bluuebunny/crossref_metadata_2025_split huggingface dataset:
Radio Galaxy Zoo: Leveraging latent space representations from variational autoencoder
Authors: Andrianomena Sambatra , Tang Hongming 
Published: June 2024

Abstract
We propose to learn latent space representations of radio galaxies, and train a very deep
variational autoencoder (VDVAE) on RGZ DR1, an unlabeled dataset, to this end. We show
that the encoded features can be leveraged for downstream tasks such as classifying galaxies in
labeled datasets, and similarity search. Results show that the model is able to reconstruct its
given inputs, capturing the salient features of the latter. We use the latent codes of galaxy
images, from MiraBest Confident and FR-DEEP NVSS datasets, to train various non-neural network
classifiers. It is found that the latter can differentiate FRI from FRII galaxies achieving
accuracy ≥ 76%, roc-auc ≥ 0.86, specificity ≥ 0.73 and
recall ≥ 0.78 on MiraBest Confident dataset, comparable to results obtained in
previous studies. The performance of simple classifiers trained on FR-DEEP NVSS data
representations is on par with that of a deep learning classifier (CNN based) trained on images in
previous work, highlighting how powerful the compressed information is. We successfully exploit
the learned representations to search for galaxies in a dataset that are semantically similar to a
query image belonging to a different dataset. Although generating new galaxy images (e.g. for data
augmentation) is not our primary objective, we find that the VDVAE model is a relatively
good emulator. Finally, as a step toward detecting anomaly/novelty, a density estimator — Masked
Autoregressive Flow (MAF) — is trained on the latent codes, such that the log-likelihood
of data can be estimated. The downstream tasks conducted in this work demonstrate the
meaningfulness of the latent codes.
First tungsten radiation studies in DIII-D’s ITER baseline demonstration discharges
Authors: Turco F. , Luce T.C. , Sips ACC. , Greenfield C. , Osborne T. , Odstrcil T. , Hanson J.M. , McLean A. , Hyatt A.W. 
Published: June 2024

Abstract
ITER Baseline Scenario plasmas were studied in DIII-D using krypton and xenon gases as a proxy for the tungsten that will be present in ITER. These impurities were chosen for having the same radiative loss rate Lz as tungsten would exhibit in the hotter ITER core. Results show that the scenario with these core radiators spans the range of impurity concentration and W radiated fraction expected for ITER, and up to 50% higher values, explored at zero injected torque, as well as 1 Nm and full co-torque injection with T ∼ 3 Nm. Stationary discharges with duration >2–4 τR are achieved with f
rad

⩾

30% leading to a reduction in confinement of ∼10%, and a comparison with real metal radiators in the same range of f
rad shows that the higher Lz at the lower temperatures in these plasmas yields too pessimistic results on the survivability and performance of this scenario in ITER. Simulations of ITER power balance including W radiation show that with concentration up to three times higher than in the DIII-D plasmas the scenario can be stationary, remaining at acceptable core radiated fraction values.
Bi-LSTM-based load forecasting for power grid
Authors: Xu Shouzhi , Sun Haowen , Li Bitao , Zhao Dongpeng , Li Hao , Wang Ke , Liu Jingwen , Wang Xiaoyu 
Published: June 2024

Abstract
It is very important to optimize the power supply and alleviate the conflict of electric load demand, so an accurate and reasonable power load prediction technology has been the focus of research. An evolutional LSTM named Bi-LSTM processes two independent input sequences with two opposite time directions, which aims to improve the efficiency of load forecasting for the power grid. The four main processes are illustrated, which include preprocessing the historical electrical load data, setting the parameters of the model, training the model, and testing the model. Comprising with typical models of RNN, LSTM, and GRU, the performance is verified, and the comparison result shows that it is better in several typical evaluation metrics. The proposed Bi-LSTM model effectively leverages historical data to forecast the imminent state of the power grid, providing valuable insights for electrical load management.
Real-time operation risk monitoring method for power grid based on cloud edge collaboration technology
Authors: Yang Sheng , Luo Tianyun , Shen Siqi , Zhou Bo , Chen Zhangguo 
Published: June 2024

