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All‐solid‐state batteries (ASSBs), configured with solid electrolytes, have received considerable attention as a future energy solution across diverse sectors of modern society. Unlike conventional liquid electrolytes in particular, sulfide solid electrolytes have various advantages, such as high ionic conductivity (>10−3 S cm−1), good ductile properties, and thermal stability. Despite these advantages, the practical application of sulfide solid electrolytes in ASSBs is still limited due to their interfacial instability with commercial cathode materials. Unfortunately, the spontaneous formation of a space charge layer (SCL) at the interface between the cathode material and the solid electrolyte leads to heightened interfacial resistance, obstructing Li+ transport. To address this issue, proper interfacial engineering is still required to facilitate smooth Li+ migration across the interfaces. In this respect, various functional materials have been under exploration as buffer layers, which are intended to suppress the formation of the SCL at these interfaces. Herein, focus is given on the critical significance of these buffer layers between cathode materials and sulfide solid electrolytes in the development of ASSBs. Considering the present limitations, future research directions for next‐generation ASSBs are discussed. These insights are poised to offer valuable guidance for the strategic design and development of highly reliable ASSBs.

Abstract Rapid and efficient cell line development (CLD) process is essential to expedite therapeutic protein development. However, the performance of widely used glutamine‐based selection systems is limited by low selection efficiency, stringency, and the inability to select multiple genes. Therefore, an AND‐gate synthetic selection system is rationally designed using split intein‐mediated protein ligation of glutamine synthetase (GS) (SiMPl‐GS). Split sites of the GS are selected using a computational approach and validated with GS‐knockout Chinese hamster ovary cells for their potential to enable cell survival in a glutamine‐free medium. In CLD, SiMPl‐GS outperforms the wild‐type GS by selectively enriching high producers. Unlike wild‐type GS, SiMPl‐GS results in cell pools in which most cells produce high levels of therapeutic proteins. Harnessing orthogonal split intein pairs further enables the selection of four plasmids with a single selection, streamlining multispecific antibody‐producing CLD. Taken together, SiMPl‐GS is a simple yet effective means to expedite CLD for therapeutic protein production.

This review explores the challenges and advancements in the development of high‐energy lithium‐ion batteries (LIBs), particularly focusing on the electrochemical and structural stability of Ni‐rich cathode materials. Despite their potential to increase the energy density of LIBs, these cathode materials encounter issues such as irreversible phase transitions and structural degradation during cycling, which ultimately affect their electrochemical performance. Elemental doping/substitution has emerged as promising strategies to address these challenges. However, the precise mechanisms underlying their performance enhancement remain unclear. The objective is to elucidate the complex reaction mechanisms triggered by doping and substitution in Ni‐rich cathode materials by employing in situ operando analyses to uncover their effects on electrochemical behavior and structural integrity during cycling. This comprehensive investigation aims to clarify the roles of elemental dopants or substituents in the crystal structures of Ni‐rich cathode materials, thereby offering valuable insights for the structural engineering of cathode materials in high‐energy LIBs. By elucidating these intricate mechanisms, this review provides a practical roadmap for future research and significantly contributes to LIB technology by guiding material design and optimization strategies in the development of advanced LIBs.

Abstract Prior research shows that political uncertainty affects general economic and firm-related outcomes. However, little is known about how firm-specific political uncertainty is related to corporate risk-taking. Taking advantage of a recent construct and using a large sample of non-financial U.S. firms covering the period between 2002 and 2021, we find compelling evidence that firm-specific political uncertainty is positively related to corporate risk-taking, especially for larger firms. On average, a one standard deviation rise in the level of political risk at the firm-level results in a 2.53% increase in risk-taking. Further analysis shows that the impact of firm-level political uncertainty on corporate risk-taking is more pronounced for firms that spend more on corporate lobbying. Our results are significant and robust to alternative risk-taking measures and endogeneity tests.

