bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-Time Monitoring of Coastal & Offshore Construction Noise for Immediate Decision Making
Authors: Troussard Corentin , Dufrechou Laurent , Tremblin Pierre-Alain , Eustache Yvan 
Published: October 2023

Abstract
With the global awareness of the need to make our energies cleaner, marine constructions, typically wind farms, and especially offshore, have multiplied in recent years, and we can expect to see these numbers increase even more rapidly. The presence of marine mammals during offshore infrastructure works (pile driving, drilling, dredging) is now a major environmental concern, as it has been proven that they could be severely harmed by exceeding noises.
In order to safeguard species and their natural habitats, more and more local legislations impose a cap on sound levels caused by all offshore activities. As of 2023, this is the mainly the case in Europe (for instance in the United Kingdom [Joint Nature Conservation Committee - JNCC, Southall et al., 2007; Popper and Hasting, 2009], Germany [Bundesamt fur Seeschifffahrt und Hydrographie - BSH (Federal Maritime and Hydrographic Agency), Muller-BBM, 2011], The Netherlands [Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek - TNO (Netherlands Organisation for Applied Scientific Research), 2011], Belgium or Denmark [Danish Energy Agency - DEA, Tougaard et al., 2016]), but Asian countries (Taiwan [Environmental Protection Administration - EPA, 2019] being the best example) and American (USA, Canada) are also implementing similar rules. Wind farms developers therefore are required to measure, monitor, and mitigate noise caused by building work, and, today, underwater noise monitoring regulations are enforced as a means of protecting aquatic life.
Optimizing Corrosion Protection: A Data-Driven Approach to Impressed Current Cathodic Protection (ICCP) Systems for Large Crude Carriers
Authors: Priyadarshan Amit 
Published: October 2023

Abstract
The use of Impressed Current Cathodic Protection (ICCP) systems in large crude carriers such as very large crude carriers (VLCC) and ultra large crude carriers (ULCC) is universally regarded as an effective means of corrosion protection. However, traditional ICCP systems have certain drawbacks that can be overcome by using data-driven methodologies. This research describes a unique data-driven ICCP system aimed to optimise energy consumption and ICCP system efficacy for large crude carriers. The objective of this project was to develop an AI-based ICCP system that could address existing ICCP system shortcomings such as overprotection, inefficiency, maintenance, corrosion detection, and environmental factors. To achieve this objective, we employed a machine learning approach that utilized historical data to train our AI-based ICCP system.
Our methodology involved collecting data on the environmental conditions, the ICCP system output, and the corrosion rates for several large crude carriers. We then utilised this data to train machine learning algorithms that could anticipate the best current output needed to protect the hull while consuming the least amount of energy. The procedures involved testing the AI-based ICCP system in a simulated setting.
Our study shows that the data-driven ICCP system provides significant benefits for the corrosion protection of large crude carriers. Our results demonstrate that the AI-ML approach is effective in reducing overprotection and optimizing energy consumption, while also predicting maintenance requirements, detecting localized corrosion, and adjusting the ICCP system output based on real-time environmental data. Our observations show that the data-driven ICCP system is able to detect localized corrosion more accurately than traditional ICCP systems, which can lead to early detection and prevention of corrosion. The system is also able to adjust the current output based on real-time environmental data, providing better protection against external factors such as temperature, salinity, and currents.
This research describes a revolutionary data-driven approach to ICCP systems that tackles the shortcomings of standard ICCP systems used in large crude carriers. Our research delivers new insights into optimising energy consumption, reducing overprotection, forecasting maintenance requirements, identifying localised corrosion, and changing the ICCP system output based on real-time environmental data by utilising AI-ML technologies. These results have the potential to significantly help the oil and gas industry by improving the efficiency and effectiveness of corrosion prevention for large crude transporters.
Redefining Dynamic Positioning (DP2) Power Plant Requirements for Sustainable Upstream Operations using Unique Hybrid Technology
Authors: Venkataraman Manoj , Ionescu Adrian , Zrncic Daniel , Santellanes Claudio , surnameAsokan Anish 
Published: October 2023

The topics of reduction in emissions, ESG & Sustainability are currently being discussed widely in all modes of media. Not a day passes when there is no coverage of these topics. Associated with them are the topics of increasing concentration of greenhouse gases, carbon dioxide emissions caused by fossil fuels, hybrid technology, renewable energy generation, sustainability of the earth's limited resources, and so on. The issues involved are complex. Even though a very large majority of the Sustainability science community agrees on the causes of emissions, differing views do exist on ways to mitigate its adverse effects.
Sustainability is expected to influence the work done by companies/ businesses. The purpose of this paper is to provide insight into cutting edge technology, implemented, and further being optimized on a continuous basis by visionaries of Rawabi Energy with unaverred support from the leadership of the biggest oil major in KSA. Some background on the science of Dynamic positioning, its impacts, keyways to minimize the impact and the roles that the technology can play in redefining rules for Dynamic positioning in upstream operations are hereby included.
Methodological review of NMA bias concepts provides groundwork for the development of a list of concepts for potential inclusion in a new risk of bias tool for network meta-analysis (RoB NMA Tool)
Authors: Lunny Carole , Veroniki Areti-Angeliki , Higgins Julian PT , Dias Sofia , Hutton Brian , Wright James M , White Ian R. , Whiting Penny , Tricco Andrea C 
Published: October 2023

