bluuebunny/crossref_metadata_2025 huggingface dataset:
The impact of Chatbot-Assisted Self Assessment (CASA) on intentions for sexual health screening in people from minoritised ethnic groups at risk of sexually transmitted infections
Authors: Nadarzynski Tom , Knights Nicky , Husbands Deborah , Graham Cynthia A. , Llewellyn Carrie D. , Buchanan Tom , Montgomery Ian , Khlafa Nuha , Tichackova Jana , Odeyemi Riliwan , Johnson Samantha , Jesuthas Neomi , Tahia Syeda , Ridge Damien
Published: July 2024

Background Sexually transmitted infections (STIs) present a significant global public health issue, with disparities in STI rates often observed across ethnic groups. The study investigates the impact of Chatbot-Assisted Self Assessment (CASA) on the intentions for sexual health screening within minoritised ethnic groups (MEGs) at risk of STIs as well as the subsequent use of a chatbot for booking STI screening. Methods A simulation within-subject design was utilised to evaluate the effect of CASA on intentions for STI/HIV screening, concern about STIs, and attitudes towards STI screening. Screening intentions served as the dependent variable, while demographic and behavioural factors related to STI/HIV risk were the independent variables. ANCOVA tests were conducted to measure the impact of CASA on these perceptions. Results Involving 548 participants (54% women, 66% black, average age = 30 years), the study found that CASA positively influenced screening intentions t(547) = −10.3, P < 0.001], concerns about STIs t(544) = −4.96, P < 0.001, and attitudes towards sexual health screening [t(543) = −4.36, P < 0.001. Positive attitudes towards CASA were observed (mean, 13.30; s.d., 6.73; range, −17 to 21). About 72% of users who booked STI screening appointments via chatbot were from MEGs. Conclusion CASA increased motivations for STI screening intentions among ethnically diverse communities. The intervention’s non-judgemental nature and the chatbot’s ability to emulate sexual history-taking were critical in fostering an environment conducive to behavioural intention change. The study’s high acceptability indicates the potential for broader application in digital health interventions. However, the limitation of not tracking actual post-intervention behaviour warrants further investigation into CASA’s real-world efficacy.
Appropriate Biological Standardisation to Bring Confidence to Omics Data: The Example of the Microbiome
Authors: Lamaudiere Matthew , Sergaki Chrysi
Published: July 2024

Research into the microbiome can provide invaluable information which is being increasingly used to improve knowledge on its fundamental influence over human health, and crucially, its potential to aid diagnostic and therapeutic efforts. However, the methods used to study this ecosystem are flawed and introduce bias at every stage of the process, from sample collection to DNA extraction and sequencing analysis. Groups across the world have rushed to conduct sophisticated omics studies of the microbiome with limited knowledge of these biases, as they are not yet fully understood. This has led to contradictory findings, a lack of comparability between seemingly similar studies, and a deterioration of overall trust in the field. Tackling this major issue requires biological standardisation; physical reference materials that provide a ground truth to which microbiome data can be compared. Through this comparison, we can identify the nature of biases and the stages at which they are introduced, and crucially, how the biases can be overcome by optimising and eventually improving the methods used. With the use of biological standards, the field can thus become harmonised, and the accuracy and reliability of the data produced can be improved. This would facilitate innovation across the different disciplines involved, through the development of novel approaches to overcome the current hurdles encountered when translating the vast promise of the microbiome to fulfil public health needs.

Information

© The Authors 2024
On the origin of magnetic anisotropy in 4f-free ferromagnets based on the CaCu5 structure*
Authors: Samolyuk German D , Belashchenko K D , Parker David S
Published: July 2024

Using first-principles calculations, we investigate the origin of magnetocrystalline anisotropy in a series of 4f-electron-free intermetallics with CaCu5-based structures: YCo5, YCo4B, and Y3Co13B2. The electronic structure of these compounds is characterized by a set of narrow 3d bands near the Fermi level. In YCo5 the easy-axis anisotropy originates primarily in the spin–orbit coupling-induced mixing of the electronic states with Co

d

x
2

−

y
2

and

d

x
y

character. The analysis of k-resolved anisotropy shows that positive contributions accumulate from the entire Brillouin zone but are particularly large near the

k
z

=
0

plane. The analysis of the single-site and two-site terms reveals a large positive single-site contribution to the magnetocrystalline anisotropy from the Co atoms on the honeycomb sublattice, along with two-site contributions from both honeycomb and kagome sublattices.
The algorithm for denoising point clouds of annular forgings based on Grassmann manifold and density clustering
Authors: Zhang Yu-Cun , Wang An , Kong Tao , Fu Xian-Bin , Fang Dong-Qing
Published: July 2024

