bluuebunny/crossref_metadata_2025 huggingface dataset:
Direct numerical simulations of immiscible two-phase flow in rough fractures: Impact of wetting film resolution
Authors: Krishna R. , Méheust Y. , Neuweiler I.
Published: July 2024

The immiscible displacement of a wetting fluid by a non-wetting fluid in rough fractures is crucial in many subsurface applications. Hydrodynamic-scale modeling of such drainage flows is challenging due to the complex interaction between the forces at play, the intricate geometry, and the required modeling of moving contact lines. In addition, a remaining critical open question is to what extent not resolving the films of wetting fluid deposited on fracture walls degrades numerical predictions. We address this question by solving the Navier–Stokes equations, employing the volume-of-fluid method to capture fluid–fluid interfaces and considering numerical meshes that result in either resolved films (RF) or unresolved films (UF) in the simulations. The numerical model, implemented in OpenFOAM, is validated in the classical Saffman–Taylor (ST) viscous instability configuration using the original ST experimental measurements; at capillary numbers (Ca) larger than 10−3, UF simulations overpredict ST finger widths. We then address two synthetic fracture geometries: one with sinusoidally varying apertures and one with stochastic geometric properties typical of geological fractures. Predictions of RF and UF simulations are compared quantitatively for Ca ranging between 10−5 and 10−3. Wetting film thicknesses follow a power law of Ca similar to Bretherton's law. RF and UF approaches both predict similar invasion patterns, but the latter underestimates interfacial lengths and macroscopic pressure drops, as compared to RF simulations, while overpredicting invading fluid saturations and breakthrough times. These discrepancies increase with Ca, whereas the disordered nature of the geological fracture tends to limit them. For Ca<10−5, the discrepancies are negligible.
Super-resolution reconstruction of flow fields coupled with feature recognition
Authors: Tang Fazhi , Liu Feng , Wu Anping , Wang Qingfeng , Huang Jun , Li Yubo
Published: July 2024

Traditional super-resolution reconstruction methods for flow fields use end-to-end mapping to determine the relationship between high- and low-resolution flow field data. The reconstruction quality of these methods depends on the accuracy of the low-resolution data. Ensuring the accuracy of low-resolution data has, thus, become a precondition for super-resolution tasks, and it imposes strict limitations on the applicability of super-resolution reconstruction methods in practical engineering applications. This paper proposes a flow field super-resolution reconstruction method coupled with feature recognition (FRNet) to reduce the dependence on the accuracy of low-resolution data. FRNet uses a feature extractor with identification capabilities to determine the effectiveness of low-resolution flow field characteristics. It recognizes the effective characteristics using a feature distance distribution. Meanwhile, a representation of the obstacle shape and freestream information is introduced to compensate for invalid features and to suppress the influence of low-precision flow field characteristics on the reconstruction results. Different downsampling factors, different density grids, and noise are used to simulate a variety of engineering application scenarios to verify the effectiveness and applicability of the proposed method. The results demonstrate that FRNet has significant advantages over traditional super-resolution reconstruction methods. Our method does not rely on the accuracy of low-resolution data and can effectively mitigate the impact of low-resolution flow field data that do not conform to physical phenomena. This characteristic allows FRNet to exhibit outstanding performance when handling flow field data affected by noise from wind tunnel wall and rack interferences. Consequently, FRNet should prove highly beneficial for the optimization of complex flow fields using super-resolution reconstruction methods.
Global, regional and local surveillance of antimicrobial resistance in Neisseria gonorrhoeae
Authors: Lahra Monica M. , George C. Robert R. , van Hal Sebastiaan J.
Published: July 2024

Neisseria gonorrhoeae disease control strategies rely almost entirely on effective antibiotic treatment. Antimicrobial resistance (AMR) makes the future of gonococcal treatment uncertain. With the rapid evolution of gonococcal AMR, and a largely unmapped AMR landscape globally, it is very evident why concerns have been raised at the highest level. Urgent actions have been undertaken for better surveillance, new treatments and improved prevention. Most critical, however, is the need for a vaccine for disease prevention and control.
The bigger they are, the higher they go: Australian insectivorous bats confirm Bergmann’s 175-year-old prediction
Authors: Herr Alexander
Published: July 2024

