bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between Phytoestrogen Consumption and Female Reproductive Health: A Systematic Review of Experimental Models
Authors: Suhaimi Nur Nadhirah , Waras Maisarah Nasution , Ansor Nurhuda Mohamad 
Published: September 2023

AbstractPhytoestrogens have been shown as promising therapeutic agents for the treatment of menopausal symptoms, osteoporosis, breast cancer, and cardiovascular diseases. However, due to its unique chemical structure, phytoestrogen may cause unintended estrogenic and/or antiestrogenic effects on the human body, especially with regard to female reproductive health and performance. Hence, this systematic review aims to provide a critical evaluation of in vitro and in vivo evidence from the literature regarding the adverse effects of phytoestrogens on female reproductive health. The literature search was performed on four electronic databases including Scopus, Science Direct, PubMed, and Google Scholar following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A total of 965 studies were screened but only 58 of them were found to be relevant and assessed for eligibility. Of these, 23 studies met the eligibility criteria while the remaining studies were excluded due to insufficiently described methods and lack of clear findings being reported. From the review, phytoestrogens may alter the development of reproductive organs, prolong the estrus cycle, induce the accumulation of fluid in the uterus, and inhibit ovulation. The concentration and exposure duration of phytoestrogens may have different effects on the reproductive organs. Thus, further studies are warranted on the toxicodynamic, toxicokinetic, mode of action, and mechanism of actions of phytoestrogens on the female reproductive system to establish recommendations regarding phytoestrogen supplement consumption for women.
Prognostication in Prolonged and Chronic Disorders of Consciousness
Authors: Carlson Julia M. , Lin David J. 
Published: September 2023

AbstractPatients with prolonged disorders of consciousness (DOCs) longer than 28 days may continue to make significant gains and achieve functional recovery. Occasionally, this recovery trajectory may extend past 3 (for nontraumatic etiologies) and 12 months (for traumatic etiologies) into the chronic period. Prognosis is influenced by several factors including state of DOC, etiology, and demographics. There are several testing modalities that may aid prognostication under active investigation including electroencephalography, functional and anatomic magnetic resonance imaging, and event-related potentials. At this time, only one treatment (amantadine) has been routinely recommended to improve functional recovery in prolonged DOC. Given that some patients with prolonged or chronic DOC have the potential to recover both consciousness and functional status, it is important for neurologists experienced in prognostication to remain involved in their care.
How does digital transformation relieve the employment pressure in China? Empirical evidence from the national smart city pilot policy
Authors: Ling Xiao , Luo Zhangwei , Feng Yanchao , Liu Xun , Gao Yue 
Published: September 2023

AbstractThe impact of digital transformation on employment has been increasingly noticed by the academic community, while the internal mechanism still remains as a black box, especially in terms of specific pilot policy, such as the national smart city pilots policy in China. Based on the city-level and firm-level panel data, we investigate the impact of China’s national smart city pilot on the employment pressure of urban job seekers using difference-in-differences model. The results show that the national smart city pilots significantly reduces the employment pressure in the pilot cities. In addition, by bringing configuration optimization and technological upgrading, smart city pilots affect firm selection at the micro level, generating siphoning effects, factor substitution effects, and efficiency gains, and further affect the macro economy by promoting urban economic agglomeration, industrial structure transformation, and regional innovation thereby affecting employment pressure. Furthermore, the reduction effect of China’s national smart city pilot on employment pressure are heterogeneous in terms of cities, firms, and workers’ education levels. Finally, conclusions and policy implementations are provided to highlight the theoretical and practical values.
Climate change, counter-terrorism and capitalist development in Somalia
Authors: Mueller Jason C. 
Published: September 2023

ABSTRACT
Somalia is often referred to as a ‘failed state’. In addition to ineffective governance, feeble economic development, and a large anti-government insurgency, it faces increasingly severe climate change-induced devastation. This article offers a critical discussion of the role of capitalist interests and ideology as a factor in the climate crisis. It explores interlocking issues of (1) the relationship between the ruling political class of Somalia and capitalist mining interests; (2) the largely covert US-backed ‘war on terror’ in Somalia; and (3) the ongoing, capitalism-induced climate crisis. The article analyses current US and Somali proposals to address these issues. Many of these proposals remain trapped in the politico-ideological deadlock of capitalist developmentalism, oriented towards fossil fuel extraction and militarised accumulation. The trajectory of this current path in Somalia is leading to immiseration, oppression, displacement for millions of people, and the destruction of an already deteriorating environment. Alternative paths to avert these catastrophes require transnational solidarity, cooperation and assistance.
Pressure gradient in an incompressible fluid as a reaction force and the preservation of the principle of ‘cause and effect’
Authors: Simeonov Lachezar S 
Published: September 2023