Abstract

Introduction: Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs. Methods and Analysis: We included papers that present items related to bias, reporting, or methodological quality, papers assessing the quality of NMAs, or methods papers. We searched MEDLINE, the Cochrane library, and unpublished literature (July 2020). We extracted items related to bias in NMAs. An item was excluded if it related to general systematic review quality or bias, and included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions). Results: 181 articles were assessed at full text and 57 were included. Of these articles, 12 were tools, checklists or journal standards, 12 were guidance documents for NMAs, 26 were articles related to bias or methods, and 7 were papers that assessed the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The items we included were reworded into 22 concepts specific to bias in NMAs. Conclusions: A list of 22 concepts were included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool.
A week in the life of the human brain: stable states punctuated by chaotic transitions
Authors: Wang Maxwell , G'Sell Max , Castellano James F. , Richardson R. Mark , Ghuman Avniel 
Published: October 2023

Abstract
Many important neurocognitive states, such as ones related to performing natural activities and fluctuations of arousal, shift over minutes-to-days in the real-world. We analyzed 3-12 days of continuous intracranial recordings in twenty participants that freely socialized, used digital devices, slept, etc. to understand how neural dynamics form and change with behavior. Brain networks formed stable states that were predictive of both behavior and physiology. Behavior changes were associated with bursts of rapid neural fluctuations where brain networks chaotically explored many configurations before settling into new states. These trajectories traversed an hourglass-like structure, with awake and sleep at opposite ends, and an attractor state represented by default mode network activation in between. These findings illustrate ways our brains balance stability and flexibility to produce real-world behavior.
Voluntary participation and optimism: How and when outside individual options can facilitate group collaboration
Authors: Mori Ryutaro , Hanaki Nobuyuki , Kameda Tatsuya 
Published: October 2023

Abstract
Whether in business ventures or academic research, voluntary participation is at the heart of collaboration. Yet, previous studies concerning collaboration have primarily focused on the freeriding problem, largely overlooking this mundane but critical aspect. We address this gap by modeling collaborative situations as people’s voluntary choices between joining an uncertain public goods provisioning in groups and pursuing a certain but less profitable individual option. Theoretically and experimentally, we show that this voluntary nature in collaboration increases the likelihood of group success by filtering out otherwise pessimistic defectors or encouraging them toward cooperation. Further, we reconcile our findings with the existing literature highlighting detrimental effects of individual alternatives to collaborative solutions. We argue that whether individual options aid or hinder collaboration hinges on the degree of the “externality” of loners (who pursue the individual option) to the group. As long as the collaboration pertains to local public goods, the externality remains limited, ensuring that the existence of an individual option robustly aids collaborative success by fostering cooperation through improved optimism within groups.
Enhancing the anticancer activity of attenuated Listeria monocytogenes by cell wall functionalization with “clickable” doxorubicin
Authors: Lepori Irene , Roncetti Marta , Vitiello Marianna , Barresi Elisabetta , Tentori Paolo , Baldanzi Caterina , Evangelista Monica , Signore Giovanni , Tedeschi Lorena , Gravekamp Claudia , Cardarelli Francesco , Taliani Sabrina , Settimo Federico Da , Siegrist M. Sloan , Poliseno Laura 
Published: October 2023

Abstract
Among bacteria used as anticancer vaccines, attenuated Listeria monocytogenes (Lmat) stands out because it spreads from one infected cancer cell to the next, induces a strong adaptive immune response, and is suitable for repeated injection cycles. Here we use click chemistry to functionalize the Lmat cell wall and turn the bacterium into an “intelligent carrier” of the chemotherapeutic drug doxorubicin. Doxorubicin-loaded Lmat retains most of its biological properties and has enhanced cytotoxicity against melanoma cells compared to the control fluorophore-functionalized bacteria. Our results provide the first demonstration of a two-in-one therapeutic approach that in principle combines immunotherapy with chemotherapy through the covalent loading of Lmat cell wall with the drug of interest.
Tunable magnetism and electron correlation in titanium-based Kagome metals RETi3Bi4 (RE = Yb, Pr, and Nd) by rare-earth engineering
Authors: Wang Gang , Chen Long , Zhou Ying , Zhang He , Ji Xuecong , Liao Ke , Ji Yu , Li Ying , Guo Zhongnan , Shen Xi , Yu Richeng , Yu Xiao Hui , Weng Hongming 
Published: October 2023

