Hasil untuk "Science (General)"

Emily Dunn, Renfu Shao

The mitochondrial (mt) genomes of animals, including humans, are typically a single circular chromosome containing all mt genes. In several animal lineages, however, mt genomes have become fragmented, with genes distributed on multiple minichromosomes. How fragmented mt genomes are transcribed is still poorly understood. In this study, we investigated the transcription of the extensively fragmented mt genomes of the human head louse (<i>Pediculus humanus capitis</i>) and the human body louse (<i>Pediculus humanus corporis</i>). RNA-seq reads of both subspecies were retrieved from the NCBI Sequence Read Archive database and mapped to their mt genomes. The transcription level of each mt gene, minichromosome, motif, coding region and non-coding region, measured by RPKM (Reads Per Kilobase of transcript per Million mapped reads), TPM (Transcripts Per Million) or read coverage, was analysed statistically. In both subspecies, mt minichromosomes were transcribed entirely, with coding regions transcribed at much higher levels than non-coding regions. The 37 mt genes are transcribed unevenly, with <i>rrnL</i>, <i>cox1</i>, <i>cox2</i>, <i>cox3</i> and <i>atp6</i> transcribed at significantly higher levels than several other genes. Many transcription events terminate near a GC-rich motif in the non-coding regions; however, some transcription events pass this motif, leading to the transcription of entire non-coding regions. Despite the drastic difference in mt genome organisation, the human lice share several transcriptional features with humans, but also have unique features related to their fragmented mt genome organisation. The current study represents the first effort into the transcription of fragmented mt genomes. As more RNA-seq data become available, further studies on other animals with fragmented mt genomes are necessary to fully understand how genome fragmentation affects transcription.

Guoqiang Liang, Jingqian Gong, Mengxuan Li et al.

Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course towards AGI and emerging as a central issue in the global technological race. This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning. Additionally, it traces the historical development of Science of Science (SciSci) and presents a forward looking perspective on the potential applications of LLMs within the scientometric domain. Furthermore, it discusses the prospect of an AI agent based model for scientific evaluation, and presents new research fronts detection and knowledge graph building methods with LLMs.

Pavel Pereslavtsev, Christian Bachmann, Joelle Elbez-Uzan et al.

There is widespread use of nuclear radiation for medical imagery and treatments. Worldwide, almost 40 million treatments are performed per year. There are also applications of radiation sources in other commercial fields, e.g., for weld inspection or steelmaking processes, in consumer products, in the food industry, and in agriculture. The large number of neutrons generated in a fusion reactor such as DEMO could potentially contribute to the production of the required radioactive isotopes. The associated commercial value of these isotopes could mitigate the capital investments and operating costs of a large fusion plant. The potential of producing various radioactive isotopes was studied from material pieces arranged inside a DEMO equatorial port plug. In this location, they are exposed to an intensive neutron spectrum suitable for a high isotope production rate. For this purpose, the full 3D geometry of one DEMO toroidal sector with an irradiation chamber in the equatorial port plug was modeled with an MCNP code to perform neutron transport simulations. Subsequent activation calculations provide detailed information on the quality and composition of the produced radioactive isotopes. The technical feasibility and the commercial potential of the production of various isotopes in the DEMO port are reported.

Xinyue Wang, Weifan Lin, Weiting Zhang et al.

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.

Evan Cleave, Cailin Wark, Emmanuel Kyeremeh

For cities, immigration is now considered a vital part of local economic and community development. Over the past half-century, many cities have experienced a series challenges caused by the impacts of late-stage demographic transition; the slow bleeding of skilled domestic workers to larger metropolitan areas; and the decline of traditional economic sectors. As a result, there has been a prioritization of attracting and retaining high-skilled and well-educated immigrants by local governments through locally-focused, place-based policies. Within this context, this paper examines the ways that cities in the Province of Ontario, Canada are constructing and implementing immigrant attraction, integration, and retention strategies. To achieve this goal, we identified and examined the local immigration policies of the 52 cities in Ontario, 36 of which have a formal immigration policy document. A comprehensive content analysis was conducted on these available to identify the ways that immigration is conceptualized, and the specific policies and approaches that local governments are implementing. Statistical analysis was used to determine if there was variation in policy across different types of cities. Based on this analysis, local governments are generally developing holistic, place-based policies – however, there is variation in approaches across cities of different sizes and geographies. These place-specific policies draw on local assets and advantages (i.e. existing migrant communities; local amenities and attractions; economic and education opportunities) while also work to enhance enhancing local capacity (i.e. building networks and immigration partnerships; training employers and city workers).

Farshid Danesh, Samaneh Kesht Karan, Lili Banihashemi et al.