Abstract
When considering the motion of an incompressible fluid, it is common practice to take the curl on both sides of the Navier–Stokes (or Euler) equations and cancel the pressure force. The governing equations are sufficient to derive the velocity field of the fluid without any knowledge of the pressure. In fact, the pressure is only calculated after obtaining the velocity field. This raises a number of conceptual problems. For instance, why is the pressure unnecessary for obtaining the velocity field? Traditionally, forces have been considered as the ‘causes’ of motion, and the resulting acceleration as the ‘effect’. However, the acceleration (the effect) and the resulting velocity field can be obtained without any recourse to the pressure (the cause), seemingly violating the principle of ‘cause’ and ‘effect’. We address these questions by deriving the pressure force of an incompressible fluid, starting from d’Alembert’s principle of virtual work, as a ‘reaction force’ that maintains the incompressibility condition. Next, we show that taking the curl on both sides of the Navier–Stokes (or Euler) equations is equivalent to using d’Alembert’s principle of virtual work, which cancels out the virtual work of the pressure gradient. This shows that abstract procedures, such as taking the curl on both sides of an equation, can actually be tacit applications of rich physical principles, without one realizing it. This can be quite instructive in a classroom of undergraduate students.
From Galilean relativity to special relativity: the pedagogical contribution of the space-proper time diagrams
Authors: Kirèche Alain 
Published: September 2023

Abstract
After detailing the characteristics of space-proper time diagrams for Galilean relativity and special relativity, we show how they can be used to better visualize the progression of ideas about the absolute or relative nature of space and time, from Galilean relativity to special relativity. We conclude that space-proper time diagrams could be an interesting new tool for introducing special relativity.
Mittag–Leffler expansions for inverse spectral problems with mixed data
Authors: Wei Zhaoying , Wei Guangsheng 
Published: September 2023

Abstract
We consider the inverse spectral problem for the Sturm–Liouville problem in the interval

[
0
,
1
]

with the Dirichlet boundary conditions at both ends. We provide a method for the unique reconstruction of the potential that is known a priori on the subinterval

[
0
,
r

/

m
]

, where m and r being positive integers and satisfying

r
<
m

/

2

, by using the spectral data set consisting of all the eigenvalues and a subset of the norming constants. The method is based on the Mittag–Leffler decomposition which can help us to decompose an entire function of exponential type into two functions of smaller types. This decomposition allows the use of Lagrange interpolation in order to reconstruct the potential in the Sturm–Liouville problem. We also provide necessary and sufficient conditions related to the existence issues.
Highly-sensitive expandable microsphere-based flexible pressure sensor for human–machine interaction
Authors: Wang Ye , Duan Shengshun , Liu Jiachen , Zhao Fangzhi , Chen Pinzhen , Shi Qiongfeng , Wu Jun 
Published: September 2023

Abstract
Flexible microstructural pressure sensors (FMPSs) have been widely used in different areas including health monitoring, human–machine interaction and electronic skin for their good reliability, easy fabrication, and highly sensitive sensing performance. Although the use of a sensing layer with microstructure improves the sensor sensitivity, it also results in limited sensitive pressure range, slow response and poor cyclic stability, which prevents the sensors from being utilized in applications requiring wide detect range, real-time response and high durability. Here, we propose an advanced micro-structured sensing layer through doping thermally expandable microspheres into the screen-printing carbon black slurry to enhance its mechanical properties. An FMPS is then developed based on this enhanced micro-structured sensing layer. Benefiting from the expanded microsphere-like microstructure, the as-prepared sensor features high sensitivity (37.16 kPa−1), fast response/recovery time (126/52 ms), and good mechanical stability (over 3000 cycles). In addition, a home-made and effective collision avoidance system consisting of a robotic arm, the pressure sensor, a MEGA2560 microcontroller unit and a computer is constructed to indicate the sensor’s outstanding tactile perception characteristic, making it a promising candidate for intelligent robotic applications such as human–robot collaboration. Furthermore, we develop a pressure sensor array and demonstrate its ability to spatial pressure distribution perception. The developed microsphere-based pressure sensor and its array show great potential to be adopted in various monitoring and interactive applications such as humanoid robots, smart home, human–machine interaction, etc.
Hyperfine transition induced by atomic motion above a paraffin-coated magnetic film
Authors: Sekiguchi Naota , Usui Hiroaki , Hatakeyama Atsushi 
Published: September 2023

