bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance optimization of the neutron-sensitive image intensifier used in neutron imaging
Authors: Tan 谭 Jinhao 金昊 , Song 宋 Yushou 玉收 , Zhou 周 Jianrong 健荣 , Yang 杨 Wenqin 文钦 , Jiang 蒋 Xingfen 兴奋 , Liu 刘 Jie 杰 , Zhang 张 Chaoyue 超月 , Zhou 周 Xiaojuan 晓娟 , Xia 夏 Yuanguang 远光 , Liu 刘 Shulin 术林 , Yan 闫 Baojun 保军 , Liu 刘 Hui 辉 , Wang 王 Songlin 松林 , Zhao 赵 Yubin 豫斌 , Zhuang 庄 Jian 建 , Sun 孙 Zhijia 志嘉 , Chen 陈 Yuanbo 元柏
Published: May 2024

As a non-destructive testing technology, neutron imaging plays an important role in various fields, including material science, nuclear engineering, and fundamental science. An imaging detector with a neutron-sensitive image intensifier has been developed and demonstrated to achieve good spatial resolution and timing resolution. However, the influence of the working voltage on the performance of the neutron-sensitive imaging intensifier has not been studied. To optimize the performance of the neutron-sensitive image intensifier at different voltages, experiments have been performed at the China Spallation Neutron Source (CSNS) neutron beamline. The change in the light yield and imaging quality with different voltages has been acquired. It is shown that the image quality benefits from the high gain of the microchannel plate (MCP) and the high accelerating electric field between the MCP and the screen. Increasing the accelerating electric field is more effective than increasing the gain of MCPs for the improvement of the imaging quality. Increasing the total gain of the MCP stack can be realized more effectively by improving the gain of the standard MCP than that of the nMCP. These results offer a development direction for image intensifiers in the future.
Intrinsic polarization conversion and avoided-mode crossing in X-cut lithium niobate microrings
Authors: Tan 谭 Zelin 泽林 , Zhang 张 Jianfa 检发 , Zhu 朱 Zhihong 志宏 , Chen 陈 Wei 伟 , Shao 邵 Zhengzheng 铮铮 , Liu 刘 Ken 肯 , Qin 秦 Shiqiao 石乔
Published: May 2024

Compared with well-developed free space polarization converters, polarization conversion between TE and TM modes in the waveguide is generally considered to be caused by shape birefringence, like curvature, morphology of waveguide cross section and scattering. Here, we study the polarization conversion mechanism in 1-THz-FSR X-cut lithium niobate microrings with multiple-resonance condition, that is the conversion can be implemented by birefringence of waveguides, which will also introduce an avoided-mode crossing. In the experiment, we find that this mode crossing results in severe suppression of one sideband in local nondegenerate four-wave mixing and disrupts the cascaded four-wave mixing on this side. Simultaneously, we propose one two-dimensional method to simulate the eigenmodes (TE and TM) in X-cut microrings, and the mode crossing point. This work will provide one approach to the design of polarization converters and simulation for monolithic photonics integrated circuits, and may be helpful to the studies of missed temporal dissipative soliton formation in X-cut lithium niobate rings.
V0405 Dra: A New Deep and Low Mass Ratio Contact Binary with Extremely Fast Decrease in the Orbital Period
Authors: Li Chao-Yue , Jiang Lin-Qiao , Zheng Jie , Liu Yi-Man , Long Xiao-Yu , Sun Min , Zhang Han-Lu , Tian Xiao-Man
Published: May 2024

V0405 Dra is a W UMa-type binary star. Based on the TESS data, we have conducted an orbital period study and performed a light curve analysis for the system. The orbital period study reveals that the O − C curve for V0405 Dra exhibits secular decrease at an extremely high rate of dP/dt = −2.71 × 10−6 day year−1, along with periodic variations characterized by an amplitude of A
3 = 0.0032 day and a period of P
3 = 1.413 years. The orbital periodic change is possibly due to the light-travel time effect resulting from an additional third body in the system, for which we estimate a minimum mass of M
3 = 0.77M
⊙. By employing the 2013 version of the Wilson-Devinney (W-D) method to synthesize a light curve, we derived photometric solutions indicating that V0405 Dra is a new deep (f = 68.7%) and low-mass ratio (q = 0.175) contact binary. The fast decrease in its orbital period is likely caused by mass transfer from the more massive primary star to the less massive secondary star, or due to angular momentum loss. With further mass transfer and loss of angular momentum, the binary will gradually evolve into a tighter contact configuration, eventually leading to a merger into a single star, following the evolutionary paths suggested for such deep and low mass ratio contact binaries.
On Building Predictive Digital Twin Incorporating Wave Predicting Capabilities: Case Study on UMaine Experimental Campaign - FOCAL
Authors: Alkarem Yuksel R. , Huguenard Kimberly , Kimball Richard W. , Hejrati Babak , Ammerman Ian , Nejad Amir R. , Fontaine Jacob , Heshami Reza , Grilli Stephan
Published: May 2024