In the industrial sector, annular forgings serve as critical load-bearing components in mechanical equipment. During the production process, the precise measurement of the dimensional parameters of annular forgings is of paramount importance to ensure their quality and safety. However, owing to the influence of the measurement environment, the manufacturing process of annular forgings can introduce varying degrees of noise, resulting in inaccurate dimensional measurements. Therefore, researching methods for three-dimensional point cloud data to eliminate noise in annular forging point clouds is of significant importance for improving the accuracy of forging measurements. This paper presents a denoising approach for three-dimensional point cloud data of annular forgings based on Grassmann manifold and density clustering (GDAD). First, within the Grassmann manifold, the core points for density clustering are determined using density parameters. Second, density clustering is performed within the Grassmann manifold, with the Cauchy distance replacing the Euclidean distance to reduce the impact of noise and outliers on the analysis results. Finally, a search tree model was constructed to filter out incorrect point cloud clusters. The fusion of clustering results and the search tree model achieved denoising of point cloud data. Simulation experiments on annular forgings demonstrate that GDAD effectively eliminates edge noise in annular forgings and performs well in denoising point-cloud models with varying levels of noise intensity.
Sensor-based intelligent tool online monitoring technology: applications and progress
Authors: Huang Jiashuai , Chen Guangjun , Wei Hong , Chen Zhuang , Lv Yingxin
Published: July 2024

With the continuous development of the aerospace, defense, and military industry, along with other high-end fields, the complexity of machined parts has gradually increased. Consequently, the demand for tool intelligence has also strengthened. However, traditional tools are prone to wear during cutting due to high cutting forces, high temperatures, and vibrations. Intelligent tools, in contrast to traditional ones, integrate sensors into their design, allowing for real-time monitoring of the cutting status and timely prediction of tool wear. The application of intelligent tools in machining significantly enhances machining quality, increases productivity, and reduces production costs. In this review, first, the tool wear monitoring methods were classified and discussed. Second, the intelligence and innovation of sensors in monitoring cutting force, temperature, and vibration were introduced, and the commonly used types of sensors for online monitoring of cutting force were detailed. Furthermore, different types of sensors in tool wear were discussed, and the advantages of multi-sensor monitoring were summarized. Some urgent issues and perspectives that need to be addressed were proposed, providing new ideas for the design and development of intelligent tools.
A self-supervised learning method for fault detection of wind turbines
Authors: Zhi Shaodan , Shen Haikuo
Published: July 2024

As promising solutions to condition-based maintenance of wind turbines, artificial intelligence-based techniques have drawn extensive attention in the era of industry 4.0. However, accurate fault detection is still challenging owing to volatile operating conditions in real-world settings. To handle this problem, a novel method is proposed for fault detection of wind turbines. Specifically, a data augmentation scheme is developed to simulate the effects of time-varying environments and noise. Then, a self-supervised proxy task of variant prediction is designed and conducted. In this way, valid data representations can be extracted to represent the health status of wind turbines. Additionally, the compactness of data representations is guaranteed by the directional evolution, which can relieve the confusion of health conditions. The effectiveness of the proposed method is verified with actual measurements. Using the proposed method, several faults can be detected more than 10 d earlier, and blade breakage can be identified more than 22 h earlier. Furthermore, the developed method outperforms several benchmark approaches.
Millisecond-resolved infrared spectroscopy study of polymer brush swelling dynamics
Authors: Jorissen K F A , Veldscholte L B , Odijk M , de Beer S
Published: July 2024

We present the study of millisecond-resolved polymer brush swelling dynamics using infrared spectroscopy with a home-built quantum cascade laser-based infrared spectrometer at a 1 kHz sampling rate after averaging. By cycling the humidity of the environment of the polymer brush, we are able to measure the swelling dynamics sequentially at different wavenumbers. The high sampling rate provides us with information on the reconformation of the brush at a higher temporal resolution than previously reported. Using spectroscopic ellipsometry, we study the brush swelling dynamics as a reference experiment and to correct artefacts of the infrared measurement approach. This technique informs on the changes in the brush thickness and refractive index. Our results indicate that the swelling dynamics of the polymer brush are poorly described by Fickian diffusion, pointing toward more complicated underlying transport.
Fault diagnosis method and experimental research of reciprocating seal based on CFD-GAN-AE
Authors: Zhang Yi , Hu Ling , He Wei
Published: July 2024

Hydraulic impactors are crucial for oil and gas exploration, but seal failure is a common issue, having an effective technique for diagnosing sealing faults can provide dependable operational and maintenance assistance for hydraulic impactors. However, identifying wear failures is challenging and there is limited data available, there has been significant interest in intelligent defect diagnosis technology that is based on deep learning in recent years. Therefore, we propose a method to enhance the data and identify faults through deep learning. Initially, the computer fluid dynamics method was used to simulate seal leakage and determine whether factors such as pressure can indicate varying levels of leaking in the seal, this approach provides a theoretical foundation for signal gathering experiments. Next, the empirical mode decomposition approach is used to separate the non-smooth pressure signal from the seal experiment, revealing fault features that indicate the extent of leakage. Finally, the improved generative adversarial network method is suggested to balance imbalanced samples by utilizing the sample overlap rate, it is paired with the auto-encoder algorithm to categorize different levels of leakage. Furthermore, a comparative analysis is conducted between the proposed methodology and several classical fault diagnosis methods. This work investigates seal damage through the lens of computational fluid dynamics and the fault identification of uneven seal samples is accomplished.
Correction of scatter in CBCT with a new grid design
Authors: Cui Han , Su Ting , Zhang Xin , Zhu Jiongtao , Tan Yuhang , Zheng Hairong , Liang Dong , Ge Yongshuai
Published: July 2024