Background: Cancer is a life-threatening disease in Korean women. Female cancer patients in Korea have reported poor physical functioning and psychological distress after diagnosis. However, some patients experience post-traumatic growth (PTG), which includes positive feelings and changes. Objective: The main purpose of this study was to examine the relationships between PTG and its associated factors in Korean female cancer patients to understand how female cancer patients achieve PTG while experiencing cancer diagnosis and treatment. This study aimed to improve the quality of life of many Korean female patients with cancer. Methods: This study investigated the structural model of the paths from the disruption of core beliefs (CBD), coping strategies, and resilience to PTG in a sample of female cancer patients. In total, 164 middle-aged women diagnosed with cancer were included in the final sample. Results: First, it was determined that the proposed structural model was substantial and had high fit indices. Second, problem-solving was positively associated by the CBD routes. Third, it was also favorably expected that problem-solving would lead to resilience and resilience would lead to PTG. These findings are important for developing future interventions for Korean female cancer patients and can be considered as an important variable to improve their PTG.

This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the Pacific Ocean, Indian Ocean, and Sea of Korea. The seawater temperature data consist of 1414 profiles including 1218 normal and 196 abnormal profiles. This dataset has an imbalance problem in which the amount of abnormal data is insufficient compared to that of normal data. Therefore, we generated abnormal data with oversampling techniques using duplication, uniform random variable, Synthetic Minority Oversampling Technique (SMOTE), and autoencoder (AE) techniques for the balance of data class, and trained Interquartile Range (IQR)-based, one-class support vector machine (OCSVM), and Multi-Layer Perceptron (MLP) models with a balanced dataset for anomaly detection. In the experimental results, the F1 score of the MLP showed the best performance at 0.882 in the combination of learning data, consisting of 30% of the minor data generated by SMOTE. This result is a 71.4%-point improvement over the F1 score of the IQR-based model, which is the baseline of this study, and is 1.3%-point better than the best-performing model among the models without oversampling data.

IntroductionExpressive Writing (EW) is an intervention that focuses on individuals’ writing down their thoughts and feelings about trauma or stressful events. Meta-analyses on EW studies have confirmed that EW has a positive effect. However, the heterogeneity of studies is high, so many studies have investigated boundary conditions and moderators. One of these moderators is the cultural difference in emotional suppression. Since EW focuses on the expression of suppressed thoughts and emotions, its effect might be slightly different for people in Asian cultures who show a high tendency to suppress their emotions. This study attempted to confirm the effect size of the EW interventions in Korea and examine whether these studies have different effect size from those based on Western cultures.MethodA total of 29 studies published in Korea until 2021 were analyzed. The effect size was calculated using the “dmetar,” “meta,” and “metafor” packages of the statistical program R 4.0.4.ResultsThe results were as follows. First, the effect size of EW intervention was 0.16, and we found that studies in the Korean context showed no significant difference from studies based on western meta-analysis. Second, the moderating variables that influenced the EW intervention were the writing type, the number of sessions, the time per session, and the measurement time.DiscussionThe results of this study suggest that EW interventions benefit Koreans. And it is at least harmless and has a positive effect considering the efficiency and conciseness of interventions. Furthermore, the finding shows that EW interventions can be helpful even in the general population without apparent psychological problems. By considering moderators, we could structure more effective form of EW interventions for Koreans.

IntroductionWomen still being a minority in engineering majors, they are reported to face discriminatory treatment in a collegiate environment. “Chilly climate,” referring to such a sexist environment, may have a negative impact on women’s mental health, academics, and careers. But, what exactly is it that female students in engineering perceive as chilly, and how chilly is it? This study aimed to explore the chilly campus climate perceived by female undergraduate engineering students in South Korea using the concept mapping method.MethodsSemi-structured interviews were conducted with 13 participants enrolled for more than four semesters at four-year coeducational universities. After extracting 52 representative statements, the participants were asked to classify them according to content similarity and rate the influence of each statement on their perception of the chilly climate. For concept mapping analysis, multidimensional scaling analysis (ALSCAL), hierarchical cluster analysis (Ward’s method), and non-hierarchical cluster analysis (K-means method) were performed.ResultsFifty-two statements were extracted under the following four clusters: (i) “Exclusion and alienation inherent in the culture (Cluster 1),” (ii) “Sexual objectification and lack of gender sensitivity (Cluster 2),” (iii) “Male-centered academic situations (Cluster 3),” and (iv) “Prejudice and generalization (Cluster 4).” A concept map was two-dimensional: an X-axis named “context dimension,” with “task: academic” and “non-task: social” at both ends, and a Y-axis named “sexism dimension”, having “explicit” and “implicit” at both ends. The order of higher scores in the influence rating is as follows: Cluster 2, Cluster 3, Cluster 1, and Cluster 4.DiscussionThis study is significant because it conceptualizes the subjective experience of minorities in a collegiate environment and provides influence rating results for prioritized measures. The findings will be helpful in formulating educational policies, psychological counseling, and social advocacy activities. Future research should target larger populations, and cover more diverse cultures, majors, and age groups.