The response of floating wind turbines (FWT) are susceptible to stochastic wave variations. For the optimal operation of FWT, a comprehensive understanding of the phaseresolved wave dynamics and the consequential system response is crucial for real-time monitoring and control. A multi-variate, multi-step, long short term memory (MLSTM), a type of recurrent neural network (RNN) is used to capture complex system dynamics for real-time application. Results indicate that the integration of a wave prediction-reconstruction (WRP) model substantially enhances prediction accuracy by 50% on average relative to the baseline model. The improvement is consistent across various wave extremity and prediction horizons, thereby significantly broadening the scope for timely and precise predictive capabilities.
An Onshore Deployment of Advanced Dual-Doppler Radar for Wind Energy Applications
Authors: Hirth B D , Schroeder J L , Guynes J G
Published: May 2024

Two advanced X-band radars have been developed and deployed by Texas Tech University to provide continuous wind measurements serving project AWAKEN, including the construction of near real-time dual-Doppler synthesized wind fields covering a large 35 km x 35 km analysis domain. This deployment represents the first overland long duration utilization of the technology, which was configured to document a wide range of wind energy relevant flows spanning turbine inflow/wake flow to regional wind flows and wind plant interactions. The overland environment coupled with technology innovation has led to very high data availability yielding a growing archive of measurements documenting diverse atmospheric phenomena including numerous instances of wind plant interaction. Through the first few project months, data availability greater than 90%, 80% and 70% has been assessed at the ranges of 8 km, 16 km, and 25 km, respectively. Robust data availability coupled with fast radar scan speeds and high along beam measurement resolution position advanced Doppler radar technology to broadly contribute to diverse wind energy measurement applications covering domains sizes that far exceed existing industry standard measurement systems.
Wind Speed Ramp Rate Predictions Using Wind Farm SCADA Data Assimilation and a WRF Ensemble
Authors: Diallo M , Letang L , Totel B , Poncet P , Andri P P Y
Published: May 2024

This study presents a novel method for improving wind power ramp events forecasts up to six hours ahead by utilizing data assimilation of SCADA measurements with an ensemble of Weather Research and Forecasting (WRF) models estimates. Leveraging data from nine wind farms in France and Belgium, the approach aims to improve WRF model predictions for wind speed and ramp event timing. The methodology employs grid and observational nudging techniques, enhancing model accuracy by incorporating real-time observational data. Key findings demonstrate that nudging significantly reduces Mean Absolute Error (MAE), decreases the Time Distortion Index (TDI), and increases the Probability of Detection (POD) of ramp events. Nudged ensemble members outperform non-nudged counterparts, exhibiting better accuracy in identifying true ramp events and reducing false alarms. MAE, TDI and POD improvements are as high as 3.7%, 8.5% and 37%, respectively. The study also explores the benefits of an ensemble approach, highlighting improved accuracy in predicting ramp rate magnitudes and providing valuable insights for grid stability management. This research contributes to wind power forecasting, showcasing the importance of integrating SCADA data into predictive models.
Reconstructing turbulent wind-fields using inverse-distance-weighting interpolation and measurements from a pulsed mounted-hub lidar
Authors: Soto Sagredo Esperanza , Hannesdóttir Ásta , Rinker Jennifer M. , Courtney Michael
Published: May 2024

This study evaluates a numerical multi-beam pulsed lidar mounted on the hub of the NREL 5MW reference wind turbine using the HAWC2 v13.1 numerical sensor for synthetic lidar measurement generation. While initially designed for single-beam operations, it facilitates multi-beam configuration simulations. We conducted an analysis of full-rotor longitudinal wind speed reconstruction by combining inverse-distance-weighting with synthetic sensor data from HAWC2. Utilizing a Mann-generated turbulence box for wind input at U = 11.4 m/s, we examined three lidar configurations for efficacy. The hub-mounted lidar proved efficient in capturing the incoming flow towards the turbine, showing a 60% improvement in overall reconstruction accuracy across the plane compared to the baseline, where hublidar measurements are simple average across the rotor plane. The rotor average wind speed showed a 30% enhancement compared to the baseline. Crucially, the lidar configuration, which impacted the spatial distribution across the rotor plane, emerged as a pivotal factor for effective reconstruction. Proper configuration assessment is essential, especially given the implications of rotational sampling and its impact under various wind conditions, for optimal performance. The proposed method, combining inverse-distance weighting with hub-lidar data for high spatial resolution measurements across the rotor plane, shows significant potential for real-time windflow estimation and lidar-assisted control applications.
Influence of wind gusts on aeroelastic loads of NREL 5MW wind turbine
Authors: Paul Manelil Neeraj , Nicolaas Theron Johannes , Höning Leo , Stoevesandt Bernhard
Published: May 2024

