bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of power generation and maintenance using AOC‐ResNet50 network
Authors: Chu Yueqiang , Cao Wanpeng , Xiao Cheng , Song Yubin 
Published: August 2024

AbstractWith the continuous expansion of the photovoltaic industry, the application of solar photovoltaic power generation systems is becoming increasingly widespread. Due to the obvious intermittency and volatility of photovoltaic power generation, integration of large‐scale photovoltaic power generation into the power grid can cause certain impacts on the security and stability of the grid. Photovoltaic power prediction is essential to solve this problem, as it can improve the quality of photovoltaic grid connection, optimize grid scheduling, and ensure the safe operation of the grid. In this article, the deep learning method is selected for photovoltaic power prediction. Based on the analysis of the OctConv (Octave Convolution) network structure, the AOctConv (Attention Octave Convolution) convolutional neural network structure is proposed, which is combined with the ResNet50 backbone network to obtain AOC‐ResNet50. It is then applied to the prediction of the generation of photovoltaic power. The prediction performance is compared with the ResNet50 network and the Oct‐ResNet50 network, and it is found that the AOC‐ResNet50 network has the best prediction performance, with an MAE of only 0.176888. Based on the exemplar work, a framework is proposed to illustrate this method. Its general application is discussed.
A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach
Authors: Abbas Ghulam , Zhi Wu , Ali Aamir 
Published: August 2024

AbstractThe uncertainty of distributed energy resources (DERs) and loads in distribution networks poses challenges for reactive power optimization and control timeliness. The computational limitations of the traditional algorithms and the development of artificial intelligence (AI) based technologies have promoted the advancement of hybrid model‐data‐driven algorithms. This article proposes a two‐stage reactive power optimization method for distributed networks (DNs) based on a hybrid model‐data‐driven approach. In the first stage, based on the topology and line parameters of the DN, as well as forecasts of loads and renewable energy outputs, a mixed‐integer second‐order cone programming (MISOCP) algorithm is used to control the on‐load tap changer (OLTC) positions on an hourly day‐ahead basis. In the second stage, leveraging deep learning technology, the real‐time reactive power output of photovoltaics (PV) and wind power units is controlled at a 5‐min time scale throughout the day. Specifically, using traditional solvers, the global optimal reactive power output for PV and wind power units is determined first, corresponding to various load and renewable energy output scenarios. Then, neural networks are trained to map node power to the optimal reactive power outputs of renewable energy units, capturing the complex physical relationships. For the second stage, a transformer network framework with a self‐attention mechanism and multi‐head attention for deep learning training is applied to uncover the intrinsic and physical spatial relationships among high‐dimensional features. The proposed method is tested on a modified IEEE 33‐bus system with multiple distributed renewable energy sources. The case study results demonstrate that the proposed hybrid model‐data‐driven algorithm effectively coordinates day‐ahead and real‐time controls of various devices, achieving real‐time model‐free optimization throughout the day. Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.
Nonparametric Bayesian reconstruction of Galactic magnetic fields using information field theory
Authors: Tsouros Alexandros , Bendre Abhijit B. , Edenhofer Gordian , Enßlin Torsten , Frank Philipp , Mastorakis Michalis , Pavlidou Vasiliki 
Published: August 2024