Context Some insectivorous bats are some of the smallest flying endotherm. They have a high energy demand to maintain body temperature. Therefore, one can expect that larger animals of a species and larger species occur in colder environments as a result of improved energy conservation related to reduced surface to volume ratio in larger endotherm animals. Evidence of this general rule is scarce in bats, although Bergmann predicted this some 175 years ago for closely related species. Aims In this work, I investigated whether bat body size increases with above-sea-level elevation-related temperature decrease for three closely related Australian bat species of the genus Vespadelus. The purpose of this was two-fold. First, to investigate whether there is a relationship between bat size and elevation by using more recent computational techniques of Bayesian multilevel modelling (BMM). Second, to provide an example of applying recent advances in BMMs to wildlife research and to predict potential consequences of climate warming for these bats. Methods I investigated whether bat size relates to elevations of bat-capture locations. I included measurement errors for elevation and forearm length measurements by using a BMM in an high-performance computing environment. This model uses measurements of 775 bats from locations in the western slopes of the Australian Alps. Key results The BMM analysis showed that bat forearm length increased 0.11 mm for every 100 m elevation, with a low standard error of 0.01 mm, indicating a high precision. The standard deviations of the variables species and sex within species were large. This means that they did not provide sufficient explantory power for the overall model and predictions to warrant inclusion. Conclusions This study showed that there is a linear increase of bat size with elevation. This is the first study to show that bat size is related to elevation (and associated temperature decline) in three sympatric, closely related species of the same genus and it confirmed what Bergmann predicted over 175 years ago. Implications Under a warming climate, the results predict that bats become smaller on average. When incorporating average temperature-lapse rate to calculate elevations that assume a 1.5 and 3°C change in future average climate, the study coarsely quantified reduction in suitable habitat for the largest of the three species, V. darlingtoni, of up to 3%.
Using Tonal Data to Recover Japanese Language History
Authors: de Boer Elisabeth M.
Published: July 2024

This book challenges several assumptions commonly encountered in Japanese dialectology: that the pitch-accent analysis of modern Tōkyō Japanese is an appropriate basis for describing the suprasegmental phonology of other dialects and earlier stages of Japanese; that the Kyōto-type dialects have been more conservative than dialects to their east and west; that the first split in proto-Japanese was the separation of proto-Ryūkyūan; and so on. De Boer brings together evidence from recent fieldwork, premodern texts, and other sources to establish a theory of dialect divergence that avoids the problems these assumptions entail. Building on De Boer 2010, this book brings the author’s theory up to date with research published in the interim, explains why Japanese is best understood as a restricted tone language, and why mergers in the large tone classes of nouns and verbs are especially reliable markers of dialect divergence.
Emoji and illocutionarity
Authors: Herring Susan C. , Ge-Stadnyk Jing
Published: July 2024

Emoji can modify a textual utterance, constitute a
stand-alone speech act, or virtually perform an action. These three broad
types of pragmatic function are usually treated separately in the literature
when they are treated at all. We classify these functions in a systematic,
unified manner by drawing on the classic speech act notion of
illocutionarity. We present a conceptually motivated typology that accounts
for the three basic types of emoji function, as well as most pragmatic
functions reported in previous emoji research, illustrating it with data
from American and Chinese social media. The scheme can serve as a practical
heuristic to guide empirical research on emoji use and a theoretical anchor
for pragmatic studies of other graphicon types.
PTPN2 deficiency: Amping up JAK/STAT
Authors: Tobin Joshua M. , Cooper Megan A.
Published: July 2024

Identification of monogenic causes of immune dysregulation provides insight into human immune response and signaling pathways associated with autoimmunity. Here, Jeanpierre et al. (https://doi.org/10.1084/jem.20232337) identify new germline variants in the gene encoding PTPN2 associated with loss of regulatory function, enhanced JAK/STAT signaling, and early-onset autoimmunity.
Design analysis of a low-power, high-speed 8 T SRAM cell using dual-threshold CNTFETs
Authors: Haq Shams ul , Abbasian Erfan , Khurshid Tabassum , Basha Shaik Javid , Sharma Vijay Kumar
Published: July 2024

Recently, carbon nanotube field-effect transistors (CNTFETs) have garnered significant attention from VLSI engineers due to their exceptional electrical properties. This paper proposes a novel high-speed, low-power eight-transistor (8 T) static random-access memory (SRAM) cell based on 32-nm CNTFET technology. The SRAM cell was simulated using the HSPICE tool with a VDD of 0.9 V. The high-speed and low-power characteristics of the SRAM design are attributed to the high subthreshold slope and high carrier mobility of metal-oxide-semiconductor field-effect transistor (MOSFET)-like CNTFETs utilized in the simulations. The implementation of dual threshold transistors, coupled with a transmission gate for bitline access, contributes to the enhanced performance. Key performance metrics such as noise margins, power consumption, delay, and SRAM electrical quality metric (SEQM) of the proposed SRAM have been evaluated and compared with existing CNTFET-based SRAM designs. The proposed cell demonstrates reductions of 73.73%, 43.18%, and 58.70% in read power, write power, and hold power, respectively, compared to the lowest respective power values of other examined SRAM designs. The proposed SRAM ranks second, third, and second in write static noise margin (WSNM), hold static noise margin (HSNM), and read static noise margin (RSNM), respectively, among other designs. Additionally, the proposed SRAM exhibits the least sensitivity to parametric variations compared to other designs. The SEQM, which provides a comprehensive assessment of access times, noise margins, and power usage for the SRAM cell, has been calculated. The SEQM of the proposed SRAM is 10.6, 1.89, 13.15, and 1.82 times higher than that of C6T, BLP8T, Mani’s 10 T, and LP8T, respectively.
Energy-dependent boosted dark matter from diffuse supernova neutrino background
Authors: Das Anirban , Herbermann Tim , Sen Manibrata , Takhistov Volodymyr
Published: July 2024