This study employs blade-resolved computational fluid dynamics simulations to investigate the aero-elastic loads on a yawed NREL 5MW wind turbine subject to full and partial wind gusts. A novel spatial gust representation, tailored towards application to large eddy simulations, is introduced. This methodology permits the injection of gusts with customizable spatial dimensions inside the flow domain. Simulations are conducted with gusts having the same diameter as the turbine rotor, half rotor diameter, and asymmetric impact with respect to the center of rotation of the turbine. Simulations of the turbine under yawed conditions are also considered. The flow characteristics are reported using contours and iso-surfaces of velocity magnitude and Q-criteria. Insights into the load characteristics of the turbine are described using torque, thrust, and root bending moments. This research highlights that partial gust impact contributes to noteworthy load fluctuations and blade tip deformations, stemming from the imbalance in force distribution across the rotor plane.
Assessing aerodynamic influences on offshore foundation design for large wind farms
Authors: Wen K Z , Dehtyriov D , Byrne B W
Published: May 2024

An evaluation of the impact of aerodynamic interactions on offshore wind turbine (OWT) monopile design in large wind farms is presented. The interactions between turbines within a wind farm, and between the atmosphere and the entire wind-farm, act to reduce the mean effective loads across the farm. This reduction impacts the operational performance of OWT foundations in two notable ways: a decrease in turbine structural loads which affects design to serviceability limit state and a shift in excitation frequencies of the passing blades which impacts fatigue performance. To assess the implications on monopile design, we conduct static and dynamic analyses using a 1-D finite element model (FEM). We show that the farmatmosphere interaction effect leads to marked reduction in monopile lengths between ∼ 5 − 25% across an entire wind farm. The dynamic analysis reveals a competing balance between shifting frequency bands and reduced wind loads on the fatigue response. The monopile design is found to be strongly dependent on wind farm size and ratio of wind to wave loading, with wave loading coupled to the farm-atmosphere interaction effect. Exploiting these interactions plays a pivotal role in reducing the levelised cost of wind energy and ensuring robust design of OWTs.
Cultivating Eco-Friendly Nanomaterials: Mimosa Pudica-Enhanced Zinc Oxide Nanoparticles for Enhanced Health and Environmental Well-being
Authors: Prashanth G.K. , Rao Srilatha , Gadewar Manoj , Lalithamba H.S. , Mahadeva Swamy M. , Sowmyashree A.S.
Published: May 2024

In this work, we report the development of ZnO NPs by using environmentally friendly SCS using Mimosa pudica (MP) leaves. The obtained ZnO-MP-NPs were subjected to extensive characterization techniques such as PXRD, BET, SEM, & TEM. Further, ZnO-MP-NPs were evaluated for their antioxidant ability by DPPH method. Their anticancer capability was assessed by MTT assay on human breast cancer cell ine MCF-7. The results indicated the potential applications of ZnO-MP-NPs both as antioxidant and anticancer agents.
Impact force attenuation capabilities of elastomer, springs and their combination
Authors: ThejeshKumar P , RaviKumar L. , Raghu Yogaraju
Published: May 2024

In various engineering applications like automotive safety, structural engineering, and industrial machinery, mitigating impact forces is of at most importance to protect structures, equipment and most importantly, human lives. Understanding the impact force attenuation capabilities of dampers, springs, and their combination is crucial. Dampers like elastomers are known for their exceptional ability to absorb and dissipate energy due to their visco-elastic properties. Spring on the other hand, store and release mechanical energy when compressed, offering a different approach to impact forces attenuation. Study is experimental and parametric in nature, where different damping materials like elastomers, polystyrene & foam are individually and in combination with springs of different stiffness are tested for their impact energy absorption capabilities. Tests are conducted in a dedicated test rig, wherein specimen is kept on a rigid plate which intern mounted on a peizo-electric load sensor and weights are dropped from same heights and force transmitted with time of travel is measured for each specimen and compared. Results revealed that the energy absorbed by springs is around 30% individually, which is less compared to dampers and, the combination of damper & springs. In conclusion the specimen with combination of damper & springs has high potential in absorbing impact force.
Analysis of a refrigeration system using carbon nanotubes (CNTs) lubricant and a combination of R290/R600 refrigerants
Authors: Babarinde T.O , Madyira D.M
Published: May 2024

A combination of R290/R600 refrigerants with low global warming and zero ozone-depleting potential refrigerant was tested with a based lubricant and CNTs nanolubricant in a refrigeration system to improve its performance efficiency and minimise the environmental impact of refrigerants and energy demand by refrigeration systems. CNTs-nanolubricant, an alternative lubricant for the refrigeration system, was made by dispersing CNTs in the based lubricant. By connecting measuring devices at each system component, temperatures and pressures were determined. The experiment’s measured outputs were used to determine the system’s performance. When compared to a 290/600 in the based lubricant, CNTs nanolubricant showed a reduction in work input ranging from 39.8%, a shorter pulldown time, a lower evaporator temperature, an increased coefficient of performance (COP), and a higher cooling capacity of 9.1%. Therefore, the lubricant made of carbon nanotubes (CNTs) presents a viable substitute for based lubricant in a vapour compression system.
Optical Properties of Gd2O3 with Silver doped Zinc Borate Glasses
Authors: Shwetha K M , Eraiah B
Published: May 2024

