bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling the relationships between STEM learning attitude, computational thinking, and 21st century skills in primary school
Authors: Küçükaydın Menşure Alkış , Çite Hakan , Ulum Hakan 
Published: February 2024

AbstractStudents enter the science, technology, engineering, and mathematics (STEM) pipeline in primary school, but leak out of it over time for various reasons. To prevent leaks, it is important to understand the variables that affect attitudes towards STEM learning from an early age. This study sought to examine the predictors of young students' STEM learning attitudes. In the study, 493 primary school students (Mage = 9.62, SD = .72) from a Turkish sample were reached through a survey. We recruited our participants using the convenience sampling technique. Data were collected with the STEM learning attitude scale, the Multidimensional 21st Century Skills Scale, and the Computational Thinking (CT) test. Descriptive and correlational analyses were performed on the data. Then the relationship between variables was tested with a structural equation modeling. The results of the analyses showed that STEM learning attitudes and CT skills of primary school students demonstrated good fit indexes. Also results showed that twenty-first century skills mediated the relationship between STEM learning attitudes and CT skills. The results of the analysis are discussed, and recommendations are presented in terms of strengthening young students' place in the STEM pipeline.
Linguistic and non-linguistic factors impacting EMI academic success: a longitudinal study
Authors: Soruç Adem , Yuksel Dogan , Horzum Barış , McKinley Jim , Rose Heath 
Published: February 2024

AbstractThis study explored changes in English language proficiency and several non-linguistic factors during four years of English medium instruction (EMI) in two academic disciplines in a Turkish university setting. Moreover, it also investigated whether changes (if any) had a predictive impact on the academic success of EMI students. In addition, potential differences between disciplines were also investigated. The participants were 241 EMI students from Business Administration (n = 117) and Mechanical Engineering (n = 124) programmes. Our findings revealed that in addition to the language proficiency scores, various non-linguistic factors, including self-efficacy, ideal L2 self, motivation, self-regulation skills, and anxiety levels, changed throughout EMI education. However, only English proficiency and instrumental motivation emerged as positively significant predictors of EMI success.Our findings also revealed that the increase in participants' intrinsic motivation scores was a significant negative predictor of EMI success. These results are discussed and implications are given regarding the impact of linguistic and non-linguistic factors in EMI contexts.
Access Interval Prediction by Partial Matching for Tightly Coupled Memory Systems
Authors: Razilov Viktor , Wittig Robert , Matúš Emil , Fettweis Gerhard 
Published: February 2024

AbstractIn embedded systems, tightly coupled memories (TCMs) are usually shared between multiple masters for the purpose of hardware efficiency and software flexibility. On the one hand, memory sharing improves area utilization, but on the other hand, this can lead to a performance degradation due to an increase in access conflicts. To mitigate the associated performance penalty, access interval prediction (AIP) has been proposed. In a similar fashion to branch prediction, AIP exploits program flow regularity to predict the cycle of the next memory access. We show that this structural similarity allows for adaption of state-of-the-art branch predictors, such as Prediction by Partial Matching (PPM) and the TAgged GEometric history length (TAGE) branch predictor. Our analysis on memory access traces reveals that PPM predicts 99 percent of memory accesses. As PPM does not lend itself to hardware implementation, we also present the PPM-based TAGE access interval predictor which attains an accuracy of over 97 percent outperforming all previously presented implementable AIP schemes.
Multi-channels Prototype Contrastive Learning with Condition Adversarial Attacks for Few-shot Event Detection
Authors: Zhang Fangchen , Tian Shengwei , Yu Long , Yang Qimeng 
Published: February 2024