Editorial board members (EBMs) of journals play a pivotal role in authentic international scientific journals. Editorial Board Interlocking (EBI) phenomenon reflects the effectiveness and importance of the scholarly journal's editorial boards in various scientific fields. The primary purpose of this paper is to conduct a Social Network Analysis (SNA) of EBI phenomena from the perspective of astronomy and astrophysics journals. The present study is applied research based on EBI, SNA, and the descriptive-analytical approach. The statistical population of this study consists of the editorial board members of all journals of astronomy and astrophysics indexed in the JCR and official journal websites. There are 1597 job positions in 67 astronomy and astrophysics journals occupied by the 1394 scholars. Data analysis shows EBI for 95 scholars and 79 organizations. "Aleksei A. Starobinsky" from Russia and the Russian Academy of Sciences, "Daniel J. Scheeres" from the United States, and the University of Colorado Boulder have the highest EBI contributions in five journals. "Daniel J. Scheeres," with a centrality of 39, has the highest degree of centrality measurement among the EBMs. The presence of more than five times as many men as women indicates that astronomy and astrophysics journals are considered "masculine" by the editorial board. The EBI phenomenon is observed in astronomy and astrophysics journals due to the limited number of peop le eligible for the editorial board. Due to EBI, a limited number of famous scholars are made macro-policies such as publishing the articles, referees selections, and the reviewing process. Astronomy and astrophysics journals have "elite" academic networks. Gender inequality exists among EBMs, and the majority of them are male. Accordingly, these journals are "men's journals."

Soonil Hong, Byoungwook Park, Chandran Balamurugan et al.

Efforts to commercialize organic solar cells (OSCs) by developing roll-to-roll compatible modules have encountered challenges in optimizing printing processes to attain laboratory-level performance in fully printable OSC architectures. In this study, we present efficient OSC modules fabricated solely through printing methods. We systematically evaluated the impact of processing solvents on the morphology of crucial layers, such as the hole transport, photoactive, and electron transport layers, applied using the doctor blade coating method, with a particular focus on processability. Notably, deposition of charge transport layer using printing techniques is still a challenging task, mainly due to the hydrophobic characteristic of the organic photoactive layer. To overcome this issue, we investigated the solvent effect of a well-studied cathode interlayer, bathocuproine (BCP). We were able to form a uniform thin BCP film (∼10 nm) on a non-fullerene based organic photoactive layer using the doctor bladed coating method. Our results showed that the use of volatile alcohols in the BCP processing required a delicate balance between wettability and vaporization, which contrasted with the results for spin-coated films. These findings provide important insights into improving the efficiency of printing techniques for depositing charge transport layers. The fully printed OSC modules, featuring uniform and continuous BCP layer formation, achieved an impressive power conversion efficiency of 10.8% with a total area of 10.0 cm2 and a geometrical fill factor of 86.5%.

Sylvain Thierry, Sarah Maadadi, Aurore Berton et al.

Toll-like receptor 3 (TLR3) is an innate immune receptor that recognizes double-stranded RNA (dsRNA) and induces inflammation in immune and normal cells to initiate anti-microbial responses. TLR3 acts also as a death receptor only in cancer cells but not in their normal counterparts, making it an attractive target for cancer therapies. To date, all of the TLR3-activating dsRNAs used at preclinical or clinical stages have major drawbacks such as structural heterogeneity, toxicity, and lack of specificity and/or efficacy. We conducted the discovery process of a new family of TLR3 agonists that are chemically manufactured on solid-phase support and perfectly defined in terms of sequence and size. A stepwise discovery process was performed leading to the identification of TL-532, a 70 base pair dsRNA that is potent without transfection reagent and is highly specific for TLR3 without activating other innate nucleic sensors such as RIG-I/MDA5, TLR7, TLR8, and TLR9. TL-532 induces inflammation in murine RAW264.7 myeloid macrophages, in human NCI-H292 lung cancer cells, and it promotes immunogenic apoptosis in tumor cells in vitro and ex vivo without toxicity towards normal primary cells. In conclusion, we identified a novel TLR3 agonist called TL-532 that has promising anticancer properties.

Tai Le Quy, Gunnar Friege, Eirini Ntoutsi

Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.

A. Moullet, T. Kataria, D. Lis et al.

PRIMA (The PRobe for-Infrared Mission for Astrophysics) is a concept for a far-infrared (IR) observatory. PRIMA features a cryogenically cooled 1.8 m diameter telescope and is designed to carry two science instruments enabling ultra-high sensitivity imaging and spectroscopic studies in the 24 to 235 microns wavelength range. The resulting observatory is a powerful survey and discovery machine, with mapping speeds better by 2 - 4 orders of magnitude with respect to its far-IR predecessors. The bulk of the observing time on PRIMA should be made available to the community through a General Observer (GO) program offering 75% of the mission time over 5 years. In March 2023, the international astronomy community was encouraged to prepare authored contributions articulating scientific cases that are enabled by the telescope massive sensitivity advance and broad spectral coverage, and that could be performed within the context of GO program. This document, the PRIMA General Observer Science Book, is the edited collection of the 76 received contributions.

