bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness of turbulent fountains in frost mitigation and pollution control
Authors: Caporale Daniel Freire , Sarasúa Luis G. , Barrere Nicasio , Marti Arturo C.
Published: January 2025

We investigate the efficacy of turbulent fountains as a tool for frost mitigation and pollution control in stratified ambient fluids. By examining the interaction between the ejected fountain fluid and the lower atmospheric layers, we develop predictive models to assess their impact on temperature distribution and the return of polluted ejected fluid to the ground. This analysis covers a range of fountain parameters, including Fr−2 and u′/U, where Fr is the Froude number and u′/U represents the turbulent intensity at the inlet. Notably, we identify an optimal set of parameter values where the temperature rise is maximized. This optimal condition occurs in the so-called semi-collapse regime, as described by Sarasúa et al. [“The influence of source Froude number and turbulent fluctuations on the development of turbulent fountains in stratified ambient,” Flow, Turbul. Combust. 112(4), 1009–1025 (2024)], where the returning fluid, mixed with warmer upper ambient layers, significantly increases local temperature. Our findings underscore the importance of carefully tuning fountain parameters to balance their effects on the surrounding environment, offering valuable insights for the practical use of turbulent fountains in environmental management.
New higher-order super-compact finite difference scheme to study three-dimensional natural convection and entropy generation in power-law fluids
Authors: Punia Ashwani , Ray Rajendra K.
Published: January 2025

This work introduces a new higher-order super-compact implicit finite difference scheme for analyzing three-dimensional (3D) natural convection and entropy generation in generalized Newtonian fluids governed by the power-law model. The proposed scheme achieves fourth-order accuracy in space and second-order accuracy in time. “Super-compact” term signifies its efficiency, utilizing 19 grid points at the current time level (nth time level ) and only seven directly adjacent grid points at the subsequent time level [(n+1)th time level] around which the finite difference discretization is made. To the best of our knowledge, this is the first higher-order accurate finite difference scheme proposed to study 3D natural convection and entropy generation in power-law fluids. This advancement marks a significant step forward in the computational modeling of power-law fluids, addressing the limitations of lower-order methods that often compromise accuracy and computational efficiency. A time-marching technique is applied, where pressure corrections are addressed using a modified artificial compressibility method. Numerical simulations explore the effects of Rayleigh numbers (Ra=102–105) and power-law indices (n = 0.75–1.25) on natural convection and entropy generation within a 3D cubic cavity. Validation of the proposed scheme demonstrates excellent agreement with existing benchmark results. The numerical study reveals that as Ra increases, the maximum value of average Nusselt number (Nuavg) also increases, whereas an opposite trend is observed with n values. Shear-thinning fluids demonstrate the highest convection efficiency compared to Newtonian and shear-thickening fluids at any specific Ra.
Effects of legume-based aquafaba on batter rheology and quality characteristics of microwave-infrared baked cakes
Authors: Akbin Alara Cagdan , Turabi Yolacaner Elif , Sumnu Gulum
Published: January 2025

The aim of this study was to determine the effects of egg white replacement by aquafaba (obtained from different legume sources that are cowpea and lupin) at different levels (0%–50%–100%) in cake formulation while examining the batter rheology and cake quality characteristics baked in conventional oven and microwave-infrared hybrid oven (MW-IR). The physical properties (emulsion stability, foaming capacity, and foaming stability) and steady shear measurements of aquafaba samples, the rheological characteristics of cake better samples (flow behavior and dynamic oscillatory behavior), and quality parameters of cake samples (weight loss, color, texture profile analysis, and specific volume) were examined. Emulsion stability of egg replacers (different levels of aquafaba) was not as high as egg white. Although lupin aquafaba had higher foaming capacity than cowpea aquafaba, their foaming stability were not significantly different (p ≤ 0.05). Flow characteristics of egg replacers and cake batters were modeled by power law (r2 = 0.989–0.999), Casson models (r2 = 0.994–0.999), Herschel–Bulkley (r2 = 0.998–0.999), and Bingham models (r2 = 0.988–0.999), indicating all samples had a shear-thinning behavior where cowpea aquafaba and the cake batter had the highest apparent viscosity. Oscillatory measurements of cake batter samples resulted in a solid elastic-like behavior, tan δ < 1, where the lowest tan δ values belonged to cowpea aquafaba batter. Egg replacement at all levels affected the color of cake samples. The texture of the cake samples was not significantly affected from the different oven types, and the eggless cake samples were the softest. Cakes containing egg white had the highest springiness, chewiness, and gumminess values. Aquafaba cakes were the least springy across both baking methods and were much less chewy, particularly in conventional baking. Aquafaba cakes were the least gummy, with MW-IR baking reducing gumminess further for these formulations. The full replacement of egg white resulted in a significant decrease in specific volume in microwave-infrared baked cakes. Substituting 50% of egg white by lupin aquafaba could be a valid alternative in MW-IR baked cakes.
TIP
4
P
2005
Ice
: Simulating water with two molecular states
Authors: Sedano Lucía F. , Vega Carlos , Noya Eva G. , Sanz Eduardo
Published: January 2025

