bluuebunny/crossref_metadata_2025_split huggingface dataset:
Citizenship and new urban realities
Authors: Indovina Francesco 
Published: April 2022

AbstractCities have always been the place where 'diversity' has settled, within a substantial ethnic, cultural, religious and linguistic identity. The author's emphasis on the need for a cultural policy on migration and welcoming not in 'moral' terms but in relation to a mutual interest between different peoples and cultures. A policy which highlights 'diversity' and allows for peaceful coexistence and sharing, provides the basis for what can be called a policy of coexistence, or a strategy for coexistence. Such a policy can only be the result of a collective debate and effort which can propose new ideas of citizenship.
Barriers to collecting student participation and completion data for a national STEM education grant program in the United States: a multiple case study
Authors: Ruhf Robert J. , Williams Cody T. , Zelinsky Megan , Becho Lyssa Wilson 
Published: April 2022

Abstract
Background
Billions of dollars are spent annually on grant-funded STEM (science, technology, engineering, and mathematics) education programs. These programs help students stay on track toward STEM careers when standard educational practices do not adequately prepare them for these careers. It is important to know that reliable and accurate student participation and completion data are being collected about these programs. This multiple case study investigates how student data are collected and reported for a national STEM education program in the United States, the National Science Foundation (NSF) Advanced Technological Education (ATE) program. Our overall aim is to provide insights to funding agencies, STEM education faculty, and others who are interested in addressing issues related to the collection and reporting of student participation and completion data within their own contexts. Emphasis is placed on the barriers encountered in collecting participation and completion data, particularly with regard to unduplicated participation counts and marketable credential data. The ATE program was selected for this study because there is already a mechanism (known as the ATE Survey) in place for annually collecting systematic data across all projects within the program.

Results
A multiple case study, including interviews of primary investigators, allowed for in-depth analysis of the ATE Survey’s point-in-time data on project-level participation in various activities, and for identification of the following barriers to tracking student-level data: lack of time and help to gather these data, lack of a consistent system for tracking students across different institutions, and a perceived lack of guidance from the funding agency about what data to track. We also saw that different data are needed from different projects to determine a project’s true impact. Defining “success” the same way across all projects is inadequate.

Conclusions
Although, due to the limited sample size, these findings cannot be generalized to the larger ATE population, they provide specific insights into the various barriers that projects encounter in collecting participation and completion data.
The chitosan/carboxymethyl cellulose/montmorillonite scaffolds incorporated with epigallocatechin-3-gallate-loaded chitosan microspheres for promoting osteogenesis of human umbilical cord-derived mesenchymal stem cell
Authors: Wang Jin , He Wubo , Tan Wen-Song , Cai Haibo 
Published: April 2022

AbstractEpigallocatechin-3-gallate (EGCG) is a plant-derived flavonoid compound with the ability to promote the differentiation of human bone marrow-derived mesenchymal stem cells (MSCs) into osteoblasts. However, the effect of EGCG on the osteogenic differentiation of the human umbilical cord-derived mesenchymal stem cells (HUMSCs) is rarely studied. Therefore, in this study, the osteogenic effects of EGCG are studied in the HUMSCs by detecting cell proliferation, alkaline phosphatase (ALP) activity, calcium deposition and the expression of relevant osteogenic markers. The results showed that EGCG can promote the proliferation and osteogenic differentiation of the HUMSCs in vitro at a concentration of 2.5–5.0 μM. Unfortunately, the EGCG is easily metabolized by cells during cell culture, which reduces its bioavailability. Therefore, in this paper, EGCG-loaded microspheres (ECM) were prepared and embedded in chitosan/carboxymethyl cellulose/montmorillonite (CS/CMC/MMT) scaffolds to form CS/CMC/MMT-ECM scaffolds for improving the bioavailability of EGCG. The HUMSCs were cultured on CS/CMC/MMT-ECM scaffolds to induce osteogenic differentiation. The results showed that the CS/CMC/MMT-ECM scaffold continuously released EGCG for up to 22 days. In addition, CS/CMC/MMT-ECM scaffolds can promote osteoblast differentiation. Taken together, the present study suggested that entrainment of ECM into CS/CMC/MMT scaffolds was a prospective scheme for promotion osteogenic differentiation of the HUMSCs.
Graphical Abstract
Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments
Authors: Dzhivelikian Evgenii , Latyshev Artem , Kuderov Petr , Panov Aleksandr I. 
Published: April 2022