Balaram Panda

Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure

M. Schweizer, N. Straumann, A. Wipf

We adapt the post-Newtonian gravitational-radiation methods developed within general relativity by Epstein and Wagoner to the gravitation theory with torsion, recently proposed by Hehl et al., and show that the two theories predict in this approximation the same gravitational radiation losses. Since they agree also on the first post-Newtonian level, they are at the present time - observationally - indistinguishable.

Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Yukio Mizuno et al.

The demand for the condition monitoring of induction motors is increasing in various fields, such as industry, transportation, and daily life. Bearing faults are the most common faults, and many fault diagnosis methods have been proposed using artificial pitting as the fault factor in most cases. However, the validity of a fault diagnosis method for other kinds of faults does not seem to be evaluated. Considering onsite scenarios and other possibilities of faults, this paper introduces scratches on the outer raceways of bearings. A study was performed on the detection of several kinds of bearing scratches using a proposed method that was based on an auto-tuning convolutional neural network. The developed approach was also compared with other diagnostic methods for validation. The results showed that the proposed technique provides the possibility of diagnosing several kinds of scratches with acceptable accuracy rates.

Kamel Arafet, Rafael Berlanga

The generation of electricity through renewable energy sources increases every day, with solar energy being one of the fastest-growing. The emergence of information technologies such as Digital Twins (DT) in the field of the Internet of Things and Industry 4.0 allows a substantial development in automatic diagnostic systems. The objective of this work is to obtain the DT of a Photovoltaic Solar Farm (PVSF) with a deep-learning (DL) approach. To build such a DT, sensor-based time series are properly analyzed and processed. The resulting data are used to train a DL model (e.g., autoencoders) in order to detect anomalies of the physical system in its DT. Results show a reconstruction error around 0.1, a recall score of 0.92 and an Area Under Curve (AUC) of 0.97. Therefore, this paper demonstrates that the DT can reproduce the behavior as well as detect efficiently anomalies of the physical system.

Armenia Androniceanu, Doina-Mihaela Marton

The Covid-19 pandemic generated a global crisis involving most countries in the world. State governments worldwide were forced to take appropriate measures impacting different fields. The Romanian government and other local public authorities developed special measures to curb the spread of the SARS-COV-2 virus in Romania in general and in Bucharest, the country’s capital. The purpose of our research was to identify the social and psychological impact of the governmental measures on the citizens of Bucharest. The research was conducted between 28 November and 25 December 2020, but refers to the measures taken by the government and local authorities from the onset of the Covid-19 pandemic to date. The sample, representative for Bucharest, included 421 citizens living in Bucharest, aged between 19 and 40 years. The data were collected using an online questionnaire. Other methods used to analyse the results and verify the hypothesis included the multiple regression model and some applications in Excel. The results confirmed that governmental measures had a strong social and psychological impact on citizens, changing their social behaviour and causing psychological disorders, such as depression and anxiety. Our research results can help the Romanian authorities avoid problems among the population and adapt their measures to better meet the population’s psychosocial needs in time of the Covid-19 pandemic crisis, which is far from over.

Nadiya Dubrovina, Russell Gerrard, Stanislav Filip et al.

Healthcare is one of the most important sectors of the public economy in the EU countries. An important task in the analysis and prediction of the values for healthcare funding is the development and application of quantitative models based on different mathematical methods. Three of the most popular indicators used for the macroeconomic description of the funding of healthcare are: (1) total government expenditure on health as a percentage of GDP; (2) total government expenditure on health as a percentage of total general government expenditure; and (3) total government expenditure on health per capita. The aim of this research is to study the trends for the main indicators of healthcare funding on the macroeconomic level and to develop models based on time series methods for analysis of the features of the trends and the prediction of the values for the next time period.