Abstract
We measured transitions between the hyperfine levels of the electronic ground state of potassium-39 atoms (transition frequency: 460 MHz) as the atoms moved through a periodic magneto-static field produced above the magnetic-stripe domains of a magnetic film. The period length of the magnetic field was 3.8 µm. The atoms were incident to the field as an impinging beam with the most probable velocity of 550 m s−1 and experienced a peak oscillating field of 20 mT. Unwanted spin relaxation caused by the collisions of the atoms with the film surface was suppressed by the paraffin coating on the film. We observed increasing hyperfine transition probabilities as the frequency of the field oscillations experienced by the atoms increased from 0 to 140 MHz for the atomic velocity of 550 m s−1, by changing the incident angle of the atomic beam with respect to the stripe domains. Numerical calculation of the time evolution of the hyperfine states revealed that the oscillating magnetic field experienced by the atoms induced the hyperfine transitions, and the main process was not a single-quantum transition but rather multi-quanta transitions.
Deep learning assisted microwave-plasma interaction based technique for plasma density estimation
Authors: Ghosh Pratik , Chaudhury Bhaskar , Purohit Shishir , Joshi Vishv , Kothari Ashray , Shetranjiwala Devdeep 
Published: September 2023

Abstract
The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) are based on the accurate estimations of plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. The article proposes a deep learning (DL) assisted microwave-plasma interaction-based non-invasive strategy, which can be used as a new alternative approach to address some of the challenges associated with existing plasma density measurement techniques. The electric field pattern due to microwave scattering from plasma is utilized to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of symmetric (Gaussian-shaped) and asymmetrical density profiles, in the range 1016–1019 m−3, addressing a range of experimental configurations have been considered in our study. Real-life experimental issues such as the presence of noise and the amount of measured data (dense vs sparse) have been taken into consideration while preparing the synthetic training data-sets. The DL-based technique has the capability to determine the electron density profile within the plasma. The performance of the proposed DL-based approach has been evaluated using three metrics- structural similarity index, root mean square logarithmic error, and mean absolute percentage error. The obtained results show promising performance in estimating the 2D radial profile of the density for the given linear plasma device and affirms the potential of the proposed machine learning-based approach in plasma diagnostics.
Anisotropic optical conductivities of model topological nodal-line semimetals
Authors: Kandel Sita , Gumbs Godfrey , Berman Oleg L 
Published: September 2023

Abstract
With the use of simple models, we investigated the optical conductivity of a nodal-line semimetal (NLSM) whose crossing of the conduction and valence bands near the origin (O point) in the (

k
x

,

k
y

) plane of a small cubic region can be adjusted by a parameter α. The Hamiltonian of the NLSM is based on the

k

⋅

p

model for the low-lying energy bands. When α = 0, these bands touch each other along a continuous closed loop but the opening of a band gap corresponding to finite values of α and the varying of the carrier concentration can be adjusted. This provides a tunable semiconductor gap, around the O point and the valence and conduction bands can meet at a pair of points within the small cubic region in k space. The optical conductivity of such a NLSM is calculated using the Kubo formula with emphasis on the optical spectral weight redistribution, deduced from appropriate Green’s functions, brought about by changes in gap and chemical potential due to modifying α. We derived closed-form semi-analytic expressions for the longitudinal components of the optical conductivity for these model systems of NLSM and compare results for chosen α and chemical potential. We also present results for the heat capacity when the system is in thermal equilibrium for various chosen α and chemical potential.
Bearing fault diagnosis under non-stationary conditions based on a speed signal resonance component demodulation algorithm
Authors: Ye Ming , Zhang Xiaojun , Yang Jiaqiang 
Published: September 2023

Abstract
This paper proposes a novel bearing fault diagnosis indicator, the resonance component, for motor speed signals. The speed signal was modulated into a higher-frequency band using a double-inertia system, and the bearing fault information was carried and reserved in this high-frequency resonance component. Variational mode decomposition was then used to separate the components unrelated to the resonance based on the parameters optimized using the artificial bee colony algorithm. After envelope demodulation and angular resampling, the multipoint optimal minimum entropy deconvolution adjusted algorithm was utilized to enhance the amplitude of the order spectrum at the corresponding faulty order. Additionally, experiments were successfully conducted on both the outer and inner raceway bearing faults under time-varying conditions using the proposed method. The results indicate that the ordinary diagnosis method using the basic bearing fault characteristic frequency as an indicator is less effective.
Parametric study of intensity-ratio-based phosphor thermometry using Mg4FGeO6:Mn4+ for instantaneous temperature measurement
Authors: Cheng Weilun , Liu Yingzheng , Peng Di 
Published: September 2023

