Original Database Research Articles

Optimization of random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations

The stability evaluation of underground entry-type excavations is a prerequisite of the entry-type mining method, which directly affects whether workers can be provided with a safe and reliable working environment and whether subsequent mining operations can be carried out normally. The design and stability assessment of entry-type excavations in current mining engineering largely relies on an empirical design method called the critical span graph, which has been widely applied in the initial span design of various cut and fill stopes. In recent years, with the wide application of various intelligent algorithms in the field of mine engineering, models based on intelligent algorithms provide new research methods and ideas for the assessment of rock stability in entry-type excavations. This study aims to introduce several hybrid models based on the random forest (RF) algorithm into the stability evaluation work to find new data-driven methods with higher accuracy to update the critical span graph. To pursue better classification performance, this paper selects three optimization strategies, namely multi-verse optimizer (MVO), grey wolf optimizer (GWO) and moth-flame optimization (MFO) algorithm, to optimize two core parameters of RF, and establishes three corresponding hybrid models, abbreviated as MVO-RF, GWO-RF and MFO-RF, based on the database containing 399 samples from seven Canada mines. There are two input parameters in the database, i.e., opening span and rock mass condition (expressed as RMR), and the output parameter is rock mass stability, which is specifically divided into three categories: stable, potentially unstable and unstable. In addition, five commonly used measurement indexes applicable to multiclassification problems were adopted to verify the classification ability of the models, i.e., the accuracy (ACC), precision calculated using macro-average (PREM), recall calculated using macro-average (RECM), F1 score calculated using macro-average (F1M) and Kappa index (Kappa). The results indicate that the three hybrid models performed well based on the test set accounting for 25 % of the original database, in which the accuracy of the MFO-RF model was the highest: ACC = 0.9300; PREM = 0.9288; RECM = 0.8983; F1M = 0.9116; Kappa = 0.8666. To evaluate whether the three optimization strategies can effectively improve the performance of RF and judge the degree of improvement, the performance of an unoptimized RF model was discussed in this study. In addition, two support vector machine (SVM) models with different kernel functions were selected as references for performance evaluation. The results indicated that compared with the RF and two SVM models, the classification accuracy of the three hybrid models was obviously more satisfactory. The classification accuracy of the three hybrid models reached 0.91, which was sufficient to explain the excellent classification ability of these models. After tuning the RF hyperparameters of each hybrid model, the critical span graph was further updated according to the optimized classification models, which was the focus of this research. By comparing the critical span graphs obtained by the three hybrid models with the single RF model and two kinds of SVM models, it is certain that the three hybrid models proposed in this paper, MVO-RF, GWO-RF and MFO-RF, are promising in the study of evaluating the stability of entry-type excavations and may be deemed auxiliary decision tools to define the stability region of the critical span graph.

Process of Changes with Renewal/Loss of Tenement Houses in densely built-up areas of wooden buildings

Urban regeneration is key to restoring areas damaged by natural disasters. The research of Professor Toshiya Yamamoto, Department of Architecture, Meiji University, Japan, encompasses the promotion of the renovation of rows of houses called Nagaya, urban planning and transformation, housing reconstruction and regional revitalisation. His research is conducted in the urban area of Kyojima, Sumida Ward, Tokyo, which is a traditional Japanese living environment. A key concern in the restoration of Nagaya is ensuring that the buildings are as safe as possible so that the impact of earthquakes can be minimised. Another important element of the work is to promote a symbiotic relationship between residents and the community, with strong links between art projects and restoration. Yamamoto and the team's work will include performing analyses of the reconstruction project underway in the area of the Great East Japan Earthquake, analysing map data and the creation and categorisation of an original database constructed from building information and registration data. This data will then be repeatedly analysed in different relevant contexts. The goal of the research is to improve the structural understanding of recent urban transformation and evaluate the urban policies and plans that have strongly influenced it. The team also developed a manual for producing so-called 'evacuation maps'.