AbstractFew-shot Event Detection (FSED) is a sub-task of Event Detection that aims to accurately identify event types with limited training instances and enable smooth transfer to newly-emerged event types. Recently, the dominant works have used the prototypical network to accomplish this task and employ contrastive learning to alleviate the issue of semantically-close categories. Nevertheless, these methods still suffer from two serious problems: (1) inadequate learning of prototype representations resulting from limited training data; (2) hard-easy sample imbalance and categories imbalance caused by the large number of non-trigger word("O" tags) in the token-level classification task. To address the problems, this paper proposes the Multi-channels Prototype and Contrastive learning method with Conditional Adversarial attack, which introduces the improved multi-channels prototype and contrastive networks to alleviate the categories and hard-easy samples imbalance. Moreover, we devise a constrained adversarial attack to improve the problem of limited training data. Extensive experimental results show that our model performs better than other FSED methods. All the code and data will be available for online public access.
Technology sourcing ambidexterity in corporate venture capital: limitations of learning from open innovation
Authors: Anokhin Sergey Alexander , Hess Manuel , Wincent Joakim 
Published: February 2024

Abstract
In the rapidly evolving landscape of open innovation, understanding the dynamics of learning is pivotal for corporate success. Yet, the constraints and thresholds inherent in the novel technology sourcing processes remain under-explored. We study 163 firms during the third wave of corporate venture capital activity characterized by the primary focus on innovation to investigate the effects of technology sourcing ambidexterity (the simultaneous pursuit of internal and external sources of innovative ideas) on corporate patenting and realized innovation. Acknowledging limitations of organizational learning, our results indicate the presence of the inverted U-shaped relationship between technology sourcing ambidexterity and innovation and suggest that beyond a certain threshold, increases in ambidexterity are detrimental to organizational learning and corporate innovation because boundary conditions to experimentation as a key element to learning arise. Such restrictions are alleviated by organizational slack, which enhances organizational abilities to orchestrate resources and take calculated risks to go beyond existing internal competencies.
Optimal test case generation for boundary value analysis
Authors: Guo Xiujing , Okamura Hiroyuki , Dohi Tadashi 
Published: February 2024

AbstractBoundary value analysis (BVA) is a common technique in software testing that uses input values that lie at the boundaries where significant changes in behavior are expected. This approach is widely recognized and used as a natural and effective strategy for testing software. Test coverage is one of the criteria to measure how much the software execution paths are covered by the set of test cases. This paper focuses on evaluating test coverage with respect to BVA by defining a metric called boundary coverage distance (BCD). The BCD metric measures the extent to which a test set covers the boundaries. In addition, based on BCD, we consider the optimal test input generation to minimize BCD under the random testing scheme. We propose three algorithms, each representing a different test input generation strategy, and evaluate their fault detection capabilities through experimental validation. The results indicate that the BCD-based approach has the potential to generate boundary values and improve the effectiveness of software testing.
Metagenomic analysis of carbohydrate-active enzymes and their contribution to marine sediment biodiversity
Authors: López-Sánchez Rafael , Rebollar Eria A. , Gutiérrez-Ríos Rosa María , Garciarrubio Alejandro , Juarez Katy , Segovia Lorenzo 
Published: February 2024