Rigid, non-polarizable water models are very efficient from a computational point of view, and some of them have a great ability in predicting experimental properties. There is, however, little room for improvement in simulating water with this strategy, whose main shortcoming is that water molecules do not change their interaction parameters in response to the local molecular landscape. In this work, we propose a novel modeling strategy that involves using two rigid non-polarizable models as states that water molecules can adopt depending on their molecular environment. During the simulation, molecules dynamically transition from one state to another depending on a local order parameter that quantifies some local structural feature. In particular, molecules belonging to low- and high-tetrahedral order environments are represented with the TIP4P/2005 and TIP4P/Ice rigid models, respectively. In this way, the interaction between water molecules is strengthened when they acquire a tetrahedral coordination, which can be viewed as an effective way of introducing polarization effects. We call the resulting model TIP4P2005Ice and show that it outperforms either of the rigid models that build it. This multi-state strategy only slows down simulations by a factor of 1.5 compared to using a standard non-polarizable model and holds great promise for improving simulations of water and aqueous solutions.
An accurate temperature field reconstruction method using schlieren imaging and optical flow for high-speed flow fields
Authors: Wu Jun , Zhang Zhen , Zhu Yuheng , Tang Yuanhong , Guo Runxia , Chen Jiusheng , Xing Zhiwei
Published: January 2025

The temperature distribution in the tail jet field of aircraft engines is crucial for reflecting combustion efficiency and understanding the formation mechanism of pollutants. The traditional temperature sensor measurement method has a series of shortcomings such as single point measurement, destruction of the flow field and often limited spatial and temporal resolution, so it is not suitable for the application scenarios of the tail jet temperature field measurement. Schlieren method, as a visual technique for measuring flow fields, is an effective method for real-time measurement of flow field parameters, with the characteristics of large measuring range, fast response speed and simple testing equipment. To improve the accuracy of the traditional schlieren method, this paper presents a temperature field distribution reconstruction method by decoupling flow velocity and density field. First, the light deflection angle of the schlieren image is obtained by the change of brightness and darkness in the picture, and then the density distribution of the flow field is obtained indirectly. Then, through the schlieren images of continuous frames, the flow velocity distribution is obtained by using the optical flow velocity measurement algorithm. The optical flow algorithm is based on constraint condition for brightness invariance and global smoothing assumption. After that, the obtained density and velocity information can be used to calculate the pressure distribution of the flow field by applying the central difference to the simplified Navier–Stokes equation. Finally, the temperature distribution of the flow field can be obtained by using the obtained flow velocity, density and pressure information through the numerical calculation of the energy equation.
Comprehensive deep learning for combustion chemistry integration: Multi-fuel generalization and a posteriori validation in reacting flow
Authors: Li Han , Yang Ruixin , Xu Yangchen , Zhang Min , Mao Runze , Chen Zhi X.
Published: January 2025

The application of deep neural networks (DNNs) holds considerable promise as a substitute for the direct integration of combustion chemistry in reacting flow simulations. However, challenges persist in ensuring high precision and generalization across various fuels and flow conditions, particularly in a posteriori time-evolving flame simulations. This study performs comprehensive deep learning with multi-fuel generalization and computational fluid dynamics (CFD) validations. The process begins with generating thermochemical base states from low-dimensional canonical laminar flames to facilitate generalization and minimize the complexity of data generation. An effective perturbation and data augmentation strategy is then employed to broaden the coverage of the composition space for multi-dimensional flame configurations. Without the need for extensive tuning, three DNNs were consistently trained for three representative fuels: hydrogen, ethylene, and Jet-A. These DNN models were subsequently integrated into our recently developed open-source CFD package, DeepFlame (https://github.com/deepmodeling/deepflame-dev), for a posteriori reacting flow simulations and thoroughly validated against laminar flames and two representative turbulent premixed flames. The DNNs strongly agreed with the direct integration results across various combustion characteristics, including laminar and turbulent flame speeds, dynamic flame structures influenced by turbulence-chemistry interactions, and conditional scalar profiles. These findings underscore the exceptional accuracy and generalization capability of the employed deep learning approach. Moreover, by leveraging graphics processing units for model inference, the integration of DNN into CFD simulations resulted in significant speed-ups, achieving factors of approximately 72 for ethylene/air flames and 102 for Jet-A/air flames. The integrated DNN-CFD solver and test cases (https://www.aissquare.com) are openly shared, providing valuable tools to advance DNN development for chemical kinetics.
Gauge invariance applies to statistical mechanics too
Authors: Miller Johanna L.
Published: January 2025

Mathematical tools from the abstract world of quantum fields have surprising relevance to the seemingly more concrete realm of particles in boxes.
Every Rock Has a Story
Authors: van Cuylenburg Kitty , Hart Cher
Published: January 2025

Australia is full of unique geological formations, fascinating fossils and intriguing minerals. Every Rock Has a Story: An A to Z of Australian Geology explores our incredible landscapes and offers captivating insights into our exceptional continent.

Discover pink diamonds and stromatolites, the Great Artesian Basin and Uluru, and granite rocks formed billions of years ago. With stunning illustrations, engaging text and packed with fun facts, this is the perfect book for anyone curious about Australian geology.