Context. Ultrahigh-energy cosmic rays (UHECRs) are charged particles with energies surpassing 1018 eV. Their sources remain elusive because they are obscured by deflections caused by the Galactic magnetic field (GMF). This challenge is further complicated by our limited understanding of the 3D structure of the GMF because current GMF observations primarily consist of quantities that are integrated along the line of sight (LOS). Nevertheless, data from upcoming stellar polarization surveys along with Gaia stellar parallax data are expected to yield local GMF measurements.
Aims. This study is the second entry in our exploration of a Bayesian inference approach to the local GMF that uses synthetic local GMF observations that emulate forthcoming local GMF measurements, and attempts to use them to reconstruct its 3D structure. The ultimate aim is to trace back observed UHECRs and thereby update our knowledge about their possible origin.
Methods. In this proof-of-concept work, we assumed as ground truth a magnetic field produced by a dynamo simulation of the Galactic ISM. We employed methods of Bayesian statistical inference in order to sample the posterior distribution of the GMF within part of the Galaxy. By assuming a known rigidity and arrival direction of an UHECR, we traced its trajectory back through various GMF configurations drawn from the posterior distribution. Our objective was to rigorously evaluate the performance of our algorithm in scenarios that closely mirror the setting of expected future applications. In pursuit of this, we conditioned the posterior to synthetically integrated LOS measurements of the GMF, in addition to synthetic local plane of sky-component measurements.
Results. Our results demonstrate that for all locations of the observed arrival direction on the plane of sky, our algorithm is able to substantially update our knowledge on the original arrival direction of UHECRs with a rigidity of E/Z = 5 × 1019 eV, even without any LOS information. When the integrated data are included in the inference, the regions of the celestial sphere in which the maximum error occurs are greatly reduced. The maximum error is diminished by a factor of about 3 even in these regions in the specific setting we studied. Additionally, we are able to identify the regions in which the largest error is expected to occur.
Staging of Early-Stage Lung Cancer without Routine PET in Candidates for Segmentectomy
Authors: Lopez-Pastorini Alberto , Tatli Zehra , von Bargen Antonia , Faltenberg Dennis , Beling Hendrik , Koryllos Aris , Galetin Thomas , Stoelben Erich 
Published: August 2024

Abstract
Introduction We aimed to investigate the accuracy of clinical staging without the routine use of positron emission tomography/computed tomography (PET/CT) in patients with cIA1 and cIA2 non-small-cell lung cancer (NSCLC) scheduled for segmentectomy.
Methods A total of 305 consecutive segmentectomies were retrospectively analyzed. Overall survival was calculated using the Kaplan–Meier method. Logistic regression was performed to investigate factors independently associated with pathologic upstaging.
Results The Union for International Cancer Control (UICC) upstaging was found in 86 patients (28%). Upstaged patients had longer operative times (146 ± 46 vs. 131 ± 44 minutes, p = 0.009), a higher number of lymph node resection (17 ± 10 vs. 13 ± 8, p = 0.001), and a higher rate of L1 involvement (34 vs. 16%, p < 0.001) than nonupstaged patients. N1 was found in 10 patients (3%) and N2 in 13 patients (4%). Nodal positive patients had longer operation times (154 ± 50 vs. 133 ± 44 minutes, p = 0.031) and higher rates of R1 (9 vs. 1%, p = 0.006) and L1 (39 vs. 20%, p < 0.026) than patients without nodal involvement. The 3- and 5-year overall survival rates for nonupstaged and upstaged patients were 85 and 67% and 67 and 54%, respectively (p = 0.040). In logistic regression, L1 involvement (odds ratio [OR]: 2.394, p = 0.005) and the number of dissected lymph nodes (OR: 1.037, p = 0.016) were independently associated with upstaging. Patients who received PET as part of clinical staging did not have a significantly lower nodal upstaging.
Conclusion Selective use of PET/CT based on the results of CT may be a viable option for patients with proven or suspected NSCLC up to 2 cm in size.
Evaluation of Cardiac Function in Patients with Supratentorial Tumors and Raised Intracranial Pressure: HABIT-ICP, a Prospective Observational Study Using Transthoracic Echocardiography
Authors: Sethuraman Manikandan , P. Ajay Prasad Hrishi , Ajayan Neeraja , Prathapadas Unnikrishnan , Vimala Smita , S. Ranganatha Praveen C. 
Published: August 2024