Objective. This study aims at developing a simple and rapid Compton scatter correction approach for cone-beam CT (CBCT) imaging. Approach. In this work, a new Compton scatter estimation model is established based on two distinct CBCT scans: one measures the full primary and scatter signals without anti-scatter grid (ASG), and the other measures a portion of primary and scatter signals with ASG. To accelerate the entire data acquisition speed, a half anti-scatter grid (h-ASG) that covers half of the full detector surface is proposed. As a result, the distribution of scattered x-ray photons could be estimated from a single CBCT scan. Physical phantom experiments are conducted to validate the performance of the newly proposed scatter correction approach. Main results. Results demonstrate that the proposed half grid approach can quickly and precisely estimate the distribution of scattered x-ray photons from only one single CBCT scan, resulting in a significant reduction of shading artifacts. In addition, it is found that the h-ASG approach is less sensitive to the grid transmission fractions, grid ratio and object size, indicating a robust performance of the new method. Significance. In the future, the Compton scatter artifacts can be quickly corrected using a half grid in CBCT imaging.
Development and validation of an MR-driven dose-of-the-day procedure for online adaptive radiotherapy in upper gastrointestinal cancer patients
Authors: Semeniuk Oleksii , Shessel Andrea , Velec Michael , Fodor Tudor , Rocca Cathy-Carpino , Barry Aisling , Lukovic Jelena , Yan Michael , Mesci Aruz , Kim John , Wong Rebecca , Dawson Laura A , Hosni Ali , Stanescu Teo
Published: July 2024

Objective. To develop and validate a dose-of-the-day (DOTD) treatment plan verification procedure for liver and pancreas cancer patients treated with an magnetic resonance (MR)-Linac system. Approach. DOTD was implemented as an automated process that uses 3D datasets collected during treatment delivery. Particularly, the DOTD pipeline’s input included the adapt-to-shape (ATS) plan—i.e. 3D-MR dataset acquired at beginning of online session, anatomical contours, dose distribution—and 3D-MR dataset acquired during beam-on (BON). The DOTD automated analysis included (a) ATS-to-BON image intensity-based deformable image registration (DIR), (b) ATS-to-BON contours mapping via DIR, (c) BON-to-ATS contours copying through rigid registration, (d) determining ATS-to-BON dosimetric differences, and (e) PDF report generation. The DIR process was validated by two expert reviewers. ATS-plans were recomputed on BON datasets to assess dose differences. DOTD analysis was performed retrospectively for 75 treatment fractions (12-liver and 5-pancreas patients). Main results. The accuracy of DOTD process relied on DIR and mapped contours quality. Most DIR-generated contours (99.6%) were clinically acceptable. DICE correlated with depreciation of DIR-based region of interest mapping process. The ATS-BON plan difference was found negligible (<1%). The duodenum and large bowel exhibited highest variations, 24% and 39% from fractional values, for 5-fraction liver and pancreas. For liver 1-fraction, a 62% variation was observed for duodenum. Significance. The DOTD methodology provides an automated approach to quantify 3D dosimetric differences between online plans and their delivery. This analysis offers promise as a valuable tool for plan quality assessment and decision-making in the verification stage of the online workflow.
Long-term phase angle muscle imaging under electrical muscle stimulation (EMS) by phase angle electrical impedance tomography
Authors: Sun Bo , Sejati Prima Asmara , Shirai Tomoyuki , Takei Masahiro
Published: July 2024

Objectives. Phase angle muscle imaging has been proposed by phase angle electrical impedance tomography (ΦEIT) under electrical muscle stimulation (EMS) for long-term monitoring of muscle quality improvement, especially focusing on calf muscles. Approach. In the experiments, twenty-four subjects are randomly assigned either to three groups: control group (CG, n = 8), low voltage intensity of EMS training group (LG, n = 8), and optimal voltage intensity of EMS training group (OG, n = 8). Main results. From the experimental results, phase angle distribution images Ф are cleared reconstructed by ФEIT as four muscle compartments over five weeks experiments, which are called the M
1 muscle compartments composed of gastrocnemius muscle, M
2 muscle compartments composed of soleus muscle, M
3 muscle compartments composed of tibialis-posterior muscle, flexor digitorum longus muscle, and flexor pollicis longus muscle, and M
4 muscle compartment composed of the tibialis anterior muscle, extensor digitorum longus muscle, and peroneus longus muscle. Ф is inversely correlated with age, namely the Ф decreases with increasing age. A paired samples t-test was conducted to elucidate the statistical significance of spatial-mean phase angle in all domain <Ф>Ω and in each muscle compartment <Ф>
M
with reference to the conventional phase angle Ф by bioelectrical impedance analysis, muscle grey-scale G
muscle by ultrasound, and maximal dynamic strength S
Max by one-repetition maximum test. Significance. From the t-test results, <Ф>Ω have good correlation with Ф and S
Max. In the OG, <Ф
W5>Ω, Ф
W5, and (S
Max)W5 were significantly higher than in the first week (n = 8, p < 0.05). A significant increase in the phase angle of both M
1 and M
4 muscle compartments is observed after five weeks in LG and OG groups. Only the OG group shows a significant increase in the phase angle of M
2 muscle compartment after five weeks. However, no significant changes in the spatial-mean phase angle of M
3 compartment are observed in each group. In conclusion, ФEIT satisfactorily monitors the response of each compartment in calf muscle to long-term EMS training.
Cold ion effects in density-asymmetric collisionless magnetic reconnection
Authors: Sun Mengmeng , Mao Aohua , He Xianglei , Divin Andrey , Zou Jitong , Wang Zhibin , Zhou Tianchun , Wang Xiaogang
Published: July 2024