AbstractMarine sediments constitute the world’s most substantial long-term carbon repository. The microorganisms dwelling in these sediments mediate the transformation of fixed oceanic carbon, but their contribution to the carbon cycle is not fully understood. Previous culture-independent investigations into sedimentary microorganisms have underscored the significance of carbohydrates in the carbon cycle. In this study, we employ a metagenomic methodology to investigate the distribution and abundance of carbohydrate-active enzymes (CAZymes) in 37 marine sediments sites. These sediments exhibit varying oxygen availability and were isolated in diverse regions worldwide. Our comparative analysis is based on the metabolic potential for oxygen utilisation, derived from genes present in both oxic and anoxic environments. We found that extracellular CAZyme modules targeting the degradation of plant and algal detritus, necromass, and host glycans were abundant across all metagenomic samples. The analysis of these results indicates that the oxic/anoxic conditions not only influence the taxonomic composition of the microbial communities, but also affect the occurrence of CAZyme modules involved in the transformation of necromass, algae and plant detritus. To gain insight into the sediment microbial taxa, we reconstructed metagenome assembled genomes (MAG) and examined the presence of primary extracellular carbohydrate active enzyme (CAZyme) modules. Our findings reveal that the primary CAZyme modules and the CAZyme gene clusters discovered in our metagenomes were prevalent in the Bacteroidia, Gammaproteobacteria, and Alphaproteobacteria classes. We compared those MAGs to organisms from the same taxonomic classes found in soil, and we found that they were similar in its CAZyme repertoire, but the soil MAG contained a more abundant and diverse CAZyme content. Furthermore, the data indicate that abundant classes in our metagenomic samples, namely Alphaproteobacteria, Bacteroidia and Gammaproteobacteria, play a pivotal role in carbohydrate transformation within the initial few metres of the sediments.
Evaluation of stability of (1R,2 S)-(−)-2-methylamino-1-phenyl-1-propanol hydrochloride in plasma and urine samples-inoculated with Escherichia coli using high-performance liquid chromatography (HPLC)
Authors: Almaary Khalid S. , Alharbi Naiyf S. , Al-Huwaymil Rayan M. , Kadaikunnan Shine , Alobaidi Ahmed S. , Khaled Jamal M. 
Published: February 2024

AbstractThe preservation of drug stability in biological evidence during the processes of collection and storage poses a substantial obstacle to the progress of forensic investigations. In conjunction with other constituents, the microorganisms present in the samples play a vital role in this investigation. The present investigation employed the high-performance liquid chromatography (HPLC) technique to assess the stability of (1R,2 S)-(–)-2-methylamino-1-phenyl-1-propanol hydrochloride in plasma and urine samples that were inoculated with Escherichia coli. These samples were subjected to storage conditions of 37 °C for 48 h and − 20 °C for a duration of 6 months. Minimal inhibitory concentration (MIC) and Minimal bactericidal concentration (MBC) of MPPH against E. coli were determined using microdilution method. The stability of MPPH in plasma and urine samples inoculated with E. coli was investigated using HPLC method. The results showed the MIC and MBC of MPPH were 87.5 ± 25 ppm and 175 ± 50 ppm, respectively. While MPPH remained stable in plasma for 48 h at 37 °C, it showed a notable decrease of about 11% in stability when stored in urine for the same period and temperature. From the beginning of the first month, a decrease in the stability of the compound appeared in all samples that were stored at − 20 °C, and the decrease reached 7% for plasma samples and about 11% for urine samples. The decrease in the stability reached its peak in the sixth month, reaching more than 30% and 70% of plasma and urine samples preserved at − 20 °C. This work concluded that E. coli can negatively affect the stability of MPPH in plasma and urine samples. This may lead to incorrect conclusions regarding the analysis of biological samples in criminal cases.
Energy security-related risks and the quest to attain USA’s net-zero emissions targets by 2050: a dynamic ARDL simulations modeling approach
Authors: Usman Ojonugwa , Ozkan Oktay , Alola Andrew Adewale , Ghardallou Wafa 
Published: February 2024

AbstractThe Russia-Ukraine war and other similar conflicts across the globe have heightened risks to the United States of America's (USA’s) energy security. However, little is known about the severity of the effect of energy security risks on the USA’s quest to attain net-zero emissions targets by 2050. To this end, we examine the effect of energy security risks on the load capacity factor (LCF) in the USA. Employing a time series dataset spinning from 1970 to 2018, the results of the Dynamic Autoregressive Distributed Lag (ARDL) simulations model suggest that energy security-related risk hampers the long-term net-zero emissions targets with its effect decreasing over time until it varnishes in about 5 years time. The results also show that foreign direct investment (FDI) inflows, renewable energy consumption, and green technology have long- and short-run positive effects on the LCF. Conversely, economic expansion and urbanization impede environmental quality by lowering the LCF both in the long run and short run. These findings are upheld by the outcomes of the multivariate quantile-on-quantile regression. Therefore, the study advocates for the consumption of renewable energy, investment in green technologies, and FDI inflows to mitigate energy security-related risks and attain the net-zero emissions targets by 2050 in the USA.
New race-free creatinine- and cystatin C-based equations for the estimation of glomerular filtration rate and association with cardiovascular mortality in the AtheroGene study
Authors: Ntaios George , Brederecke Jan , Ojeda Francisco M. , Zeller Tanja , Blankenberg Stefan , Schnabel Renate B. 
Published: February 2024