Pregnancy and childbirth during incarceration: A qualitative systematic review of lived experiences.

Incarcerated individuals who experience pregnancy or childbirth in correctional facilities face unique considerations for obstetric care and consequently are at greater risk of adverse maternal and fetal outcomes. To characterise patient experiences regarding pregnancy and childbirth during incarceration via qualitative synthesis. Medline-OVID, EMBASE, CINAHL, Sociological Abstracts, Social Work Abstracts, Web of Science, Scopus and PsycInfo were systematically searched from inception to 24 December 2020. Supplementary searches were performed using the Scopus database. Only original, peer-reviewed literature was examined. Eligible studies were assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. After screening 4173 original database citations, 24 studies that met inclusion criteria were included and analysed via thematic analysis. The 24 studies included perspectives from 645 female patients who had experienced incarceration, 69 healthcare providers and 70 prison staff. Key patient-reported concerns for the well-being of pregnant individuals during incarceration included mental health challenges, dehumanisation of prenatal care and delivery, lack of privacy, stigma, psychological trauma, lack of emotional support and shackle usage during pregnancy and/or labour. The studies reported a lack of support for patients to access female correctional officers or guards, privacy during intimate examinations, timely medical care and support for breastfeeding. Above all, the psychological trauma of separation from one's newborn after birth was of utmost devastation. Our systematic review highlights the dire need for accountability and interventions to improve pregnancy and childbirth care for incarcerated individuals. This systematic review describes lived experiences of pregnancy & childbirth during incarceration, including dehumanisation, psychological trauma, and use of shackles.

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images

Background and Objective:Automatic detection of stenosis on X-ray Coronary Angiography (XCA) images may help diagnose early coronary artery disease. Stenosis is manifested by a buildup of plaque in the arteries, decreasing the blood flow to the heart, increasing the risk of a heart attack. Convolutional Neural Networks (CNNs) have been successfully applied to identify pathological, regular, and featured tissues on rich and diverse medical image datasets. Nevertheless, CNNs find operative and performing limitations while working with small and poorly diversified databases. Transfer learning from large natural image datasets (such as ImageNet) has become a de-facto method to improve neural networks performance in the medical image domain.Methods:This paper proposes a novel Hierarchical Bezier-based Generative Model (HBGM) to improve the CNNs training process to detect stenosis. Herein, artificial image patches are generated to enlarge the original database, speeding up network convergence. The artificial dataset consists of 10,000 images containing 50% stenosis and 50% non-stenosis cases. Besides, a reliable Fréchet Inception Distance (FID) is used to evaluate the generated data quantitatively. Therefore, by using the proposed framework, the network is pre-trained with the artificial datasets and subsequently fine-tuned using the real XCA training dataset. The real dataset consists of 250 XCA image patches, selecting 125 images for stenosis and the remainder for non-stenosis cases. Furthermore, a Convolutional Block Attention Module (CBAM) was included in the network architecture as a self-attention mechanism to improve the efficiency of the network.Results:The results showed that the pre-trained networks using the proposed generative model outperformed the results concerning training from scratch. Particularly, an accuracy, precision, sensitivity, and F1-score of 0.8934, 0.9031, 0.8746, 0.8880, 0.9111, respectively, were achieved. The generated artificial dataset obtains a mean FID of 84.0886, with more realistic visual XCA images.Conclusions:Different ResNet architectures for stenosis detection have been evaluated, including attention modules into the network. Numerical results demonstrated that by using the HBGM is obtained a higher performance than training from scratch, even outperforming the ImageNet pre-trained models.