The coexistence of low-energy (cold) ions and thermal (warm) ions is commonly observed in space and laboratory plasmas, such as those in magnetopause and fusion fueling processes. In certain events, the cold ion proportion may play a crucial role in plasma processes, especially magnetic reconnection. In this paper, magnetic reconnection with density-asymmetric cold ions is investigated in implicit particle-in-cell (iPIC) simulations. It is found that in such events the reconnection rate decreases as the cold ion distribution depth into the current sheet increases, mainly due to the mass-loading effect. Particularly, a density-peak structure of cold ions is developed in the reconnection region owing to the bounce motion of cold ions entering from the opposite inflow region. In the y–vy
phase space where the y-direction is normal to the current sheet, a cold ion ring structure related to the bounce motion is formed and amplified by the Hall electric field. Furthermore, the cold ions become a notable current carrier due to its shorter inertial scale than the warm ions. Consequently, the asymmetry of the cold ion distribution significantly breaks the symmetry in the Hall magnetic field, eventually leading to asymmetric cold ion density peak structure. Such structures can be taken as significant signals of cold ion existence in in situ spacecraft observations.
Bolt looseness localization with connection-stiffness-varying flange
Authors: Huang Hao , He Qingbo
Published: July 2024

Electromechanical impedance analysis is a traditional method to identify the occurrence of bolt looseness, but accurate localization of blot looseness is hard to realize on the flange. In this study, a flange model with bolt connection stiffness varying with position is proposed. The location of bolt looseness can be then determined from the impedance spectrum of the model since the uniformity and symmetry of the flange are broken. The analytic model is established to reveal the distinguishability of the eigenfrequency shifting characteristics when the connection stiffness at different positions changes. The frequency shifting sequence is extracted from the coupling impedance spectrum as a feature, and the correlation between the sequences corresponding to bolt looseness at different positions is low. The relationship between the sequence and the degree of looseness is highly related so that the unknown degree of looseness can be matched with the calibrated sequence to realize the localization of the looseness. Based on the distinguishability of the frequency shifting sequence, the connection-stiffness-varying model shows great potential in the field of flanges or other connecting structures for structural health monitoring and damage localization.
High-Q film bulk acoustic resonator with high quality AlN film based on transfer method
Authors: Wang Yaxin , Gao Chao , Yang Chaoxiang , Yang Tingting , Liu Yan , Ma Ye , Ren Xiaoning , Cai Yao , Sun Chengliang
Published: July 2024

Thin film bulk acoustic resonator (FBAR) with high quality factor (Q) is preferred for many communication applications. AlN film deposited in conventional FBAR fabrication process is poor, resulting in unsatisfactory device performance. This study demonstrated the comparison of two Si-substrate FBARs, adopting the same AlN deposition process by physical vapor deposition (PVD) method, however using different device fabrication method. Instead of depositing AlN film on Mo/SiO2/Si substrate in existing fabrication process, we deposited AlN directly on Si substrate by PVD to get better crystal quality piezoelectric layer of the FBAR. The full width at half maximum (FWHM) of rocking curve and surface roughness of AlN directly deposited on Si substrate are 1.4° and 1.96 nm, while those of AlN deposited on Mo/SiO2/Si substrate are 8.5° and 5.54 nm, respectively, indicating AlN deposited directly on Si substrate has a better crystal quality than that on Mo/SiO2/Si substrate. The dielectric loss of FBAR using AlN developed on Si substrate decreases from 0.4 Ω to 0.11 Ω leading to the increase of Q

m
from 470 to 830. By preserving the high quality AlN deposited on Si substate with film transfer method, the FBAR device enjoys an increasing of Q-values as high as 76%.
Mehler-Heine asymptotics and zeros of some Meijer G-functions
Authors: Mañas–Mañas Juan F , Moreno–Balcázar Juan J , Rodríguez–Perales Cristina
Published: July 2024

In this contribution we investigate the asymptotic behavior of the zeros of some Meijer G–functions. To achieve this, we analyze the Mehler–Heine asymptotics for these G–functions. The findings are then illustrated through numerical experiments.
Interfacial physico-chemical equilibrium control of lotus-type pore formation in solid
Authors: Ou Y T , Wei P S
Published: July 2024

