bluuebunny/crossref_metadata_2025_split huggingface dataset:
An uncertainty-aware deep learning ensemble approach for effective cutting tool predictive maintenance decision-making
Authors: Wang Yue , Wang Ganlong , Wu Yanxia , Zhang Guoyin , Wu Maopu 
Published: December 2024

Abstract
As the manufacturing sector increasingly emphasizes production efficiency and product quality, predicting cutting tool remaining useful life (RUL) and conducting failure analysis become essential for ensuring continuity and reducing costs. However, the uncertainties in the tool wear process complicate accurate predictions and decision-making. This paper introduces a predictive maintenance decision-making approach using an ensemble model of convolutional neural network and bidirectional long short-term memory quantile regression (CNN-BiLSTMQR), enhanced by an attention mechanism and kernel density estimation (KDE). Firstly, signals related to tool wear are collected from various sensors, and a CNN captures spatial features and local patterns in the data. These features are then input into a BiLSTM network, which integrates a temporal attention mechanism and QR to predict RULs at multiple quantiles. Subsequently, KDE generates the probability density distribution of the tool RULs. Finally, the costs and time associated with maintenance decisions are assessed to derive cost functions for tool replacement and ordering. By minimizing the two functions, the optimal timing for tool replacement and spare parts procurement is identified. Validation with a publicly available tool wear prediction dataset demonstrates that the proposed method effectively reduces maintenance costs while ensuring the safe and reliable operation of tools.
Cross-alignment of silver nanowires network for efficient nanowelding
Authors: Wang Chao , Song Bo , Zhai Xin , Zhang Che , Du Mengyang , Miao Yanqin , Dong Peng 
Published: December 2024

Abstract
The performance of silver nanowire (AgNW) network flexible transparent electrodes is limited by large contact resistance, making it necessary to perform nanowelding to improve conductivity of the network. However, not all nanowire junctions can be welded. Our work indicates that the welding kinetics between nanowires depend on the crossing angle, with higher surface diffusion velocity prone to welding and fracture at nanowire junctions of crossing angles close to 90 degrees. The impact of nanowire crossing angles on the welding process makes it difficult to achieve simultaneous welding of random AgNWs networks. To address this issue, we adopted an improved Meyer rod coating method to prepared a cross-aligned nanowire network based on a layer-by-layer assembly strategy. Compared to randomly distributed AgNWs networks (11.17 Ω sq−1, 85.2%), the cross-aligned AgNWs network achieved simultaneous welding of nanowire junctions during thermal annealing, further enhancing the optoelectronic performance (10.8 Ω sq−1, 90.3%) of the AgNWs network, resulting in a superior figure of merit value of 421.
Effect of MoS2 layers on the performance of FET-based biosensors: a comprehensive study
Authors: Datar Rishikesh , Bacher Gautam 
Published: December 2024

Abstract
MoS2-based transistors have attracted significant attention in several sensing applications owing to their ultra-thin structure and high mobility. In this work, the performance of MoS2 field-effect transistor-based biosensors (BioFETs) was systematically analysed with device-level modelling, considering different biomolecules such as uricase, streptavidin, cholesterol oxidase (ChOx), and aminopropyltriethoxysilane (APTES). The BioFETs were modelled utilizing appropriate physics related to the site-binding principle, the surface charge density of biomolecules, and the transport of diluted species in electrolytes. First, MoS2-based field-effect transistors with different top-oxide materials were evaluated for pH sensing, where HfO2 provided a near-Nernstian voltage sensitivity of 58.88 mV pH−1. Additionally, a MoS2-based BioFET model was developed with biomolecules on top of the HfO2 surface and analysis was carried out for single- and double-gated (DG) configurations. BioFET performance was evaluated based on drain-to-source current, subthreshold swing,

I

ON

/

I

OFF

, and voltage sensitivities. The effect of the MoS2 layers on the performance of the BioFET was analysed. For the monolayer MoS2-based BioFET, a maximum top-gate voltage sensitivity (

S

TV

) of 129.95% and a bottom-gate voltage sensitivity (

S

BV

) of 254.55% were obtained for APTES. However, the performance of a quadlayer MoS2-based BioFET in a DG configuration (