Justice from below: corporate accountability in Argentina

ABSTRACT Argentina has taken a protagonist role in corporate accountability for crimes against humanity committed during the past authoritarian regime (1976-1983). This study examines in-depth the factors that allowed for those advances. It highlights the role of victims, human rights groups, and their advocates in demanding justice for gross human rights violations perpetrated by an alliance of economic and state actors involved in them. It considers the role institutional innovators within the judicial realm played to advance these demands, translating them into legal actions. This combination of forces ‘from below’ has made Argentina a leader in corporate accountability, capable of overcoming barriers posed by a powerful veto by the business sector. Not all from below processes advance victims’ rights, however. Using an original database of cases, the article develops an Archimedes' Lever approach for explaining how cases move along an accountability continuum. Specifically, in the right context and with the right tools, even relatively weak victims in the Global South can lift the weight of corporate accountability. The article concludes by highlighting the tools that are transferable to other country contexts.

Influence of construction age on seismic vulnerability of masonry buildings damaged after 2009 L'Aquila earthquake

The aim of this study is to describe the evolution of vulnerability over the years for masonry buildings characterized by a good quality layout and/or a regular texture. To this aim, data collected shortly afterwards the L'Aquila 2009 earthquake are considered, recently released by the Italian Department of Civil Protection (DPC) through the Da.D.O. (Database di Danno Osservato, Database of Observed Damage) platform (Dolce et al., 2019) [1].The taxonomy has been defined reflecting the need to consider all the parameters available from post-earthquake inspections and the obtainment of reliable and homogeneous sample.A time-consuming data processing has been performed to obtain a generalized version of the original database, which has been integrated with census data to avoid bias in vulnerability analysis. Then, damage analysis has been done considering 5 + 1 damage grades defined for the whole building based on the conversion of damage for vertical structures in sight of the classification of European Macroseismic Scale. The analysis of mean damage values reveals the general trends as a function of the main influential parameters, i.e. construction age and horizontal structural types, having fixed the vertical structural type and the quality layout.Vulnerability curves were derived assuming a lognormal statistical model and peak ground acceleration as intensity measure, through a minimization procedure of the distance between predicted and observed mean damage.In addition, the general criteria established to define the taxonomy had allowed to accomplish a direct comparison also between vertical structures, namely buildings with good quality and regular texture analysed herein and those with bad quality and irregular layout thoroughly analysed in Del Gaudio et al. (2021) [2]; fully characterizing the behaviour of masonry buildings under lateral loads.

Relocation Decisions in Uncertain Times: Brexit and Financial Services

abstract This article examines the impact of uncertainty and profound political, economic, and regulatory changes on the process of geographic reorganization of the financial industry in the course of Brexit. It draws on historic lessons of massive relocations within the financial industry in Europe to conjecture three scenarios: (1) concentrated relocation to build a new European lead financial center (FC), (2) least necessary relocation to meet regulatory requirements for operation in the EU single market, and (3) selective relocation and cumulative functional specialization of regional FCs. Drawing on official statistics, corporate and media reports, as well as on qualitative interviews and participant observation in the field, we build an original database of published plans and confirmed practices of geographic reorganization. Our analysis of the relocations of financial service firms from London to five leading FCs on the European continent supports the least necessary relocation as well as selective relocation scenarios. We conclude that Brexit-induced reorganization actually reproduces the existing geographic architecture while simultaneously deepening the divisions of labor among the established European FCs.

A bibliometric analysis of the research on preeclampsia in the first two decades of the twenty-first century.

Preeclampsia still remains one of the leading causes of maternal and perinatal mortality worldwide. Despite the concerted efforts of researchers, only a little improvement has been seen. Clinical decision-making is based on the published literatures. With the explosive growth of medical documents in recent decades, a bibliometric method is essential for assessing the intellectual contributions, major components and potential trends. Web of Science Core Collections was selected as the original database and datasets were retrieved consisting of literatures published from 2000 to 2020. Different bibliometric software were employed to visualize the co-authorship network, citation analysis and research theme detection. A total of 25497 articles and 3668 reviews were obtained. Despite the number of publications increased annually, the quantity of high-quality contributions did not elevate accordingly. Clinical practitioners should be alerted to the false bloom of achievements and the yield of improvement in future research. Nicolaides Kypros H was found to be the most productive and influential researcher. University of Pittsburgh was the most productive institution whereas Harvard University showed its leading academic status. America located at the central point in global collaboration and scholarship network. Reference citation analysis revealed the top landmark articles. Moreover, keywords co-occurrence analysis and burst detection certificated the lack of novel themes in this field, which needs further efforts. This study provides the overall landscape of science mapping in recent two decades in the field of preeclampsia, with the aim of identifying evolution of research topics and promoting potential concentration or collaboration in the future.