This study presents a challenging analysis of interfacial equilibrium conditions that control the evolution of lotus-type pores in both metals and nonmetals during solidification. It incorporates Henry’s or Sieverts’ law, affecting solute transfer at the cap and top free surface, and pore evolution. The significance of the directional and lightweight characteristics of lotus-type porous materials makes them vitally important in functional heat sinks, energy absorption, biomedical devices, and other applications. The study extends previous solute transfer models based on solute concentration deviations in the liquid from the top surface and convection-affected segregation at the advancing liquid–solid interface by further considering the effects of interfacial equilibrium conditions on pore development. Typical data selected for the dimensionless Henry’s law constant at the cap and top free surface is 0.175, while the Sieverts’ law constant at the cap and top free surface is 0.03. MATLAB Simulink and Simscape (version R2020b) with the solver ode113 are utilized to solve the resulting simultaneous system of unsteady first-order differential equations. The results show that the size of lotus-type pores increases as the Henry’s law constant at the cap decreases while the Henry’s law constant at the top free surface increases. Similar results are observed for Sieverts’ law. Lotus-type pores readily form as the Henry’s law constant at the cap increases while that at the top free surface decreases. The lotus pore length can also be determined and interpreted algebraically using solute content conservation. The model’s predictions closely match analytical findings previously validated by experimental data
A Big Ring on the sky
Authors: Lopez A.M. , Clowes R.G. , Williger G.M.
Published: July 2024

We present the discovery of `A Big Ring on the Sky' (BR), the
second ultra-large large-scale structure (uLSS) found in Mg II-absorber catalogues, following the previously reported Giant Arc
(GA). In cosmological terms the BR is close to the GA — at the same
redshift z ∼ 0.8 and with a separation on the sky of only ∼
12°. Two extraordinary uLSSs in such close configuration raises the
possibility that together they form an even more extraordinary cosmological
system. The BR is a striking circular, annulus-like, structure of diameter
∼ 400 Mpc (proper size, present epoch). The method of discovery is as
described in the GA paper, but here using the new Mg II-absorber
catalogues restricted to DR16Q quasars. Using the Convex Hull of Member
Spheres (CHMS) algorithm, we estimate that the annulus and inner absorbers
of the BR have departures from random expectations, at the density of the
control field, of up to 5.2σ. We present the discovery of the BR,
assess its significance using the CHMS, Minimal Spanning Tree (MST),
FilFinder and Cuzick & Edwards (CE) methods, discuss it in the context of the
GA+BR system, and suggest some implications for the origins of uLSS and for
our understanding of cosmology. For example, it may be that unusual
geometric patterns, such as these uLSSs, have an origin in cosmic strings.
CMB spectrum in unified EFT of dark energy: scalar-tensor and vector-tensor theories
Authors: Aoki Katsuki , Gorji Mohammad Ali , Hiramatsu Takashi , Mukohyama Shinji , Pookkillath Masroor C. , Takahashi Kazufumi
Published: July 2024

We study the cosmic microwave background (CMB) radiation in the unified description of the effective field theory (EFT) of dark energy that accommodates both scalar-tensor and vector-tensor theories. The boundaries of different classes of theories are universally parameterised by a new EFT parameter αV characterising the vectorial nature of dark energy and a set of consistency relations associated with the global/local shift symmetry. After implementing the equations of motion in a Boltzmann code, as a demonstration, we compute the CMB power spectrum based on the wCDM background with the EFT parameterisation of perturbations and a concrete Horndeski/generalised Proca theory. We show that the vectorial nature generically prevents modifications of gravity in the CMB spectrum. On the other hand, while the shift symmetry is less significant in the perturbation equations unless the background is close to the ΛCDM, it requires that the effective equation of state of dark energy is in the phantom region w
DE<-1. The latter is particularly interesting in light of the latest result of the DESI+CMB combination as the observational verification of w
DE>-1 can rule out shift-symmetric theories including vector-tensor theories in one shot.
Fast identification of transients: applying expectation maximization to neutrino data
Authors: Karl M. , Eller P.
Published: July 2024

We present a novel method for identifying transients suitable for both strong signal-dominated and background-dominated objects. By employing the unsupervised machine learning algorithm known as expectation maximization, we achieve computing time reductions of over 104 on a single CPU compared to conventional brute-force methods. Furthermore, this approach can be readily extended to analyze multiple flares. We illustrate the algorithm's application by fitting the IceCube neutrino flare of TXS 0506+056.
Testing scale-invariant inflation against cosmological data
Authors: Cecchini Chiara , De Angelis Mariaveronica , Giarè William , Rinaldi Massimiliano , Vagnozzi Sunny
Published: July 2024

There is solid theoretical and observational motivation behind the idea of scale-invariance as a fundamental symmetry of Nature. We consider a recently proposed classically scale-invariant inflationary model, quadratic in curvature and featuring a scalar field non-minimally coupled to gravity. We go beyond earlier analytical studies, which showed that the model predicts inflationary observables in qualitative agreement with data, by solving the full two-field dynamics of the system — this allows us to corroborate previous analytical findings and set robust constraints on the model's parameters using the latest Cosmic Microwave Background (CMB) data fromPlanckandBICEP/Keck. We demonstrate that scale-invariance constrains the two-field trajectory such that the effective dynamics are that of a single field, resulting in vanishing entropy perturbations and protecting the model from destabilization effects. We derive tight upper limits on the non-minimal coupling strength, excluding conformal coupling at high significance. By explicitly sampling over them, we demonstrate an overall insensitivity to initial conditions. We argue that the modelpredictsa minimal level of primordial tensor modes set byr≳ 0.003, well within the reach of next-generation CMB experiments. These will therefore provide a litmus test of scale-invariant inflation, and we comment on the possibility of distinguishing the model from Starobinsky and α-attractor inflation. Overall, we argue that scale-invariant inflation is in excellent health, and possesses features which make it an interesting benchmark for tests of inflation from future CMB data.
Research on key technology of smart grid based on holographic data
Authors: Zhang Tao , Li Shuang , Fu Yuhui , Wu Yiming , Wang Liang
Published: July 2024