S

BV

of 221.69% for APTES) was comparable to that of a monolayer MoS2-based BioFET. Overall, this work highlights the significant role of MoS2 layers and gate configurations for BioFET performance in sensing applications.
Gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall gravity
Authors: Wang Tongzheng , Cui Yuhao , Lin Kai 
Published: December 2024

Abstract
In this article, we apply the WKB approximation method, matrix method, and finite difference method to study the gravitational quasi-normal modes of charged spherically symmetric black holes surrounded by quintessence fluid in Rastall gravity. By comparing the spherically symmetric spacetime metric of charged black holes surrounded by quintessence fluid in Rastall gravity with that of general relativity, we can find that the modifications to general relativity in this modified gravity theory can be described by parameters such as λ, Q, and Ca
, etc. In four-dimensional spacetime, we investigate the impact of charge Q and parameter Ca
on the gravitational quasi-normal modes of charged black holes surrounded by quintessence field in Rastall gravity. The aim is to search for observational evidence of such black holes in astrophysical observations and, consequently, test the validity of Rastall theory. In five-dimensional spacetime, we study the impact of the parameter Ca
on the gravitational quasi-normal modes of Rastall black holes surrounded by quintessence field and summarize the corresponding variation patterns.
Dynamical analysis and localized waves of the n-component nonlinear Schrödinger equation with higher-order effects
Authors: Lou 娄 Yu 瑜 , Xu 许 Guoan 国安 
Published: December 2024

Abstract
Under investigation is the n-component nonlinear Schrödinger equation with higher-order effects, which describes the ultrashort pulses in the birefringent fiber. Based on the Lax pair, the eigenfunction and generalized Darboux transformation are derived. Next, we construct several novel higher-order localized waves and classified them into three categories: (i) higher-order rogue waves interacting with bright/antidark breathers, (ii) higher-order breather fission/fusion, (iii) higher-order breather interacting with soliton. Moreover, we explore the effects of parameters on the structure, collision process and energy distribution of localized waves and these characteristics are significantly different from previous ones. Finally, the dynamical properties of these solutions are discussed in detail.
Two-photon Dissociation of BeH+ with a Middle Ultraviolet Band Laser
Authors: Zhang Qian-Yu , Bai Wen-Li , Ao Zhi-Yuan , Peng Wen-Cui , He Sheng-Guo , Tong Xin 
Published: December 2024

Abstract
Two-photon dissociation of BeH+ ions is studied by detecting the fluorescence changes of Be+-BeH+ bi-component Coulomb crystal in a linear Paul trap. BeH+ ions generated by an exothermic reaction between electronically excited Be+ ions and residual H2 in the vacuum chamber are photon-dissociated with two photons scanning over the range of 201 nm to 208 nm. Our experiment provides a novel method to maintain the number of Be+ ions stable in a Coulomb crystal with a middle ultraviolet band dissociation laser. This two-photon dissociation method extends the wavelength range of the dissociation laser for BeH+ compared to the one-photon dissociation, and the method can be utilized to all alkaline earth atomic ions which require suppression of the reaction with residual H2 gas in vacuum.
Up to half of companies would be behind on their climate targets under stricter scope 2 accounting rules
Authors: Bjørn Anders , Lund Jens Friis , Brander Matthew 
Published: December 2024

Abstract
Companies have been accused of overstating their scope 2 emission reductions through purchases of ineffective renewable energy certificates (RECs). Therefore, several actors have proposed stricter accounting rules targeting additionality and deliverability. In this paper we explore how such restrictions would quantitatively affect the scope 2 emissions and target progress reported by companies. We consider three accounting restrictions: geographical proximity, facility age, and power purchase agreement. The study involves adjusting the market-based scope 2 emissions reported by 206 companies based on the portion of their purchased RECs that would become invalid under the proposed accounting restrictions. We find that the sample companies have more than doubled their purchases of RECs between their base year (typically 2019) and 2022, including an increase in RECs that would be invalid under the proposed accounting restrictions. The introduction of all three accounting restrictions would entail that the combined scope 2 emission reduction reported by the sample companies falls from 21% to 17%. Similarly, the share of sample companies that were behind on their climate target in 2022 would increase from 28% to 50% under all three accounting restrictions. However, a small subset of the company sample has over time replaced potentially invalid RECs with valid RECs, and the accounting restrictions would lead to improved target performance for these companies. Our findings can inform the revision of the greenhouse gas protocol and emerging accounting standards around renewable fuels.
Entry points for assessing ‘fair shares’ in national mitigation contributions
Authors: Pelz Setu , Ganti Gaurav , Pachauri Shonali , Rogelj Joeri , Riahi Keywan 
Published: December 2024