Abstract
Objective An acute increase in intracranial pressure (ICP) has been shown to affect cardiac function due to brain ischemia and the associated increased sympathetic activity. However, there is limited literature on the changes in cardiac function in clinical scenarios where there is a gradual and progressive increase in ICP, such as in brain tumors. We aimed to assess and compare the cardiac function in patients with primary supratentorial brain tumors presenting with and without raised ICP for neurosurgery.
Materials and Methods In this prospective observational study, we included 60 patients; Group I (30 patients without features of raised ICP) and Group II (30 patients with features of raised ICP). Transthoracic echocardiography was performed on the day before the surgery and the seventh postoperative day. Hemodynamic, electrocardiographic, and echocardiographic parameters were obtained during pre-, intra-, and postoperative periods and were used for statistical analysis.
Results We found an increased relative wall thickness and an increased incidence of systolic (22%) and diastolic dysfunction (33.3%) in Group II compared with Group I patients. There was an increased incidence of intraoperative adverse events such as postinduction hypotension and vasopressor use in Group II patients. In the postoperative period, there was an improvement in the systolic function; however, the chamber dimensions and diastolic dysfunction did not improve significantly.
Conclusion Our study suggests that raised ICP might contribute to the pathophysiology of sympathetic overactivity and sympathetically driven cardiac dysfunction, which does not entirely revert in the immediate postoperative period.
First-principles study on pressure-induced superconductivity and structural design on transition metal diborides
Authors: Zhou Chao , Yu Hongyu , Zhang Zihan , Yu Zekun , Zhu Jinming , Bao Kuo , Cui Tian 
Published: August 2024

Recent experiments on α-MoB2 with MgB2-type structure achieved superconductivity at ∼32 K under 90 GPa, the highest among transition-metal diborides, rekindling interest in their superconducting properties. Our study systematically investigates the band structures of AlB2-type transition metal diborides. We found that the superior superconductivity of MoB2, WB2, and TcB2 correlates with their von Hove singularities near the Fermi level (EF), potentially linked to electron–phonon coupling. These three diborides exhibit similar critical temperature (Tc) trends under pressure: rising initially, peaking around 60 GPa, and then declining. While unstable at ambient pressure, their thermodynamic and dynamical stability limits vary significantly, possibly explaining experimental discrepancies. To stabilize MoB2 at ambient pressure, we designed MoXB4 compounds (X = other transition metals) by substituting every other Mo layer in MoB2 with an X layer. This modification aims to stabilize the structure and enhance superconductivity by reducing d-electron concentration at EF. This principle extends to other potential superconducting diborides, such as WB2 and TcB2. Using Nb as an example, we found that Nb atoms in AlB2-type MoNbB4 may exhibit random occupancy, potentially explaining disparities between theoretical predictions and experimental results. Our study offers valuable insights into superconductivity in transition metal diborides, paving the way for future research and applications.
Decision tree learning with random forest models using agricultural and ecological field data incorporating multi-factor studies and covariate structure
Authors: Dhanoa M.S. , Sanderson R. , Lister S.J. , Cardenas L.M. , Ellis J.L. , López S. , France J. 
Published: August 2024

Abstract
In this study, the decision learning methods of regression tree and random forest analysis are investigated as complements to standard statistical methods such as analysis of variance and grouped regression. For this purpose, three diverse data sets were used. The first set is large and multidimensional and describes nitrous oxide emissions from sites across different geo-positions in the UK receiving various fertilisation treatments. The second set is based on Gliricidia tree provenances and has a small number of samples and an imbalanced distribution of factor classes. Random forest modelling was found to be a very viable option in the case of the first data set but failed in the case of second. The third data set, based on count observations recording osprey egg incubation times, lends itself to tree and forest modelling. These decision learning methods therefore appear well suited to handling the diverse, multi-dimensional and complex data sets that often arise in carrying out agricultural and ecological field experiments.
Photon traps in nonlinear electrodynamics
Authors: Goulart Érico , Bittencourt Eduardo 
Published: August 2024

Abstract
We demonstrate the existence of photon traps within the framework of nonlinear electrodynamics. The trapping mechanism is based on the fact that, for null background fields, the optical metric reduces to the Kerr–Schild form, which plays a prominent role in the context of black hole physics. We then construct explicit examples where photons are confined in a region of spacetime, such that a distant observer cannot interact with them. Finally, we argue that the trapping scheme is quite universal, being entirely compatible with causality, energy conditions, and hyperbolicity.
Impact of dark sector preheating on CMB observables
Authors: Garcia Marcos A.G. , Pereyra-Flores Aline 
Published: August 2024