AbstractRenal function is associated with cardiovascular outcomes and mortality. Among equations used to eGFR, CKD–EPI equations show more accurate association with cardiovascular risk and mortality than MDRD. Recently, new CKD–EPI equations were proposed which do not include race and would be considered sufficiently accurate to estimate eGFR in clinical practice. It is unknown if these new race-free equations are comparably well associated with cardiovascular outcomes in high-risk individuals. The analysis was performed in the AtheroGene Study cohort including patients at high cardiovascular risk. eGFR was determined using the established as well as the recently developed formulas which are calculated without the otherwise existing coefficient for black race. The outcome was cardiovascular death. Analyses included Cox-proportional hazard regression and area-under-the-curve calculation. The analysis included 2089 patients followed up for a median of 3.8 years with a maximum of 6.9 years, corresponding to an overall period of 7701 patient-years. Cardiovascular death occurred in 93 (4.45%), corresponding to an annualized rate of 1.2/100 person-years. In all Cox regression analyses, the estimated adjusted GFR was an independent predictor of cardiovascular death. The equations which included cystatin C showed higher C-index compared to those which did not include cystatin C (0.75–0.76 vs. 0.71, respectively). The equations for the estimation of eGFR which include cystatin C are better associated with cardiovascular death compared to the race-free equations which include only creatinine. This finding adds on the related literature which supports the elimination of race in GFR-estimating equations, and promotion of the use of cystatin C.
Effects of observation mode on ratings of teaching quality in secondary mathematics classrooms
Authors: Jentsch Armin , Benecke Kirsten , Blömeke Sigrid , König Johannes , Kaiser Gabriele 
Published: February 2024

AbstractIn educational research, teaching quality is extensively studied because of its role of a mediator between teacher characteristics and student learning. However, empirical evidence on differences between video and live scoring of teaching quality is rare. In the present study, thirty lessons from 15 secondary mathematics classrooms in a German metropolitan area were observed. Lessons were scored both live in the classroom and using video recordings. Live and video scoring was conducted by (different) trained observers. Ratings were obtained with a “hybrid” observational instrument that covers generic and subject-specific characteristics of teaching quality in mathematics classrooms. Generalizability analysis and paired t tests were performed to investigate mode effects. The findings showed that in live scoring, classroom management was rated lower, and cognitive activation was rated higher. Rankings of lessons or classrooms were very similar across modes, and reliabilities did not differ to a meaningful extent either, except for classroom management reaching better results for live ratings. This suggests that based on the present findings, classroom observation performed with our hybrid framework of teaching quality generalizes across observation mode only under certain circumstances. Further research is necessary to better understand the relation between observation mode and teaching quality ratings, as well as the impact of the scoring procedures. We discuss the implications of our findings for educational research and practice.
Preoperative Strategies for Locally Advanced Colon Cancer
Authors: Nair Kanika G. , Kamath Suneel D. , Chowattukunnel Nivan , Krishnamurthi Smitha S. 
Published: February 2024