Glasses with chemical composition ZnO-B2O3-Gd2O3-Ag2O were prepared using melt quenching method. The synthesized glass was characterized using X-ray diffraction method. The densities of these glasses were measured using the Archimedes method, and the corresponding molar volumes were calculated using the corresponding formula. Absorption spectra were recorded using a UV-visible spectrometer. The energy bandgap was investigated using the Tauc’s diagram. The variation of band gap energy in relation to Gd2O3 concentration has been discussed. Study the structure and functional groups of the prepared samples were studied using FTIR spectra. Emission and excitation spectra were recorded using a luminescence spectrometer. Photoluminescence spectra provides stable blue light emission in rich in Gd2+ ions glass, Color coordination has also been decided using CIE chromaticity diagram and showing that blue emission was observed in all samples with slightly different positions.
A study of continuous double diffusive convection of binary liquid in an alloy form by employing Lapwood-Brinkman model with cross diffusion effect.
Authors: Satisha M , Pranesha Setty A
Published: May 2024

In this paper we study the effect of DuFour and Soret on linear double diffusive convection of binary liquid in a two dimensional rectangular channel filled with anisotropy porous material using Lapwood-brinkman model with cross diffusion effect. Darcy model without a time derivative is used in the momentum equation. Linear stability assessment is used to investigate the walls of channels that are heated and salted from below due to Soret and DuFour effects. The anisotropic parameters, the solute Rayleigh number, and the impacts of Soret and DuFour on the onset of convection are examined and same is plotted graphically.
Ligin-derived graphene-like ultrathin carbon materials for ORR catalysis
Authors: Liu Wenting , Wu Xiaoyu
Published: May 2024

Low-cost nitrogen-doped carbon materials with graphene-like structures were designed and prepared from extensive renewable biomass waste lignin. The obtained carbon materials exhibited well-designed ultrathin two-dimensional structures with high specific area and mesoporous characteristics. Benefiting from the well-designed structures with extensive catalytic active sites and efficient mass transfer and diffusion, as well as the modified electronic characteristics from nitrogen doping, the lignin-derived carbon materials exhibited pronounced ORR catalytic activity and methanol tolerance. Considering these, the lignin-derived carbon materials hold great application potentials as ORR catalysts and catalyst supports.
Effects of strain induced martensite on high temperature electrochemical performance of 321 stainless steel in the simulated PWR first circuit environment
Authors: Li Dongxing , Cao Han , Gao Junxuan , Zheng Quan , Zhang Peng , Zhong Weihua
Published: May 2024

The electrochemical properties of 321 stainless steel with different cold strains in simulated PWR first circuit environment were studied mainly by electrochemical impedance spectroscopy (EIS). The results show that with the increase of strain (0, 20%), the charge transfer resistance increases and the film resistance decreases. By combining the electrochemical experiments and microscopic characterization results of different specimens, including solution annealed, cold worked and hot worked, it is found that the charge transfer resistance increases with the increase of deformation degree, the increase of strain induced martensite content enriches Cr in the oxide layer, but with the decreased material corrosion resistance.
Color-changing Fluorescent Carbon Dots Synthesized by Simple Hydrothermal Method for Anti-counterfeiting Pattern Study
Authors: Chen Qidan , Xu Youyi , Sun Maolin , Zang Xinyao , Qiu Jinglan
Published: May 2024

A simple hydrothermal method was employed to synthesize the carbon dots with dopamine hydrochloride used as the mainly carbon source and o-phenylenediamine as the nitrogen source. In the study, the fluorescent carbon dots which have good water solubility and alcohol solubility showed visible pink under daylight and orange under UV after purification by column chromatography, and dichloromethane: methanol (v: v =20:1) was selected as the separation solvents. Meanwhile, the pink fluorescent carbon dots (λmax =595 nm under 577 nm excitation) was observed that it can be changed to bright yellow (λmax =530 nm under 442 nm excitation), the conditions of different stocked solvents and time were explored in detail. The IR spectra for the pink carbon dots indicated its color-changing property due to the unsatisfaction group of its chemical structure. For UV and fluorescence characterization, the quantum yield of pink carbon dots is ∼11.0%. In addition, the application of color-changing carbon dots as anti-counterfeiting ink for high-quality anti-counterfeit patterns on paper was investigated, and the preliminary results showed good potential of the prepared carbon dots as effective and eco-friendly anti-counterfeiting materials.
A Surface Composition Study of Niobium after BCP for SRF Cavity in Particle Accelerator
Authors: Luo Didi , Wang Changlin , Pan Feng
Published: May 2024