Reading level varies from child to child, but we recommend this book for ages 8 to 12.
An exploratory analysis of forest fine fuel consumption and accumulation using forest inventory data and fire history
Authors: Nguyen Trung H. , Jones Simon , Reinke Karin J. , Soto-Berelov Mariela
Published: January 2025

Background Estimating changes in fine fuel loads (FFL) is essential for carbon monitoring and fire management. Field measurements of post-fire fuel response are challenging, leading to reliance on generalised fuel types in operational models. Aims This study presents a proof-of-concept for estimating fine fuel consumption and accumulation by integrating forest inventory and fire records, aiming to refine fuel dynamics estimates and enhance current practices. Methods We estimated FFL changes across vertical strata in southeast Australian eucalypt forests, considering burn severity, fire type and forest cover. Fuel consumption was estimated by correlating pre-fire observations with combustion factors defined by burn severity. Fuel accumulation was predicted using modified Olson models with dynamic input parameters. Key results Wildfires typically occurred in forests with higher FFL and consumed more fuels than prescribed burns. Closed forests experienced greater fuel loss compared with open and woodland forests. Increasing fire severity led to lower decomposition rates and a longer time to reach pre-fire FFL, with denser forests showing higher accumulation rates. Conclusions Integrating forest inventory and fire history data offers valuable insights into fuel dynamics, potentially enhancing existing fuel hazard models. Implications The approach is applicable in regions with mature forest inventories and advanced fire severity mapping.
A synapse-specific refractory period for plasticity at individual dendritic spines
Authors: Flores Juan C. , Sarkar Dipannita , Zito Karen
Published: January 2025

How newly formed memories are preserved while brain plasticity is ongoing has been a source of debate. One idea is that synapses which experienced recent plasticity become resistant to further plasticity, a type of metaplasticity often referred to as saturation. Here, we probe the local dendritic mechanisms that limit plasticity at recently potentiated synapses. We show that recently potentiated individual synapses exhibit a synapse-specific refractory period for further potentiation. We further found that the refractory period is associated with reduced postsynaptic CaMKII signaling; however, stronger synaptic activation fully restored CaMKII signaling but only partially restored the ability for further plasticity. Importantly, the refractory period is released after one hour, a timing that coincides with the enrichment of several postsynaptic proteins to preplasticity levels. Notably, increasing the level of the postsynaptic scaffolding protein, PSD95, but not of PSD93, overcomes the refractory period. Our results support a model in which potentiation at a single synapse is sufficient to initiate a synapse-specific refractory period that persists until key postsynaptic proteins regain their steady-state synaptic levels.
The magnitude of legal wildlife trade and implications for species survival
Authors: Michael Marshall Benjamin , Alamshah Aubrey L. , Cardoso Pedro , Cassey Phillip , Chekunov Sebastian , Eskew Evan A. , Fukushima Caroline S. , García-Díaz Pablo , Gore Meredith L. , Lockwood Julie L. , Rhyne Andrew L. , Sinclair James S. , Thomas Strine Colin , Stringham Oliver C. , Tlusty Michael F. , Valdez Jose W. , Watters Freyja , Hughes Alice C.
Published: January 2025

The unsustainable use of wildlife is a primary driver of global biodiversity loss. No comprehensive global dataset exists on what species are in trade, their geographic origins, and trade’s ultimate impacts, which limits our ability to sustainably manage trade. The United States is one of the world’s largest importers of wildlife, with trade data compiled in the US Law Enforcement Management Information System (LEMIS). The LEMIS provides the most comprehensive publicly accessible wildlife trade database of non-the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) listed species. In total, 21,097 species and over 2.85 billion individuals were traded over the past 22 y (2000-2022). When LEMIS data are combined with CITES records, the United States imported over 29,445 wild species, including over 50% of all globally described species in some taxonomic groups. For most taxa, around half of the individuals are declared as sourced from the wild. Although the LEMIS provides the only means to assess trade volumes for many taxa, without any associated data on most wild populations, it is impossible to assess the impact on biodiversity, sustainability of trade, or any potential risk of pest or pathogen spread. These insights underscore the considerable underestimation of trade and the urgent need for other countries to adopt similar mechanisms to accurately record trade.
When performance studies meet discourse theory
Authors: Aiolfi Théo
Published: January 2025

Despite its versatility as a methodological tool, discourse analysis suffers from a logocentric bias, a tendency
to primarily focus on text while ignoring the non-textual components of discourse. Although poststructural approaches to discourse
analysis like Discourse Theory (DT) have developed an understanding of discourse going beyond language, there are few practical
methodological tools for scholars from these traditions to engage with these non-textual elements. This article fills that gap by
providing an original qualitative methodological tool, the Political Performance Analysis Protocol (PPAP) which adapts one of the
signature methods of Performance Studies to political performances. It does so by describing the four constitutive elements of
political performances — background symbols and foreground scripts, actor, audience and mise-en-scène — and empirically
illustrating them through the case of Greta Thunberg’s “How dare you?” performance at a United Nations to show the PPAP’s
relevance as a tool complementing other forms of discourse analysis.
Google Translate versus DeepL in Spanish to English translation of Don Quixote
Authors: Ibáñez Moreno Ana , Esther Domínguez Mora María
Published: January 2025