Abstract
As a huge and complex system, the power grid involves multiple levels and various interconnected components, making it difficult to monitor operational risks in real-time. Therefore, this study proposes a real-time risk monitoring method in the power grid operation based on cloud-edge collaboration technology. Through cloud edge collaboration technology, data processing results are designed and an extreme gradient boosting clustering (XGBoost) algorithm is used to complete data clustering. The risk level of real-time operation in the power grid is calculated and the real-time operation risk monitoring in the power grid is completed. The experimental results indicate that the application process of the research method has a shorter delay and higher accuracy.
Design of safe sharing method of power core data in smart grid environment
Authors: Li Ce , Su Rui , Yan Runzhen , Wen Jinmei , Wang Yating , Li Yijing , Huang Jianxiong , Peng Jian 
Published: June 2024

Abstract
In order to ensure the effective intercommunication of information resources in the power supply chain, a safe sharing method of power core data in a smart grid environment is proposed. By constructing a feature extraction process of power core data security sharing under a smart grid, processing similar heterogeneous data based on each node center, taking the result as a standard, eliminating similar data, thereby determining the node center value and obtaining the power core data sharing encryption result, the power core data to be shared is shared so that the power core data can be shared safely. The experimental results show that the shared throughput of this method is high. It can take less time to complete the calculation in the same number of samples and it has higher running efficiency.
Research on system planning methods for large-scale grid connection of photovoltaics
Authors: Jiang Yumen , Li Haoen , Qi Yuchen , Miao Linxin , Huang Ruanming , Song Tianli , Xue Kun , Cui Min , Liu Ziqiu 
Published: June 2024

Abstract
With technological advancements, the extent of distributed photovoltaic grid connections has expanded. Optimizing the integration capacity of photovoltaic sources into the grid is key to reducing network loss and voltage deviations in power systems. This study first examines the effects of photovoltaic grid connections on these parameters, then it introduces a calculation method for grid integration capacity using the particle swarm optimization algorithm. The performance of this method, compared against two other algorithms, is validated through simulations in an IEEE33 node system.
Research on bundling optimization of wind-solar-hydro based on wind-solar cluster convergence
Authors: Zhao Yuanyuan , Wu Yaowu , Yao Xun , Lou Suhua , Yang Xiaojuan , Liu Peiyu 
Published: June 2024

Abstract
To solve the problem of cooperative dispatching of wind-solar power stations and cascade hydropower stations along the coast of the basin, an optimization method of bundling of wind-solar-hydro (WSH) in the basin based on the convergence of wind-solar (WS) clusters is proposed. This method takes into account the spatial distribution characteristics and output characteristics of WS-power stations in the basin and constructs the cluster division strategy based on the geographical location distribution peculiarity of power stations and the cluster convergence criterion based on the complementary properties of wind and solar. To optimize the load-matching characteristics of the output of the WSH system, based on the hydraulic coupling characteristics of upstream and downstream cascade hydropower stations, an optimization model of cascade wind-solar-hydro bundling is constructed. Taking the data of wind, solar, and hydro stations in a river basin as an example, the results show that the method proposed in this paper is suitable for solving the problem of gradual bundling of wind-solar-hydro in the basin.
Fast power flow calculation method for power system based on heterogeneous edge graph convolutional neural network
Authors: Wu Gang , Liu Shangjia , Xie Tao , Luo Wenbo , Liu Junlei 
Published: June 2024