Abstract
Fairness considerations have long been central to the international climate change mitigation discourse, generating numerous scientific and philosophical debates. Yet, there remains a pressing need for practical guidance on developing assertions of fairness in national mitigation contributions. The Paris Agreement mandates that subsequent nationally determined contributions (NDCs) submitted under Article 4 represent a progression compared to previous NDCs. Further decisions under the Paris Agreement mandate that NDCs include clear and transparent considerations of fairness, as recalled in the first Global Stocktake. We propose a practical approach to this, comprising a set of ‘entry points’ that represent key stages where decisions are taken in ‘fair share’ quantifications, from interpreting foundational principles to selecting indicators and presenting results. By bridging the gap between scholarly debates and practical application, this work supports the integration of clear and transparent fairness considerations into climate policy commitments. We demonstrate the use of our approach through a case study.
Photocatalytic performance of sulfur-doped zinc oxide: green synthesis via matoa leaf extract mediation
Authors: Rini A Sulistyo , Syahrul M , Rati Y 
Published: December 2024

Abstract
The rapidly expanding industrial sector frequently generates hazardous waste for the environment. Semiconductor materials can be employed to remove contaminants in the photocatalyst process. In this paper, sulfur-doped ZnO (at 1%–5% S) has been prepared using matoa leaf extract as a biostabilizer. The synthesized sample was characterized using XRD, FESEM, EDX, UV–vis, and spectroscopy to study its crystallinity, morphology, elemental composition, absorption, and functional group. XRD analysis confirmed a wurtzite hexagonal-shaped crystal with a high purity of ZnO without other phases. Dopant S does not significantly affect the size of the crystal. Nanoflakes of ZnO have changed to nanospheres as dopant S increases. The S dopants reveal broadening absorption peaks with narrow bandgap energy from 2.82–3.08 eV. Sulfur-doped ZnO successfully degrades 4-nitrophenol by 98.79% after illumination under UV light for 120 min. Therefore, S-doped ZnO green synthesis has a great opportunity to be applied to degrade pollutants found in the aquatic environment.
The effects of leaching and pyrolysis on particle synthesis and their impact on thermal conductivity and cooling rates of waste PCB-dispersed quenchants
Authors: Putra Wahyuaji Narottama , Ariati Myrna , Siradj Eddy S , Suharno Bambang 
Published: December 2024

Abstract
This study investigates the effects of pre-treatment processes—leaching and pyrolysis—on synthesizing particle-dispersed quenchants derived from waste printed circuit boards (PCBs) for enhanced heat treatment applications. The process begins with milling PCB powder with and without pre-treatment, followed by thermal conductivity and cooling rate analysis of synthesized quenchants. Findings reveal that full pre-treated particles, which undergo leaching and pyrolysis, achieve significantly smaller sizes, up to 80.4%, compared with the half-pre-treated particles. The better improvement was due to the breakdown of epoxy resin used in the PCB. The particle size reduction enhances the quenchant’s thermal conductivity up to 17.47% for the quenchant with 0.5% dispersed particle. Similarly, the maximum cooling rate was improved by 16.93% for the same quenchant. Quenching tests on S45C medium carbon steel demonstrate that particle-dispersed quenchants increase the hardness of the quenched steel, maximized at 57.8 HRC, because of the higher density of the Martensite phase developed by the higher cooling rate. This research highlights the value of pre-treatment in optimizing quenchant performance, with potential implications for improved efficiency in industrial heat treatments.
Effect of Al contents on the microstructure and cavitation erosion resistance of TiSiN/NiTiAlxCrCoN nanomultilayer films
Authors: Yan Hongjuan , Cheng Fangying , Dou Zhaoliang , Si Lina , Yang Ye , Liu Fengbin 
Published: December 2024