This paper analyses the effectiveness of neural machine translation when applied to literary translation and, more specifically, to the translation of collocations, one of the most difficult aspects in machine translation (Corpas-Pastor 2015; Shraiden and Mahadin 2015). Literary translation continues to constitute one of the biggest challenges for machine translation (Toral and Way 2018), where cohesion errors are amongst the most frequent (Voigt and Jurafsky 2012). A comparative analysis of the translation of the first chapter of the world literature masterpiece El ingenioso hidalgo don Quijote de la Mancha — known as Don Quixote in English — was carried out, paying close attention to collocations. The human translation done by Tom Lathrop (Don Quixote)1 was compared to the target texts obtained with the two biggest neural machine translation systems today, Google Translate and DeepL, to see which provided more accurate results. The results confirm that neural machine translation offers highly reliable results. On a quantitative level the margins are very narrow when determining which system, DeepL or Google Translate, is better. DeepL scored better in terms of accuracy and recall, but in the BLEU metrics Google Translate scored 28.10 and DeepL 26.63. On a qualitative level and from a subjective point of view, we found DeepL’s translation to be somewhat more fluid and natural than Google Translate’s.
Applying neural machine translation and ChatGPT in the teaching of business English writing
Authors: Xu Jun , Wang Qingran
Published: January 2025

As language teaching becomes more complex and diverse, there has been a rapid increase in the demand for advanced technology, driving the widespread adoption of neural machine translation (NMT) and ChatGPT in the field. This study contributes to the literature on the use of technology in language teaching by evaluating the application of NMT technology and ChatGPT in teaching English as a foreign language (EFL) writing in three business fields: finance, economics, and business administration. By building six comparable corpora consisting of students’ direct-writing and post-edited writing based on machine-translated texts, we examined whether NMT can help improve students’ performance in business English writing classes, and whether ChatGPT can complement NMT. Our statistical analyses show that in general, NMT can enhance the proficiency of students’ academic writing, but its improvement effect works on different dimensions for those students studying in different majors. Specifically, for finance students, NMT can improve their academic writing at the word and syntax levels and mechanics, while it harms the organizational dimension. For students in economics, the improvement effect of NMT mainly focuses on enhancing the dimensions of syntax, cohesion, and mechanics, whereas for students in business administration, NMT works primarily on the dimensions of content, cohesion, and mechanics. As for the dimensions where NMT performs poorly, our analysis of students’ essay writing shows that ChatGPT can complement NMT by making improvements and providing feedback to students. Our paper adds value to existing research on the use of technology in language teaching by investigating the application of NMT and ChatGPT in teaching EFL writing, and by proposing potential directions for their use in the teaching of writing business English.
Spatiotemporal dynamics of fetal liver hematopoietic niches
Authors: Mesquita Peixoto Márcia , Soares-da-Silva Francisca , Bonnet Valentin , Zhou Yanping , Ronteix Gustave , Santos Rita Faria , Mailhe Marie-Pierre , Nogueira Gonçalo , Feng Xing , Pereira João Pedro , Azzoni Emanuele , Anselmi Giorgio , de Bruijn Marella F.T.R. , Perkins Archibald , Baroud Charles N. , Pinto-do-Ó Perpétua , Cumano Ana
Published: January 2025

Embryonic hematopoietic cells develop in the fetal liver (FL), surrounded by diverse non-hematopoietic stromal cells. However, the spatial organization and cytokine production patterns of the stroma during FL development remain poorly understood. Here, we characterized and mapped the hematopoietic and stromal cell populations at early (E12.5–14.5) FL stages, revealing that while hepatoblasts were the primary source of hematopoietic growth factors, other stromal cells—including mesenchymal, mesothelial, and endothelial cells—also contributed to this signaling network. Using a dedicated image analysis pipeline, we quantified cell distances to tissue structures and defined neighbor relationships, uncovering that different hematopoietic progenitors exhibit distinct preferences for neighboring stromal cells and show developmental changes in spatial distribution. Notably, our data suggest that the sub-mesothelium region plays a prominent role in early fetal hematopoiesis. This approach offers a valuable tool for studying complex cellular interactions in biological systems, providing new insights into hematopoietic niche organization during development.
Using neural networks: a guidance with application in inverse heat conduction problem
Authors: Shang Yuanbin , Tan Chaofa , Yu Xueling , Hu Xiaoyu , Jiang Hongquan , Ma Wenjiang , Liu Donghuan
Published: January 2025