Abstract
A deep learning-based power flow calculation model can directly fit the mapping relationship between the initial and final values of the power flow in a system, providing extremely fast computational speeds without causing ill-conditioned power flow issues. However, existing deep learning power flow calculation methods are often based on homogeneous graph neural networks, which do not differentiate between different types of nodes in power flow calculations and neglect the influence of line admittance on power flow distribution. A power flow calculation method based on a heterogeneous edge graph convolutional neural network is proposed in response to this issue. The proposed model underwent comprehensive simulations on a dataset expanded from the IEEE 57-node system, generating 10, 000 samples of different system configurations. Simulation experiments validated that the proposed model’s computational time is approximately one-tenth of the Newton-Raphson method, with a power flow distribution calculation accuracy reaching 98.44%. Comparative experiments further confirmed the effectiveness of the proposed model’s improvements in graph convolution.
An instruction level attack detection method for new power load system based on LSTM
Authors: Yao Qigui , Zhang Xiaojian , Fei Jiaxuan , Yao Minglu , Li Ye 
Published: June 2024

Abstract
To realize the efficient instruction level attack detection of the new power load resource management system under the load resource coordination and control interactive service scenario, this article proposes a novel LSTM adaptive sequence classification detection method combining MTM feature modeling and GAN countermeasure sample generation. In this method, the system operation instruction model based on MTM is established, and the generalized instruction features are expressed in detail. Simultaneously, large quantities of confrontation samples are generated by the GAN Network, which is similar to the real samples, and the problem of data imbalance in model training is solved. Finally, this paper proposes an adaptive coefficient-based LSTM for system instruction feature learning and testing and realizes the detection and recognition of hidden instruction level attacks in the new power load resource management system, which is more accurate and efficient than other methods.
Satellite mapping and demarcation analysis for coastal regulation zones assessment
Authors: Eldo Joel , Ntantis Efstratios L 
Published: June 2024

Abstract
Rapid urbanization and expanding tourism have led to widespread encroachments, particularly in flood-prone areas, where land degradation is more likely. Conventional methods of on-foot inspections for detecting these encroachments are intricate and time-consuming. A scarcity of data has resulted in undocumented violations, prompting the need for advanced technologies like satellite remote sensing. The current paper focuses on demonstrating the effectiveness of satellite remote sensing in detecting large-scale encroachments near flood-prone zones. Due to frequent undetected illegal constructions, Coastal Regulation Zones (CRZ) are a specific area of interest. This study employs a well-established method of analysis acquired from an extensive literature review to investigate illegal construction and encroachments via remote sensing advances. The research advocates for a practical analysis to combat illegal constructions in CRZs, presenting a comprehensive database through a concise portal for easy accessibility and analysis. The case study in Kerala, India, a region prone to natural disasters like monsoon floods, provides crucial data for measuring the boundaries required to preserve the ecosystem. The research underscores the significance of satellite remote sensing in efficiently addressing environmental threats posed by encroachments, offering insights applicable to regions facing similar challenges. This analysis has proven effective in diverse global settings and offers a standardized approach for demarking CRZ throughout the globe. Its adaptability and reliability across different terrain types and climatic conditions underscore its potential as a valuable tool in mapping encroachments. The current study contributes to the discourse on sustainable urban development, aiming to inform policies promoting resilience and ecological consciousness in rapid urbanization and tourism expansion.
Optimizing friction stir welding of AA7075 and AA8090 aluminum alloys: a desirability-driven investigation into mechanical and microstructural enhancement
Authors: Singh Naveen , Gupta Meenu 
Published: June 2024

Abstract
This research explores the multifaceted analysis of a friction-welded joint, employing Central Composite Design of Response Surface Methodology. The study integrates microstructural investigations and fracture analyses to explain the effect of process parameters on mechanical properties. The optimum settings for Friction Stir Welding of AA7075 and AA8090 were determined by assessing desirability indices. These settings comprised a tool rotation speed of 1927.7 rpm, a tool travel speed of 35 mm min−1, and a tool tilt angle of 0.9°. This specific combination yielded a noteworthy combined desirability index of 0.79, considering both Ultimate Tensile Strength (UTS) and Tensile Elongation (TE). Microstructural examinations revealed distinct characteristics in the Heat-Affected Zone (HAZ), Thermo-Mechanically Affected Zone (TMAZ), and Nugget Zone (NZ). Notably, fine grain structure in the NZ was attributed to the stirring effect created by the tool pin. Fracture analyses indicated ductile fractures, with dimple size variation correlating to tensile strength. Lower dimple density in low-strength joints suggested insufficient material mixing during welding. The maximum tensile strength sample exhibited a high dimple density. These findings contribute to a comprehensive understanding of the welding process’s influence on microstructure and fracture characteristics, providing valuable insights for optimizing mechanical properties in friction-welded joints.
Introduction to liquids
Authors: Abraham Nitzan 
Published: June 2024