Diffuse neutrinos from past supernovae in the Universe present us with a unique opportunity to test dark matter (DM) interactions. These neutrinos can scatter and boost the DM particles in the Milky Way halo to relativistic energies allowing us to detect them in terrestrial laboratories. Focusing on generic models of DM-neutrino and electron interactions, mediated by a vector or a scalar boson, we implement energy-dependent scattering cross-sections and perform detailed numerical analysis of DM attenuation due to electron scattering in-medium while propagating towards terrestrial experiments. We set new limits on DM-neutrino and electron interactions for DM with masses in the range ∼ (0.1, 104) MeV, using recent data from XENONnT, LUX-ZEPLIN, and PandaX-4T direct detection experiments. We demonstrate that consideration of energy-dependent cross-sections for DM interactions can significantly affect constraints previously derived under the assumption of constant cross-sections, modifying them by multiple orders of magnitude.
Optical appearance of black holes surrounded by a dark matter halo
Authors: Macedo Caio F.B. , Rosa João Luís , Rubiera-Garcia Diego
Published: July 2024

Black holes in General Relativity are described by space-time metrics that are simpler in comparison to non-vacuum compact objects. However, given the universality of the gravitational pull, it is expected that dark matter accumulates around astrophysical black holes, which can have an impact in the overall gravitational field, especially at galactic centers, and induce non-negligible effects in their observational imprints. In this work, we study the optical appearance of a spherically symmetric black hole both when orbited by isotropically emitting light sources and when surrounded by a (geometrically and optically thin) accretion disk, while immersed in a dark matter halo. The black hole geometry plus the dark matter halo come as a solution of Einstein's field equations coupled to an anisotropic fluid whose density component follows a Hermquist-type distribution. We analyze in some depth the circular geodesic structure in both perturbative and non-perturbative regimes, investigating particular possible consequences for the structure of accretion disks. Despite this, however, even in situations in which the geodesic description differs profoundly from the isolated black hole case, we find minor modifications to the primary and secondary tracks of the isotropic orbiting sources, and to the width, location, and relative luminosity of the corresponding photon rings as compared to the Schwarzschild black hole at equal black hole mass and emission models. This shows that physical structures are crucial for understanding black hole images and points the limitations of drawing conclusions from more artificial imaging profiling. More profoundly, this fact points towards troubles distinguishing between both geometries using present observations of very-long baseline interferometry.
Local clustering of relic neutrinos: comparison of kinetic field theory and the Vlasov equation
Authors: Holm Emil Brinch , Zentarra Stefan , Oldengott Isabel M.
Published: July 2024

Gravitational clustering in our cosmic vicinity is expected to lead to an enhancement of the local density of relic neutrinos. We derive expressions for the neutrino density, using a perturbative approach to kinetic field theory and perturbative solutions of the Vlasov equation up to second order. Our work reveals that both formalisms give exactly the same results and can thus be considered equivalent. Numerical evaluation of the local relic neutrino density at first and second order provides some fundamental insights into the frequently applied approach oflinear responseto neutrino clustering (also known as the Gilbert equation). Against the naive expectation, including the second-order contribution does not lead to an improvement of the prediction for the local relic neutrino density but to a dramatic overestimation. This is because perturbation theory breaks down in a momentum-dependent fashion and in particular for densities well below unity.
Heavy dark matter in white dwarfs: multiple-scattering capture and thermalization
Authors: Bell Nicole F. , Busoni Giorgio , Robles Sandra , Virgato Michael
Published: July 2024

We present an improved treatment for the scattering of heavy dark matter from the ion constituents of a white dwarf. In the heavy dark matter regime, multiple collisions are required for the dark matter to become gravitationally captured. Our treatment incorporates all relevant physical effects including the dark matter trajectories, nuclear form factors, and radial profiles for the white dwarf escape velocity and target number densities. Our capture rates differ by orders of magnitude from previous estimates, which have typically used approximations developed for dark matter scattering in the Earth.
We also compute the time for the dark matter to thermalize in the center of the white dwarf, including in-medium effects such as phonon emission and absorption from the ionic lattice in the case where the star has a crystallized core. We find much shorter thermalization timescales than previously estimated, especially if the white dwarf core has crystallized.
We illustrate the importance of our improved approach by determining the cross section required for accumulated asymmetric dark matter to self-gravitate.
Enhanced soft magnetic properties of SiO2-coated FeSiCr magnetic powder cores by particle size effect
Authors: Ge 葛 Mingyue 铭悦 , Xiao 肖 Likang 礼康 , Liu 刘 Xiaoru 潇如 , Pan 潘 Lin 嶙 , Zhou 周 Zhangyang 章洋 , Lan 蓝 Jianghe 江河 , Xiong 熊 Zhengwei 政伟 , Wu 吴 Jichuan 冀川 , Gao 高 Zhipeng 志鹏
Published: July 2024