In recent years, significant progress has been made in the development and application of artificial intelligence, spanning various fields and presenting powerful potential applications. The investigation of how to integrate the achievements of artificial intelligence with traditional physical problems has become a crucial task. Neural networks are considered as a pivotal technology of artificial intelligence, showcasing their distinctive ability to comprehend intricate nonlinear relationships through simulation and learning, and has showed exceptional performance in handling large-scale data and addressing inverse problems. This article begins by introducing the fundamental principles of neural networks, providing a detailed explanation of the process involved in constructing classical neural networks. A neural network was constructed on the MATLAB platform, and the practical application process was illustrated through lively demonstrations using simple examples. Tailored primarily for physics education, this article is designed to be suitable for undergraduate-level educational purposes. Through the integration of neural networks with practical inverse heat conduction problem, we not only provide a more interactive and dynamic learning experience but also enrich undergraduate students’ comprehension and interest in relevant issues within higher education. This integrated teaching approach has the potential to propel interdisciplinary collaboration, nurturing innovation and advancement within the field of physics education.
2D hybrid simulation of a dual-frequency capacitively coupled nitrogen discharge: effects of gas pressure and low-frequency voltage on generation and distribution of species
Authors: Zhou Fang-Jie , Zhang Yu-Ru , Wen De-Qi , Liu Yong-Xin , Wang You-Nian
Published: January 2025

The effect of the gas pressure and the low frequency voltage,

V

LF

, on the spatial distributions of electron energy distribution function (EEDF), electron and neutral densities in a dual-frequency (DF 2/60 MHz) driven capacitive discharge in nitrogen (N2) is investigated by employing a two-dimensional fluid model with electron collision processes treated by Monte Carlo model. It is found that by increasing the pressure the region of strong ionization shrinks immensely towards the sheath edge of the powered electrode and the ionization maximum occurs below the edge of the powered electrode, leading to edge-high density profiles of the electron and excited-state nitrogen molecules at a higher pressure, while the nitrogen atom density remains uniform in space, due to higher transport coefficient. By increasing

V

LF

, the discharge becomes more symmetrical in axial direction, the ionization rate changes from a radial uniform to an edge-high profile, leading to an edge-high profile of electron density. The EEDF is found to exhibit a dip at

ε

= 2.35 eV, due to depletion of electron via the electron-impact vibrational excitation of nitrogen molecule. This dip of the EEDF is most significant at the discharge center, and become less pronounced towards the sheath edge and the sidewall, due to low-energy electrons being heated by local strong electric field. Besides, at a higher

V

LF

, the dip becomes less significant, due to enhanced electron heating.
Characterization of a single emitter passively fed electrospray ion source: part I - luminescence imaging analysis
Authors: Kerber Thomas V , Allwine Nathaniel J , Lemmer Kristina M , Taylor Nicholas R
Published: January 2025

Luminescence at the face of ionic liquid ion sources and nearby facility surfaces is a commonly reported radiative phenomenon that requires thorough examination. In this study, we present magnified images of a single emitter porous-media ionic liquid electrospray in profile, which provides spatial information on the origin of the luminescence. To determine what role facility interactions play in luminescence at the electrospray face, we varied the distance and material of a downstream beam target on which the ion plume terminated. The effect of applied emitter voltage on luminescence was also examined. Analysis of luminescence images and corresponding telemetry data demonstrate that the formation of luminescence near the emitter tip is not only a function of the surrounding test facility, but also dependent on the electrospray device, itself. The data also indicate the majority of luminescence observed within our experimental system is formed primarily from high-energy collisions between emitted propellant ions and propellant accumulated on the orifice of the extractor electrode.
Characterization of a single emitter passively fed electrospray ion source: part II-luminescence spectral analysis
Authors: Kerber Thomas V , Allwine Nathaniel J , Lemmer Kristina M , Taylor Nicholas R
Published: January 2025

Luminescence spectroscopy was used to examine the dynamics of propellant dissociation near the emitter tip of a single-emitter porous electrospray thruster loaded with the ionic liquid EMI-BF4. Luminescence spectra from CH, C2, CN, NH, BH, H
α
, and H
β
were observed and confirmed by comparison with simulated spectra. Analysis of the CH (A 2Δ, v′ = 0) spectra yielded a rotational temperature 3082 ± 30 K while the C2 (d 3Πg− a 3Πu) Swan system yielded rotational temperatures 6252 ± 92 K and 5914 ± 75 K for Δv = 0 and Δv = +1, respectively. Examination of the integrated spectral signals from acquired CH (A 2Δ), BH (A 1Π), and H
α
spectra showed a strong correlation with measured extractor current in both positive and negative polarity mode. The evidence suggests the formation of these electronically excited species is due to dissociative excitation induced by high-energy collisions between emitted ions and propellant accumulated on the extractor orifice. A weak broadband signal was also observed and is likely due to dissociative excitation of the anion, BF4
−, leading to the formation of electronically excited BF2. Analysis of the neutral gas within the test chamber with a mass spectrometer confirmed the presence of BF2, providing strong evidence the observed broadband signal is the result of BF2.
A density functional theory perspective on the structural, elastic, thermal, and electronic properties of copper nitride doped with transition metal (Fe, Co, and Ni)
Authors: Sahoo Guruprasad , Jena Ajit , Patro L N , Behera Gangadhar
Published: January 2025