Abstract

Rare-earth engineering is an effective way to introduce and tune the magnetism in topological Kagome magnets, which has been acting as a fertile platform to investigate the quantum interactions between geometry, topology, spin, and correlation. Here we report the structure and properties of three newly discovered titanium-based Kagome metals RETi3Bi4 (RE = Yb, Pr, and Nd) with various magnetic states. They all crystalize in the orthogonal space group Fmmm (No. 69), where slightly distorted Ti Kagome lattice, RE triangular lattice, Bi honeycomb and triangular lattices stack along the a axis. By changing the rare earth atoms on RE zag-zig chains, the magnetism can be tuned from nonmagnetic YbTi3Bi4 to short-range ordered PrTi3Bi4 (Tanomaly ~ 8.2 K), and finally to ferromagnetic NdTi3Bi4 (Tc ~ 8.5 K). The resistivity and specific heat capacity measurements demonstrate an evolution of electron correlation and density of states near the Fermi level with different rare earth atoms. In-situ resistance measurements of NdTi3Bi4 under high pressure further reveal a potential relationship between the electron correlation and ferromagnetic ordering temperature. These results highlight RETi3Bi4 as a new family of topological Kagome magnets to explore nontrivial band topology and exotic phases in Kagome materials.
Energy Storage Materials Performance and Assessment of Single Solar Distiller using a Natural Dye created by doping TiO2 with Dragon Fruit
Authors: Nagendrababu Arigela , Shanmugan Sengottaiyan , Kabeel Abd-Elnaby , Somlal Jarupula , Awad Mohamed M. , Essa Fadl A. 
Published: October 2023

Abstract
There is no longer a decent water shortage in rural or urban locations, and solar distillers' lower productivity is a result of their low cost and benefits. Therefore, design modifications were developed and tested in the summer (S) and winter (W) to maximize the output of the single solar distiller (SSD). This is accomplished by coating the distiller's absorber plate's backbone with dye made from Dragon fruit peel extract with TiO2 nanoparticles (DFPETN). The absorption spectra of the DFPTEN has a peak value of 535 nm and studied using UV-Vis. techniques. Finally, both conventional solar still (CSS) with & without coated DFPETN using SSD is evaluated in terms of their thermal, exergy, environmental, and economic possibilities. The outcomes showed that the SSD by DFPETN@30% yield was superior to that of the other DFPETN ratios. Moreover, the benefit of DFPETN@30% with coated the basin area to more yield of SSD has been reported as 4.026 kg/m2.daytime in summer (S), 3.958 kg/m2.daytime in winter (W) with a compared to 1.637 kg/m2daytime (S) and 1.629 kg/m2daytime (S) for the CSS, accordingly with improvement by 179% in SSD. The overall SSD efficacies with DFPETN@30% during the S and W have increased by around 38.87% and 34.69%, the exergy efficiency is 5.8% respectively, and CSS is 8.23% in the S and 7.14% in the W. The SSD with DFPETN@30% has an environmental parameter of 12 tones CO2 year and water costs is 0.24 and 0.10 $/L, respectively. We successfully demonstrated the utility of the SSD is utilizing dragon fruit as TiO2 in the creation of an inexpensive, environmental friendly solar distiller.
Research on the Innovative Application of Shen Embroidery Cultural Heritage Based on Convolutional Neural Network
Authors: Zhu Changyong , Zhu Jiajun 
Published: October 2023

Abstract

This paper proposes an innovative research approach using convolutional neural networks to address the issues of limited Shen Embroidery works, insufficient image datasets, and low recognition rates. Firstly, the experimental dataset was expanded through methods such as flipping, rotating, and color variations. Secondly, the MobileNet V1 network, based on deep learning, was selected for image recognition of Shen Embroidery. Then, transfer learning was applied to fine-tune the MobileNet V1 network. Finally, the avg pool in the MobileNet V1 was replaced with Spatial Pyramid Pooling (SPP) to achieve the fusion of local and global features. The experimental results showed that the improved MobileNet V1 achieved a recognition accuracy of 98.45%, which was 2.3% higher than the baseline MobileNet V1 network. The experiments demonstrated that the improved convolutional neural network can efficiently recognize Shen Embroidery and provide technical support for the intelligent development of intangible cultural heritage.
GPT-PACK: Can GenAI act as TPACK Peer Tutor for Preservice Teachers? A Comparative Study on Objective TPACK of Humans and ChatGPT 3.5
Authors: Çelik Ferdi , Ersanlı Ceylan Yangın , Drummond Aaron 
Published: October 2023

