task to investigate how social cue numerosity and social information about an individual affected attentional allocation in such multi-agent settings. The learning phase was a standard gaze-cuing procedure in which a stimulus face could be either uninformative or informative about the upcoming target. The test phase was a group-cuing procedure in which the stimulus faces from the learning phase were presented in groups of three. The target could either be cued by the group minority (i.e., one face) or majority (i.e., two faces) or by uninformative or informative stimulus faces. Results showed an effect of cue numerosity, whereby responses were faster to targets cued by the group majority than the group minority. However, responses to targets cued by informative identities included in the group minority were as fast as responses to targets cued by the group majority. Thus, previously learned social information about an individual was able to offset the general enhancement of cue numerosity, revealing a complex interplay between cue numerosity and social information in guiding attention in multi-agent settings. [ABSTRACT FROM AUTHOR]

Serious games, or else educational games, for programming are considered to have a positive impact on learning programming. Specifically, serious games are considered to motivate students and engage them in playing and learning programming. However, more research is required in order to study their effects in learning programming, as well as their added value in comparison with typical teaching approaches. In this study the effects of teaching programming to lower secondary school students with the serious game CodeCombat and the typical teaching approach are compared. Specifically, fifty-nine students formed an experimental group that was taught programming with CodeCombat and a control group that was taught programming through lecturing and problem solving in Python with a typical programming environment. The study aimed to investigate whether a game like CodeCombat that is based on a text-based real programming language: improves students' performance in basic programming concepts; brings better learning outcomes in comparison with typical teaching methods; engages students' interest. Data was collected through a pre and post test, as well as a survey prior the intervention and another one based on the Technology Acceptance Model at the end. It was concluded that the experimental group performed better than the control group, but this difference was not found to be statistically significant. CodeCombat was evaluated positively in terms of its perceived ease of use and usefulness, as well as the attitude towards its use. The results were neutral in terms of students' behavioral intention to use CodeCombat, but were positive in using serious games for programming in general. [ABSTRACT FROM AUTHOR]

Diffusion of low-carbon technologies is critical to global decarbonization efforts aimed at achieving the Paris Agreement climate goals. In this paper, we represent a social system as a network of agents and model the process of technology diffusion as a contagion propagating in such a network. By setting the necessary conditions for an agent to switch (i.e. to adopt the technology), we address the question of how to maximize the contagion of a technology subject to learning effects (e.g. solar PV) in a network of agents. We focus the analysis on the influence of the network structure and technological learning on diffusion. Our numerical results show that clusters of agents are critical in the process of technology contagion although they generate high levels of variance in aggregate diffusion. Whatever the network structure, learning effects ease the technology contagion in social networks. Key policy insights: Policy makers should take advantage of, or favour, clustered organizations to deploy renewables (e.g. cooperatives of farmers, online social networks) Social dimensions are critical in the design of environmental policies (e.g. peer effects) Technologies subject to learning effects spread further in the population When considering social connections, policy makers should design policies to cope with uncertainty in diffusion (clustered organizations exhibit high levels of uncertainty in adoption). [ABSTRACT FROM AUTHOR]

Sequence dependent set-up times scheduling problems (SDSTs), availability constraint and transportation times are interesting and important issues in production management, which are often addressed separately. In this paper, the SDSTs job shop scheduling problem with position-based learning effects, job-dependent transportation times and multiple preventive maintenance activities is studied. Due to learning effects, jobs processing times are not fixed during plan horizon and each machine has predetermined number of preventive maintenance activities. A novel mixed integer linear programming model is proposed to formulate the problem for minimizing Make Span. Owing to the high complexity of the problem; we applied Grey Wolf Optimizer (GWO) and Invasive Weed Optimizer (IWO) to find nearly optimal solutions for medium and large instances. Finally, the computational Results are provided for evaluating the performance and effectiveness of the proposed solution approaches.

This study investigates the optimization of an integrated production–inventory system that consists of an original equipment manufacturer (OEM) supplier and an OEM brand company. The cognitive and skill learning effect, imperfect quality, and fuzzy random demand are incorporated into the integrated two-echelon supply chain model to minimize the total cost. We contribute to dividing the learning effect into cognitive learning and skill learning, we build a new learning curve to resemble the real complexity more closely and avoid the problem that production time tends towards zero after production is stable. In total, five production–inventory models are constructed. Furthermore, a solution procedure is designed to solve the model to obtain the optimal order quantities, and the optimal shipment size. Additionally, the symbolic distance method is used to deal with the inverse fuzzification. Then numerical analysis shows that the increase of the cognitive learning coefficient and skill learning coefficient will reduce the total cost of the production–inventory system. With the increase of the cognitive learning coefficient, the gap between the total cost of cognitive learning and skill learning, and that of Wright learning, correspondingly decreases consistently. However, with the increase of the skill learning coefficient, there is a consistent corresponding increase. The total cost of cognitive learning and skill learning shows hyperbolic characteristics. The important insights of this study for managers are that employees’ knowledge plays an important role in reducing costs in the early learning stage and humanistic management measures should be taken to reduce employees’ turnover. Compared with the skill learning training for production technicians, we should pay more attention to the training of cognitive learning.