It has been known that metal FeSiCr powders with large average particle sizes have been typically employed to prepare magnetic powder cores (SMCs), with few studies reported on the influence of magnetic properties for original powders with various average particle sizes less than 10 μm. In this work, SiO2-coated FeSiCr SMCs with different small particle sizes were synthesized using the sol–gel process. The contribution of SiO2 coating amount and voids to the soft magnetic properties was elaborated. The mechanism was revealed such that smaller particle sizes with less voids could be beneficial for reducing core loss in the SMCs. By optimizing the core structure, permeability and magnetic loss of 26 and 262 kW/cm3 at 100 kHz and 50 mT were achieved at a particle size of 4.8 μm and ethyl orthosilicate addition of 0.1 mL/g. The best DC stacking performance, reaching 87%, was observed at an ethyl orthosilicate addition rate of 0.25 mL/g under 100 Oe. Compared to other soft magnetic composites (SMCs), the FeSiCr/SiO2 SMCs exhibit significantly reduced magnetic loss. It further reduces the magnetic loss of the powder core, providing a new strategy for applications of SMCs at high frequencies.
Peak structure in the interlayer conductance of Moiré superlattices
Authors: Tao 陶 Yizhou 懿洲 , Liu 刘 Chao 超 , Xiao 肖 Mingwen 明文 , Fang 方 Henan 贺男
Published: July 2024

We investigate the peak structure in the interlayer conductance of Moiré superlattices using a tunneling theory we developed previously. The theoretical results predict that, due to the resonance of two different partial waves, the double-peak structure can appear in the curve of the interlayer conductance versus twist angle. Furthermore, we study the influences of the model parameters, i.e., the chemical potential of electrodes, the thickness of Moiré superlattice, and the strength of interface potential, on the peak structure of the interlayer conductance. In particular, the parameter dependence of the peak structure is concluded via a phase diagram, and the physical meanings of the phase diagram is formulized. Finally, the potential applications of the present work is discussed.
Enhancing VTOL Performance: Shrouded Rotor BLDC Motor Model and Validation
Authors: Dayhoum Abdallah , Etewa Mohamed , Ramirez-Serrano Alejandro , Martinuzzi Robert
Published: July 2024

One prominent area of current research interest, driven by a multitude of factors and considerations, centers around drone technology. A primary challenge currently faced in the realm of small-scale Unmanned Aerial Vehicles (UAVs) is the increasing demand for increased power coupled with the constraint of limited flight duration provided by available battery technology. These vehicles fall into two overarching categories based on the presence or absence of shrouding around their rotor(s). Shrouded rotors play a pivotal role in augmenting aerodynamic performance by enhancing thrust while mitigating blade-tip vortex losses and numerous other aspects that are not presented in open rotors. Although such characteristics contribute to an expanded effective rotor diameter and optimized airflow within the shroud. There are numerous aspects related to power, shroud and rotor characterization, etc. that need to be understood before shrouded rotors are commonly used. This paper presents the implementation of an effective mathematical model for Brushless Direct Current (BLDC) motors, specifically tailored for application in conjunction with shrouded rotors aimed at enhancing thrust and reducing power consumption. The proposed model serves to predict the performance characteristics of the utilized motor within the context of the specified shrouded rotor combination. In order to identify the effects of the induced load torque on the rotor’s dynamic response, a methodical analysis is conducted on the numerical simulation of the proposed model. The results are experimentally verified for hover flying in a case study of a scalable and highly maneuverable vertical takeoff and landing aircraft developed for operations in highly confined spaces. The adoption of this modeling technique is anticipated to significantly streamline the shrouded rotor combination design and selection process, particularly in the selection of an appropriate motor.
Gradient-based aerodynamic shape optimization using a discrete adjoint approach on a graphics processing unit
Authors: Yang L , Yang J
Published: July 2024

This paper presents a gradient-based aerodynamic shape optimization framework that utilizes a Graphics Processing Unit (GPU) for solving both flow and adjoint equations. It is built based on a GPU-accelerated flow solver that has been developed previously. Hence, the focus of this work is on how to solve the adjoint equations on the GPU and subsequently compute the gradients. The adjoint equations are right-preconditioned by a block Incomplete Lower Upper (ILU) preconditioner and solved by a restarted Generalized Minimum Residual (GMRES) method. The exact residual Jacobian matrix in the adjoint equations is computed using finite difference and a distance-2 graph coloring algorithm. With the adjoint-based gradients, the steepest descent method with momentum is employed for constrained aerodynamic shape optimization of a wing-body configuration at a transonic flow condition.
Micro-vibration simulation of laser communication satellite subjected to the disturbance of momentum wheels
Authors: Zhang Jie , Yin Jiacong , Ye Sang , Pei Shengwei , Wang Yihong
Published: July 2024

As the laser gradually replaces the microwave for satellite communication, micro-vibration environment of satellites in orbit become the key factor to keep laser link and communication quality. Due to the limited use of vibration suppression measures, micro-vibration simulation covering the whole frequency range up to 1000 Hz is required for laser communication satellites, which is inevitably more difficult than optical satellites focusing on low-frequency characteristics. This paper proposes an engineering algorithm that can quickly and efficiently simulate the micro-vibration environment of satellites within 1000Hz, and the simulation results are confirmed by experimental data.
A Hybrid Algorithm for Predicting Head Related Transfer Function based on Physiological Parameters
Authors: Lu Dongdong , Zhu Jianbin , Shen Zhiqiang , Liu Chuang , Xin Qi
Published: July 2024