ObjectiveRapid changes in post-COVID-19 higher education have increased students’ academic stress. This study focused on graduate students’ academic stress in South Korea and compared the results for Korean graduate students and those for international graduate students.MethodUsing the online survey results, the study verified the relationships between faculty interactions, a sense of belonging, and academic stress among Korean and international graduate students using a mediating effects analysis and a multigroup path analysis.ResultsThe results were as follows. First, Korean students experienced greater academic stress, faculty interactions, and a sense of belonging, but no statistically significant difference was observed. Second, a sense of belonging had a mediating effect on the relationship between faculty interactions and academic stress. Unlike in previous studies, all paths were found to be statistically significant. Faculty interactions had a negative effect on academic stress and a positive effect on a sense of belonging. A sense of belonging had a negative effect on academic stress. Third, the comparison of Korean and international graduate students showed that international students had a greater effect of faculty interactions on academic stress.ConclusionThrough these results, we explored the post-COVID-19 academic lives of Korean and international graduate students in South Korea and built grounds for effective interventions for alleviating academic stress.

IntroductionThis study investigated the moderating effects of resilience on the relationship between academic stress and school adjustment among Korean adolescents. We examined the moderating effect for the total scores of school adjustment and the two subscales of school adjustment (academic adjustment and general school adjustment).MethodsData were obtained in Pusan, Korea, using the convenience sampling method, and a total of 674 participants' responses were used for the final analysis. Hierarchical regression analysis and multiple regression analysis were performed to examine our research questions.ResultsThe results of the study showed that academic stress was a significant predictor of school adjustment among Korean adolescents. More specifically, academic stress was related to poor school adjustment. The study also found that resilience has a significant mediating effect on the relationship between academic stress and school adjustment.DiscussionOur findings suggest that resilience is likely to alleviate the harmful effect of academic stress on school adjustment. Finally, implications for school-based intervention were discussed for providing practical academic assistance.

IntroductionAlthough prior studies have supported the effectiveness of Multi-Tiered Systems of Support (MTSS) on addressing social, emotional, behavioral, and academic challenges faced by youth at-risk, educators using MTSS often do not consider contextual factors which may also influence youth at-risk and the interventions targeting them. This study thus aimed to identify youth at-risk who should be referred to targeted instructions within MTSS by examining the risk profiles of Korean adolescents. Based on the identified risk profiles, we also tried to investigate the effect of contextual factors on deciding youth at-risk and confirm whether and/or what contextual factors should be considered when implementing targeted interventions for them.MethodTo accomplish the research goal, a latent profile analysis on risk factors of Korean adolescents was performed, using the first year data of “Korean Children and Youth Panel Study (KCYPS) 2018.”ResultsFour risk profiles were identified, using low academic motivation, low academic behavior, attention deficit, aggression, social withdrawal, and depression as indicators: the high risk, M-SEB (Moderate-social, emotional, & behavioral) risk, M-ACA (Moderate-academic) risk, and low risk group. The covariates of this study, home and school environmental variables, worked as predictors of adolescents included in the high group.ConclusionThe results of this study suggest students in the high risk group (16.8%) should be given targeted instructions combining academic and SEB support within MTSS so as to prevent negative outcomes in the future among all adolescents. Those instructions need to be planned with consideration of contextual factors accompanied by teacher's careful understanding of social dynamics surrounding each student.