With the increasing scale and complexity of power projects, designers face more and more challenges in terms of design quality and efficiency. In order to improve design quality and reduce human-caused errors, utilizing technologies such as big data and strong artificial intelligence, this manuscript proposes a key technology for smart grid based on holographic data. Firstly, the concept and key technologies of holographic intelligent design were proposed. Then, the overall plan of holographic smart grid was constructed, and the implementation methods of the main functions of holographic smart grid were elaborated. Finally, the construction process of the holographic digital twin platform was explored.
Research on Intelligent Heating for Urban Systems Based on Digital Twin
Authors: Jing Gao , Zhongxiao Du , Shuxiang Yang , Yingqi Xu , Sibo Xu
Published: July 2024

This article focuses on the intelligent heating platform driven by digital twins, analyzing the overall framework of the system according to the sensor layer, application layer, and network layer, and building an information service platform for intelligent heating networks. The data of the entire heating system, including heat sources, primary networks, heating stations, secondary networks, and heat users, is remotely collected, analyzed, and diagnosed. Subsequently, statistical analysis is conducted on the energy consumption of each heat source and heating station, achieving data sharing and data mining among various business systems. This article conducts research on several key technologies for optimizing decision-making methods of heating system operation scheduling based on simulation models and develops software systems to support the intelligent upgrading of heating systems. The results show that intelligent heating system based on digital twins can better meet the balance between supply and demand in urban heating systems, and optimize the overall operating costs and environmental benefits of the system under multiple constraints.
Research on optimal investment decision of distribution network projects under the construction of the new power system
Authors: Jiao Jie , Zhang Jiyuan , Yang Jie , Zhang Wenwen , Wen Xin
Published: July 2024

This article establishes a ranking model of distribution network projects to make optimal investment decisions. The project optimization ranking model consists of the LCC model and the project comprehensive evaluation model. When constructing the evaluation model of distribution network projects, the evaluation index system is first set, and then the weight allocation for each index is calculated based on the G1-antientropy weight comprehensive integrated weighting method, so as to calculate the comprehensive benefit of the unit cost of a project for optimal sorting. Taking a distribution network planning project in Sichuan Province as an example, the empirical analysis is conducted, and the ranking results are obtained by using the above optimization ranking model for distribution network projects depending on the comprehensive benefit index of unit cost of the project A-E, which confirms the reasonability and practicability of the decision-making system and realizes the optimization of investment decisions for distribution network projects.
Privacy-preserving method for sensitive partitions of electricity consumption data based on hybrid differential privacy and k-anonymity
Authors: Zhai Feng , Liang Xiaobing , Qin Yu , Li Baofeng , Shen Lixiang , Xie Jingyi
Published: July 2024

With the development of smart grid, traditional privacy protection methods are no longer sufficient to cope with the mining and analysing of users’ electricity consumption data, which can easily lead to the leakage of users’ privacy. In this paper, for the challenge of privacy protection of user electricity data in smart grid, we propose a privacy protection method for sensitive partition of electricity data based on hybrid differential privacy and k-anonymity technique, which identifies pattern-specific quasi-identifiers by random forest technique and divides the data into privacy-invasive and non-privacy-invasive partitions, and adopts the Laplacian noise addition (numerical quasi-identifiers) and data generalisation (sub-types of quasi-identifiers) techniques to achieve differential privacy protection for different partitions. Finally, it is experimentally verified that the method performs better in terms of information reduction accuracy and execution time, effectively striking a balance between protecting user privacy and maintaining data utility.
Correlation analysis of wind power and meteorology based on the ramp detection method with adaptive sliding window
Authors: Ming Huang Ruan , En Li Hao , Xin Miao Lin , Bing Li Can , Fei Xu Yu , Deng Xiang , Ying Zhang Qi
Published: July 2024

Since the penetration of wind power rises rapidly, the coupling between wind power and meteorology strengthens the dependence of the new power system on meteorology. The fluctuation of wind power and the ramp events become key challenges that affect the safe and stable operation of the power system. To balance the accuracy and efficiency of wind power ramp event detection, an adaptive sliding time window-based ramp event detection method is proposed, which includes three major steps: dataset preprocessing and compression, wind power ramp trend pattern extraction considering bump event screening, and wind power ramp detection based on adaptive sliding time window. The correlation between the ramp event of wind power and meteorological factors like temperature, wind speed and direction are analyzed, and the feasibility of the proposed method is verified. The results show that wind speed is the decisive meteorological factor influencing ramp events, and wind power is also affected by wind direction. However, the correlation between wind power and temperature is weak, which only works in a few ramp events.
Short-term Power Load Forecasting Based on EMD-GWO-BP
Authors: Bai Hong , Guan Yan , Cai Yinong , Wang Mingqi
Published: July 2024