The present study demonstrates the structural, elastic, thermal, and electronic properties of copper nitride (Cu3N) doped with transition metal M (M: Fe, Co, and Ni) using pseudopotential-based density functional calculations as implemented in the Quantum ESPRESSO simulation code. The exchange-correlation is approximated by Perdew-Burke-Ernzerhof functional within the framework of generalized gradient approximation. The doped matrices, represented as Cu3NM, are verified to be stable structures, both thermodynamically and mechanically. Tailoring of elastic properties and their anisotropy due to M doping is successfully demonstrated through a comprehensive analysis of the computed elastic stiffness coefficients, elastic moduli, elastic anisotropy factors, and spatial variation of the elastic moduli, which have not been explored yet. An increase in bulk modulus due to M doping ensures enhanced mechanical stability under isotropic stress. Conversely, while doping of Co and Ni enhances the shear resistance of the host material, Fe doping slightly reduces it. The superior ductile nature of all the studied systems predicts their suitability for applications in flexible electronics. It is evident that doping of M substantially reduces the elastic anisotropy of Cu3N. Using the calculated elastic moduli, the velocity of acoustic waves and its anisotropy for Cu3N and Cu3NM are also predicted. The anisotropy in the acoustic velocity of the studied materials recommends their potential application in acoustic devices with directional selectivity. It is also noticed that, while the average acoustic velocity is reduced due to Fe doping, it increases for Co and Ni doping. Furthermore, analysis of the computed Debye temperature and minimum thermal conductivity forecasts their employability as thermal barrier coatings. Finally, the calculations reveal the ferromagnetic nature of Cu3NFe and Cu3NCo with respective induced magnetic moments of 2.71 and 1.47 μ
B/cell, recommending their potential applications in spintronics. It is also proved that the M-d–Cu-d coupling stabilizes the ferromagnetic ordering in such magnetic systems. On the other hand, Cu3NNi is observed to be non-magnetic.
Multi-parameter study of a diamond magnetometer
Authors: Jani Mona , Lazda Reinis , Gāhbauer Florian , Asare Antra , Mrózek Mariusz , Wojciechowski Adam M , Gawlik Wojciech , Auziņš Mārcis
Published: January 2025

Negatively charged nitrogen-vacancy (NV) diamond magnetometers are promising candidates for room-temperature magnetic field sensing. However, systematic studies of magnetic field sensors that evaluate the sensitivity of NV-diamond magnetometers as a function of multiple parameters are sparse. By comparing optically detected magnetic resonance (ODMR) dips for four single-crystal diamonds, we explored how the photon shot-noise-limited sensitivity and experimental magnetic noise floor were influenced by the density of the NV centers and methods of their formation. The lowest shot-noise-limited sensitivity was 380 pT √Hz−1; it was significantly affected by ODMR fluorescence, contrast, and linewidth. Using dual-resonance modulation in ODMR, the experimental lowest magnetic noise floor based on power spectral density was 1.14 nT √Hz−1. These values can be improved with an increase in laser power. This study will facilitate the selection of appropriate NV diamonds when designing magnetometers for various applications.
Discussion on modified Sigmund sputtering formula
Authors: Shang Shengfei , Yang Xiaoning , Yang Yong , Cai Guobiao , Wang Weizong , He Bijiao , Zhang Baiyi
Published: January 2025

The modified Sigmund sputtering formula offers high computational accuracy for calculating sputtering yields and the energy distribution of sputtered atoms. This formula introduces a parameter called ‘relative threshold energy’ (E
z), which, like the sputtering threshold energy (E
th), serves as a reference for the minimum energy required for sputtering. However, E
z is significantly larger than E
th, rendering the modified Sigmund model inaccurate for sputtering calculations when E < E
z. Additionally, our previous research provided an empirical expression for E
z, but it was complex and lacked theoretical justification. To develop a more concise, theoretically grounded expression, we explored the theoretical form of E
z and introduced E
th as a parameter to address the modified Sigmund formula’s failure to account for the sputtering threshold. Furthermore, we proposed an explicit calculation method for the model when E < E
z. The results, including sputtering yields and energy distributions, demonstrate that the new method for calculating E
z and the improvements to the modified Sigmund formula under E < E
z align well with experimental data.
Degradation of 4-nitrophenol using Ar/air thermal plasma in submerged conditions: plasma characteristics, degradation chemical kinetics and degradation pathways
Authors: Nandy Nanditta , Pasupathi Amarnath , Subramaniam Yugeswaran
Published: January 2025

This study explores the application of thermal plasma in submerged conditions for the efficient degradation of 4-nitrophenol (4-NP), a high-priority toxic pollutant. A customized 10 kW DC non-transferred arc thermal plasma torch was employed to generate the plasma using argon (Ar) and air as plasma-forming gases. The plasma jet was characterized through arc voltage fluctuations, optical emission spectroscopy, and submerged jet dynamics, offering detailed insights into the physical and chemical properties of the Ar/air plasma. The plasma treatment of an initial 50 mg l−1 concentration of 4-NP in an aqueous medium resulted in 94% degradation and 58% mineralization within 30 min at the plasma input power of 6.2 kW. The removal process followed a pseudo-first-order kinetic model with a reaction rate constant of 0.0684 min–1. The possible degradation intermediates and products were identified via Liquid chromatography-mass spectroscopy and Ion chromatography. The submerged thermal plasma technique demonstrated enhanced compound removal efficiency, making it a promising method for the treatment of toxic dissolved organic compounds in wastewater.
Nanoparticle measurement system based on laser self-mixing technique and FPGA
Authors: Wang Huarui , Wu Shangge
Published: January 2025