Social-emotional learning (SEL) is an educational model for improving social-emotional competences of all students and a long-term education program connecting school, home, and community. Although there has been active research to establish evidence-based practice (EBP) of SEL programs worldwide, the quality of SEL intervention studies which is an integral part of evaluating EBP was rarely investigated. In addition, prior meta-analytic studies focused only on the effectiveness of SEL programs conducted in Western society. In this sense, in order to contribute to establishing EBP of SEL programs, the current research sought to analyze both quality and effectiveness of SEL intervention studies conducted in Korea where SEL has been investigated and applied in classroom since 2010. To conduct this study, we selected 22 peer-reviewed articles (about 23 SEL programs) and analyzed their quality by Evidence-Based Intervention (EBI) indicators and calculated effect sizes using a meta-analysis. The results of the quality analysis revealed that SEL intervention studies had some limitations with a statistical analysis, use of measurement, a control group design, intervention fidelity, and external validity. The global effect size of SEL programs was 0.27, and the results from the effect size analyses by controlling variables showed that group compositions, the number of sessions, and session length were accountable for the variability of effect sizes. Based on these findings, we discussed the directions for future research and practice on the EBP of SEL programs that can be appreciated by researchers worldwide.

Within Organization for Economic Cooperation and Development (OECD) nations, South Korea has the highest suicide rate for which immediate prevention measures are sought including effective therapeutic counseling interventions. As such, the present study explored and examined experienced South Korean counselors' perception of therapeutic interventions for the prevention or delaying of completed suicide, using concept mapping methodology. The semi-structured interviews were provided to 15 study participants of experienced counselors having a minimum of 5 years of professional counseling career and at least 10 suicide crisis counseling sessions. A total of 77 statements were extracted with 8 major clusters: “Securing Safety,” “Active Advocacy for Client,” “Coping Skills Training,” “Conceptualization of Suicide Crisis,” “Emotional Identification and Validation,” “Empowerment,” “Counselor Self-Disclosure,” “Counselor Self-Awareness and Regulation.” From the results, the present study described unique findings in Korean counselors' perceptions of suicide crisis therapeutic intervention. Study limitations and future implications are further discussed.

With the emergence of a new concept called 'Internet Gaming Disorder' in DSM-5 and ICD-11, related research is underway around the globe. However, not enough literature on Internet gaming addiction has considered the diversity in game genres. Internet gaming addiction may take on a different form according to the particular characteristics of a game being played. To better understand adolescent Internet gaming addiction, this study sought to identify the differences in Internet gaming addiction and personality characteristics based on the game genre played. A total of 3,217 elementary and middle school students across Korea participated in the survey that included the Maladaptive Game Use Scale and the Adolescent Personality Questionnaire. ANOVA analysis revealed that Internet gaming addiction and personality characteristics varied according to the game genre played. In particular, post-hoc tests showed that Real-Time Strategy (RTS) and First-Person Shooter (FPS) game users have higher levels of tolerance, withdrawal, and neglect of everyday life compared to other genres such as Role-Playing Game (RPG), Racing, and Arcade/Shooting. Also, Internet gamers users of particular genres showed significantly lower self-esteem (Arcade/Shooting), conscientiousness (Racing), empathy (RPG), and sense of community (Racing and RTS) scores than comparison group. The implications of the study results were discussed with a special emphasis on Internet gaming addiction interventions.

Introduction: Globally, more people are spending time on the Internet and gaming since the outbreak of the Coronavirus Disease 2019 (COVID-19). Consequently, concerns about developing behavioral addiction of adolescents have been raised. Such risk could be greater for adolescents in South Korea where the majority of adolescents have access to the Internet and own a smartphone. In fact, statistics indicate that Korean youths are spending significantly more time on the Internet and gaming during the COVID-19 pandemic. Previous studies on the patterns of time spent on the Internet and Internet gaming show inconsistent results. The aim of this study is to investigate the latent profiles of the Internet and Internet game usage among adolescents in South Korea.Method: Data from a national survey on elementary and middle school students across South Korea were used. The sample consists of 3,149 respondents, and 2,984 responses were analyzed after removing missing responses. Latent profile analysis was performed to investigate the number of latent profiles for the Internet and Internet game usage time. To validate the profiles, differences in problematic gaming behavior, sex, and neuroticism were examined.Results: Seven profiles were found: Casual User, Moderate User, Smartphone User, Internet User, PC Internet Gamer, Heavy User, and Excessive User. Validation of the profiles indicated differences in problematic gaming behavior, sex, and neuroticism among selected profiles.Conclusion: This study presented different profiles of the Internet and Internet game usage among adolescents in South Korea. Profiles with higher game usage time scored higher in problematic game use compared to other profiles. Males were more likely to be in the profiles with high gaming time, and females were more likely to be in Internet and Smartphone User profiles. The results indicate that Internet and Internet gaming usage patterns could be classified by the type of device used and the content of the Internet.