Against Putin and Corruption, for Navalny and the “Revolution”?

This article seeks to provide a better understanding of the dynamics of the nationwide protests that appeared in Russia as a result of the large-scale political campaigns of 2017–18. On the basis of an original database devoted to six protests, organized in this period by different anti-systemic opposition leaders and organizations, the study explores the turnout and geographic scope of these events and the repertoire of frames that were used to mobilize the protesters. The analysis contrasts three types of frames (an anti-corruption protest frame, election campaign event frame, and anti-systemic protest frame) and demonstrates that appropriate framing was a necessary condition of successful protest mobilization. In combination with other factors, such as the quality of protest organization and the impact of repressive actions of the authorities, the changes of protest frames contributed to the protests’ turnout dynamics. Alexei Navalny, the most popular anti-systemic leader, succeeded in organizing the initial mobilization by framing it as an anti-corruption protest, but then, under increasing repression, the opposition failed to convert this dissent into a longer-term campaign with broader electoral or anti-systemic frames.

Measuring the responsiveness of the SF-36 in the Cardiac and Pulmonary Rehabilitation Literature: Combined Results of a Comparative Effectiveness Review and Meta-Analysis

Research Objectives Objective; To investigate the responsiveness of Patient Outcome Measures (POMs) in Cardiac (CR) and Pulmonary (PR) Rehabilitation programs. Design Data Sources: Data was sourced from the main medical databases with the search terms; Rehabilitation programs, Quality of Life measures and Cardiac and Pulmonary Rehabilitation. Setting Study Selection: 1557 articles were selected from the original database search of study terms. The study team decided articles were excluded if the Quality of Life POMs were other than questionnaires, if the rehabilitation did not involve a multi-disciplinary team, and if the outcome of interest was not rehabilitation (e.g. drug therapy). Articles included were studies involving outpatient rehabilitation programs and those with a pre to post rehabilitation assessment. Participants N/A. Interventions Data extraction: The 83 articles for the comparative effectiveness review used a modified version of the Cochrane Database assessment of bias. The meta-analysis conducted correlation analysis between SF-36 pre and post “within” (per interventional group) mean scores for the CR literature. In the PR literature a correlation analysis between SF-36 and Chronic Respiratory Questionnaire (CRQ) “within” change scores was undertaken. Main Outcome Measures N/A. Results Data synthesis: The most commonly used POM was the SF-36. In the CR literature the SF-36 MCS domain is the most responsive of the composite SF-36 domains. In the individual domains Role Emotional scored r = 0.52, p <= 0.001 with only 27% of the variance explained and Role Physical with r = 0.49, p <=0.005 had only 24% of the variance explained. In the PR literature Spearman's rank correlation coefficient shows that SF-36 PCS has a weaker correlation to the CRQ at 0.39, than the SF-36 MCS which was 0.63. Conclusions The surveyed literature found no "gold standard" POM for either CR or PR and the meta-analysis found that the SF-36 is not suited as a pre to post program assessment tool for CR and PR. Author(s) Disclosures The authors are not aware of any conflicts of interest at this time. Objective; To investigate the responsiveness of Patient Outcome Measures (POMs) in Cardiac (CR) and Pulmonary (PR) Rehabilitation programs. Data Sources: Data was sourced from the main medical databases with the search terms; Rehabilitation programs, Quality of Life measures and Cardiac and Pulmonary Rehabilitation. Study Selection: 1557 articles were selected from the original database search of study terms. The study team decided articles were excluded if the Quality of Life POMs were other than questionnaires, if the rehabilitation did not involve a multi-disciplinary team, and if the outcome of interest was not rehabilitation (e.g. drug therapy). Articles included were studies involving outpatient rehabilitation programs and those with a pre to post rehabilitation assessment. N/A. Data extraction: The 83 articles for the comparative effectiveness review used a modified version of the Cochrane Database assessment of bias. The meta-analysis conducted correlation analysis between SF-36 pre and post “within” (per interventional group) mean scores for the CR literature. In the PR literature a correlation analysis between SF-36 and Chronic Respiratory Questionnaire (CRQ) “within” change scores was undertaken. N/A. Data synthesis: The most commonly used POM was the SF-36. In the CR literature the SF-36 MCS domain is the most responsive of the composite SF-36 domains. In the individual domains Role Emotional scored r = 0.52, p <= 0.001 with only 27% of the variance explained and Role Physical with r = 0.49, p <=0.005 had only 24% of the variance explained. In the PR literature Spearman's rank correlation coefficient shows that SF-36 PCS has a weaker correlation to the CRQ at 0.39, than the SF-36 MCS which was 0.63. The surveyed literature found no "gold standard" POM for either CR or PR and the meta-analysis found that the SF-36 is not suited as a pre to post program assessment tool for CR and PR.