The short-range prediction of electricity demand holds immense importance in the strategic planning and advancement of the energy sector. However, the actual load sequence data reflects multiple complex properties, such as nonlinearity, non-stationarity, and temporal variation. In order to accurately forecast the load, this paper presents a three-level hybrid integrated short-term load prediction method composed of empirical mode decomposition (EMD), Grey Wolf Optimization (GWO) and BP neural network (BP). EMD is used to decompose load data to obtain good power consumption characteristics; GWO is used to obtain the optimal weight and threshold required for BP prediction. The hybrid integration method (EMD-GWO-BP) was evaluated using the 2017 annual data of Chaoyang County, Liaoning Province. The EMD-GWO-BP method was compared with the other two mainstream coupling methods (BP, GWO-BP). The statistical analysis Indicates that the suggested method within this document shows better forecast precision on three standard scales of MAPE, MAD and RMSE, which reflects the advanced nature of this method.
The SHADOWS experiment at the CERN SPS
Authors: Cicero V. ,   the SHADOWS collaboration
Published: July 2024

SHADOWS (Search for Hidden And Dark Objects With the SPS) is a proposed proton beam-dump experiment for the search of a large variety of Feebly-Interacting Particles (FIPs) at the CERN SPS. It will exploit the potential for searches and discoveries at the intensity frontier offered by the upgrade of the ECN3 beam line.
SHADOWS will be located off-axis, which allows the optimisation of the signal-to-background ratio, and will collect data from up to 5×1019 protons of 400 GeV on target in 4 years of operations.
The experiment has a transversal size of 2.5×2.5 m2 and is composed by an upstream veto, a 20 m long decay volume and a spectrometer with a tracking system in a dipole magnet, a timing detector, a calorimeter and a muon system. The conceived detector offers excellent
tracking and timing performance for the identification and reconstruction of most of the visible final states of FIP decays. SHADOWS will allow to explore a large parameter space region of many FIPs, like light dark scalars, axion-like particles and heavy neutral leptons, with masses ranging between 0.1 and 10 GeV.
This paper reports about the status of the proposal of the SHADOWS experiment, with focus on the detector challenges.
Status of edge electron density and temperature measurements with Helium Beam Emission Spectroscopy (He-BES) on EAST
Authors: Ding G.F. , Ye Y. , Chen R. , Xu G.S. , Yu Y. , Lin X. , Yang Q.Q. , Zhang W. , Li Y. , Yan N. , Liu S.C. , Wang L. , Zhang T. , Zhou T.F. , Wu D.G.
Published: July 2024

Helium Beam Emission Spectroscopy (He-BES) diagnostic has
been developed on EAST, which is able to measure the edge electron
density and temperature profiles simultaneously using a helium line
intensity ratio method. The diagnostic includes the beam injector
and the detection system. There are 20 observation channels within
an observation range of 80 mm in the detection system at the low
filed side, which can cover the whole scrape-off layer (SOL) and
part of the pedestal region of EAST. The beam injector system has
been upgraded to Supersonic Molecular Beam Injector (SMBI) system to
realize deeper helium injection since the 2021 campaign. Four
spectral lines at wavelengths of 728.1 nm, 706.5 nm, 667.8 nm and
656.3 nm are detected by the He-BES. The first three spectral
lines, including 728.1 nm, 706.5 nm, 667.8 nm, are measured for
calculating edge n
e and T
e profiles based on the
collisional-radiative model (CRM) model, and the last spectral line
(656.3 nm) is used for the measurement of D
α
emission. The edge electrostatic fluctuations can be obtained from
the power spectrum of D
α emission. The electron density
and temperature profiles calculated from the 667.8/728.1 and
728.1/706.5 nm line ratios are in good agreement with those from
other diagnostics in the edge region of plasma. The self-consistency
of He-BES diagnostic is also verified, such as the density pump out
caused by LHW and the lower edge temperature caused by the lower
heating power.
Expected performance of Cosmic Muon Veto Detector
Authors: Shah Raj , Majumder Gobinda , Shukla Prashant ,   on behalf of INO collaboration
Published: July 2024

The India-based Neutrino Observatory (INO) collaboration has
established a miniICAL detector, at the transit campus of IICHEP,
Madurai, India, which serves as a prototype detector of the larger
Iron-Calorimeter detector (ICAL). The purpose of miniICAL lies in
unraveling the intricate physics and engineering challenges inherent
in constructing and operating a substantial ICAL-type detector. To
explore the feasibility of building a large-scale neutrino
experiment at shallow depths the collaboration has embarked upon the
construction of a Cosmic Muon Veto Detector (CMVD) around the
miniICAL detector. The primary objective of this endeavor revolves
around attaining a veto efficiency surpassing 99.99%, while
simultaneously maintaining a false-positive rate lower than
10-5. The CMVD system is based on extruded plastic
scintillators (EPS) and utilizes wavelength-shifting fibers to
collect scintillation photons and uses silicon photomultipliers
(SiPMs) as photo-transducers. A software tool is developed for CMVD
and is integrated with the existing miniICAL consisting of RPC
detectors. The simulation is tuned to include properties of EPSs and
WLS fibers, measured efficiencies, and time resolutions of
EPSs. Measured spectra and noise in SiPMs are also taken into
account. The muon tracks in the RPCs are used to estimate the muon
veto efficiency of CMVD to arrive at efficient muon veto
criteria. With improved veto efficiency of cosmic muons, the CMVD
experiment will help to pave the way for future large-scale
shallow-depth neutrino experiments e.g. INO-type experiments,
enhancing our understanding of neutrino properties.
Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with cycle generative adversarial network
Authors: Huang Bohan , Liu Zhu , Duan Qingyun , Rajib Adnan , Yin Jina
Published: July 2024