The number of English Language Learners (ELLs) has been growing worldwide. ELLs are at risk for reading disabilities due to dual difficulties with linguistic and cultural factors. This raises the need for finding practical and efficient reading interventions for ELLs to improve their literacy development and English reading skills. The purpose of this study is to examine the evidence-based reading interventions for English Language Learners to identify the components that create the most effective and efficient interventions. This article reviewed literature published between January 2008 and March 2018 that examined the effectiveness of reading interventions for ELLs. We analyzed the effect sizes of reading intervention programs for ELLs and explored the variables that affect reading interventions using a multilevel meta-analysis. We examined moderator variables such as student-related variables (grades, exceptionality, SES), measurement-related variables (standardization, reliability), intervention-related variables (contents of interventions, intervention types), and implementation-related variables (instructor, group size). The results showed medium effect sizes for interventions targeting basic reading skills for ELLs. Medium-size group interventions and strategy-embedded interventions were more important for ELLs who were at risk for reading disabilities. These findings suggested that we should consider the reading problems of ELLs and apply the Tier 2 approach for ELLs with reading problems.

ObjectivesThis study developed an information and communication technology (ICT)-based comprehensive health and social-needs assessment (CHSNA) system based on the International Classification of Functioning, Disability, and Health (ICF) with the aim of enhancing person-centered community care for community residents and supporting healthcare professionals and social workers who provide healthcare and social services in the community.MethodsItems related to a CHSNA tool were developed and mapped with ICF codes. Experts validated the CHSNA system design and process using the Delphi method, and a pilot test of the initial version of the system was conducted.ResultsThe following three steps of CHSNA were embedded in the system, which had a user-friendly screen and images: basic health assessment, life and activity assessment, and in-depth health assessment. The assessment results for the community residents were presented with visualized health profiles, including images, graphs, and an ICF model.ConclusionsThe developed CHSNA system can be used by healthcare professionals, social workers, and community residents to evaluate the reasoning underlying health and social needs, to facilitate the identification of more appropriate healthcare plans, and to guide community residents to receive the best healthcare services. A CHSNA system can improve the implementation of standardized terminology utilizing the ICF and the accuracy of needs assessments of community residents.

Data-based instruction (DBI) is an ongoing process to utilize students' data for determining when and how to intensify intervention. It is an educational approach that is suggested as effective to enhance achievements of struggling learners, particularly for those who did not respond to intensive intervention in usual ways. In Korea, DBI was introduced and applied for students with learning difficulties especially since 2000 when the first Korea curriculum-based measurement (CBM) was developed as the name of Basic Academic Skills Assessment. Despite a number of studies accumulated since then, there has been a lack of research that examined the level of evidence-based practice (EBP) of DBI research. Thus, the present study sought to synthesize the DBI research so far in Korea by analyzing the effectiveness of DBI for school-aged students with learning difficulties via meta-analysis and evaluating the quality of the research. In this study, a total of 32 single-subject design studies were used. Multilevel meta-analysis revealed that the mean effect size of DBI was statistically significant (B = 1.34) and there was significant variance across participants in effect sizes. The results from the conditional model showed that exceptionality type, the number of sessions, and the length of each session were significantly accountable for the variability of effect sizes. In addition, the results of the qualitative analysis revealed the acceptable quality of the overall DBI research with some limitations. Based on these findings, implications and study limitations were discussed.

