Leila Omidi, Vahid Salehi, Seyed Abolfazl Zakerian
et al.
Stress, fatigue, and work situation awareness are key contributors to accidents and unsafe behaviors in process industries. Given the significance of these factors, this study aimed to assess the employees' perceptions of the effects of stress, fatigue, and work situation awareness on safety performance in a process industry. The data of this study were collected through a questionnaire, and their reliability was evaluated and confirmed. The Data Envelopment Analysis (DEA) method was used to identify and analyze the most influential factors and sub-factors influencing employees' perceptions of safety performance. Additionally, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied to rank alternatives and validate the DEA results. Sensitivity analysis revealed that work situation awareness significantly affected safety performance compared to stress and fatigue. Furthermore, the findings showed that distraction, chronic fatigue, and demands were the most influential sub-factors of work situation awareness, fatigue, and stress, respectively. The Pearson correlation test confirmed a strong agreement between the DEA and TOPSIS results. Given these findings, stress, fatigue, and work situation awareness play an important role in safety performance of employees in the process industries.
History of scholarship and learning. The humanities, Social sciences (General)
Intercultural skills have become essential for functioning in contemporary society, where daily interaction with members of diverse cultures and adaptation to different cultural environments are increasingly necessary. Integration into such environments requires the development of new forms of behavior and approaches. Core values such as openness to diversity and strong communication skills have become key factors for functioning in these social conditions, and should be encouraged from an early age. An upbringing that enables children to participate in multicultural communities significantly contributes not only to their personal development but also to the improvement of society as a whole. The ability to develop intercultural competences helps individuals navigate relationships within various ethnic, religious, and national groups, which is crucial for the overall progress of society. This paper analyzes how primary school students assess their own intercultural competences, taking into account the location of the school they attend. Data were collected using a self-assessment scale for intercultural competences. The results of the study indicate statistically significant differences in self-assessment, favoring students attending urban schools. These findings open avenues for further research to better understand the underlying causes of these differences.
History of scholarship and learning. The humanities
This article examines an intellectual debate between N.I. Kareev and L.Z. Slonimsky, which unfolded in Russian journals in 1883, following the publication of N.I. Kareev’s monograph “Key Issues in the Philosophy of History” based on his doctoral dissertation, which introduced his original but controversial perspective of the hierarchy of sciences, the nature of the laws of history, and the “progress” formula. New, previously unexplored anthropological contexts of the polemical exchange between the two scholars are explored that help uncover the reasons behind the sharp differences in how they understood the structure of the historical process and the place of the human being in it. The results show that, with respect to the idea and theory of the nation perceived as the central theoretical components of intellectual pursuits at that time, N.I. Kareev and L.Z. Slonimsky’s views reflect their fundamentally different personal experiences, as well as the alternative analytical and rhetorical traditions they accepted. Because they pursued different goals in their theorizing, their reasonings developed at different levels of meta-reflection.
History of scholarship and learning. The humanities
This paper examines the direct and spatial impacts of the Belt and Road Initiative (BRI) on China’s OFDI globally and tests the moderating effect of the BRI, considering economic development, resources and institution quality for 186 countries from 2008 to 2019. The results imply that the BRI has significantly improved China’s OFDI globally, with a much larger effect in Asia and Africa. This study finds that institutional quality has no direct impact on OFDI, with the exception of the corruption index, where China’s OFDI seem to favour countries with higher corruption. However, when institutional quality is used as an interactive term that controls heterogeneity, the results suggest that institutional quality significantly strengthens the effects of the BRI on OFDI. In other words, in countries that engage with BRI, higher institutional quality positively improves the attractiveness of China’s OFDI. The results also reveal that the impact of the BRI is moderated by higher levels of economic development but not by resource-rich host countries. Interestingly, the increasing Chinese OFDI in BRI countries has a spatial suppression effect on OFDI in non-BRI countries, suggesting industrial agglomeration effects due to the BRI. The results are validated by various robustness tests, and this study concludes with policy implications.