With the development of Artificial Intelligence (AI) and its sharp growth in demand, AI chatbot has spread to other fields, including the field of language education on AI-based smart learning. This empirical study aims to review the learning effects of Korean education using AI chatbots in smart learning. In addition, the perceived learning effects of Korean as second language (KSL) learners were examined. Additionally, as the learning style of the learners could influence the learning effect, this study also compared the differences in perceived learning effect according to the students' learning style (visual, auditory, and hands-on). The participants of this study used a Korean language education chatbot application and answered a survey on the learning effect, namely the earning content, learning flow, learning anxiety, learning efficacy, and learning satisfaction. The results showed that KSL learners considered Korean language education through chatbots was as effective as existing Korean language education. The chatbot also received positive evaluation in terms of decreasing learning anxiety and increasing learning efficacy. Furthermore, the study revealed that among the different learning styles, visual learners showed a significant reduction in learning anxiety. [ABSTRACT FROM AUTHOR]

This paper studies a two-level closed-loop supply chain under vendor managed inventory contract and learning effects. The proposed problem is formulated by a mixed-integer non-linear programming model. The main supposition of the proposed problem is to assume a known probability density function for the defective products rate, then the mean standard deviation utility function is used to minimize the mean cost of the system while taking the standard deviation costs. This paper deals with a multi-product model which is NP-hard and very difficult to solve. Hence, another main contribution of this work is to develop a hybrid metaheuristic with regards to three well-known and recent efficient metaheuristics. The results confirm the applicability and efficiency of the proposed hybrid algorithm in comparison with individual ones in this context and encourage the development and application of this approach more broadly. [ABSTRACT FROM AUTHOR]

In Taiwan, people are getting more aware of beauty treatments and the concept of health care. This study hopes to bring all relevant concepts such as aromatherapy, essential scented oils, medical foods, human meridians, Chinese five elements therapy, and beauty business management into the curriculum design, and seriously considered many factors when designing the curriculum. After that, a teaching experiment was conducted to evaluate if the courses were well planned. The experiment method incorporated a one-group pretest-posttest quasi-experimental design. The sample group consisted of a training class for beauty and body shaping in Pingtung County. After the class was over, a curriculum satisfaction survey was also conducted. All data was analyzed by SPSS with descriptive statistics and t-test for dependent samples. The results are as follows: 1. After the experiment, the curriculum proved to be effective in teaching. 2. Students majoring in nursing or cosmetology did not show any statistical difference in learning achievement. 3. After the class was over, students' grades improved and they displayed more knowledge about aromatherapy. 4. Most students gave positive feedback regarding the learning activities of the aromatherapy class. 5. Participants thought that this course was helpful in promoting self-interest in aromatherapy class in the future. Finally, the researcher provided some suggestions for further study and future reference according to the research results. [ABSTRACT FROM AUTHOR]

Cognitive abilities allow robots to learn and reason from their environment. The gained knowledge can then be incorporated into the robot's actions which in turn affect the environment. Therefore, a cognitive robot is no longer a static system that performs actions based on a pre-defined set of rules but a complex entity that dynamically adjusts over time. With this, challenges arise for production systems that need to observe and ideally anticipate the cognitive robot's behavior. Often, digital twins are employed to test and optimize production control systems. This paper presents a generic approach to characterize, model and simulate learning processes and formalized knowledge in hybrid production systems assuming different station types with learning effects. Thereby, quantitative and qualitative learning processes are mapped including knowledge sharing and transfer across entities. A modular and parameterizable design enables the adjustment to different use cases. Eventually, the model is instantiated as a digital twin of a real production system for product disassembly employing cognitive-autonomous robots among human operators and rigidly automated machines. The model shows great potential to be integrated into test beds for planning and control systems of cognitive factories. [ABSTRACT FROM AUTHOR]

In the ramp-up phase, or time to volume of new products, pronounced learning effects are observed. They are present especially on assembly lines producing mass-goods because of a high number of repetitions of the tasks. Shortening the ramp-up phase and reaching the steady-state production as soon as possible generates main advantages for firms that introduce new products. Moreover, a careful planning of the ramp-up stage is getting even more important in view of shorter product life cycles and a growing importance of the ‘time to payback’ financial indicators. Former studies on incorporation of learning effects into assembly line balancing have limited applicability, because they rely on unrealistic assumptions. We model learning effects, based on general and realistic assumptions, as an extension of the Simple Assembly Line Balancing Problem. We propose exact and heuristic solution procedures and perform extensive computational tests. We found that for instances similar to the problems, which arise in firms, the duration of the learning stage can be reduced by up to 10% if our specialised methods are applied. [ABSTRACT FROM PUBLISHER]