Abstract
This chapter discusses concepts from the physics of liquid used later in this book. (1) Statistical mechanics of classical liquids. (2) Time and ensemble average. (3) Reduced configurational distribution functions. (4) Observable implications of the pair correlation function (X-ray scattering, the average energy, pressure). (5) The potential of mean force and the reversible work theorem. (5) The virial expansion and the second virial coefficient.
Introduction to stochastic processes
Authors: Nitzan Abraham 
Published: June 2024

Abstract
This chapter introduces and discusses concepts from the theory of stochastic processes. (1) The nature of stochastic processes. (2) Stochastic modeling of physical processes. (3) The random walk problem (time evolution, moments, the probability distribution). (4) Some concepts from the general theory of stochastic processes (distributions and correlation functions, Markovian stochastic processes, Gaussian stochastic processes, a digression on cumulant expansions). (5) Harmonic analysis (the power spectrum, the Wiener–Khinchin theorem, application to absorption, the power spectrum of a randomly modulated harmonic oscillator). (6) Shot noise.
An overview of quantum electrodynamics and matter–radiation field interaction
Authors: Davidson P A 
Published: June 2024

Abstract
This chapter reviews the theory of light–matter interaction in the quantum and classical regimes. (1) Classical electrodynamics. (2) Quantum electrodynamics. (3) Spontaneous emission. (4) Classical spontaneous emission. (5) Spontaneous emission in confined environments: The Purcell effect. (6) Light scattering. (7) Photon detection.
Residential proximity to agricultural pesticide use and cardiovascular disease risk factors among adult Latina women in California’s Salinas Valley
Authors: Calderon Lucia , Warner Marcella , Gunier Robert B , Rauch Stephen , Hazard Kimberly G , Kogut Katherine , Eskenazi Brenda , Torres Jacqueline M 
Published: June 2024

Abstract
Cardiovascular disease is a leading cause of death worldwide. There is limited evidence that exposure to current-use pesticides may contribute to cardiovascular disease risk. We examined the association between residential proximity to the application of agricultural pesticides and cardiovascular risk factors among 484 adult women in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) Study, a cohort based in an agricultural region of California. Outcome assessment was completed between 2010 and 2013. Using participant residential addresses and California’s Pesticide Use Reporting database, we estimated agricultural pesticide use within 1 km of residences during the 2-year period preceding outcome assessment. We used Bayesian hierarchical modeling to evaluate associations between exposure to 14 agricultural pesticides and continuous measures of waist circumference, body mass index, and blood pressure. Each 10-fold increase in paraquat application around homes was associated with increased diastolic blood pressure (β = 2.60 mm Hg; 95% credible interval [CrI], 0.27-4.89) and each 10-fold increase in glyphosate application was associated with increased pulse pressure (β = 2.26 mm Hg; 95% CrI, 0.09-4.41). No meaningful associations were observed for the other pesticides examined. Our results suggest that paraquat and glyphosate pesticides may affect cardiovascular disease development in women with chronic environmental exposure.
This article is part of a Special Collection on Environmental Epidemiology.
Klotho accelerates the progression of polycystic ovary syndrome through promoting granulosa cell apoptosis and inflammation
Authors: Ye Wenting , Xia Siyu , Xie Tingting , Ye Huiyun , Yang Yi , Sun Yan , Cai Jing , Luo Xiaoqing , Zhou Lili , Song Yali 
Published: June 2024