Wahib Assyahri, Mercya Vaguita

Aplikasi Siskeudes ini merupakan suatu sistem yang dikembangkan oleh BPKP dalam meningkatkan kualitas tata kelola keuangan desa. Pemerintah Padang Pariaman sudah menetapkan Siskeudes untuk diaplikasikan namun pada kenyataannya masih banyak nagari yang ada di Kecamatan 2x11 Kayutanam khususnya nagari kayu tanam ini mengalami kendala dalam mengaplikasikannya. Penelitian ini bertujuan untuk mengevaluasi pelaksanaan aplikasi Siskeudes di Nagari Kayu dalam mewujudkan pengelolaan keuangan nagari yang transparan dan secara akuntabel. Penelitian ini merupakan penelitian kualitatif dengan menggunakan pendekatan deskriptif. Data penelitian didapatkan melalui observasi, studi dokumentasi, dan wawancara secara mendalam, kemudian dianalisis dengan tiga tahapan untuk penelitian ini. Hasil penelitian menunjukkan bahwa (1) Penerapan Aplikasi Siskeudes di Nagari Kayu Tanam sudah cukup baik dalam penggunaannya, dilihat dari aspek efisiensi, efektif, dan kesesuaian dalam aplikasi tersebut melalui tahap perencanaan dan tahap pelaksanaan, sedangkan belum efektif dan belum ada kesesuaian pada tahap pelaporan dan tahap pertanggungjawaban, tetapi masih banyak kendala yang dihadapi dalam pelaksanaannya, yaitu; kurangnya sumber daya manusia yang ada; pelatihan-pelatihan dari pihak kabupaten belum maksimal; keterlambatan dalam penyusunan laporan pertanggungjawaban; dan partisipasi masyarakat yang masih kurang. (2) Faktor-faktor penyebab kendala yang ditemukan dalam penggunaan sistem keuangan desa tersebut, yaitu adanya kesalahan teknis, adanya gangguan lingkungan, dan adanya kelalaian manusia. Berdasarkan hasil penelitian dan pembahasan, dapat disimpulkan bahwa penggunaan aplikasi sistem keuangan desa (Siskeudes) di Nagari Kayu Tanam Kecamatan 2x11 Kayutanam sudah cukup baik diterapkan tetapi dalam pelaksanaannya masih ada kendala-kendala yang terjadi.

Christine O'Donnell, Edward E. Prather, Peter Behroozi

General-education college astronomy courses offer instructors both a unique audience and a unique challenge. For many students, such a course may be their first time encountering a standalone astronomy class, and it is also likely one of the last science courses they will take. Thus, in a single semester, primary course goals often include both imparting knowledge about the Universe and giving students some familiarity with the processes of science. In traditional course environments, students often compartmentalize information into separate "life files" and "course files" rather than integrating information into a coherent framework. The astronomy course created through this project, taught at the University of Arizona in Spring 2019, was designed around inclusivity-driven guiding principles that help students engage with course content in ways that are meaningful, relevant, and accessible. Our course bridges the gap between students' "life" and "course files", encourages and respects diverse points of view, and empowers students to connect course content with their personal lives and identities. In this paper, we provide insight into the guiding principles that informed our course design and share research results on the effectiveness of the instructional strategies and assessment techniques implemented in the course.

Mike Thelwall

Researchers may be tempted to attract attention through poetic titles for their publications, but would this be mistaken in some fields? Whilst poetic titles are known to be common in medicine, it is not clear whether the practice is widespread elsewhere. This article investigates the prevalence of poetic expressions in journal article titles 1996-2019 in 3.3 million articles from all 27 Scopus broad fields. Expressions were identified by manually checking all phrases with at least 5 words that occurred at least 25 times, finding 149 stock phrases, idioms, sayings, literary allusions, film names and song titles or lyrics. The expressions found are most common in the social sciences and the humanities. They are also relatively common in medicine, but almost absent from engineering and the natural and formal sciences. The differences may reflect the less hierarchical and more varied nature of the social sciences and humanities, where interesting titles may attract an audience. In engineering, natural science and formal science fields, authors should take extra care with poetic expressions, in case their choice is judged inappropriate. This includes interdisciplinary research overlapping these areas. Conversely, reviewers of interdisciplinary research involving the social sciences should be more tolerant of poetic license.

Gretchen R. Stahlman, P. Bryan Heidorn

"Long tail" data are considered to be smaller, heterogeneous, researcher-held data, which present unique data management and scholarly communication challenges. These data are presumably concentrated within relatively lower-funded projects due to insufficient resources for curation. To better understand the nature and distribution of long tail data, we examine National Science Foundation (NSF) funding patterns using Latent Dirichlet Analysis (LDA) and bibliographic data. We also introduce the concept of "Topic Investment" to capture differences in topics across funding levels and to illuminate the distribution of funding across topics. This study uses the discipline of astronomy as a case study, overall exploring possible associations between topic, funding level and research output, with implications for research policy and practice. We find that while different topics demonstrate different funding levels and publication patterns, dynamics predicted by the "long tail" theoretical framework presented here can be observed within NSF-funded topics in astronomy.