Opinion statementNeoadjuvant chemotherapy is safe for patients with locally advanced colon cancer (LACC). The FOxTROT trial demonstrated a reduction in residual and recurrent cancer at 2 years with neoadjuvant chemotherapy for patients with cT3-4 LACC. Preoperative chemotherapy should be avoided, if possible, for patients with dMMR LACC, as over 50% of dMMR cancers have no pathologic response. Early universal testing of MMR status is critical to selecting the appropriate neoadjuvant therapy. Concerns about CT staging of LACC have limited uptake of neoadjuvant chemotherapy, as approximately 25% of patients with cT3-T4 cancer on CT have low-risk stage II disease. Development of CT criteria for malignant nodes should reduce the risk of over-staging. A multidisciplinary approach is needed to identify patients for neoadjuvant therapy. Neoadjuvant immunotherapy is safe and results in dramatic pathologic responses in patients with dMMR LACC. Longer follow-up is needed to determine if the exceptionally high pathologic response rates observed will translate into long-term remission. Remarkably, neoadjuvant immunotherapy has been found to cause major pathologic responses in a subset of patients with pMMR LACC, indicating the potential to cure more patients with this common cancer. Patients with cT4 LACC, whether stage II or III, have a substantial risk of recurrence despite adjuvant fluoropyrimidine plus oxaliplatin chemotherapy. We recommend neoadjuvant systemic therapy for all patients with cT4b LACC (dMMR and pMMR). Features of T4b disease are routinely reported by radiology. We use three cycles of FOLFOX chemotherapy for patients with cT4b pMMR LACC, due to the high rate of compliance and improvement in residual and recurrent disease. Patients with cT4b dMMR LACC should receive neoadjuvant immunotherapy, if there are no contraindications. Clinical trials of neoadjuvant therapy for LACC are of great interest and should provide training for radiologists to identify eligible patients. Results are anticipated from multiple ongoing trials of neoadjuvant chemotherapy, immunotherapy, and targeted therapy for pMMR LACC and immunotherapy for dMMR LACC.
Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds
Authors: Panza Luigi , Bruno Giulia , Antal Gabriel , De Maddis Manuela , Russo Spena Pasquale 
Published: February 2024

AbstractThe quality of resistance spot welding (RSW) joints is strongly affected by the condition of the electrodes. This work develops a machine learning-based tool to automatically assess the influence of electrode wear on the quality of RSW welds. Two different experimental campaigns were performed to evaluate the effect of electrode wear on the mechanical strength of spot welds. The resulting failure load of the joints has been used to define the weld quality classes of the machine learning tool, while data from electrode displacement and electrode force sensors, embedded in the welding machine, have been processed to identify the predictors of the tool. Some machine learning algorithms have been tested. The most performing algorithm, i.e., the neural network, achieved an accuracy of 90%. This work provides important theoretical and practical contributions. First, the decreasing thermal expansion of the weld nugget as the electrode degradation advances results in a strong correlation between the difference of the maximum displacement value and the last value recorded during the welding and the relative failure load. Then, this work offers a practical decision support tool for manufacturers. In fact, the automatic detection of low-quality welds allows to reduce or eliminate unnecessary redundant welds, which are performed to compensate for the uncertainty of electrode wear. This leads to savings in time, energy, and resources for manufacturers. Finally, general recommendations for the timing of redressing or replacing the electrode are provided in the manuscript based on the company willingness to accept some non-compliant welds or not.
The role of spillovers when evaluating regional development interventions: evidence from administrative upgrading in China
Authors: Zhang Xiaoxuan , Li Chao , Gibson John 
Published: February 2024

Abstract
Direct effects of regional development interventions on targeted areas may be amplified by positive spillovers from elsewhere or offset by negative spillovers. Yet spillovers are often ignored in the applied literature, where impact analyses based on difference-in-differences typically treat spatial units as independent of their neighbours. We study spatial spillovers from a popular regional development intervention in China—converting counties to cities. China’s top-down approach lets only central government bestow city status on an area, with over ten percent of counties upgraded to cities in the last two decades. A growing literature estimates impacts of these conversions, with spatial units typically treated as independent of their neighbours. In contrast, our spatial econometric models use a 20-year panel for almost 2500 county-level units to allow indirect spillover effects on indicators of local economic activity. The positive direct effects on GDP and luminosity of a county being upgraded are amplified through positive indirect effects, especially in the eastern regions of China where economic activity and population are more densely concentrated. The models without spatial lags that ignore spillovers give estimated effects of converting counties to cities that are only two-fifths to two-thirds as large as the estimated effects coming from the spatial models.