History of scholarship and learning. The humanities, Social Sciences
Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick
et al.
Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.
Abstract The study aims to identify and describe the theory of economic development according to the thinking of Indonesia Raya Incorporated (IRI) in managing the interest of natural resources included in strategic economic resources. This study used a qualitative method through a grounded theory approach with constructivism and criticism as the interpretation approach. The data collected through the Focus Group Discussion (FGD) technique was processed using a componential analysis approach. The study results reveal the content of the main variables of economic development, namely the role and function of the government and state enterprises, namely State-Owned Enterprises (SOEs) and Regional-Owned Enterprises (ROEs), related to the potential of managing natural resources and other strategic economic resources which are determinants of the economic strata of population or for improving the welfare of the people. These novel findings highlights the significance of natural resource governance and strategic economy, namely the IRI’s perspective on economic development. The theory and conception contribute to deepening knowledge previously proposed in IRI and Murakabi economics. So, this knowledge has implications for natural resources management practices by the government and corporate strategies within the body of SOEs and ROEs in Indonesia and the global world. This includes the possibility of strategies for national and multinational private companies whose main business positions are based on natural resources and strategic economics.
History of scholarship and learning. The humanities, Social Sciences
Abstract This systematic review examined the association between students’ digital cultural and social capital and their learning outcomes, focusing on the characteristics, related factors, and impact of their digital cultural and social capital. Through a literature search process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 21 studies were identified for inclusion in the review. We found that digital cultural and social capital provides a useful theoretical basis for understanding the underlying effects of the digital divide on student development. Results of the review allow us to propose operational definitions of digital cultural and social capital, as well as refine our conceptualization of these forms of capital, including their roles in the reproduction of educational inequalities. Lastly, strategies that could be implemented by schools, parents, and other stakeholders in educational systems to bolster students’ digital cultural and social capital are suggested.
History of scholarship and learning. The humanities, Social Sciences
Abstract Using the Intimate Partner Behaviour Control Scale, the Autonomy Needs Questionnaire, the Intimacy Quality Questionnaire, and the Chinese version of the Brief Barratt Impulsiveness Scale, we conducted a survey of 416 college students (204 males and 212 females) with a mean age of 24.99 (SD = 7.94) years. The aim of this research was to explore the relationship between intimate partner behaviour control and impulsivity with a focus on the roles of autonomy needs satisfaction and intimacy quality in the context of college students in romantic relationships. The findings indicated that (1) intimate partner behaviour control significantly and positively predicts impulsivity among college students; (2) autonomy needs satisfaction plays a mediating role in the relationship between intimate partner behaviour control and impulsivity among college students; (3) intimacy quality also plays a mediating role in the relationship between intimate partner behaviour control and impulsivity among college students; (4) autonomy needs satisfaction and intimacy quality jointly play a chain mediating role in the relationship between intimate partner behaviour control and impulsivity among college students. These results highlight the close relationship between intimate partner behaviour control and impulsivity, in which context autonomy needs and intimacy quality play a vital role. The findings of this research offer practical insights for mental health practitioners with regard to preventing and intervening in impulsive behaviour among college students as well as guidance for policy plans that aim to promote healthy relationships and prevent intimate partner violence.
History of scholarship and learning. The humanities, Social Sciences
Ayano Hiranaka, Shang-Fu Chen, Chieh-Hsin Lai
et al.
Controllable generation through Stable Diffusion (SD) fine-tuning aims to improve fidelity, safety, and alignment with human guidance. Existing reinforcement learning from human feedback methods usually rely on predefined heuristic reward functions or pretrained reward models built on large-scale datasets, limiting their applicability to scenarios where collecting such data is costly or difficult. To effectively and efficiently utilize human feedback, we develop a framework, HERO, which leverages online human feedback collected on the fly during model learning. Specifically, HERO features two key mechanisms: (1) Feedback-Aligned Representation Learning, an online training method that captures human feedback and provides informative learning signals for fine-tuning, and (2) Feedback-Guided Image Generation, which involves generating images from SD's refined initialization samples, enabling faster convergence towards the evaluator's intent. We demonstrate that HERO is 4x more efficient in online feedback for body part anomaly correction compared to the best existing method. Additionally, experiments show that HERO can effectively handle tasks like reasoning, counting, personalization, and reducing NSFW content with only 0.5K online feedback. The code and project page are available at https://hero-dm.github.io/.
Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optimal classifiers, this perspective is limited in that it does not account for the choice of learning algorithm. Since classifiers seldom behave like Bayes classifiers and are influenced by the choice of learning algorithms along with their inherent biases, in this work we initiate the study of how the choice of the learning algorithm plays a role in the success of a collective in practical settings. Specifically, we focus on distributionally robust optimization (DRO), popular for improving a worst group error, and on the ubiquitous stochastic gradient descent (SGD), due to its inductive bias for "simpler" functions. Our empirical results, supported by a theoretical foundation, show that the effective size and success of the collective are highly dependent on properties of the learning algorithm. This highlights the necessity of taking the learning algorithm into account when studying the impact of collective action in machine learning.
Abstract A series of social media posts on 4chan then 8chan, signed under the pseudonym ‘Q’, started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of the texts written by Q and to those written by a list of suspects provided by journalistic investigation. To identify the authors of these posts, serious challenges have to be addressed. The ‘Q drops’ are very short texts, written in a way that constitute a sort of literary genre in itself, with very peculiar features of style. These texts might have been written by different authors, whose other writings are often hard to find. After an online ethnography of the movement, necessary to collect enough material written by these thirteen potential authors, we use supervised machine learning to build stylistic profiles for each of them. We then performed a ‘rolling analysis’, looking repeatedly through a moving window for parts of Q’s writings matching our profiles. We conclude that two different individuals, Paul F. and Ron W., are the closest match to Q’s linguistic signature, and they could have successively written Q’s texts. These potential authors are not high-ranked personality from the US administration, but rather social media activists.
Abstract Automated character identification in movies and TV series has been typically carried out through face detection in video and the association of faces with characters’ names extracted from dialogues or cast lists. We propose a deep learning architecture to identify characters based on subtitles only, precisely through the lexicon those characters employ. The identification task is formalized as a multi-class classification task. We apply our technique to the complete set of episodes in the Gomorrah TV series and achieve an average identification accuracy beyond 94 per cent on the full set of characters.
AbstractNative language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years. We focus on a different facet of the NLI task, i.e. that of analysing the internals of an NLI classifier trained by an explainable machine learning (EML) algorithm, in order to obtain explanations of its classification decisions, with the ultimate goal of gaining insight into which linguistic phenomena ‘give a speaker’s native language away’. We use this perspective in order to tackle both NLI and a (much less researched) companion task, i.e. guessing whether a text has been written by a native or a non-native speaker. Using three datasets of different provenance (two datasets of English learners’ essays and a dataset of social media posts), we investigate which kind of linguistic traits (lexical, morphological, syntactic, and statistical) are most effective for solving our two tasks, namely, are most indicative of a speaker’s L1; our experiments indicate that the most discriminative features are the lexical ones, followed by the morphological, syntactic, and statistical features, in this order. We also present two case studies, one on Italian and one on Spanish learners of English, in which we analyse individual linguistic traits that the classifiers have singled out as most important for spotting these L1s; we show that the traits identified as most discriminative well align with our intuition, i.e. represent typical patterns of language misuse, underuse, or overuse, by speakers of the given L1. Overall, our study shows that the use of EML can be a valuable tool for the scholar who investigates interlanguage facts and language transfer.