Abstract
The prediction of a nearly scale-invariant spectrum of curvature and tensor fluctuations is among the main features of cosmic inflation. The current measurements of the primordial fluctuations in the cosmic microwave background (CMB) provide tight constraints on the amplitude of the scalar and tensor spectra, and the scalar tilt. However, the precise connection between these observables and a given inflationary model, depends on the expansion history between the end of inflation and the beginning of the radiation dominated era, which corresponds to the reheating epoch. This mapping between horizon exit and reentry of fluctuations, parametrized by the number of e-folds N*, can therefore be affected by the presence of a transient epoch of non-perturbative particle production during reheating (preheating). Using a combination of perturbative and lattice computations, we quantify the impact of preheating in a non-equilibrated dark matter sector on the CMB observables, under the assumption of a simultaneous perturbative decay of the inflaton into Standard Model particles. Combined with structure formation constraints, this allows us to impose stringent bounds on the post-inflationary reheating temperature.
Induced gravitational wave interpretation of PTA data: a complete study for general equation of state
Authors: Domènech Guillem , Pi Shi , Wang Ao , Wang Jianing 
Published: August 2024

Abstract
We thoroughly study the induced gravitational wave interpretation of the possible gravitational wave background reported by PTA collaborations, considering the unknown equation of state w of the early universe. We perform a Bayesian analysis of the NANOGrav data using the publicly available PTArcade code together with SIGWfast for the numerical integration of the induced gravitational wave spectrum. We focus on two cases: a monochromatic and a log-normal primordial spectrum of fluctuations. For the log-normal spectrum, we show that, while the results are not very sensitive to w when the GW peak is close to the PTA window, radiation domination is out of the 2σ contours when only the infra-red power-law tail contributes. For the monochromatic spectrum, the 2σ bounds yield 0.1 ≲ w ≲ 0.9 so that radiation domination is close to the central value. We also investigate the primordial black hole (PBH) abundance for both monochromatic and log-normal power spectrum. We show that, in general terms, a larger width and stiffer equation of state alleviates the overproduction of PBHs. No PBH overproduction requires w ≲ 0.57 up to 2-σ level for the monochromatic spectrum. Furthermore, including bounds from the cosmic microwave background, we find in general that the mass range of the PBH counterpart is bounded by 10-5
M
⊙ ≲ M
PBH ≲ 10-1
M
⊙. Lastly, we find that the PTA signal can explain the microlensing events reported by OGLE for w ~ 0.7. Our work showcases a complete treatment of induced gravitational waves and primordial black holes for general w for future data analysis.
Incremental Conductance Method in Maximum Power Point Tracking
Authors: Andrasto Tatyantoro , Primadiyono Yohanes , Hidayat Syahroni , Sandyawan Bernadus , Fauzi Ahmad Rizqi , Khairul Azis Mukhammad 
Published: August 2024

Abstract
The power characteristic curve in photovoltaic system is non-linear fluctuating based on changes in solar irradiation and module surface temperature. MPPT is a system of tracking the maximum power point of solar panels at any change in irradiation and temperature. Incremental conductance is one of the maximum power tracking methods that can be applied to the MPPT with the input of solar panel voltage and solar panel current. DC-DC Converter is inseparable from the role of MPPT system. MPPT is integrated by implementing DC-DC Converter synchronous buck topology. The system is validated through computational simulations in simulink. Implementation of MPPT product design based on incremental conductance using Arduino Uno to be applied to a 50Wp single solar panel stand-alone. Measurement parameters are displayed on the LCD and data streamer as a monitoring system. The results of the study were conducted by comparing the two topologies of DC-DC converter on the MPPT system, from the comparison of the two topologies of DC-DC converter effect of Drain-Source resistance and forward voltage internal diode to the output power of solar panels showed that the MPPT system that implements the DC-DC converter synchronous buck is able to. Efficiency by implementing DC-DC converter synchronous buck reached 98.2% while the implementation using DC-DC Conveter buck topology reached 97.2% at 1000W/m2.
Characteristics Visitors of Indonesia Kaya Park, Semarang, Central Java
Authors: Harjanti Intan Muning , Budiati Lilin , Ardanareswari Ayu Sagita 
Published: August 2024