The Head Related Transfer Function (HRTF) characterizes the interaction between sound and the physiological structure of the listener, therefore, HRTFs have personalized characteristics. This paper proposes a hybrid algorithm for predicting HRTFs based on anthropometric parameters. This algorithm reduces the dimensionality of physiological parameters and HRTF, and then constructs a regression model between physiological parameters and HRTF. Firstly, principal component analysis is used to reduce the dimensionality of HRTF, followed by sparse principal component analysis to reduce the dimensionality of measured physiological parameters. Finally, a prediction model from physiological parameters to principal component coefficients is constructed through least squares regression. The predicted results were analyzed with spectral distortion. The result indicates that this prediction method can effectively predict HRTF.
AHA-Kriging surrogate model method for surface partition flaw tolerance assessment of turbine blisk
Authors: Wen Jiongran , Zheng Baiyang , Fei Chengwei
Published: July 2024

The high-pressure turbine blade in aero-engine power system may experience microstructural degradation due to uncertain flaws, multi-physical fields and loads during manufacturing, processing, installation, and maintenance, leading to serious structure deterioration that affects safety and reliability in service. Therefore, it is necessary to assess the influence of random flaws and loads on the fatigue performance of turbine blades from a probabilistic perspective. In this study, we propose a novel method based on the Artificial Hummingbird Algorithm and Kriging surrogate model (AHA-Kriging), for flaw tolerance assessment in the surface partition of the turbine blade. The results indicate that in the hazardous zone, the flaw tolerance reliability is 0.9984, corresponding to a LCF life of 1520 cycles. In the safe zone, the flaw tolerance reliability is 0.9991, corresponding to a LCF life of 2501 cycles. The primary factor influencing LCF life is flaw size, followed by factors such as the strength coefficient, gas temperature, and fatigue strength exponent. Besides, the AHA-Kriging approach exhibits higher modeling precision and simulation efficiency compared to other methods. This paper presents a practical engineering approach for assessing flaw tolerance in the surface partition of complex components, which is of significant value.
A proposal of drilling unit for robotic machining
Authors: Kondo Yasuo , Arimura Jho , Oh-hori Tom
Published: July 2024

In the assembly of large aircraft, drilling is often performed onsite after components are assembled, and many holes are still drilled by manual labor. In this unusual condition, it is difficult to apply conventional CNC machining. The authors have been proposing soft machining, a machining method that copies human drilling, as a machining method suitable for robotic drilling. In soft machining, thrust force is controlled to be smaller than that in CNC machining, and hole widening and burrs are less likely to be generated. In addition, vibration can be controlled by optimizing arm layout and drill shape. A twist drill with a 135° tip angle that was resharpened to a 115° tip angle at the center of the drill showed high performance in suppressing vibration. These indicate that soft machining can be applied to robotic drilling.
MSA-GAN: A novel method for inpainting EBSD image via cellular automation and deep learning
Authors: Zheng Baiyang , Wen Jiongran , Fei Chengwei
Published: July 2024

Electron backscatter diffraction (EBSD) is an important technique for analyzing the microstructure of materials. Unfortunately, EBSD images obtained by experimental means often suffer from different types of information loss, which affect the subsequent experimental analysis. Our study aims to study an EBSD image restoration method based on deep learning, which can solve the problem of various defects in EBSD imaging process. This paper proposes MSA-GAN: A multi-scale image restoration method based on generative adversarial networks (GAN) and feature pyramids (ASPP). To construct a dataset for training the model, a mask generation method based on cellular automata is proposed to simulate the information loss in EBSD images. The experimental results show that the MSA-GAN method can effectively restore large, medium and small types of defects in EBSD images, and achieve better results than other restoration methods, improving the quality and accuracy of the digital microstructure images. Our study provides a new technical means for the analysis of material microstructures, which has important theoretical and practical value.
Improvement of damage identification in GFRP plates using self-heating based vibrothermography and image processing
Authors: Katunin A , Amraei J , Lis K , Wachla D
Published: July 2024

Self-heating based vibrothermography (SHVT) is the non-destructive testing technique developed by the authors, which does not require external heat excitation. Thermal excitation is performed using viscoelastic heating induced by resonant vibration of a tested polymeric composite structure. This is especially useful in cases where direct thermal excitation cannot be applied. In this study, the authors implemented the SHVT technique for two-dimensional plate-like structures with simulated damage, which allowed initial detection of damage. Advanced image processing methods make it possible to significantly enhance the detectability of damage compared to the raw thermographic images acquired during inspection. The analysis of applied image processing methods allowed analysing the increase in damage detectability and selecting the most effective ones to achieve the best improvement of raw thermograms. The results of this study may find application in the inspection of polymeric composite components, especially in aircraft industry, where timely detection of damage is crucial for ensuring structural integrity and safety.
Effects of strong solar flares on the ionospheric electron density over the Philippines
Authors: Domingo Zane Nikia C. , Macalalad Ernest P.
Published: July 2024