AbstractBiologically plausible models of learning may provide a crucial insight for building autonomous intelligent agents capable of performing a wide range of tasks. In this work, we propose a hierarchical model of an agent operating in an unfamiliar environment driven by a reinforcement signal. We use temporal memory to learn sparse distributed representation of state–actions and the basal ganglia model to learn effective action policy on different levels of abstraction. The learned model of the environment is utilized to generate an intrinsic motivation signal, which drives the agent in the absence of the extrinsic signal, and through acting in imagination, which we call dreaming. We demonstrate that the proposed architecture enables an agent to effectively reach goals in grid environments.
Individual differences in skill acquisition and transfer assessed by dual task training performance and brain activity
Authors: Reddy Pratusha , Shewokis Patricia A. , Izzetoglu Kurtulus 
Published: April 2022

AbstractAssessment of expertise development during training program primarily consists of evaluating interactions between task characteristics, performance, and mental load. Such a traditional assessment framework may lack consideration of individual characteristics when evaluating training on complex tasks, such as driving and piloting, where operators are typically required to execute multiple tasks simultaneously. Studies have already identified individual characteristics arising from intrinsic, context, strategy, personality, and preference as common predictors of performance and mental load. Therefore, this study aims to investigate the effect of individual difference in skill acquisition and transfer using an ecologically valid dual task, behavioral, and brain activity measures. Specifically, we implemented a search and surveillance task (scanning and identifying targets) using a high-fidelity training simulator for the unmanned aircraft sensor operator, acquired behavioral measures (scan, not scan, over scan, and adaptive target find scores) using simulator-based analysis module, and measured brain activity changes (oxyhemoglobin and deoxyhemoglobin) from the prefrontal cortex (PFC) using a portable functional near-infrared spectroscopy (fNIRS) sensor array. The experimental protocol recruited 13 novice participants and had them undergo three easy and two hard sessions to investigate skill acquisition and transfer, respectively. Our results from skill acquisition sessions indicated that performance on both tasks did not change when individual differences were not accounted for. However inclusion of individual differences indicated that some individuals improved only their scan performance (Attention-focused group), while others improved only their target find performance (Accuracy-focused group). Brain activity changes during skill acquisition sessions showed that mental load decreased in the right anterior medial PFC (RAMPFC) in both groups regardless of individual differences. However, mental load increased in the left anterior medial PFC (LAMPFC) of Attention-focused group and decreased in the Accuracy-focused group only when individual differences were included. Transfer results showed no changes in performance regardless of grouping based on individual differences; however, mental load increased in RAMPFC of Attention-focused group and left dorsolateral PFC (LDLPFC) of Accuracy-focused group. Efficiency and involvement results suggest that the Attention-focused group prioritized the scan task, while the Accuracy-focused group prioritized the target find task. In conclusion, training on multitasks results in individual differences. These differences may potentially be due to individual preference. Future studies should incorporate individual differences while assessing skill acquisition and transfer during multitask training.
Successful salvage surgery followed by second ALK–TKI after alectinib failure in a patient with ALK-positive NSCLC
Authors: Hashimoto Hiroshi , Komori Kazuyuki , Kameda Koji , Taguchi Shinichi , Ozeki Yuichi 
Published: April 2022

Abstract
Background
Anaplastic lymphoma kinase (ALK)–tyrosine kinase inhibitors (TKIs) have been approved for the therapy of locally advanced non-small cell lung cancer (NSCLC) caused by ALK rearrangement. However, its treatment after failure of initial ALK–TKI therapy remains controversial.