This paper introduces a laser self-mixing nanoparticle sizer based on field-programmable gate array (FPGA). This system combines the compactness of laser self-mixing technique with the advantages of FPGA’s parallel processing, achieving rapid and accurate measurement of nanoparticle sizes in solutions. Through the parallel operation of analog-to-digital conversion, autocorrelation processing, and wireless transmission modules within the FPGA, significant improvements in signal processing speed and data throughput are achieved. Experimental results demonstrate that the system can effectively extract the autocorrelation curves of nanoparticles and derive the particle size distribution accordingly. Measurements of standard polystyrene microspheres with nominal sizes of 30 nm, 60 nm, 100 nm, and 200 nm yield particle size results close to actual values, verifying the feasibility and potential of the system. However, there is still some dispersion in the measurement results, particularly for larger particles, indicating that further optimization of the system is needed to improve measurement accuracy and stability in the future.
A wavelet packet transform-based domain adaptation method for rolling bearings fault diagnosis with fusion of local and global time–frequency features
Authors: Ren Chen , Li Xiangyu , Wang Weijie , Xiong Xiaoyan , Wu Bing
Published: January 2025

Fault diagnosis transfer learning models commonly employ deep neural networks (DNNs) to analyze time–frequency features. However, excessively DNNs can result in diminished generalization capabilities, leading to subpar performance of the model across various working conditions. Furthermore, inappropriate domain adaptation (DA) strategies significantly constrain the accuracy of the model. To address this issue, a robustly optimized residual-network and vision transformer (ViT) domain adaptation model is proposed in this article, combining wavelet packet transform (WPT), residual networks, and self-attention mechanisms. Firstly, the WPT is utilized to construct multi-band wavelet coefficient matrix (MWCM) and corresponding multi-band wavelet coefficient time–frequency feature matrix (MWSM) with small size and feature aggregation. Subsequently, a shallow robustly optimized residual network is designed to effectively extract features from MWCM, considering the spatial distance dependencies of features. Additionally, ViT is employed for time–frequency global feature extraction from MWSM. Furthermore, domain adversarial neural network and multi-kernel maximum mean discrepancy are employed to extract domain-invariant features from signals of different operating conditions and fault types. At last, three fault diagnosis experiments are conducted in multi-condition scenarios of bearings. The experimental results illustrate the superiority and effectiveness of the proposed model.
Intelligent fault diagnosis based on multi-sensor data fusion and multi-scale dual attention enhanced network
Authors: Zhang Weizhong , Yan Xiaoan , Ye Maoyou , Hua Xing , Jiang Dong
Published: January 2025

As a significant component of rotating machinery, the health of rolling bearings and gears directly impact the normal operation of rotating machinery. Traditional single-sensor fault diagnosis approaches often fail to extract sufficient fault information, resulting in low diagnostic accuracy in practical engineering applications. Additionally, conventional multi-sensor fusion diagnosis methods exhibit the low robustness in noisy environments. To tackle these challenges, this paper presents a novel fault diagnosis approach derived from multi-sensor data fusion and a multi-scale dual attention enhanced network (MSDF-MSDAENet). Initially, a MSDF strategy is presented. This method reduces the dimensionality of high-dimensional data from multiple sensors to extract embedded low-dimensional effective information, which is then fused into a three-dimensional pixel matrix image. Subsequently, a MSDAENet is constructed, incorporating both time–frequency information feature extraction modules and position information extraction modules. These enhancements significantly enhance the model’s capacity to extract fault features. By dividing the RGB fused images into a well-organized dataset and inputting it into the network for fault diagnosis, the method achieves intelligent diagnosis of samples with different fault types. Tests were carried out on three datasets, and the presented method was compared with various existing methods. The ultimate experimental results demonstrate the efficacy and superiority of the presented method.
ALDNet: a lightweight and efficient drone detection network
Authors: Cai Hao , Zhang Jinhong , Xu Jianlong
Published: January 2025

With the growing use of unmanned aerial vehicles (UAVs), it poses a potential threat to public safety, so drone detection has become an increasingly important research direction. However, the irregular shooting angles, deformations, significant target scale variations, and complex backgrounds in UAV imagery pose challenges for existing object detection models. To address these issues, we propose ALDNet, a precise and lightweight UAV image object detection network built on an improved RT-DETR model. First, we introduce the position accurate continuous convolution (PAC), which dynamically adjusts convolution kernel coordinates, allowing flexibility in detecting objects of varied shapes. PAC improve the ability of backbone network to capture objects with different shapes. Second, we present an enhanced multi-scale feature fusion approach that integrates spatial information more effectively, reducing the loss of fine-grained details during fusion. Additionally, we propose the Inner-Generalized Intersection over Union loss function, which reduces interference and accelerates the convergence of bounding box optimisation, thus improving the overall detection performance. We conducted experiments on three UAV image datasets, Det-Fly, ARD-MAV and VisDrone, and compared the ALDNet results with the RT-DETR model. The experimental results show that ALDNet achieved 98.3%, 97.4% and 48.1% mAP50 values on Det-Fly, ARD-MAV and VisDrone, which are 1.5%, 0.4% and 1.9% higher than RT-DETR, respectively, and the number of parameters and computation amount of ALDNet are 25% and 14% less than the baseline, respectively. Experimental results show that ALDNet achieves greater efficiency and effectiveness with less computational power.
Mean field limits of particle-based stochastic reaction-drift-diffusion models*
Authors: Heldman M , Isaacson S A , Liu Q , Spiliopoulos K
Published: January 2025