The relevance of bibliometric indicators and academic criteria in economics

Research Question: Are the bibliometric indicators included in the Journal Citation Reports (JCR) and the academic criteria in the Economics domain relevant from the point of view of the assessment of the quality of the research activity? Motivation: This study is motivated by the fact that the research landscape changed significantly during the last years, with the growth of open access publishers. In addition, the number of journals and the value of the bibliometric indicators included in the JCR are growing. As such, the relevance of the academic criteria is affected. Data: Data for this study were collected from the JCR for 50 journals edited by five publishers. Tool: An original database created by the author starting from the JCR was used as a tool for the present study. Findings: The results have shown that the fast-publish editor selected for the study registers a high number of self-cites within the publishing house, and also that the self-cites are mostly directed towards the recently published papers, so that they influence the value of the bibliometric indicators. Practical implications: The importance of the findings of such research stems from providing evidence on the rise of a new type of publisher, open access, fast growing, fast publishing, fast citing. Also, the behaviour of Romanian academics and the criteria they have to comply with in their careers are affected. The results have implications for the authorities setting the criteria for academics.

The diffusion of energy technologies. Evidence from renewable, fossil, and nuclear energy patents

Technology innovation is widely recognised as a critical means in tackling climate change and fulfilling energy policy objectives. The objective of this paper is twofold: first, to provide a descriptive analysis of innovation in energy technology across countries and sectors and over time; and second, to explore the determining factors of patented knowledge diffusion of energy technologies by distinguishing between renewables and other energy patents, i.e., fossil and nuclear patents) thorough a regression analysis. The data employed in this paper consists of an original database on renewables and other energy patents applied by firms in the period 1990–2015 and contained in PATSTAT. By drawing on patent citations as an indicator of knowledge diffusion and focusing on characteristics extracted from patent documents, a set of econometric models is estimated. Our results show that those patents containing more citations to previous scientific literature and patents attain greater diffusion. Joint patents with other firms or universities exert a negligible effect on technology regarding renewables. Co-ownership with universities has a negative effect on the diffusion of other types of energy technology. Several policy implications can be determined from our results: for example, the justification for policies oriented towards enhancing the incorporation of scientific knowledge and co-inventorship in energy innovation.

Application of a Neural Network to Store and Compute the Optical Properties of Non-Spherical Particles

Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (∼127 GB) to that of the DNN parameters (∼20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.