We consider the learning effects in the coordination of production and outbound distribution for manufacturers. The objective is to minimize service span, which lasts from the beginning of production to the completion of delivery of products. In production, a batch-processing facility is used to process jobs which have different sizes. Batch-position-based learning effects are considered since workers become skillful gradually after processing batches one by one. In distribution, a vehicle with a fixed capacity is used to deliver products the customer and the transportation time from the manufacturer to the customer is a constant. We show the coordinated scheduling problem is NP-hard in the strong sense. We propose properties of optimal solutions and we provide an approximation algorithm for the problem. The absolute performance guarantee of the algorithm is 1.667 and the asymptotic performance guarantee is 1.223. Then we consider the problem where there are infinite vehicles and the performance guarantees are respectively 1.5 and 1.223. Finally we analyze the performance of the algorithm by the change of the problem scale, the learning index and operational factors. We propose managerial suggestions for decision makers of manufacturers according to our results. [ABSTRACT FROM AUTHOR]

Motivated by applications in porcelain-making companies, we consider a type of optimization problems for batch-processing operations. In production, a single batch-processing machine with a fixed capacity is used to process jobs. Several jobs can be processed together if their total size is no more than the machine capacity. Batch-position-based learning effects are considered because workers become skillful gradually after they perform the processing task repeatedly. The actual processing time of a batch is a decreasing function of its position in production. The objective is to minimize makespan and we consider three different problems. In the first problem, jobs have identical sizes and we present an algorithm which can find optimal solutions in polynomial time. In the second problem, jobs have identical processing times and we show the problem is NP-hard in the strong sense. We propose an approximation algorithm with an absolute performance guarantee of 1.5 and asymptotic performance guarantee of 1.223. In the third problem, jobs have non-identical sizes and processing times simultaneously. We propose an algorithm with an absolute and asymptotic performance guarantee of 2. Besides, we present the evolution of the performance guarantee and provide managerial insights for decision makers of manufacturing companies. [ABSTRACT FROM AUTHOR]

Abstract Background Practice effects (PEs) are improvements in performance after repeated exposure to test materials, and typically viewed as a source of bias in repeated cognitive assessments. We aimed to determine whether characterizing PEs could also provide a useful marker of early cognitive decline. Methods We conducted a systematic review of the literature, searching PsycInfo (Ebsco) and PubMed databases for articles studying PEs in aging and dementia populations. Articles published between 1920 and 2019 were included. Result We identified 259 articles, of which 27 studied PEs as markers of cognitive performance. These studies consistently showed that smaller, less‐robust PEs were associated with current diagnostic status and/or future cognitive decline. In addition, lower PEs were associated with Alzheimer's disease risk factors and neurodegeneration biomarkers. Conclusion PEs provide a potentially useful marker of cognitive decline, and could prove valuable as part of a cost‐effective strategy to select individuals who are at‐risk for dementia for future interventions.

An imprecise demand rate creates problems in profit optimization in business scenarios. The aim is to nullify the imprecise nature of the demand rate with the help of the cloudy fuzzy method. Traditionally, all items in an ordered lot are presumed to be of good quality. However, the delivered lot may contain some defective items, which may occur during production or maintenance. Inspection of an ordered lot is indispensable in most organizations and can be treated as a type of learning. The learning demonstration, a statistical development expressing declining cost, is necessary to achieve any cyclical process. Further, defective items are sold immediately after the screening process as a single lot at a discounted price, and the fraction of defective items follows an S-shaped learning curve. The trade-credit policy is adequate for suppliers and retailers to maximize their profit during business. In this paper, an inventory model is developed with learning and trade-credit policy under the cloudy fuzzy environment where the demand rate is treated as a cloudy fuzzy number. Finally, the retailer’s total profit is maximized with respect to order quantity. Sensitivity analysis is presented to estimate the robustness of the model.