Abstract
The TiSiN/NiTiAl
x
CrCoN nanomultilayer films were grown on 304 stainless steels through magnetron sputtering system. The microstructure and cross-sectional and surface images, mechanical and cavitation erosion properties of the prepared films were explored in detail. The results of the study reveal that all TiSiN/NiTiAl
x
CrCoN nanomultilayer films exhibit a face-centered cubic structure with a preferential orientation in (200) plane. The cross-sectional images of films show the columnar structure. The values of elastic modulus, hardness, and adhesive strength for the TiSiN/NiTiAl
x
CrCoN nanomultilayer films initially increase and later decrease as the Al content is increased, whereas the mass loss for the TiSiN/NiTiAl
x
CrCoN nanomultilayer films initially decrease and later increase during the cavitation erosion experiment. The TiSiN/NiTiAl0.8CrCoN nanomultilayer film provides a maximum hardness value equal to 31.3 GPa, a maximum elastic modulus equal to 218.9 GPa, a highest adhesive strength equal to 38.8 N and a least mass loss equal to 0.8 mg. The study showed that the solid solution strength and the alternating stress field enhance the properties of the TiSiN/NiTiAl
x
CrCoN nanomultilayer films.
Minimal design of multi-stage reservoir based on small-world network
Authors: Kitayama Ken-ichi 
Published: December 2024

Abstract
We will present a methodology of dividing a large single reservoir into number of small reservoirs and concatenating them in a multi-stage architecture. Our goal is to pursue the minimal design of multi-stage reservoir in terms of the reservoir size and the number of stages, while the performance is equivalent to a single reservoir having much larger number of nodes than the total number of nodes of multi-stage reservoir. A motivation behind this is to explore a good balance between the computational capability and downsizing hardware of photonic reservoir computing (RC) so that advanced photonic integrated circuits (PICs) technology such as multi-chip module and chiplet can afford in near future. To this end, we will pick up one of the multi-stage reservoir models and introduce small-world network instead of conventional random network reservoir to enable a systematic approach to synthesis of the optimum reservoir model. To validate the proposed methodology, we conduct benchmark tests, including both predictions and classification of time series data. We demonstrate that the proposed minimal design for the RC hardware allows a substantial downsizing without any loss of the performance. Finally, we will present a conceptual design of the minimal multi-stage reservoir for the photonic chiplet.
An appraisal of the competence of mathematical fuzzy logic approach via adaptive neuro-fuzzy inference system (ANFIS) in biodiesel production from algae oil
Authors: Sharma Divyangana , Goel Vivek , Kumar Sunil 
Published: December 2024

Abstract
The fuel crisis and environmental problems can be resolved using biodiesel from various basic materials. This paper uses the transesterification process and segregation to produce biodiesel from algal oil. According to the four input factors (power, methanol-to-oil percentage, catalyst utilized, and process time), 29 experiments have been conducted to manufacture biodiesel. This work focuses on modeling and estimating the processes involved in producing biodiesel, specifically from algal oil, using the (ANFIS) adaptive neuro-fuzzy inference system methodology. The determination coefficient (R-square) and the mean root-mean-square error (RMSE) were employed to reconcile the ANFIS findings with the true results of the research. During training, the RMSE statistical variables were 1.209 and the R-squared was 0.9742. This instance, involves the ANFIS Framework additionally the Gaussian membership function was used and examined. This modeling approach shows promise for use in the biodiesel manufacturing process, potentially increasing the efficacy and efficiency of the generation of biodiesel from algal oil, given the high estimation accuracy shown by the ANFIS.
Partial discharge inception voltage in PCB defect samples under different frequencies of alternating sine wave voltage
Authors: Chang Chien-Kuo , Lyu Chong-Yi , Tsai Hua-Wen , Boyanapalli Bharath Kumar 
Published: December 2024