Nitrogen contamination from post-processing significantly affects the performance of niobium superconducting radiofrequency (SRF) cavities. Therefore, several performance-boosting strategies, including nitrogen doping, infusion and chemical acid reactions, have been explored in various studies. Among these studies, some secondary ion mass spectrometry (SIMS) results showed that nitrogen contamination exists on the niobium surface after the buffered chemical polishing (BCP) process. However, the compositions of these nitrogen byproducts were hard to detect. In this work, we calculated the chemical reaction process by the density function theory (DFT), predicted the reaction product, and then analyzed the Nb sample surface after BCP with X-ray Photoelectron Spectroscopy (XPS) to determine the composition of these contaminations.
Numerical experiments on scalar transport and mixing in turbulent boundary layers
Authors: Boga Gabriele , Giancola Alfonso , Cimarelli Andrea
Published: May 2024

In this work, we present numerical experiments aimed at dynamically establishing the separate role of the inner and outer cycles on the scalar transport in the configuration of a temporally evolving boundary layer. The experiments are based on the study of the evolution of passive scalars driven by velocity fields where inner and outer cycles are alternately suppressed. Two different approaches are implemented. In the first, the discrimination between inner and outer cycle activities is based on the scale dimension of the involved motions. The second instead, discriminates on the basis of the distance from the wall of the turbulent motions. The two approaches depict the same scenario. Both the inner and outer cycles appear to be autonomous and, in a sense, independent, since their dynamics remain qualitatively unaltered despite facing two different conditions. The outer cycle faces a free boundary at the top and simply rescales according to what is supplied by the inner cycle. The inner cycle, on the other hand, resides between the wall and the outer region. As a result, the reduction of the scalar fluxes in the outer region due to the suppression of the outer cycle causes a damping in the near-wall region activities.
Spectral analysis of the correlation between temperature and wall-normal velocity in Rayleigh-Bénard convection at moderate Rayleigh numbers
Authors: Lee Myoungkyu
Published: May 2024

Turbulent Rayleigh-Bénard convection holds significant scientific and engineering implications, with one of the most debated topics in convection research being the ultimate scaling of the Nusselt number at extremely high Rayleigh numbers. A widely adopted method for investigating this issue is direct numerical simulation (DNS); however, achieving the ultimate Rayleigh number with DNS can be prohibitively expensive. To overcome this limitation, one approach is to employ a small simulation domain size for high Rayleigh number cases. This study explores the impact of simulation domain size by conducting DNS with a large geometrical aspect ratio. Specifically, we perform spectral analysis on the covariance of wall-normal velocity and temperature, along with its transport equations. The results reveal that the characteristic length scale in the intermediate length-scale region linearly increases with wall-normal distance. Moreover, the spectral analysis indicates that large-scale structures play a crucial role in the dynamics of the covariance of wall-normal velocity and temperature. However, the study suggests that these large-scale structures have a minimal impact on the scaling of the Nusselt number. This is attributed to the fact that large-scale structures remain in regions away from the wall, and no processes transfer them from large to small-scale structures. As a consequence, the dynamics of the covariance of wall-normal velocity and temperature in the close vicinity of the wall experience little influence from large-scale dynamics.
Coherent structures in elastic turbulent planar jets
Authors: Amor Christian , Corrochano Adrián , Rota Giulio Foggi , Rosti Marco Edoardo , Clainche Soledad Le
Published: May 2024

At low Reynolds numbers, the flow of a Newtonian planar jet remains laminar, thus easy to characterize. In contrast, the presence of elasticity (e.g., attained dissolving polymers in a Newtonian solvent) enables a highly-complex turbulent-like behavior termed elastic turbulence. In this work, we run data-driven modal decomposition algorithms on high-fidelity data collected from the simulation of an elastic turbulent planar jet. The large-scale motions are expressed as a finite expansion of modes that condense the dominant dynamics. The modes associated with lower frequencies weight the most on the reconstruction of the original data, thus they are further decomposed in space to investigate their implications on the sustainment of the elastic turbulent state. Our findings suggest that slower dynamics are crucial for the sustainment of elastic turbulence, which is connected to the interaction of spanwise-coherent structures, steady in space, with spanwise-periodic traveling waves, causing the breakdown of the structures close to the inlet.
Active flow control of a turbulent separation bubble through deep reinforcement learning
Authors: Font Bernat , Alcántara-Ávila Francisco , Rabault Jean , Vinuesa Ricardo , Lehmkuhl Oriol
Published: May 2024