Modelling the maintenance activities could be complex and non-linear system which consists of different parameters. In this paper, a new approach for maintenance policy and planning problem is developed. First, maintenance activities are simulated by incorporating learning effects. Production and maintenance functions are estimated using historical data. Then, simulation is carried out for different scenarios which are combinations of periodic maintenance and different policies. Several outputs including machines and operators’ availability, reliability, efficiency and queue length are computed. Since the problem is multi-criteria, data envelopment analysis method is used to select the preferred policy. In order to show the applicability of the proposed approach, the data for a series production line is used and different scenarios with different policies are investigated. Since many scenarios are needed to be simulated, Taguchi orthogonal array design is used to reduce the number of scenarios. The proposed approach of this study would help managers to identify the preferred strategy considering and investigating various parameters and policies. This is the first study that introduces an integrated multi-criteria approach for optimum maintenance policy and planning. [ABSTRACT FROM PUBLISHER]

Abstract Background Commonly in surgical randomised controlled trials (RCT) the experimental treatment is a relatively new technique which the surgeons may still be learning, while the control is a well-established standard. This can lead to biased comparisons between treatments. In this paper we discuss the implementation of approaches for addressing this issue in the ROLARR trial, and points of consideration for future surgical trials. Methods ROLARR was an international, randomised, parallel-group trial comparing robotic vs. laparoscopic surgery for the curative treatment of rectal cancer. The primary endpoint was conversion to open surgery (binary). A surgeon inclusion criterion mandating a minimum level of experience in each technique was incorporated. Additionally, surgeon self-reported data were collected periodically throughout the trial to capture the level of experience of every participating surgeon. Multi-level logistic regression adjusting for operating surgeon as a random effect is used to estimate the odds ratio for conversion to open surgery between the treatment groups. We present and contrast the results from the primary analysis, which did not account for learning effects, and a sensitivity analysis which did. Results The primary analysis yields an estimated odds ratio (robotic/laparoscopic) of 0.614 (95% CI 0.311, 1.211; p = 0.16), providing insufficient evidence to conclude superiority of robotic surgery compared to laparoscopic in terms of the risk of conversion to open. The sensitivity analysis reveals that while participating surgeons in ROLARR were expert at laparoscopic surgery, some, if not all, were still learning robotic surgery. The treatment-effect odds ratio decreases by a factor of 0.341 (95% CI 0.121, 0.960; p = 0.042) per unit increase in log-number of previous robotic operations performed by the operating surgeon. The odds ratio for a patient whose operating surgeon has the mean experience level in ROLARR – 152.46 previous laparoscopic, 67.93 previous robotic operations – is 0.40 (95% CI 0.168, 0.953; p = 0.039). Conclusions In this paper we have demonstrated the implementation of approaches for accounting for learning in a practical example of a surgery RCT analysis. The results demonstrate the value of implementing such approaches, since we have shown that without them the ROLARR analysis would indeed have been confounded by the learning effects. Trial registration International Standard Randomised Controlled Trial Number (ISRCTN) registry, ID: ISRCTN80500123. Registered on 27 May 2010.

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We consider the problem of determining an optimal release schedule for a production facility as it transitions from producing an older product to a newer one. We use simple queueing ideas to model the impact of the new product introduction on the throughput rate and cycle time of both products. We then incorporate the learning gained from accumulated experience in producing the new product to formulate a deterministic optimization model for release planning during product transitions. Since the model is nonlinear and non-convex we use a genetic algorithm to obtain near-optimal solutions. The structure of the optimal solution provides insights into the relationships between the different costs involved in the model. Computational experiments show that the release planning model produces high-quality solutions in reasonable CPU times, and reflects the behavior of the system realistically. The principal insight is that careful management of releases during product transitions has the potential to mitigate many of the adverse effects frequently observed in practice such as longer cycle times for both old and new products. [ABSTRACT FROM AUTHOR]

This paper attempts to fill a research gap of literature by constructing the dynamic model into which both ex ante and ex post patent value indicators are incorporated. A patent renewal model is tested using a large set of Pharmaceutical patents granted by the European Patent Office between 1996 and 2009. We test five ex ante indicators and single ex post indicator including family size, patent backward citations, backward references to non-patent literature, number of claims, number of inventors, renewal fee, patent age, application year, and the ex post indicator forward citations. Empirical findings show that three citation related indicators, family size, and the number of claims are positively associated with patent values, while the number of inventors, renewal fee, patent age, and application year are negatively correlated. In addition, forward citations seem to have persistent learning effects on patent values. [ABSTRACT FROM AUTHOR]

We hypothesize and test for a U-shaped relation between the cost of equity capital and the level of disclosure in annual report narratives. Using a computer-generated word-count-based index of the level of disclosure in U.K. annual report narratives, we document a negative relation with the cost of equity capital at low levels of disclosure, and a positive relation at higher levels of disclosure, together implying the presence of an optimal level of disclosure. We interpret the positive relation at higher levels of disclosure as evidence of uninformative clutter increasing the cost of equity capital. Additional analyses indicate the presence of both firm-level learning and regulatory corporate reporting initiatives as factors shaping adjustments towards optimum levels of disclosure. [ABSTRACT FROM AUTHOR]