Abstract
This study employs a single case-control design to evaluate the objective TPACK performance of ChatGPT by comparing it to human preservice teachers. A purposive sampling method selected ChatGPT 3.5 as a potential GenAI peer tutor and 93 Australian preservice teachers as the normative sample. Objective TPACK performance was measured using an adapted TPACK scale. Bayesian analysis revealed that ChatGPT significantly outperformed the human control group with a substantial effect size. Qualitative data analysis of ChatGPT’s explanations further demonstrated its comprehensive understanding of TPACK dimensions. While certain limitations were identified, including the narrow focus on Australian preservice teachers and the availability of more advanced AI models, the study emphasizes ChatGPT 3.5’s potential as a more knowledgeable other for preservice teachers to enhance their TPACK. This research encourages further investigation into the integration of AI in education and its implications for pedagogical practices. ChatGPT 3.5’s remarkable TPACK performance suggests its role as a valuable peer tutor in TPACK-related domains, complementing human educators while recognizing the irreplaceable human aspects of the learning experience.
A comprehensive study on soil parameters influencing collapsibility of loess through data analysis
Authors: Motameni Sahand , Rostami Fateme , Soroush Abbas 
Published: October 2023

Abstract
Loessial soils may settle and collapse under significant loading or water inundation. Therefore, their mechanical behavior and collapse potential must be assessed before undertaking any construction work. However, conducting a thorough empirical assessment of soil collapsibility would be both costly and time-consuming. Therefore, in this study, a dataset, including the results of 756 oedometer test records on loessial soils, as well as the physical properties of the samples, has been gathered from 27 published papers. Subsequently, a grey relational analysis was used to identify the most influential factors among the thirteen most significant factors. The results showed that the most influential factors on the collapsibility of loess are the plasticity index, dry unit weight, initial void ratio, and degree of saturation. Finally, using the multivariate non-linear regression technique, a prediction equation has been derived to predict the collapse potential of loess with an acceptable level of accuracy.
Epidemiological and Molecular Study of Providencia Rettgeri Outbreak at an University Hospital During the Covid-19 Reference Center
Authors: Pimenta Julia , Magalhaes Gerusa Luciana Gomes , Soncini João Gabriel Material , Lincopan Nilton , Vespero Eliana 
Published: October 2023

Abstract

In 2014, Brazil detected New Delhi metallo-β-lactamase (NDM)-producing Enterobacterales from a Providencia rettgeri isolate obtained through surveillance swabs in the Southern region. Subsequently, various species have reported several NDM enzymes. However, comprehensive data on the current epidemiology of NDM-producing P.rettgeri in Brazil remains limited. This study, aimed to provide a detailed characterization of the phenotypic, genotypic and epidemiological profile of clinical isolates of P. rettgeri NDM. From April 2020 to December 2022, 18 carbapenem-resistant P. rettgeri strains, previously identified using Vitek2®, were isolated at the University Hospital of Londrina. Resistance and virulence genes were assessed through genetic analysis usig ERIC PCR and NextSeq (Illumina) sequencing. Statistical analysis was conducted using SPSS version 2.0. Genomic analysis confirmed the presence of β-lactamase blaNDM-1, blaOXA-1. All isolates showed the presence of the NDM gene and genetic similarity above 90% between isolates. Clinical parameters of patients infected with P. rettgeri exhibited significant association with mechanical ventilation, prior use of carbapenems, and polymixins. We also report a significant association between P. rettgeri infection and death outcome. This study characterizes NDM-1 metallo-β-lactmases isolates, among P. rettgeri isolates from patients at the University Hospital (HU), during the COVID-19 pandemic. The emergence of this novel resistance mechanism among P. rettgeri poses a significant challenge, limiting the therapeutic optins for infections in our hospital.
Experimental characterisation of defect-induced phonon lifetime shortening
Authors: Liu Boyao , Kelsall Jack , Ward David , Jardine Andrew 
Published: October 2023

Abstract
Phonon-mediated heat transport is of wide importance, particularly in thermal management for the rapidly advancing microelectronics industry. Since lattice imperfections strongly influence phonon scattering, which in turn determine thermal conductivity, it is crucial to understand phonon lifetimes and how they are influenced by material defects. To date, direct measurements of phonon lifetimes have been extremely limited; most studies have relied on indirect measurements of thermal properties, or theoretical calculations with limited validation. Here, we present direct experimental measurement of defect-induced lifetime shortening of acoustic surface phonons. Defects are found to contribute a temperature-independent component to the linewidths of Rayleigh wave (RW) phonons on a Ni(111) surface. We also characterised the increase in phonon scattering with both surface defect density and phonon wave vector. A quantitative estimate of the strength of the interaction between a phonon-mode and a surface line-defect is extracted from the data. Looking forwards, we anticipate direct characterisation of defect-phonon interactions in 2D systems will be of significant value to the active fields of phononics and nanoscale heat flow.