Solar flares are capable of generating extreme amounts of ionizing radiation which affects Earth’s atmosphere. These are examined most notably in the X-ray and EUV regimes. The eruption of strong solar flares of classes X6.9, X2.25, and M9.7 were recorded, during which the Philippines was on Earth’s sunlit side. In this paper, the observation of the effects of solar radiation on the ionospheric electron density is done by quantifying the change in the slant total electron content (ΔSTEC). This was calculated by finding the difference between the difference relative STEC and its fitted data. The data were acquired from two GNSS receiver stations (PIMO & PTGG) in the Philippines. Moreover, it was inferred that changes in the electron density are manifest during increased EUV enhancements on the occurrences of the X6.9 and M9.7 flares. The calculated ΔSTEC ranged from ∼0.43 – 0.67 TECU during the X6.9 (PIMO) and ∼0.45 – 0.71 TECU during the M9.7 (PTGG) flares. This signifies that the contribution of the EUV radiation to the ionospheric response in the E and F ionospheric regions is more immense than that of the X-ray.
Predicting the minimum energy pathway of 1H to 1T phase transition of select 2D transition metal dichalcogenides via density functional theory and machine learning approach
Authors: Diego KY M , Putungan D B , Santos-Putungan A B
Published: July 2024

This study predicts the minimum energy pathways (MEPs) and transition barriers of two-dimensional transition metal dichalcogenides (TMDs) during their 1H to 1T structural phase transitions. The investigation utilizes density functional theory (DFT) calculations and machine learning algorithms to predict the MEPs and transition barriers. Six TMDs, namely NbSe2, ScS2, ScSe2, TiTe2, VS2, and VSe2, are selected for analysis. The DFT calculations provide reference values for comparison with the machine learning predictions. The transition barriers obtained through DFT calculations range from 0.13 eV to 0.72 eV. The machine learning models, including Linear Regression, Random Forest Regression, K-Nearest Neighbor Regression, and Gaussian Process Regression, demonstrate varying degrees of accuracy in predicting the transition barriers. Gaussian Process Regression achieved the highest accuracy among all ML models, with the absolute mean error spanning all systems being 0.0093. It has been shown that the features that were identified in the study are sufficient for the prediction of the MEPs and transition barriers of TMDs using machine learning. Overall, this study contributes to the understanding of TMD phase transitions and provides insights into the integration of machine learning in materials science research.
Detection of microplastics in surface water using a DSLR lens-based UV imaging system
Authors: Jaruda RA J , Gabayno JL F
Published: July 2024

Microplastics are any synthetic solid particle or polymeric matrix, ranging from 1 μm to 5 mm in diameter. Due to their low density and size, microplastics can easily be transported through wind and water currents, contaminating the environment with the toxic compounds they contain, harming living organisms through microplastic consumption and exposure. Despite being the top global contributor of plastic waste in 2010, the Philippines still has very limited studies regarding microplastic contamination. There is a need, then, to develop an efficient and accessible means of identifying and quantifying microplastics. To do this, this study utilized a DSLR lens attached to a UV CCD camera to capture images of microplastics, which fluoresce under 395 nm illumination. An image processing algorithm was applied to the resulting images, including binarization, adaptive segmentation, and particle counting to quantify MPs as a fast and easily accessible testing method. Results from the study revealed that despite water samples having been taken within proximity of one another, the number of particles identified within the system still showed a wide spread in values. In addition, it was found that the size distribution of the microplastics obeys the power law. As a new application for UV-based imaging, our method showed that quantitative detection of microplastics is feasible for future in situ measurement of marine water samples. However, further refinement to the counting technique and comparison with standard testing for validation is still recommended for improvement.
Integration of Smart Grid and Application of Microgrid Technologies in Western Pipeline Company
Authors: Duan Pengcheng
Published: July 2024

The energy sector is undergoing a significant transformation, driven by the urgent need to address climate change, enhance energy security, and cater to the growing demand for electricity. In the context of escalating global energy demands and environmental concerns, this study investigates the integration and application of smart grid and microgrid technologies within the Western Pipeline Company’s operations. Addressing the critical need for sustainable energy solutions, our research focuses on enhancing system efficiency, reliability, and user satisfaction while reducing environmental impacts. Our findings reveal significant improvements in peak load reduction (15.3%), total energy reduction (20.46%), and carbon emission reduction (30.01%) compared to conventional methods. Moreover, the study demonstrates a marked increase in system reliability (95.35%) and a substantial decrease in response times (2.6 seconds), highlighting the efficiency of our integrated energy management strategies. The research also underscores notable advancements in customer satisfaction (90.13%), maintenance cost reduction (50.4%), and renewable energy utilization (75.80%), affirming the effectiveness and economic viability of implementing advanced grid technologies.
Research on the performance and thermal comfort of CO2 heat pump heating system in high altitude area
Authors: Wang Xinyu , Xiong Chengyan , Luo Huilong , Xia Yuping , Du Peijian , Yang Xiaochuan
Published: July 2024