We initiate a study of computable online (c-online) learning, which we analyze under varying requirements for "optimality" in terms of the mistake bound. Our main contribution is to give a necessary and sufficient condition for optimal c-online learning and show that the Littlestone dimension no longer characterizes the optimal mistake bound of c-online learning. Furthermore, we introduce anytime optimal (a-optimal) online learning, a more natural conceptualization of "optimality" and a generalization of Littlestone's Standard Optimal Algorithm. We show the existence of a computational separation between a-optimal and optimal online learning, proving that a-optimal online learning is computationally more difficult. Finally, we consider online learning with no requirements for optimality, and show, under a weaker notion of computability, that the finiteness of the Littlestone dimension no longer characterizes whether a class is c-online learnable with finite mistake bound. A potential avenue for strengthening this result is suggested by exploring the relationship between c-online and CPAC learning, where we show that c-online learning is as difficult as improper CPAC learning.
Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning (RL) that learns from human feedback instead of relying on an engineered reward function. Building on prior work on the related setting of preference-based reinforcement learning (PbRL), it stands at the intersection of artificial intelligence and human-computer interaction. This positioning provides a promising approach to enhance the performance and adaptability of intelligent systems while also improving the alignment of their objectives with human values. The success in training large language models (LLMs) has impressively demonstrated this potential in recent years, where RLHF has played a decisive role in directing the model's capabilities towards human objectives. This article provides an overview of the fundamentals of RLHF, exploring how RL agents interact with human feedback. While recent focus has been on RLHF for LLMs, our survey covers the technique across multiple domains. We provide our most comprehensive coverage in control and robotics, where many fundamental techniques originate, alongside a dedicated LLM section. We examine the core principles that underpin RLHF, how algorithms and human feedback work together, and the main research trends in the field. Our goal is to give researchers and practitioners a clear understanding of this rapidly growing field.
Salih Katircioglu, Huseyin Arasli, Mehmet Necati Cizreliogullari
The goal of this research is to figure out the moderating act of ethical leadership on the effects of job satisfaction and psychological capital of employees. The Hotel industry in Northern Cyprus has been preferred with this respect. The study was conducted within the appropriate literature. Research-oriented data collection tools designed and used in accordance with the purposes of the research, research questions and hypotheses are surveyed. The findings of the study revealed the moderating role of ethical leadership (EL) on psychological capital (PsyCap) and job satisfaction (JS). It was also found that the participants were on the idea of working under equal conditions adopted via ethical leadership factors. The current study is a new era for the developmental issues of hospitality in terms of management and it is hoped that it will yield basic basements for further studies.
History of scholarship and learning. The humanities, Social Sciences
In this study, edited on the basis of a critical review of domestic and foreign literature, as well as authors’ own analyzes, previously presented in several articles (Słodowa-Hełpa 2015; Gorynia 2021 and 2022), mainly in two shorter texts published in popular magazines with a range of Poland (Gory-nia and Słodowa-Hełpa 2022a, 2022b), selected aspects of the concept of the common good from the perspective of the Covid-19 pandemic were presented.
History of scholarship and learning. The humanities
Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan
Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpretability. Checklists are usually designed by expert clinicians that manually collect and analyze available evidence. However, the increasing quantity of available medical data is calling for a partially automated checklist design. Recent works have taken a step in that direction by learning predictive checklists from categorical data. In this work, we propose to extend this approach to accomodate learning checklists from continuous medical data using mixed-integer programming approach. We show that this extension outperforms a range of explainable machine learning baselines on the prediction of sepsis from intensive care clinical trajectories.
The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.
This article investigates the extant literature on the correlation between narratives in corporate annual reports and corporate performance. Prior studies are reviewed for overall characteristics, research topics, theoretical foundations, and methods. Articles published between 2000 and 2018 were analyzed using the content analysis method. The results demonstrated that prior studies generally show an increasing trend with salient interdisciplinarity. Mapping and predictability between annual reports’ narratives and business performance have been the prevailing topics. The impression management and agency theories are the most frequent theoretical references. More importantly, complexity of research methods was found in data, analytical approaches, and variables. The emphasis on narratives in prior research proves the necessity of contextualizing narratives in business communication. Future work would benefit from a “narrative framework” that incorporates linguistic, socio-cultural, and organizational perspectives into the correlation study. The article presents the first study to investigate the correlation studies through content analysis.
History of scholarship and learning. The humanities, Social Sciences