Abstract
Intensity-ratio-based phosphor thermometry is advantageous for instantaneous temperature measurement because it avoids interframe displacement due to moving phosphor coated samples that is encountered with lifetime-based methods. This study was aimed at investigating the effects of exposure time, excitation energy, and wavelength on intensity-ratio-based phosphor thermometry using Mg4FGeO6:Mn4+ (MFG). First, the temperature-dependent emission spectra of MFG were obtained under 355 nm and 405 nm excitations. The spectra exhibited different trends in the two cases, potentially because of thermally enhanced phonon-assisted absorption. Next, the shot-to-shot calibration uncertainties were evaluated from the temperature calibration results under different exposure time and excitation irradiance/fluence values for light-emitting diode/laser. The uncertainties under 355 nm and 405 nm excitations exhibited similar trends, i.e. they increased and decreased with increasing temperature and exposure time, respectively. For the minimum exposure time of 4 μs under 355 nm excitation, the uncertainty increased from 1.5 K at 323 K to 3.3 K at 673 K at a constant excitation fluence of 3.14 mJ cm−2. The uncertainties under 405 nm excitation were higher than those under 355 nm excitation. In addition, the inaccuracies due to excitation non-uniformity were evaluated from the calibration results under different excitation irradiance/fluence. The calibration curves were more sensitive to excitation irradiance at higher temperatures under 405 nm excitation. Consequently, the excitation non-uniformity induced inaccuracies under 405 nm excitation and different exposure time values were small at low temperatures (0.4–1.1 K at 323 K) and increased rapidly with temperature (2.6–14.4 K at 673 K). In the case of 355 nm excitation, the calibration curves exhibited small sensitivity to excitation fluence only at low temperatures. As the temperature increased, the inaccuracies under 355 nm excitation decreased and then increased, and exhibited values between 0.5 K and 2.5 K.
Fault diagnosis of gas turbine generator bearings using enhanced valuable sample strategy and convolutional neural network
Authors: Xu Xiaozhuo , Li Zhiyuan , Zhao Yunji , Qian Wei 
Published: September 2023

Abstract
Gas turbine bearings operate continuously under complex and harsh conditions such as high temperatures, high pressures and high speeds. Bearing fault monitoring data often exhibits anomalies, noise, missing values, and strong coupling and non-linearity due to real-world random factors. In addition, the traditional convolutional neural network (CNN) is still limited by the scarcity of labeled samples in real-world conditions and cannot fully extract fault features. To address the complexities of strongly coupled fault data, inconsistent data quality, and the shortage of authentic labeled samples, this paper presents a novel method for bearing fault diagnosis. This method utilizes an enhanced valuable sample strategy in conjunction with CNNs, integrating data enhancement and active learning (AL) for preprocessing to create valuable training sets for the networks. At the outset, random dropout and scaling jitter operations are applied to the original data, effectively removing anomalies and gaps in the fault signals. This process simultaneously emulates variations in the frequency spectrum and amplitude of vibration signals under real operating conditions, thereby enhancing the quality of gas turbine bearing fault data. Subsequently, the method incorporates AL techniques, iteratively selecting unlabeled data samples with the highest information value, and annotating them using support vector machines. This strategy creates a valuable training dataset that further enhances the fault diagnosis capabilities of the model. In the subsequent phase, a three-dimensional CNN is employed to extract fault feature information efficiently from the fault data, culminating in precise fault classification. In order to validate the effectiveness and superiority of the algorithm, validation and comparison analyses were carried out using the Case Western Reserve University and XJTU open datasets. To evaluate the practical effectiveness, a gas turbine main bearing dataset collected in a real environment, named the BaiChuan dataset, is used for the engineering validation of the proposed method.
Isodrastic magnetic fields for suppressing transitions in guiding-centre motion
Authors: Burby J W , MacKay R S , Naik S 
Published: September 2023

Abstract
In a magnetic field, transitions between classes of guiding-centre motion can lead to cross-field diffusion and escape. We say a magnetic field is isodrastic if guiding centres make no transitions between classes of motion. This is an important ideal for enhancing confinement. First, we present a weak formulation, based on the longitudinal adiabatic invariant, generalising omnigenity. To demonstrate that isodrasticity is strictly more general than omnigenity, we construct weakly isodrastic mirror fields that are not omnigenous. Then we present a strong formulation that is exact for guiding-centre motion. We develop a first-order treatment of the strong version via a Melnikov function and show that it recovers the weak version. The theory provides quantification of deviations from isodrasticity that can be used as objective functions in optimal design. The theory is illustrated with some simple examples.