In view of the complex climate in high altitude areas, a high altitude CO
2 air source heat pump heating system was constructed in this study, and the energy consumption of the system in one heating season (198 days) was statistically analysed, which showed that the annual average COP of the system was 2.95. Compared with conventional heat pumps, gas boilers, and electric boilers, CO
2 heat pumps reduce annual carbon emissions in high altitude areas by 9.6t, 31.1t, and 30.4t, and the payback period is 5.7 years, 3.2 years, and 2.5 years, respectively. and 30.4t of carbon emissions, with payback payback cycles of 5.7, 3.2 and 2.5 years, respectively. At the end of the heating period, the study also conducted a thermal comfort questionnaire survey for the heat users, and the results showed that the CO
2 heat pump heating system at high altitude was able to fully satisfy the indoor thermal comfort requirements while maintaining the superior performance. This study shows that CO
2 heat pumps have significant advantages in high-altitude heating applications and provides a reference for subsequent studies.
Toward a satellite-based monitoring system for urban CO2 emissions in support of global collective climate mitigation actions
Authors: Wilmot Taylor Y , Lin John C , Wu Dien , Oda Tomohiro , Kort Eric A
Published: July 2024

Over the past decade, 1000s of cities have pledged reductions in carbon dioxide emissions. However, tracking progress toward these pledges has largely relied exclusively on activity-based, self-reported emissions inventories, which often underestimate emissions due to incomplete accounting. Furthermore, the lack of a consistent framework that may be deployed broadly, across political boundaries, hampers understanding of changes in both city-scale emissions and the global summation of urban emissions mitigation actions, with insight being particularly limited for cities within the global south. Given the pressing need for rapid decarbonization, development of a consistent framework that tracks progress toward city-scale emissions reduction targets, while providing actionable information for policy makers, will be critical. Here, we combine satellite-based observations of atmospheric carbon dioxide and an atmospheric model to present an atmospherically-based framework for monitoring changes in urban emissions and related intensity metrics. Application of this framework to 77 cities captures ∼16% of global carbon dioxide emissions, similar in magnitude to the total direct emissions of the United States or Europe, and demonstrates the framework’s ability to track changes in emissions via satellite-observation. COVID-19 lockdowns correspond to an average ∼21% reduction in emissions across urban systems over March–May of 2020 relative to non-lockdown years. Urban scaling analyses suggest that per capita energy savings drive decreases in emissions per capita as population density increases, while local affluence and economic development correspond to increasing emissions. Results highlight the potential for a global atmospherically-based monitoring framework to complement activity-based inventories and provide actionable information regarding interactions between city-scale emissions and local policy actions.
A Fusion of Histogram Equalization Technique and Fuzzy Logic for Sustained Enhancement of images
Authors: Padmavathy N , Suresh Naidu N , Harshitha V.
Published: July 2024

A primary method for visually representing ideas is through images, typicallycaptured using cameras. However, these captured images, whether in video or still form, oftensuffer from issues like blurriness, noise, low intensity, and differences in luminance and light intensityresulting from digitization procedure. Image enhancement becomes crucial in elevating the quality ofthese images. This paper introduces a novel approach that leverages fuzzy concepts onhistogram-equalized images for color image enhancement, facilitating a refined adjustment oflight and dark areas and thereby improving The global contrast characteristics exhibited by an image. The paper incorporates wavelet transform alongside fuzzy-enhanced histogram equalization to decompose the image into its fundamental spectral components. Further, employing image conversiontechniques allows for enhancing the image resolution effectively. The performance of thisproposed approach, surpasses the results that of the conventional histogram equalizationmethod. The evaluation metrics, including peak signal-to-noise ratio, and mean square errorestimation, indicate the superiority of the proposed approach. Experimental findings demonstrate that the proposed method effectively enhances low-contrast images, achieving not only improvements in global brightness and contrast but also the preservation of intricate details while effectively mitigating noise. Thiscomprehensive approach ensures sustained improvements in image quality, offering apromising solution for addressing common issues associated with digitized images
Performance Comparison of Random Forest Regressor and Support Vector Regression for Solar Energy Prediction
Authors: Lakshmi Yerrabolu Venkata , Kasireddy Idamakanti , Jasmine K M , Murali Krishna Vamsi T B , Joshua N , Shyam Kumar V , Rao DSNM
Published: July 2024

This study conducts a comparative analysis between the Random Forest Regressor (RFR) and Support Vector Regression (SVR) for solar energy prediction. Solar energy, a prominent renewable energy source, but its predictability is challenging due to changing weather conditions. Micro grid operators, responsible for managing smaller, localized energy systems, often struggle to balance supply and demand efficiently because of the unpredictable nature of solar energy. To tackle this issue, we use machine learning models, RFR and SVR, with historical weather data including dew point, temperature, cloud cover, visibility and wind. Outcomes focus the superior performance of the Random Forest Regressor process compared to the Support Vector Regression in forecasting solar energy, demonstrating its potential for enhancing reliability in solar energy prediction models.
Improving the Efficiency of Photovoltaic Systems through Hybrid Energy Storage Management for DC Applications
Authors: Arunan R , Janardhanan V , Mohana Sundaram N , Mathan Kumar M
Published: July 2024

