Abstract: Governments across the OECD are investing significant resources to address the immediate and long-term effects of the COVID-19 pandemic. Given that the crisis has affected different age groups differently and that its repercussions will be felt by many for decades to come, governments need to adopt an integrated public governance approach to COVID-19 response and recovery efforts. This policy brief presents the views of a non-representative sample of 151 youth organisations from 72 countries, including 100 youth organisations based in 36 OECD countries, on how young people have been experiencing the crisis and related government action. It is complemented by an analysis of the measures adopted across 34 OECD countries and provides recommendations on how to deliver a fair, inclusive and resilient recovery for young people through a range of public governance approaches.

Abstract: There is a growing concern within the surface science community that the massive increase in the number of XPS articles over the last few decades is accompanied by a decrease in work quality including in many cases meaningless chemical bond assignment. Should this trend continue, it would have disastrous consequences for scientific research. While there are many factors responsible for this situation, the lack of insight of physical principles combined with seeming ease of XPS operation and insufficient training are certainly the major ones. To counter that, we offer a comprehensive tutorial written in the form of a step-by-step guide starting from experimental planning, through sample selection and handling, instrument setup, data acquisition, spectra analysis, and results presentation. Six application examples highlight the broad range of research questions that can be answered by XPS. The topic selection and the discussion level are intended to be accessible for novices yet challenging possible preconceptions of experienced practitioners. The analyses of thin film samples are chosen for model cases as this is from where the bulk of XPS reports presently emanate and also where the author's key expertise lies. At the same time, the majority of discussed topics is applicable to surface science in general and is, thus, of relevance for the analyses of any type of sample and material class. The tutorial contains ca. 160 original spectra and over 290 references for further reading. Particular attention is paid to the correct workflow, development of good research practices, and solid knowledge of factors that impact the quality and reliability of the obtained information. What matters in the end is that the conclusions from the analysis can be trusted. Our aspiration is that after reading this tutorial each practitioner will be able to perform error-free data analysis and draw meaningful insights from the rich well of XPS.

Abstract: This work proposes for the first time, a metric tool that gives prominence to sample preparation. The developed metric (termed AGREEprep) was based on 10 categories of impact that were recalculated to 0–1 scale sub-scores, and then used to calculate the final assessment score. The criteria of assessment evaluated, among others, the choice and use of solvents, materials and reagents, waste generation, energy consumption, sample size, and throughput. Assessment was also based on the possibility to differentiate between criteria importance by assigning them weights. The assessment procedure was performed using an open access, intuitive software that produced an easy-to-read pictogram with information on the total performance and structure of threats. A compiled version of the open access software can be obtained from mostwiedzy.pl/AGREEprep. The applicability of AGREEprep was successfully demonstrated using six different methods as case studies.

Abstract: The ten principles of GSP are presented with the aim of establishing a road map toward the development of overall greener analytical methodologies. Paramount aspects for greening sample preparation and their interconnections are identified and discussed. These include the use of safe solvents/reagents and materials that are renewable, recycled and reusable, minimizing waste generation and energy demand, and enabling high sample throughput, miniaturization, procedure simplification/automation, and operator's safety. Further, the importance of applying green metrics for assessing the greenness of sample preparation methods is highlighted, next to the contribution of GSP in achieving the broader goal of sustainability. Green sample preparation is sample preparation. It is not a new subdiscipline of sample preparation but a guiding principle that promotes sustainable development through the adoption of environmentally benign sample preparation procedures. • The ten principles of green sample preparation are presented. • Sustainability issues on solvents, reagents and materials are considered. • Fast, miniaturized, automated, in situ and low-energy methods are preferred. • Post-sample preparation configuration for analysis is considered. • Green metrics and the impact on sustainable development are discussed.

Abstract: Routine haplotype-resolved genome assembly from single samples remains an unresolved problem. Here we describe an algorithm that combines PacBio HiFi reads and Hi-C chromatin interaction data to produce a haplotype-resolved assembly without the sequencing of parents. Applied to human and other vertebrate samples, our algorithm consistently outperforms existing single-sample assembly pipelines and generates assemblies of similar quality to the best pedigree-based assemblies.