The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at Reτ
= 180 on the upstream region before separation occurs. The TSB can resemble a separation phenomenon naturally arising in wings, and a successful reduction of the TSB can have practical implications in the reduction of the aviation carbon footprint. We find that the classical zero-net-mas-flux (ZNMF) periodic control is able to reduce the TSB by 15.7%. On the other hand, the DRL-based control achieves 25.3% reduction and provides a smoother control strategy while also being ZNMF. To the best of our knowledge, the current test case is the highest Reynolds-number flow that has been successfully controlled using DRL to this date. In future work, these results will be scaled to well-resolved large-eddy simulation grids. Furthermore, we provide details of our open-source CFD–DRL framework suited for the next generation of exascale computing machines.
Elucidating optimal nanohole structures for suppressing phonon transport in nanomeshes
Authors: Cui Haoran , Ma Tengfei , Wang Yan
Published: May 2024

Nanomeshes, often referred to as phononic crystals, have been extensively explored for their unique properties, including phonon coherence and ultralow thermal conductivity (κ). However, experimental demonstrations of phonon coherence are rare and indirect, often relying on comparison with numerical modeling. Notably, a significant aspect of phonon coherence, namely the disorder-induced reduction in κ observed in superlattices, has yet to be experimentally demonstrated. In this study, through atomistic modeling and spectral analysis, we systematically investigate and compare phonon transport behaviors in graphene nanomeshes, characterized by 1D line-like hole boundaries, and silicon nanomeshes, featuring 2D surface-like hole boundaries, while considering various forms of hole boundary roughness. Our findings highlight that to demonstrate a disorder-induced reduction in κ of nanomeshes, optimal conditions include low temperature, smooth and planar hole boundaries, and the utilization of thick films composed of 3D materials.
Synthesis of composite films for ZnO-based memristors with superior stability
Authors: Cao Boyu , Liu Hongxin , Li Tong , Gong Jixin , Zhang Sijie , Dove Martin T
Published: May 2024

Memristors have unique non-volatile characteristics that potentially can emulate biological synapses for applications in neural computing systems. However, the random formation of conductive filaments in these devices can cause various unreliability problems. In this work, films of a composite of ZnO nanoparticles and carbon nanotubes were prepared as functional layers for memristors by an in-situ growing strategy (ZnO@CNT-IS) using a straightforward high-temperature annealing treatment. This approach allowed for the formation of a high-quality films with uniform loading of ZnO nanoparticles on the carbon nanotubes, which contributed to a lower formation energy for oxygen vacancies and increased electron transfer rate. As a result, the memristors exhibited faster switching response speed, lower power consumption, and a stabilised switching ratio even after 2000 switching cycles. Based on the analog switching behaviour, the ZnO@CNT-IS-based devices showed significant biological synapse functions and plasticity, indicating their potential for high-density storage and neuromorphic computing.
Influence of preparation method on structural, optical and magnetic properties of synthesized Zn0.5Mg0.5−xNixFe2O4 nano-ferrite
Authors: Khalafallah Abdien Y , Siddig Mohamed A , Algethami Obaidallah A , Elbadawi Abdelrahman A , Mohammed Abdalskhi S
Published: May 2024

Structural, optical, and magnetic properties of ferrite series with chemical formula Zn0.5NixMg0.5−xFe2O4 (x = 0.0, 0.1, 0.2, 0.3 and 0.4) were investigated. Co-precipitation and sol–gel methods were used to synthesis the samples and to investigate further the influence of these preparation methods on the properties of Zn0.5NixMg0.5−xFe2O4 nano-ferrites. X-ray diffraction and Fourier-transform infrared spectroscopy revealed that the samples crystallize in a cubic spinel structure with space group Fd3m:1. The crystallite size of Zn0.5NixMg0.5−xFe2O4 nanoparticles was decreased from 19.15 to 11.65 nm with increasing Ni content for the samples of co-precipitation method, while the opposite behavior was observed in case of sol–gel method which was increased from 13.90 to 21.90 nm. Moreover, the optical investigation by UV-visible spectroscopy revealed that the energy band gap of the samples of co-precipitation method ranged (6.04–6.08) eV compared to (4.44–4.50) eV of sol–gel method. For both methods, substitution of Ni+2 by Mg+2 in Zn0.5NixMg0.5−xFe2O4 affects the remanent magnetization (Mr), coercivity (Hc) and saturation magnetization (Ms) suggesting the super-paramagnetic behavior of the samples. The substitution of Ni resulted in small values of coercivity ranged 0.758 to 164.62 Oe. The Ms was ranged 20.39 to 127.57 emu g−1 and an enhancement in magnetization was observed which increases the value of Ms that was found to be maximum with x = 0.4 of co-precipitation method (127.57 emu g−1). The magnetic behavior of synthesized Ni doped nanoparticles makes them suitable candidate for sensing and high-frequency applications and for use in hyperthermia treatment. The characterization outcomes may recommend that co-precipitation method is preferred over sol–gel method for refined structure and preferable properties of Zn0.5NixMg0.5−xFe2O4 nanoparticles.
Graphene reinforced ceramic matrix composite (GRCMC) – state of the art
Authors: A Madhankumar , M Anthony Xavior
Published: May 2024