Case presentation
A 47-year-old woman with a hemosputum was diagnosed with adenocarcinoma of the left lung (cT2bN3M0, stage IIIB). Gene mutation analysis indicated positive ALK translocation. Alectinib was selected as the first-line treatment. Although the treatment effect was determined as a partial response, the main tumor regrew. Alectinib was discontinued, and salvage surgery was performed without causing morbidity. The pathological diagnosis was pleomorphic carcinoma without lymph node metastasis (yp-T2bN0). After surgery, lorlatinib was administered as the second-line treatment for 8 months until the patient could not tolerate continuation. Computed tomography scan revealed no lung cancer recurrence 14 months after discontinuation.

Conclusions
Our experience with this case suggests that salvage surgery after alectinib treatment followed by lorlatinib therapy may be effective for initially unresectable ALK-positive NSCLC.
Corporate pledgeable asset ownership and stock price crash risk
Authors: Jung Hail , Choi Sanghak , Lee Junyoup , Woo Sanggeum 
Published: April 2022

AbstractWe investigate how a firm’s corporate pledgeable asset ownership (CPAO) affects the risk of future stock price crashes. Using pledgeable asset ownership and crash risk data for a large sample of U.S. firms, we provide novel empirical evidence that a firm’s risk of a future stock price crash decreases with an increase in its pledgeable assets. Our main findings are valid after conducting various robustness tests. Further channel tests reveal that firms with pledgeable assets increase their collateral value, thereby enhancing corporate transparency and limiting bad news hoarding, resulting in lower stock price crash risk. Overall, the results show that having more pledgeable assets enables easier access to external financing, making it less likely that managers will hoard bad news.
Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading
Authors: Li Yuze , Jiang Shangrong , Li Xuerong , Wang Shouyang 
Published: April 2022

AbstractIn recent years, Bitcoin has received substantial attention as potentially high-earning investment. However, its volatile price movement exhibits great financial risks. Therefore, how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers. However, empirical works in the Bitcoin forecasting and trading support systems are at an early stage. To fill this void, this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market. Two primary steps are involved in our methodology framework, namely, data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price. Results demonstrate that the proposed model outperforms other benchmark models, including econometric models, machine-learning models, and deep-learning models. Furthermore, the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation. The robustness of the model is verified through multiple forecasting periods and testing intervals.
Engaging local youths in humanitarian response is not a matter of if but how
Authors: Apollo Abigael , Mbah Marcellus Forh 
Published: April 2022

AbstractDespite being critical responders in humanitarian crises, local youths are continually left out of the humanitarian action agenda. This paper used a qualitative methodology to investigate local youths’ role in humanitarian response and their impacts and assessed how humanitarian actors influence the effectiveness of youth engagement. The data was collected through semi-structured interviews with local youths who participated in the Ebola response in Sierra Leone. Findings showed that young people are significantly contributing to crises response. However, they lack an enabling environment and support system to convert their skills into valuable humanitarian resources efficiently. Therefore, despite the rhetoric that many reports and policies reflect, this study establishes that the realities of youth engagement in humanitarian activities are often misunderstood and controlled for the self-interest of different actors other than youths themselves. It advocates for a renewed focus and support for young people’s skills as paramount for effective humanitarian response and building back resilient communities after emergencies. Besides, engaging local youths in tackling crises empowers them with transferable skills and stimulates their passion for participating in development issues within their communities.
Factors influencing COVID-19 testing: a qualitative study in Bhutan
Authors: Yangchen Sonam , Ha Solip , Assan Abraham , Tobgay Tashi 
Published: April 2022

Abstract
Background
The COVID-19 pandemic has reaffirmed an all-knowing truth—that health is central in the 2030 Sustainable Development Agenda. To fully control the infection in a community, accurate testing of suspected cases along with effective tracing and appropriate treatment (3Ts) is still crucial to slowing transmission of COVID-19 virus This study explored factors that influenced COVID-19 testing in Bhutan. The lessons learned from this study could serve as a roadmap to strengthen the current respond to COVID-19 and for future outbreaks, particularly in low- and middle-income countries.