Abstract: This article explores the natural language generation capabilities of large language models with application to the production of two types of learning resources common in programming courses. Using OpenAI Codex as the large language model, we create programming exercises (including sample solutions and test cases) and code explanations, assessing these qualitatively and quantitatively. Our results suggest that the majority of the automatically generated content is both novel and sensible, and in some cases ready to use as is. When creating exercises we find that it is remarkably easy to influence both the programming concepts and the contextual themes they contain, simply by supplying keywords as input to the model. Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students. We further discuss the implications of OpenAI Codex and similar tools for introductory programming education and highlight future research streams that have the potential to improve the quality of the educational experience for both teachers and students alike.

Abstract: Nudge interventions have quickly expanded from academic studies to larger implementation in so‐called Nudge Units in governments. This provides an opportunity to compare interventions in research studies, versus at scale. We assemble a unique data set of 126 RCTs covering 23 million individuals, including all trials run by two of the largest Nudge Units in the United States. We compare these trials to a sample of nudge trials in academic journals from two recent meta‐analyses. In the Academic Journals papers, the average impact of a nudge is very large—an 8.7 percentage point take‐up effect, which is a 33.4% increase over the average control. In the Nudge Units sample, the average impact is still sizable and highly statistically significant, but smaller at 1.4 percentage points, an 8.0% increase. We document three dimensions which can account for the difference between these two estimates: (i) statistical power of the trials; (ii) characteristics of the interventions, such as topic area and behavioral channel; and (iii) selective publication. A meta‐analysis model incorporating these dimensions indicates that selective publication in the Academic Journals sample, exacerbated by low statistical power, explains about 70 percent of the difference in effect sizes between the two samples. Different nudge characteristics account for most of the residual difference.

Abstract: This study aimed at examining the impact of E-HRM on organizational health. It focused on telecommunications companies operating in Jordan. Data were primarily gathered through self-reported questionnaires created in Google Forms and distributed to a purposive sample of senior managers via email. AMOSv24 was used to test the study hypotheses. The results of the study show that E-HRM has a positive impact on organizational health. Based on the obtained results, the researchers recommend managers and decision-makers of the telecommunications companies in Jordan to invest in electronic human resources systems, which can help them fully implement human resources practices electronically, to obtain economic savings and to be able to attract talents. The study also highlights the importance of focusing more on the electronic training and development process in order to raise individuals’ practical capabilities, which is reflected in their creativity.

Abstract: This study aims to explore the non-linear renewables and carbon emission efficiency (CEE) nexus to optimize the energy transition path. Taking 32 developed countries that have proposed carbon neutrality targets as the research objects, the super-efficiency slacks-based measure (SE-SBM) model is first used to measure their CEE from 2000 to 2018. Then, a newly developed panel threshold model with interactive fixed effects (PTIFEs) is established to comprehensively explore the non-linear impact of renewable energy development (RED) on CEE. The results show that: (1) During the sample period, there are significant differences in CEE among countries, and most countries are inefficient. (2) On the whole, RED is conductive to CEE, but there is a significant threshold effect. Specifically, this positive effect decreases with energy consumption intensity, whereas it increases with financial development, RED, and CEE. (3) The heterogeneity analysis shows that the threshold effect persists across countries with different income levels, and the direction is consistent with the entire sample. Besides, as the incomes down, the positive correlation between RED and CEE is significantly diminished. This study provides a new perspective for optimizing the energy transition path.

Abstract: In Malaysia, manufacturing industry is a major contributor to the economic advancement. As a result, cutting-edge technology like the internet of things (IoT) is projected to have a significant impact on business operations and supply chain management (SCM). However, research into the influence of IoT deployment on supply chains and organizational performance is relatively sparse. Therefore, this study is to determine the relationship between benefits and challenges of IoT adoption and organizational performance. Furthermore, this study looks into the mediating role of supply chain performance in the relationship between IoT adoption benefits and challenges and organizational performance. The population of this study is comprised of 3019 manufacturing companies in Malaysia, while the minimum sample size needed is 43 manufacturing companies.1160 complete set of survey questionnaire were distributed through email and 63 responses received representing five per cent of response rate. Partial Least Square Structural Equation Modelling (PLS-SEM) is used to assess all of the study's hypotheses. The results of this paper support six out of the seven hypotheses tested. In conclusion, the manufacturing industry in Malaysia needs to be exposed more to the benefits of IoT rather than keep discussing its challenges. This study can be a guideline to the manufacturing companies in decision making for IoT adoption. The limitations and recommendation for future study is highlighted.