Ceramic Matrix Composites (CMC) are widely used for manufacturing automobile parts aircraft components, biomedical and electronic devices. In recent years, graphene has found extensive applications in various fields like effective reinforcement in the development of metal matrix, polymer matrix and ceramic matrix composites. Literature reveals that more focus was on polymer and metal matrix composites reinforced with graphene when compared to ceramic matrix composites. In this manuscript, the effect and suitability of graphene as a reinforcement for fabricating ceramic matrix composite are presented. Further, the property evaluation, including mechanical and physical, characterization and microstructural evaluation of the CMCs developed with graphene as reinforcement is elaborated. The investigation of the reinforcing mechanisms and failure behavior of ceramic matrix composites reinforced with graphene, together with the development of novel processing techniques to solve manufacturing difficulties, are key areas of focus. Although many investigations have concentrated on enhancing the mechanical and electrical characteristics of ceramics by integrating graphene, more investigation is required to investigate the interfacial interaction between graphene and the ceramic matrix, as well as the influence of graphene size on the properties of the composite. Furthermore, there is a need for future research to explore the possibility of using graphene-reinforced ceramic composites in several multifunctional applications, including microwave absorption, electromagnetic interference shielding, ballistic armors, self-monitoring damage sensors, and energy storage and conversion. Future research should focus on developing innovative processing procedures that guarantee the uniform distribution and precise alignment of graphene sheets within the ceramic matrix. The incorporation of graphene into ceramic matrix composites presents novel prospects for augmenting the characteristics and capabilities of ceramics, rendering it a very interesting area for further investigation.
Multi-criteria optimization of Al2O3-ZrO2(Y2O3)-based self-lubricating composite cutting tools for increasing shear angle, and wear resistance in machining processes
Authors: Pradeep Kumar Bagadi , Rao Pujari Srinivasa , K Adarsha Kumar , Venkatesh Durga Janaki
Published: May 2024

In the present investigation, self-lubricating cutting tools were fabricated by adding various elements such as NiCr, Ag, Mo, SrSO4, and CaF2 to Zirconia Toughened Alumina in order to overcome the difficulties that occurred in dry machining. Three significant process parameters; cutting speed (50, 125, 200, and 275 m/min), feed rate (0.1 and 0.2 mm/rev), and depth of cut (0.1 and 0.2 mm) were selected for the machining process. The performance measures such as chip thickness ratio, shear angle, and tool wear, were investigated. The experimental runs were conducted with Taguchi L8 mixed-level orthogonal array. Technique for Order of Preference by Similarity to Ideal Solution is a multi-criteria decision-making method used to determine the best alternative from a set of options. Turning experiments have been performed on the AISI 4340 steel workpiece with various combinations of machining parameters. The findings revealed the incorporation of SrSO4 and Mo at 10Wt% and CaF2 at 5Wt% (i.e., self-lubricating cutting tool-4) achieved better mechanical properties and wear resistance due to the formation of a self-lubrication layer on the rake face of the cutting tool. A confirmation experiment is executed in order to verify the outcomes in the end. Finally, there is a noticeable improvement in the outcomes compared to the base cutting tool, i.e., chip thickness ratio is 35.77%, shear angle is 27.31%, and tool wear is 58%. The mechanism for improving performance measures is discussed in detail.
Application of self-learning interval type-2 fuzzy neural network in PM2.5 concentration prediction
Authors: Shao Kunpeng , Zhao Taoyan , Cao Jiangtao
Published: May 2024

The change of PM2.5 concentration in air quality is nonlinear and difficult to predict. Therefore, a self-learning interval type-2 fuzzy neural network (SLIT2FNN) is proposed. SLIT2FNN has two parts: online structure learning and parameter learning. In structure learning, to improve the training accuracy and speed of the model, the Possibilistic Fuzzy C-Means (PFCM) algorithm is used to process the input data and obtain the number of initial rules before model training. The PFCM algorithm introduces the concept of possibility P to Fuzzy C-Means (FCM), allowing PFCM to overcome the shortcomings of FCM that cannot accurately cluster a large number of nonlinear problems. SLIT2FNN can establish an appropriate number of rules in the preparation stage, and then use the firing strength of the antecedents of the rules to judge whether to generate fuzzy rules for online self-learning, thereby optimizing its network structure. Then, the improved Levenberg–Marquardt (ILM) algorithm is used to modify the relevant parameters of SLIT2FNN. The ILM algorithm can address the challenge of numerous parameters in the Jacobian matrix and complex calculations and improve the calculation speed and adaptive ability of SLIT2FNN parameter learning. Finally, SLIT2FNN is applied to the prediction of air quality PM2.5 concentration, and the performance is compared with other models. The experiment proves that SLIT2FNN has a high prediction accuracy and fast convergence.
Spike-based computation using classical recurrent neural networks
Authors: De Geeter Florent , Ernst Damien , Drion Guillaume
Published: May 2024