Climate change significantly impacts agricultural production, ecosystem stability, and socioeconomic development. Global climate models (GCMs) serve as the primary tool for simulating historical and future precipitation patterns. However, due to issues such as coarse resolution, boundary condition, and parameterization, model outputs require bias correction (BC). With the evolution of deep learning techniques, supervised convolutional neural network (CNN) frameworks have gained popularity in the area of climate model BC but face limitations in spatial correlation assumptions and data sparsity, particularly for extreme precipitation This study proposed an unsupervised learning approach using cycle generative adversarial network (CycleGAN) to correct the ensemble mean bias of models and compare its performance with CNN and Quantile Mapping methods. The results demonstrate that the proposed CycleGAN approach outperforms both CNN and Quantile Mapping in ensemble mean BC. It effectively learns the overall distribution of precipitation through an adversarial process and yields better extreme precipitation predictions.
Anomalous diffusion of self-propelled particles
Authors: J Sevilla Francisco , Chacón-Acosta Guillermo , Sandev Trifce
Published: July 2024

The transport equation of active motion is generalised to consider time-fractional dynamics to describe the anomalous diffusion of self-propelled particles. In the present study, we consider an arbitrary active motion pattern modelled by a scattering function that defines the dynamics of the change of the self-propulsion direction. The exact probability density of the particle positions at a given time is obtained. From it, the time dependence of the firsts moments, i.e. the mean square displacement and the kurtosis for an arbitrary scattering function, are derived and analysed. Anomalous diffusion is found with a crossover of the scaling exponent from 2α in the short-time regime to α in the long-time one,

0
<
α
<
1

being the order of the fractional derivative considered. It is shown that the exact solution found satisfies a fractional diffusion equation that accounts for the non-local and retarded effects of the Laplacian of the probability density function through a coupled temporal and spatial memory function. Such a memory function holds the complete information of the active-motion pattern. In the long-time regime, space and time are decoupled in the memory function, and the time fractional telegrapher’s equation is recovered. The theoretical framework presented here can be applied as model of active motion that exhibits anomalous diffusion.
Revisiting the spatially inhomogeneous condensates in the(1+1)-dimensional chiral Gross–Neveu model via the bosonic two-point function in the infinite-N limit
Authors: Koenigstein Adrian , Winstel Marc
Published: July 2024

This work shows that the known phase boundary between the phase with chiral symmetry and the phase of spatially inhomogeneous chiral symmetry breaking in the phase diagram of the(1+1)-dimensional chiral Gross–Neveu (GN) model can be detected from the bosonic two-point function alone and thereby confirms and extends previous results (Schön and Thies 2000At The Frontier of Particle Physics: Handbook of QCD, Boris Ioffe Festschriftvol 3 (World Scentific) ch 33, pp 1945–2032; Boehmeret al2008Phys. Rev.D78065043; Boehmer and Thies 2009Phys. Rev.D80125038; Thies 2018Phys. Rev.D98096019; Thies 2022Phys. Rev.D105116003). The analysis is referred to as the stability analysis of the symmetric phase and does not require knowledge about spatial modulations of condensates. We perform this analysis in the infinite-Nlimit at nonzero temperature and nonzero quark and chiral chemical potentials also inside the inhomogeneous phase. Thereby we observe an interesting relation between the bosonic 1-particle irreducible two-point vertex function of the chiral GN model and the spinodal line of the GN model.
Informationally overcomplete measurements from generalized equiangular tight frames
Authors: Siudzińska Katarzyna
Published: July 2024

Informationally overcomplete measurements find important applications in quantum tomography and quantum state estimation. The most popular are maximal sets of mutually unbiased bases, for which trace relations between measurement operators are well known. In this paper, we introduce a more general class of informationally overcomplete positive, operator-valued measure (POVMs) that are generated by equiangular tight frames of arbitrary rank. This class provides a generalization of equiangular measurements to non-projective POVMs, which include rescaled mutually unbiased measurements and bases. We provide a method of their construction, analyze their symmetry properties, and provide examples for highly symmetric cases. In particular, we find a wide class of generalized equiangular measurements that are conical two-designs, which allows us to derive the index of coincidence. Our results show benefits of considering a single informationally overcomplete measurement over informationally complete collections of POVMs.
Critical behavior of dirty free parafermionic chains
Authors: Pandey Akshat , Cowsik Aditya
Published: July 2024

A family of

Z

n

-symmetric non-Hermitian models of Baxter was shown by Fendley to be exactly solvable via a parafermionic generalization of the Clifford algebra. We study these models with spatially random couplings, and obtain several exact results on thermodynamic singularities as the distributions of couplings are varied. We find that these singularities, independent of n, are identical to those in the random transverse-field Ising chain; correspondingly the models host infinite-randomness critical points. Similarities in structure to exact methods for random Ising models, a strong-disorder renormalization group, and generalizations to other models with free spectra, are discussed.