Abstract: The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

Abstract: Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.

Abstract: Abstract Public acceptance is a precondition for implementing taxes and laws aimed at mitigating climate change. However, it still remains challenging to understand its determinants for the climate community. Here, we use a meta-analytic approach to examine the role of public opinion about climate change taxes and laws. Fifteen variables were examined by synthesizing 89 datasets from 51 articles across 33 countries, with a total sample of 119,465 participants. Among all factors, perceived fairness and effectiveness were the most important determinants. Self-enhancement values and knowledge about climate change showed weak relationships and demographic variables showed only weak or close to zero effects. Our meta-analytic results provide useful insights and have the potential to inform climate change researchers, practitioners and policymakers to better design climate policy instruments.

Abstract: To date, public health policies implemented during the COVID-19 pandemic have been evaluated on the basis of their ability to reduce transmission and minimise economic harm. We aimed to assess the association between COVID-19 policy restrictions and mental health during the COVID-19 pandemic.In this longitudinal analysis, we combined daily policy stringency data from the Oxford COVID-19 Government Response Tracker with psychological distress scores and life evaluations captured in the Imperial College London-YouGov COVID-19 Behaviour Tracker Global Survey in fortnightly cross-sections from samples of 15 countries between April 27, 2020, and June 28, 2021. The mental health questions provided a sample size of 432 642 valid responses, with an average of 14 918 responses every 2 weeks. To investigate how policy stringency was associated with mental health, we considered two potential mediators: observed physical distancing and perceptions of the government's handling of the pandemic. Countries were grouped on the basis of their response to the COVID-19 pandemic as those pursuing an elimination strategy (countries that aimed to eliminate community transmission of SARS-CoV-2 within their borders) or those pursuing a mitigation strategy (countries that aimed to control SARS-CoV-2 transmission). Using a combined dataset of country-level and individual-level data, we estimated linear regression models with country-fixed effects (ie, dummy variables representing the countries in our sample) and with individual and contextual covariates. Additionally, we analysed data from a sample of Nordic countries, to compare Sweden (that pursued a mitigation strategy) to other Nordic countries (that adopted a near-elimination strategy).Controlling for individual and contextual variables, higher policy stringency was associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients β=0·014 [95% CI 0·005 to 0·023] for psychological distress; β=-0·010 [-0·015 to -0·004] for life evaluation). Pandemic intensity (number of deaths per 100 000 inhabitants) was also associated with higher mean psychological distress scores and lower life evaluations (standardised coefficients β=0·016 [0·008 to 0·025] for psychological distress; β=-0·010 [-0·017 to -0·004] for life evaluation). The negative association between policy stringency and mental health was mediated by observed physical distancing and perceptions of the government's handling of the pandemic. We observed that countries pursuing an elimination strategy used different policy timings and intensities compared with countries pursuing a mitigation strategy. The containment policies of countries pursuing elimination strategies were on average less stringent, and fewer deaths were observed.Changes in mental health measures during the first 15 months of the COVID-19 pandemic were small. More stringent COVID-19 policies were associated with poorer mental health. Elimination strategies minimised transmission and deaths, while restricting mental health effects.None.

Abstract: Objectives This report describes the creation of the NHANES 2017-March 2020 prepandemic data files, including the selection of the appropriate NHANES sample design (2015-2018) to create sample weights and variance units for public-use data files. Additionally, the development of a factor applied to the primary sampling units to adjust the 2017-March 2020 data to fit the NHANES 2015-2018 sample design is described. Analyses to assess representativeness of the target population were performed, and a simulation to replicate the impact of interrupted data collection using earlier NHANES cycles was undertaken. Analytic guidance specific to use for prepandemic data files is also included. .