Spiking neural networks (SNNs) are a type of artificial neural networks in which communication between neurons is only made of events, also called spikes. This property allows neural networks to make asynchronous and sparse computations and therefore drastically decrease energy consumption when run on specialized hardware. However, training such networks is known to be difficult, mainly due to the non-differentiability of the spike activation, which prevents the use of classical backpropagation. This is because state-of-the-art SNNs are usually derived from biologically-inspired neuron models, to which are applied machine learning methods for training. Nowadays, research about SNNs focuses on the design of training algorithms whose goal is to obtain networks that compete with their non-spiking version on specific tasks. In this paper, we attempt the symmetrical approach: we modify the dynamics of a well-known, easily trainable type of recurrent neural network (RNN) to make it event-based. This new RNN cell, called the spiking recurrent cell, therefore communicates using events, i.e. spikes, while being completely differen-tiable. Vanilla backpropagation can thus be used to train any network made of such RNN cell. We show that this new network can achieve performance comparable to other types of spiking networks in the MNIST benchmark and its variants, the Fashion-MNIST and the Neuromorphic-MNIST. Moreover, we show that this new cell makes the training of deep spiking networks achievable.
Crossing Abyssal Lines
Authors: Taboada Manuela , Turner Jane
Published: May 2024

This chapter explores how decolonial thought can help us understand and recover our relationships with the more-than-human in urban design contexts. This is done through stories of place from Meanjin/Brisbane, Australia, that offer a lens to look at the tangles created by the visible and invisible ‘abyssal lines’ that separate the human from the more-than-human in the city. We start by taking a walk along a local river and finish in our homes, highlighting the encounters with and perceptions of the humans towards the more-than-human in these areas. The authors weave the stories together to reflect upon some contemporary approaches to bring the more-than-human closer to human urban ontologies, and their consequences. They offer some decolonial principles that can be applied to help us untangle our perspectives and positionings to find opportunities to return to living and sharing with the more-than-human.
Exploring More-than-Human Smart Cities
Authors: Gaver Bill , Boucher Andy , Brown Dean , Matsuda Naho , Ovalle Liliana , Sheen Andy , Vanis Mike
Published: May 2024

This chapter describes a process and line of thought developed by our design research team as we explored how smart cities might include ‘more-than-humans’, particularly living non-humans and for our team, mainly animals. The project was inspired by and pursued alongside researchers from five UK universities and two community organisations via a programme of participatory and design-led engagements with a variety of (human) stakeholders in London and elsewhere in the United Kingdom. As newcomers to the field, our design team investigated the topic by generating a workbook of design proposals and fictions: collections of simple propositions that contain a few images, words, and titles to convey an idea. The chapter provides examples from the workbook and discusses the value of an open, emergent design process and highlights the transformative potential of embracing a more-than-human perspective in reshaping our understanding and implementation of smart city designs.
The Logic of Status Competition
Authors: Beaumont Paul David
Published: May 2024

This chapter situates the book’s theoretical framework within the IR status literature. In so doing, it develops an ideal-typical account of status competition as a rule-governed competition for position whereby the relative performance of competitors is easily identifiable by virtue of the shared understanding of the rules of the game. While broadly consistent with prior research, the advantage of this ideal type is that it gives due emphasis to the rule-governed nature of status competition and the specific processual-relational logic of action it produces. The key value added is that it defines status dynamics by their processual-relational logic rather than by either the motivation or the substance of the behavior. This typology thus lays the groundwork for solving one of the methodological riddles of the introduction: differentiating status competition from other types of state action and status-seeking without defining substance of the status hierarchy prior to analysis.
The Grammar of Status Competition
Authors: Beaumont Paul David
Published: May 2024

This chapter elaborates how we can recognize theories of international status as they are manifested in discourse. To this second end, the chapter develops a metalinguistic framework—“Grammar of Status Competition”—for identifying the logic of status competition as it manifests in language. The chapter thereby leverages the processual-relational logic outlined in the previous chapter to develop a lens for identifying status and its effects without assuming (and reifying) the rules of an international social hierarchy or trying to infer motivations. Specifically, it outlines how three types of representations invoke and thus constitute competitive status hierarchies: (1) superlatives and relative comparisons, (2) positional identity constructions, and (3) sports metaphors. When uttered, each grammatical unit on its own (no matter how momentary) invokes the logic of status competition and specifies a distinct theory of status, which can be used to legitimize and delegitimize particular courses of action.
AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data
Authors: Li Zhongxiao , Napolitano Antonella , Fedele Monica , Gao Xin , Napolitano Francesco
Published: May 2024

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.