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a precursor to eventual depression as a disease. Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. We have selected a wearable band with multiple sensors for monitoring elderly people. Depression questionnaire has been surveyed periodically to be used as the labels. Instead of working with depression patients, we recruited 14 single household elderly people from a nearby community. The wearable band provided daily activity and biometric data for 71 days. From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. One general model is generated to be used as the baseline for the initial model deployment. Personal models are also generated for model refinement. The general model has a high recall of 80% in an MLP model. Individual models achieved an average recall of 82.7%. In this study, we have demonstrated that we can generate depressed mood prediction models with data collected from real daily living. Our work has shown the feasibility of using a wearable band as an unobtrusive depression monitoring sensor even for elderly people.

The purpose of this study is to examine and identify effective variables related to single-subject reading interventions for the struggling readers. This study conducted meta-analysis of 84 single-subject research articles of which 54 were published in Korean and 30 were published in English. Utilizing the Improvement Rate Difference (IRD), a total of 586 effect sizes were produced with the mean effect size of 0.77. Intervention variables were categorized into two types – participant-related and intervention-related variables. Collected studies were analyzed for each effect size calculation. Variables having large effects within grade levels, disability types, implementer and session duration are discerned along with further discussion and educational implications.

Surface mount technology is an important process in modern electronic circuit manufacturing. Quality control problems have arisen in this area because of the increased design and processing complexity of electronic circuits. Identifying the cause of a fault shortly after its occurrence is critical; however, human fault analysis is inaccurate and time-consuming. Here, we propose a data analysis method that provides actionable information that can easily be interpreted to facilitate rapid identification of fault cause in surface mount technology. The proposed method divides each input variable into a certain number of partitions, and then, the proportion of faults in a partition is calculated in comparison to the proportion of faults in the entire data set. The analytical results are provided to the user with a list that includes the fault causes and a corresponding density histogram for visualization. Real-world surface mount technology data were employed for a case study, in which raw data were preprocessed into an integrated data set consisting of 14,847 rows and 12,929 columns. The proposed method showed reasonable results in approximately 65 s, and the visualization of the results provided a suitable basis for intuitive interpretation, thus demonstrating the method’s ability to generate an efficient analysis in a practical application.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis is an autoimmune disorder which affects small- and, to a lesser degree, medium-sized vessels. ANCA-associated vasculitis encompasses three disease phenotypes: granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA). This classification is largely based on clinical presentations and has several limitations. Recent research provided evidence that genetic background, risk of relapse, prognosis, and co-morbidities are more closely related to the ANCA serotype, proteinase 3 (PR3)-ANCA and myeloperoxidase (MPO)-ANCA, compared to the disease phenotypes GPA or MPA. This finding has been extended to the investigation of biomarkers predicting disease activity, which again more closely relate to the ANCA serotype. Discoveries related to the immunopathogenesis translated into clinical practice as targeted therapies are on the rise. This review will summarize the current understanding of the immunopathogenesis of ANCA-associated vasculitis and the interplay between ANCA serotype and proposed disease biomarkers and illustrate how the extending knowledge of the immunopathogenesis will likely translate into development of a personalized medicine approach in the management of ANCA-associated vasculitis.

Machine learning has been applied successfully for faulty wafer detection tasks in semiconductor manufacturing. For the tasks, prediction models are built with prior data to predict the quality of future wafers as a function of their precedent process parameters and measurements. In real-world problems, it is common for the data to have a portion of input variables that are irrelevant to the prediction of an output variable. The inclusion of many irrelevant variables negatively affects the performance of prediction models. Typically, prediction models learned by different learning algorithms exhibit different sensitivities with regard to irrelevant variables. Algorithms with low sensitivities are preferred as a first trial for building prediction models, whereas a variable selection procedure is necessarily considered for highly sensitive algorithms. In this study, we investigate the effect of irrelevant variables on three well-known representative learning algorithms that can be applied to both classification and regression tasks: artificial neural network, decision tree (DT), and k-nearest neighbors (k-NN). We analyze the characteristics of these learning algorithms in the presence of irrelevant variables with different model complexity settings. An empirical analysis is performed using real-world datasets collected from a semiconductor manufacturer to examine how the number of irrelevant variables affects the behavior of prediction models trained with different learning algorithms and model complexity settings. The results indicate that the prediction accuracy of k-NN is highly degraded, whereas DT demonstrates the highest robustness in the presence of many irrelevant variables. In addition, a higher model complexity of learning algorithms leads to a higher sensitivity to irrelevant variables.