Abstract: The aim of the study was to examine the impact of digital marketing capabilities on organizational ambidexterity by focusing on the Information Technology Sector in UAE. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a purposive sample of managers at different levels via email. This study was conducted structural equation modeling (SEM) to test the hypotheses, which represents a contemporary statistical technique for testing and estimating the relationship between factors and variables. The results showed that the highest impact on organizational ambidexterity was for strategic approach and data content infrastructure, followed by integrating customers with employees, and finally the lowest impact belonged to the process of improving performance. Based on the study findings, the researcher hopes that the decision-makers and managers define all tasks, roles and work procedures in companies through digital marketing systems to improve their organizational ambidexterity and enhance their performance.

Abstract: The development of detection methods for oriented object detection remains a challenging task. A considerable obstacle is the wide variation in the shape (e.g., aspect ratio) of objects. Sample selection in general object detection has been widely studied as it plays a crucial role in the performance of the detection method and has achieved great progress. However, existing sample selection strategies still overlook some issues: (1) most of them ignore the object shape information; (2) they do not make a potential distinction between selected positive samples; and (3) some of them can only be applied to either anchor-free or anchor-based methods and cannot be used for both of them simultaneously. In this paper, we propose novel flexible shape-adaptive selection (SA-S) and shape-adaptive measurement (SA-M) strategies for oriented object detection, which comprise an SA-S strategy for sample selection and SA-M strategy for the quality estimation of positive samples. Specifically, the SA-S strategy dynamically selects samples according to the shape information and characteristics distribution of objects. The SA-M strategy measures the localization potential and adds quality information on the selected positive samples. The experimental results on both anchor-free and anchor-based baselines and four publicly available oriented datasets (DOTA, HRSC2016, UCAS-AOD, and ICDAR2015) demonstrate the effectiveness of the proposed method.

Abstract: As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.

Abstract: Abstract Background Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculators. Results We present and discuss four parameters (namely level of confidence, precision, variability of the data, and anticipated loss) required for sample size calculation for prevalence studies. Choosing correct parameters with proper understanding, and reporting issues are mainly discussed. We demonstrate the use of a purposely-designed calculators that assist users to make proper informed-decision and prepare appropriate report. Conclusion Two calculators can be used with free software (Spreadsheet and RStudio) that benefit researchers with limited resources. It will, hopefully, minimize the errors in parameter selection, calculation, and reporting. The calculators are available at: ( https://sites.google.com/view/sr-ln/ssc ).