Abstract
The existence of urban parks is expected to be in accordance with the needs and desires of the community, so it is necessary to involve the community in the process of providing urban parks. The purpose of this study is to determine the characteristics of visitors in Indonesia Kaya Park, which are categorized based on socioeconomic and visiting patterns. The approach used is positivistic deductive, quantitative descriptive analysis method. Data collection is done by questionnaire and field observations. The results showed that the characteristics of visitors in Taman Indonesia Kaya based on socio-economics were dominated by visitors coming from Semarang, with female gender, age range 17-25 included in the category of early adolescents, the last level of high school / vocational education, and the type of student work / student. While based on the pattern of visits, the results obtained that the majority of visitors aim to sit / gather / walk, with a frequency of visits as much as 2-4 times, and the length of visit between 1-2 hours, and most visitors go with friends. The reason visitors come is because the place is comfortable and the mode of transportation used by visitors is dominated by motorcycles. The results of this study can be used to determine patterns and behaviour of urban park users in interacting in public spaces.
Influence of HSS drill coatings on surface finish and cylindricity of AA6061/C/ZrO2 composite in CNC drilling operations under dry conditions
Authors: P Haja Syeddu Masooth , V Jayakumar , G Bharathiraja , Mekonnin Keno Teshome 
Published: August 2024

Abstract
This study aims to evaluate the effect of HSS drill tool coatings, namely single-layer TiN, single-layer AlCrN, and double-layer AlTiN + TiSiXN (Durana), on the surface roughness and cylindricity of AA6061 (90 wt%)/C (5 wt%)/ZrO2 (5 wt%) hybrid composite material processed by the stir casting method. The fabricated sample was examined for the uniform particle distribution of reinforcements using Scanning Electron Microscope. The drilling operation was carried out on the fabricated in a CNC machining center by setting the spindle speeds of 800, 1200, and 1600 rpm, depths of cut of 0.5, 1, and 1.5 mm, and feeds of 50, 100, and 150 mm/rev. An orthogonal array (L27) designed by Taguchi’s method was used as the design of the experiment for the optimization of the best cutting parameters and coating. Surface roughness and cylindricity errors were determined for the 27 experimental runs. Field emission scanning electron microscopic (FESEM) examination and Energy Dispersive Spectroscopy (EDS) were used to analyze the surface integrity and elemental composition, respectively. The results revealed that Durana had a significant effect on the surface substrate with minimum surface roughness (Ra) of 1.666 μm and obtained the minimum cylindricity error for the parameters of depth of cut 0.5 mm, spindle speed of 1200 and feed of 100 mm rev−1.
Tuning the structural and mechanical properties of SiC-Li and SiC-Na alloys for aerospace application: an ab initio study
Authors: Otieno Ochieng Victor , Ongwen Nicholas , Otieno Calford 
Published: August 2024

Abstract
Aluminum (Al) and its alloys are popular in the aerospace industry due to their high strength-to-weight ratio, corrosion resistance, and ductility. However, these properties (extreme ductility and malleability) can compromise corrosion resistance, making them susceptible to dents and scratches. Silicon Carbide (SiC) is a promising alternative to Al and it alloys due to its higher Youngs modulus and excellent wear resistance, although it has the drawbacks of brittleness and higher density. This study investigated the structural and mechanical properties of SiC alloyed with lithium (SiC-Li) or sodium (SiC-Na) using ab initio calculations with the aim of tuning the structural and mechanical properties of SiC. Modeling was done using Burai software, which offers a friendly graphical user interface for Quantum ESPRESSO, thus facilitating the creation of input files, visualization of crystal structures and analysis of results. The results from this study showed that the addition of Li and Na lowered the density as well as the mechanical properties of SiC but still being favorably better than those of Al and its alloys, suggesting that the modeled alloys could potentially replace the traditional Al and its alloys in the aerospace industry. Further experimental studies are needed to validate these findings and to explore the possibility of simultaneous alloying of SiC with both Li and Na for enhanced performance.