Methods
The study employed an exploratory qualitative design. Data collection methods included interviews with key informants with a purposively selected sample of 20 participants. The interview findings were augmented by reviewing both published literature and unpublished documents.For the analysis of qualitative interviews, a hybrid approach of inductive and deductive coding and theme development was conducted to analyze findings. A tailored version of the WHO Health System Framework incorporating the Essential Public Health Function was used to guide data interpretation.

Results
Political will through the influence and leadership of the King of Bhutan played a crucial role in raising attention to the problem, and provision of adequate financial and technical relief to ensure that all people irrespective of their socioeconomic status do not pay to get tested of COVID-19. A compassionate leadership, Whole of Society approach is backed by the scientific community, functional health systems and community-based approaches, use of information technology for awareness creation and improved surveillance system, and fast-tracked COVID-19 testing service delivery.

Conclusion
Bhutan’s success relied greatly on multi-sectoral and systematic approach during policy design, implementation and monitoring, and active collaborative efforts involving consultation and engagements with a broad range of local (community members), national and global actors for accelerated COVID-19 testing. These efforts were made possible through improved health governance and leadership at all levels of the society.
Reliability of IEC 61850 based substation communication network architecture considering quality of repairs and common cause failures
Authors: Mathebula Vonani Clive , Saha Akshay Kumar 
Published: April 2022

AbstractMission-critical IEC 61850 system architectures are designed to tolerate hardware failures to achieve the highest reliability performance. Hence, multi-channel systems are used in such systems within industrial facilities to isolate machinery when there are process abnormalities. Inevitably, multi-channel systems introduce Common Cause Failure (CCF) since the subsystems can rarely be independent. This paper integrates CCF into the Markov reliability model to enhance the model flexibility to investigate synchronous generator intra-bay SCN architecture reliability performance considering the quality of repairs and CCF. The Markov process enables integration of the impact of CCF factors on system performance. The case study results indicate that CCF, coupled with imperfect repairs, significantly reduce system reliability performance. High sensitivity is observed at low levels of CCF, whereas the highest level of impact occurs when the system diagnostic coverage is 99% based on ISO 13849-1, and reduces as the diagnostic coverage level reduces. Therefore, it is concluded that the severity of CCF depends more on system diagnostic coverage level than the repair efficiency, although both factors impact the system overall performance. Hence, CCF should be considered in determining the reliability performance of mission-critical communication networks in power distribution centres.
Management of cotton bollworms Helicoverpa armigera and Earias vittella by entomopathogenic nematodes
Authors: NAGACHANDRABOSE Seenivasan 
Published: April 2022

Abstract
Background
The bollworm complex consisting of Helicoverpa armigera and Earias vittella is a major threat in cotton production globally. The habit of developing resistance to many insecticides including Bt transgenic cotton necessitates the exploration of an alternate strategy to manage bollworms. The entomopathogenic nematodes (EPN) Steinernema carpocapsae strain APKS2 and Heterorhabditis bacteriophora strains KKMH1 and TRYH1 at different concentrations of 1 × 109 infective juveniles (IJs)·hm−2, 2 × 109 IJs·hm−2, and 3 × 109 IJs·hm−2 in 500 L of water were evaluated as a foliar spray in fields naturally infested with H. armigera and E. vittella located at Eastern Block and and Cotton Research Farm of Tamil Nadu Agricultural University, Coimbaotre, India during October 2010–February 2011 and October 2011–February 2012, respectively.