For the vast majority of independent photovoltaic (PV) systems to consistently supply electricity to the load during times of poor solar irradiation, they require an energy storage medium. Photovoltaic panels are not a dependable source for supplying the load and charging batteries for future usage due to their reliance on weather conditions and inconsistent output, making it challenging to maintain an ideal charge/discharge cycle. Combining supercapacitors/ultracapacitors and Li-ion batteries could be used to resolve the previously described issue and improve the system’s overall effectiveness and dependability. This hybrid system’s main goals are to increase battery life and offer a reliable DC power source. The hybrid energy storage system developed using MATLAB Simulink is used to control the energy management system. The chosen battery charging strategy and a maximum power point tracking (MPPT) function will both be included in the energy management system. The simulation findings demonstrate that while supplying energy to the DC motor load and other related DC load applications, the hybrid storage system may achieve greater efficiency and reliability than the battery storage system.
Real-Time Precision Irrigation System for Optimal Crop Yield and Water Conservation
Authors: Prakash Korlepara N S D , Narasimha Raju V S N , Satyanarayana P.V.V. , Sunil Kumar V. , Jahnavi Priya Y. , Harsha Vardan D.
Published: July 2024

In contemporary farming, efficient water management plays a crucial role in achieving optimal crop yields, endorsing water conservation, and upholding ecological balance. Current irrigation systems often lack precision, resulting in water wastage, suboptimal plant growth, and environmental degradation. This issue is pronounced evident in piped and micro irrigation networks, where manual control fails to adapt to fluctuating soil moisture conditions. This inconsistency in water distribution negatively affects agricultural yield and water conservation efforts. The core problem is the absence of an automated mechanism to regulate water release based on real-time soil moisture and centralised monitoring system. To tackle this, we’ve developed a real-time unified monitoring platform for automatic regulation of water release valves. Through the integration of soil moisture sensors, monitoring systems, and decision-making processes, our solution addresses the gap between water supply and crop demand, enhancing resource utilization and minimizing environmental impact. The amalgamation of instantaneous soil moisture information from various sensors into a centralized dashboard demands robust mechanisms for data collection, transmission, and processing. This real-time model, which unequivocally scrutinizes soil moisture data and associated parameters, significantly contributes to informed decisions concerning the timing and volume of water release.
Near Unity Power Factor Correction Vienna Rectifier for Plug-In Type Three Phase EV Charging Station
Authors: Kavya Santhoshi B. , Ravi kishore D. , Sailakshmi Kommushatti , Sravani Ganta D.S.L , Durga prasanna Avapati
Published: July 2024

With the growing incorporation of Electric Vehicles (EVs) into the transportation sector, there is an increasing need for reliable and efficient EV charging infrastructure. This paper introduces a near unity Power Factor Correction (PFC) Vienna rectifier is designed specifically for plug-in type three phase EV charging stations. The Vienna rectifier is selected for its superior efficiency, lower Total Harmonic Distortion (THD) and capability to handle high power density applications. The proposed rectifier system achieves near-unity power factor that is crucial for reducing reactive power and improving overall power quality. Through the comprehensive simulation studies and validations, the rectifier’s performance is analysed under various operating conditions, demonstrating its effectiveness in providing a stable high-quality DC output. This research highlights the pivotal role of advanced rectifier technology in enhancing the performance and reliability of modern EV charging stations, contributing to a more sustainable and efficient power grid.
Multi-resonance plasmonic refractive index sensor based on maze-shaped resonators for biological applications
Authors: Majidi Zahra , Ghanavati Mohammad , Karami Mohammad Azim
Published: July 2024

Herein, a plasmonic refractive index (RI) sensor based on a metal–insulator–metal waveguide coupled with maze-shaped resonators is proposed and numerically investigated using finite element method. Various geometrical parameter impacts on the transmission spectrum are examined to optimize the sensor’s performance. Additionally, the effect of using SiO2 as a dielectric material instead of air has been investigated. The proposed sensor can achieve maximum RI sensitivity, figure of merit, and sensing resolution of 3340 nm RIU−1, 143.33 RIU−1, and 2.99 × 10−6 RIU, respectively, in the 500–3500 nm wavelength range. The designed structure is investigated for potential applications in different biological fields, including detecting cancer cells, determining blood hemoglobin (HB) levels, and glucose concentrations. This sensor can detect MCF-7 cancer cells with a maximum sensitivity of 3543 nm RIU−1 and can achieve the sensitivity of 0.407 nm·l g−1 for glucose concentration and 3329.41 nm RIU−1 for blood HB level. The structure presented in this study has promising specifications, making it suitable for use in optical integrated circuits, particularly in highly sensitive sensors.
Brylinski-Radon transformation and generic projections
Authors: Liu Yongqiang , Maxim Laurentiu , Wang Botong
Published: July 2024

Using the Brylinski-Radon transformation, we prove that under a generic surjective linear map

F
:

C

n

→

C

m

F\colon \mathbb {C}^n\to \mathbb {C}^m

, only the zeroth perverse cohomology of the push-forward of a perverse sheaf is (possibly) non-constant.