Solving Collective Action Problems under Separated and Shared Powers: The Benefits of Consolidating Executive Budgetary Powers, 1895–1940

The Budget and Accounting Act of 1921 consolidated executive budgetary powers within the presidency and Congress. I evaluate how the Act reforms altered the nature of executive budgetary influence, using an original historical database from fiscal years 1895 to 1940. I show that these reforms were not only consistent with both political branches’ objective to empower presidents to rein in profligate executive budget proposals under the decentralized budgetary system but also afforded presidents greater budgetary influence when seeking higher funding levels relative to Congress. This mutually beneficial power-sharing arrangement between the president and Congress stands in contrast with existing accounts that these reforms favored one branch at the expense of the other. Finally, I offer evidence that this arrangement was made possible by solving collective action problems within the executive branch that reduced the capacity of seasoned agency heads to shape budgetary outcomes during the postreform era.

System Invariant Method for Ultrasonic Flaw Classification in Weldments Using Residual Neural Network

The industrial use of ultrasonic flaw classification using neural networks in weldments must overcome many challenges. A major constraint is the use of numerous systems, including a combination of transducers and equipment. This causes high complexity in the datasets used in the training of neural networks, which decreases performance. In this study, the performance of a neural network was enhanced using signal processing on an ultrasonic weldment flaw dataset to achieve system invariance. The dataset contained 5839 ultrasonic flaw signals collected by various types of transducers connected to KrautKramer USN60. Every signal in the dataset was from 45 FlawTech/Sonaspection weldment specimens with five types of flaw: crack, lack of fusion, slag inclusion, porosity, and incomplete penetration. The neural network used in this study is a residual neural network with 19 layers. The performance evaluation of the same network structure showed that the original database can achieve 62.17% ± 4.13% accuracy, and that the invariant database using the system invariant method can achieve 91.45% ± 1.77% accuracy. The results demonstrate that using a system invariant method for ultrasonic flaw classification in weldments can improve the performance of a neural network with a highly complex dataset.

A Bayesian Regularization Approach to Predict the Quality of Injection-Moulded Components by statistical SVM for Online Monitoring system

To evaluate the quality of injection-molded components, conventional approaches are costly, time-consuming, or based on statistical process control characteristics that are not always accurate. Machine learning might be used to categorise components based on their quality. In order to accurately estimate the quality of injection moulded components, this study uses a SVM classifier. In addition, the form of the spare components after the working method product in simulation is classified as "qualified" or "unqualified". The quality indicators have an excellent association with data recordings from the original database of various sensors such as pressure and temperature used in the proposed network model for online prediction. The outliers are removed from the input original data to minimize the deviation of precision or prediction accuracy of the model performance metrics. Data points in the "to-be-confirmed" region (which is in the fit line area) may be misjudged by this statistical SVM model since it is placed between the "qualified" and "unqualified" areas. This statistical procedure in the proposed SVM model also uses Bayesian regularisation to classify final components into distinct quality levels.

Effect of cancer treatment on sleep quality in cancer patients: A systematic review and meta-analysis of Pittsburgh Sleep Quality Index.

Sleep problems are one of the most common symptoms experienced by cancer patients. The causes of poor sleep quality might be due to treatment and its side effects. Thus, we conducted this systematic review and meta-analysis with the aims of investigating sleep quality during treatment in cancer patients. Comprehensive search strategy was conducted in the following original databases: PubMed, Web of Science (ISI), Scopus, Embase, PsycINFO, and Ovid, from 1950 to 15th February 2021. Studies that investigated the sleep quality during treatment in cancer patients were included. Two investigators extracted all relevant data, independently. For deriving mean difference, random-effects meta-analyses were used. We assessed quality of studies by Newcastle-Ottawa Scale (NOS). A total of 27 studies (1884 participants) were included in the syntheses on sleep quality. The mean global Pittsburgh Sleep Quality Index (PSQI) in cancer patients before the initiation of treatment was 7.11 (95% CI: 6.48, 7.74), during 8.31 (95% CI: 6.34, 10.27), after the treatment 7.10 (95% CI: 6.54, 7.66), and finally, the mean global PSQI in the time of follow-up was estimated 7.33 (95% CI: 6.27, 8.39); all the results were meaningful (P < 0.001). The mean difference showing the quality of sleep was better before the initiation of treatment compared to after the treatment. Cancer patients who underwent cancer treatment face lots of problems and adverse effects caused by treatment. Our results revealed that cancer patients experience poor quality of sleep during the whole trajectory of cancer even after a year from the initiation of treatment. After the end of treatment, sleep quality got better compared to during the treatment and returned to before the treatment level, but it is still poor and needs more sleep-related interventions to improve.