Abstract: PurposeThe purpose of the paper is to evaluate the essential role of corporate social responsibility (CSR) on SMEs' performance by exploring the mediating role of corporate image (CI), corporate reputation (CR) and customer loyalty (CL) between CSR and firm performance (FP) in the context of an emerging country.Design/methodology/approachBased on an extended literature review on CSR, CI, CR and CL studies, the authors evaluate the impact of these four constructs on SMEs' performance in an emerging market. The paper follows a quantitative approach. The study sample was composed of 482 responses covering top executives, managers and experts. The Smart PLS SEM version 3.3.2 was used to analyse the data of the small- and medium-sized enterprises (SMEs) of Vietnam in the year 2020–2021.FindingsThe authors' findings reveal significant and positive relationships amongst CSR, FP, CSR and CI, CSR and CR, CSR and CL, and most importantly, the findings add value to the current knowledge by exploring the mediating effect of CI, CR and CL between CSR and FP.Research limitations/implicationsThe study was conducted in Vietnam. As a result, the findings of the study might not be applicable for other countries, if the economic and environmental settings are different from that of Vietnam. Therefore, future research should consider for other countries, other regions. Second, due to the purpose and priority of the study, CI, CR, and CL was employed as mediators amongst the relationship between CSR and FP. Thus, future research should consider other mediators or moderators in such a relationship to see how CSR generates outcomes in the new associations.Practical implicationsThe study regarding the role of CSR in enhancing the performance of SMEs can motivate firm's chief executive officers (CEOs) to be proactive in getting involved and practising CSR in a consistent manner. Second, the above discussion draws a very important implication for the executive level, the management level of the enterprise, which enterprises should balance the interests of business, customers, other stakeholders, the environment and society in order to optimise CSR outcomes for improving competitiveness and developing sustainably. This implication is particularly important to the survival and development of SMEs in a challenging emerging economy.Social implicationsThe study widens the literature regarding relationship between CSR and SMEs' performance. Besides, the study supports stakeholder theory that explains why CSR positively affects firm's performance. The significant mediating roles of CI, CR and CL were positively confirmed in the study. Although previous studies determined that such roles are strategic source of competitive advantages of enterprises, however, how CSR involved in enhancing the roles has not been deeply explored and integrated. Third, the findings of the study support the resource-based view (RBV) and resource-based perspective that explains why firm should engage in CSR activities, and CI, CR and CL can enhance firm's performance by providing strategic source of competitive advantages that facilitate business to improve its performance in sustainable direction.Originality/valueTo the best of the authors' knowledge, the current literature on CSR and FP shows that, to date, there has been little empirical research on the mediating mechanism of CI, CR and CL in the link between CSR and FP for SMEs. The findings of the study may have great implications for entrepreneurs and top management with respect to the strategic perspectives to drive the businesses and to improve firm's performance in a sustainable direction in the context of emerging markets. In addition, the finding might be of great interest to – and motivate – SMEs' managers to engage with CSR actions where such businesses were or are situated during and after the coronavirus disease-2019 (COVID-19) pandemic. By that understanding, the Government might allow for innovative and groundbreaking policies or the reformation of old policies to leverage businesses to promote their strengths towards sustainable development in the new economic settings. The findings of the study may be a significant contribution to SMEs in Vietnam and in other emerging economies.

Abstract: Background Since sequencing techniques have become less expensive, larger sample sizes are applicable for microbiota studies. The aim of this study is to show how, and to what extent, different diversity metrics and different compositions of the microbiota influence the needed sample size to observe dissimilar groups. Empirical 16S rRNA amplicon sequence data obtained from animal experiments, observational human data, and simulated data were used to perform retrospective power calculations. A wide variation of alpha diversity and beta diversity metrics were used to compare the different microbiota datasets and the effect on the sample size. Results Our data showed that beta diversity metrics are the most sensitive to observe differences as compared with alpha diversity metrics. The structure of the data influenced which alpha metrics are the most sensitive. Regarding beta diversity, the Bray–Curtis metric is in general the most sensitive to observe differences between groups, resulting in lower sample size and potential publication bias. Conclusion We recommend performing power calculations and to use multiple diversity metrics as an outcome measure. To improve microbiota studies, awareness needs to be raised on the sensitivity and bias for microbiota research outcomes created by the used metrics rather than biological differences. We have seen that different alpha and beta diversity metrics lead to different study power: because of this, one could be naturally tempted to try all possible metrics until one or more are found that give a statistically significant test result, i.e., p-value < α. This way of proceeding is one of the many forms of the so-called p-value hacking. To this end, in our opinion, the only way to protect ourselves from (the temptation of) p-hacking would be to publish a statistical plan before experiments are initiated, describing the outcomes of interest and the corresponding statistical analyses to be performed.

Abstract: Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split-sample approach in hydrological modeling.

Abstract: Learning information from a single or a few samples is called few-shot learning. This learning method will solve deep learning’s dependence on a large sample. Deep learning achieves few-shot learning through meta-learning: “how to learn by using previous experience”. Therefore, this paper considers how the deep learning method uses meta-learning to learn and generalize from a small sample size in image classification. The main contents are as follows. Practicing learning in a wide range of tasks enables deep learning methods to use previous empirical knowledge. However, this method is subject to the quality of feature extraction and the selection of measurement methods supports set and the target set. Therefore, this paper designs a multi-scale relational network (MSRN) aiming at the above problems. The experimental results show that the simple design of the MSRN can achieve higher performance. Furthermore, it improves the accuracy of the datasets within fewer samples and alleviates the overfitting situation. However, to ensure that uniform measurement applies to all tasks, the few-shot classification based on metric learning must ensure the task set’s homologous distribution.