Results
In general, all three tested EPN strains reduced the larval population of H. armigera and E. vittella; reduced square and boll damage; and subsequently increased cotton yield compared with the untreated control. The S. carpocapsae APKS2 is most effective against H. armigera whereas both S. carpocapsae APKS2 and H. bacteriophora KKMH1 were equally effective against E. vittella. The higher dose of 3 × 109 IJs·hm−2 was highly significant in the reduction of H. armigera larvae. However, the doses 2 × 109 IJs·hm−2 and 3 × 109 IJs·hm−2 were equally effective for E. vittella control. The S. carpocapsae APKS2 at 3 × 109 IJs·hm−2 caused a 62.2% reduction of H. armigera larvae, 34% reduction of square damage, 58.5% reduction of boll damage, and yielded 45.5% more seed cotton than the untreated control plots. In E. vittella infested field, S. carpocapsae strain APKS2 and H. bacteriophora strain KKMH1 at 2 × 109 IJs·hm−2 resulted in 60.6%~62.4% larva reduction, 68.4%~70.7% square damage reduction, 66.6%~69.9% boll damage reduction and 45.9% yield increase over the untreated control. The effective EPN treatments were comparable to the chemical insecticide chlorpyriphos 20% emulsifiable concentrate spraying at 2 mL·L−1.

Conclusions
This study has shown that EPN have great potential in the management of the bollworm complex in cotton. Foliar spraying EPN strain S. carpocapsae (APKS2) at 3 × 109 IJs·hm−2 and S. carpocapsae (APKS2) or H. bacteriophora (KKMH1) at 2 × 109 IJs·hm−2 five times at10 days intervals are the best for the management of H. armigera and E. vittella, respectively.
TriCTI: an actionable cyber threat intelligence discovery system via trigger-enhanced neural network
Authors: Liu Jian , Yan Junjie , Jiang Jun , He Yitong , Wang Xuren , Jiang Zhengwei , Yang Peian , Li Ning 
Published: April 2022

AbstractThe cybersecurity report provides unstructured actionable cyber threat intelligence (CTI) with detailed threat attack procedures and indicators of compromise (IOCs), e.g., malware hash or URL (uniform resource locator) of command and control server. The actionable CTI, integrated into intrusion detection systems, can not only prioritize the most urgent threats based on the campaign stages of attack vectors (i.e., IOCs) but also take appropriate mitigation measures based on contextual information of the alerts. However, the dramatic growth in the number of cybersecurity reports makes it nearly impossible for security professionals to find an efficient way to use these massive amounts of threat intelligence. In this paper, we propose a trigger-enhanced actionable CTI discovery system (TriCTI) to portray a relationship between IOCs and campaign stages and generate actionable CTI from cybersecurity reports through natural language processing (NLP) technology. Specifically, we introduce the “campaign trigger” for an effective explanation of the campaign stages to improve the performance of the classification model. The campaign trigger phrases are the keywords in the sentence that imply the campaign stage. The trained final trigger vectors have similar space representations with the keywords in the unseen sentence and will help correct classification by increasing the weight of the keywords. We also meticulously devise a data augmentation specifically for cybersecurity training sets to cope with the challenge of the scarcity of annotation data sets. Compared with state-of-the-art text classification models, such as BERT, the trigger-enhanced classification model has better performance with accuracy (86.99%) and F1 score (87.02%). We run TriCTI on more than 29k cybersecurity reports, from which we automatically and efficiently collect 113,543 actionable CTI. In particular, we verify the actionability of discovered CTI by using large-scale field data from VirusTotal (VT). The results demonstrate that the threat intelligence provided by VT lacks a part of the threat context for IOCs, such as theActions on Objectivescampaign stage. As a comparison, our proposed method can completely identify the actionable CTI in all campaign stages. Accordingly, cyber threats can be identified and resisted at any campaign stage with the discovered actionable CTI.