Abstract
As the application of power electronic components in renewable and green energy technologies continues to increase, there is an increasing trend in the utilization of higher voltage and frequency levels. This shift may result in more frequent partial discharge (PD) phenomena that can accelerate the degradation of insulation materials and shorten the lifespan of the components. Therefore, it is critical to ensure the reliability and durability of these components. This study investigated the effects of voltage magnitude and frequency on the PD and insulation degradation of printed circuit board (PCB) samples. Five different test samples were fabricated using a PCB as the substrate and an epoxy resin as the insulating coating. Two distinct testing configurations were employed: one for assessments based on a commercial frequency (60 Hz) and the other for assessments based on a high frequency (600 Hz). This study examined the behavior of PD within PCBs and the resulting insulation deterioration. The results demonstrated that all the samples exhibited significantly lower PD inception voltages (PDIV) at high frequencies compared with those at the commercial frequency. Specifically, the maximum decrease in the PDIV observed was 5 kV, while the minimum reduction was 3.3 kV. These findings highlight the critical impact of frequency on the PD behavior and underscore the importance of understanding these effects to enhance the reliability of electronic components in high-frequency applications.
Benchmarking artifact of selective laser sintering (SLS) components fabricated with flexible and rigid polymers
Authors: Shen Hor Zian , Muhayudin Nor Amalina , Basaruddin Khairul Salleh , Mohamad Khan Shah Fenner Khan , Tajul Liyana , Hoang Tien-Dat 
Published: December 2024

Abstract
This study aims to assess the performance of an Additive Manufacturing (AM) machine, specifically a Selective Laser Sintering (SLS) machine, through the design and evaluation of a benchmarking artifact. Drawing from insights gained in previous research, the artifact is meticulously crafted with two distinct materials to explore potential variations in geometric accuracy. The artifact comprises two types: one featuring straight geometries and another incorporating curved elements. The research methodology involves printing both artifact types at default machine settings, followed by precise measurements using a 3D scanner. The inclusion of straight and curved features facilitates a comprehensive examination of the machine’s ability to reproduce diverse geometries. The amalgamation of these features into a combined artifact provides a holistic assessment of the machine’s overall performance. To validate the benchmarking artifact, the final design is reproduced, and its output is compared not only with the original design but also with real-life parts. The results show that flexible polymers offer higher accuracy but lower resolution, while rigid polymers provide better resolution but with a greater number of defects. This comparative analysis serves to highlight the accuracy and reliability of the benchmarking artifact in reflecting the machine’s performance in practical scenarios. In conclusion, this study endeavours to advance the understanding of an SLS machine’s capabilities by leveraging a carefully designed benchmarking artifact.
Large language models for causal hypothesis generation in science
Authors: Cohrs Kai-Hendrik , Diaz Emiliano , Sitokonstantinou Vasileios , Varando Gherardo , Camps-Valls Gustau 
Published: December 2024

Abstract
Towards the goal of understanding the causal structure underlying complex systems—such as the Earth, the climate, or the brain—integrating Large language models (LLMs) with data-driven and domain-expertise-driven approaches has the potential to become a game-changer, especially in data and expertise-limited scenarios. Debates persist around LLMs’ causal reasoning capacities. However, rather than engaging in philosophical debates, we propose integrating LLMs into a scientific framework for causal hypothesis generation alongside expert knowledge and data. Our goals include formalizing LLMs as probabilistic imperfect experts, developing adaptive methods for causal hypothesis generation, and establishing universal benchmarks for comprehensive comparisons. Specifically, we introduce a spectrum of integration methods for experts, LLMs, and data-driven approaches. We review existing approaches for causal hypothesis generation and classify them within this spectrum. As an example, our hybrid (LLM + data) causal discovery algorithm illustrates ways for deeper integration. Characterizing imperfect experts along dimensions such as (1) reliability, (2) consistency, (3) uncertainty, and (4) content vs. reasoning are emphasized for developing adaptable methods. Lastly, we stress the importance of model-agnostic benchmarks.