We consider particle-based stochastic reaction-drift-diffusion models where particles move via diffusion and drift induced by one- and two-body potential interactions. The dynamics of the particles are formulated as measure-valued stochastic processes (MVSPs), which describe the evolution of the singular, stochastic concentration fields of each chemical species. The mean field large population limit of such models is derived and proven, giving coarse-grained deterministic partial integro-differential equations (PIDEs) for the limiting deterministic concentration fields’ dynamics. We generalize previous studies on the mean field limit of models involving only diffusive motion, with care to formulating the MVSP representation to ensure detailed balance of reversible reactions in the presence of potentials. Our work illustrates the more general set of PIDEs that arise in the mean field limit, demonstrating that the limiting macroscopic reactive interaction terms for reversible reactions obtain additional nonlinear concentration-dependent coefficients compared to the purely diffusive case. Numerical studies are presented which illustrate that two-body repulsive potential interactions can have a significant impact on the reaction dynamics, and also demonstrate the empirical numerical convergence of solutions to the PBSRDD model to the derived mean field PIDEs as the population size increases.
Fast spot order optimization to increase dose rates in scanned particle therapy FLASH treatments
Authors: Wase Viktor , Widenfalk Oscar , Nilsson Rasmus , Fälth Claes , Fredriksson Albin
Published: January 2025

The advent of ultra-high dose rate irradiation, known as FLASH radiation therapy, has shown promising potential in reducing toxicity while maintaining tumor control. However, the clinical translation of these benefits necessitates efficient treatment planning strategies. This study introduces a novel approach to optimize proton therapy for FLASH effects using traveling salesperson problem (TSP) heuristics. We applied these heuristics to optimize the arrangement of proton spots in treatment plans for 26 prostate cancer patients, comparing the performance against conventional sorting methods and global optimization techniques. Our results demonstrate that TSP-based heuristics significantly enhance FLASH coverage to the same extent as the global optimization technique, but with computation times reduced from hours to a few seconds. This approach offers a practical and scalable solution for enhancing the effectiveness of FLASH therapy, paving the way for more effective and personalized cancer treatments. Future work will focus on further optimizing run times and validating these methods in clinical settings.
Cross-scale turbulence in space plasmas: old concepts, recent findings, and future challenges
Authors: Alberti Tommaso , Benella Simone , Stumpo Mirko , Sorriso-Valvo Luca , Carbone Vincenzo
Published: January 2025

Turbulence, a fascinating and intricate phenomenon, has captivated scientists over different domains, mainly for its complex cross-scale nature spanning a wide range of temporal and spatial scales. Despite significant advances in theories and observations in the last decades, some aspects of turbulence still remain unsolved, motivating new efforts to understand its underlying physical mechanisms and refine mathematical theories along with numerical models. This topical review explores recent findings from the Parker Solar Probe mission, providing a distinctive opportunity to characterize solar wind features at varying heliocentric distances. Analyzing the radial evolution of magnetic and velocity field fluctuations across the inertial range, a transition has been evidenced from local to global self-similarity as proximity to the Sun increases. This behavior has been reconciled with magnetohydrodynamic theory revising an old concept by emphasizing the evolving nature of the coupling between fields. This offers inspiration for novel modeling approaches to understand open challenges in interplanetary plasma physics as the heating and acceleration of the solar wind, as well as, its evolution within the inner Heliosphere.
Perpendicular magnetic anisotropy of Pd/Co2MnSi/Co/Pd multilayer
Authors: Qin Xiaoqi , Tan Jiaxing , Xiu Xianwu , Cao Wentian , Wang Shuyun
Published: January 2025

Pd/Co2MnSi(CMS)/Co/Pd multilayer films were designed based on the idea of combining highly spin-polarized materials with strong perpendicular magnetic anisotropy(PMA) films. The PMA of Pd/CMS/Co/Pd multilayer films were studied by optimizing the growth conditions and thickness of each film layer. The optimal structure of the multilayer films were Pd(6 nm)/CMS(5 nm)/Co(2 nm)/Pd(1 nm). Its Abnormal Hall resistance (R
Hall), coercivity (H
c) and effective magnetic anisotropy constant (K
eff) are 0.08Ω, 284Oe and 1.36Merg/cm3, respectively, which are 100%, 492%, and 183% higher than the corresponding values (0.04Ω, 48Oe, and 0.48Merg/cm3) of the Pd (6 nm)/Co (1 nm)/Pd (3 nm) trilayer films. The analysis shows that the increase of the above values are the result of Pd/CMS interface effect and CMS/Co interface ferromagnetic coupling, and it is closely related to the thickness of each film layer in the multilayer films and the growth conditions of the multilayer films.