Entrepreneurship Education: The Effects of Challenge-Based Learning on the Entrepreneurial Mindset of University Students

The aim of this paper is to investigate the implications of Challenge-Based Learning programs on entrepreneurial skills, and on the mindset and intentions of university students, through a quantitative approach. Resorting to an original database, we analyzed the pre- and post-levels of entrepreneurial skills, mindset and intention of 127 students who attended a Challenge-Based Learning program. Results show a positive and significant effect of Challenge-Based Learning programs on the entrepreneurial mindset and skills—that is, financial literacy, creativity, and planning—of the students.

Transforming the Global Plastics Economy: The Role of Economic Policies in the Global Governance of Plastic Pollution

International policy discussions on plastic pollution are entering a new phase, with more than 100 governments calling for the launch of negotiations for a new global plastics agreement in 2022. This article aims to contribute to efforts to identify effective international policy levers to address plastic pollution. It takes stock of the evolution of views and perceptions on this complex and multi-faceted topic—from concerns about marine pollution and waste management towards new strategic directions that involve the entire plastics life-cycle and include climate and health impacts associated with the proliferation of plastics. It also traces the progressive development of responses—from voluntary approaches involving multiple stakeholders to national and international approaches focused on regulation. The paper is informed by desk research, a literature review and participation by the authors in informal and formal global governance processes on plastic pollution, the environment and development in the United Nations and World Trade Organization between 2019 and 2021. It also draws on empirical findings from a novel and original database on the life-cycle of plastic trade created by the authors. The paper argues that the important focus on downstream dimensions of plastic pollution—and strategies to address them—needs to be complemented by a broad life-cycle and “upstream” perspective that addresses plastic pollution at its source. It highlights the political economy tensions and inconsistencies at hand, observing that while some countries are taking concerted efforts to reduce pollution (including through bans on certain kinds of plastic and plastic products); to promote more circular plastic economies; and to reduce the carbon footprint of plastics (as part of a wider effort to decarbonize their economies), trade and investment in the plastic industry continues to rise. The paper argues that to reduce plastic pollution, emerging global governance efforts must integrate international environmental law and cooperation with a complementary and enabling global framework that addresses the economic, financial, industrial and trade policies needed to drive the necessary transformation of the plastics sector.

Protein cycling in the eastern tropical North Pacific oxygen‐deficient zone: A de novo‐discovery peptidomic approach

AbstractPeptides are identified using a de novo‐discovery approach in suspended and sinking particles from the eastern tropical North Pacific oxygen‐deficient zone (ODZ) and in a culture of a dominant autotroph from the region, the cyanobacterium Prochlorococcus. The benchmarking experiment with Prochlorococcus shows de novo peptides to be taxonomically specific, and thus of value in augmenting database‐driven approaches. Analysis of the suspended and sinking particles using the de novo‐discovery approach reveals the presence of fungal proteins in deep sinking particles that were not in our original search database, contributing to growing recognition that fungi may play important roles in marine organic matter cycling. Cyanobacterial peptides that have been post‐translationally modified were tracked to depth, where they contribute ~ 1% of the phylum‐level identifiable peptide pool in the sediment trap sample. The majority of peptides found at depth in the detrital pool are associated with membranes, indicating that cellular location is associated with early preservation within the detrital pool. Modified amino acids in sinking and suspended particles include high levels of deamidation, suggesting that partial extracellular degradation of protein could fuel observed anammox and contribute to pools of refractory organic nitrogen.