Abstract
The morbidity of polycystic ovary syndrome (PCOS) is in highly increasing rate nowadays. PCOS not only affects the fertility in women, but also threatens the health of whole life. Hence, to find the prognostic risk factors is of great value. However, the effective predictors in clinical practice of PCOS are still in blackness. In this study, we found Klotho (KL) was increased in follicular fluid (FF) and primary luteinized granulosa cells (GCs) from PCOS patients with hyperandrogenism. Furthermore, we found follicular KL was negatively correlated with numbers of mature oocytes, and positively correlated with serum testosterone, LH, and LH/FSH levels menstrual cycle and number of total antral follicles in PCOS patients. In primary luteinized GCs, the increased KL was accompanied with upregulation of cell apoptosis and inflammation-related genes. In ovaries of PCOS mice and cultured human KGN cell line, KL was up-regulated and accompanied by apoptosis, inflammation, and mitochondrial dysfunction. Therefore, our findings suggest new mechanisms for granulosa cell injury and revealed to target inhibit KL maybe a new therapeutic strategy for treatment of PCOS.
Implementation of plate reader-based indooxine and Nessler protocols for monitoring L-asparaginase serum activity in childhood acute lymphoblastic leukaemia
Authors: Vergov Bozhidar , Sbirkov Yordan , Minchev Danail , Todorova Tatyana , Baldzhieva Alexandra , Burnusuzov Hasan , Spasova Мariya I , Sarafian Victoria 
Published: June 2024

Abstract
Monitoring the blood serum activity of L-asparaginase in children with acute lymphoblastic leukaemia (ALL) has been highly recommended to detect enzyme inactivation that can cause relapse and to avoid unwanted toxicity. Nevertheless, perhaps at least partially due to the lack of clinically approved commercially available kits or standardized and independently reproduced and validated in-house protocols, laboratory assay-based determination of the optimal doses of L-asparaginase is not carried out routinely. In this study, we adapted previously published protocols for two plate reader-based colorimetric methods, indooxine and Nessler, to measure asparaginase activity. Mock samples with dilutions of the enzyme for initial optimization steps, and patient samples were used as a proof of principle and to compare the two protocols. For the first time the indooxine and the Nessler methods are adapted for a plate reader and L-asparaginase serum activity levels are compared by both protocols. Passing–Bablok and Bland–Altman’s statistical analyses found very little difference, strong correlation (r = 0.852), and bias of only 6% between the data from the two methods when used for fresh patient samples. Furthermore, we demonstrate that the Nessler method could also be applied for frozen sera as the results, compared to fresh samples, showed little difference, strong correlation (r = 0.817), and small bias (9%). We successfully adapted and validated two methods for measuring L-asparaginase activity in cALL and provided the most detailed description to date on how to reproduce and implement them in other clinical laboratories.
Radioactive iodine therapy for thyroid cancer coexisting with Hashimoto’s thyroiditis: a systematic review and meta-analysis
Authors: Chen Xiang-Yi , Tan Yang , Wang Dawei , Wei Zhi-Xiao 
Published: June 2024

Abstract

Objectives
To investigate the clinical character of differentiated thyroid cancer (DTC) coexisting with Hashimoto’s thyroiditis (HT) and provide state-of-art evidence for personalized radioactive iodine-131 therapy (RAIT) for patients coexisting with HT.

Methods
From January 2000 to January 2023, PubMed, Embase, and Web of Science databases were searched for relevant original articles that published in English on the RAIT efficacy for DTC with HT. RevMan 5.4 and Stata 17.0 were used for data analysis.

Results
Eleven studies involving 16 605 DTC patients (3321 with HT) were included. HT was more frequent in female (OR: 2.90, 95% confidence interval [CI]: 1.77-4.76, P &lt; .00001). The size of tumour (MD: −0.20, 95% CI: −0.30 to −0.11), extrathyroidal extension rate (OR: 0.77, 95% CI: 0.67-0.90), and metastasis rate (OR: 0.18, 95% CI: 0.08-0.41) were less in HT, but tumour, node, metastasis (TNM) stage had no significant difference among HT and non-HT group. Disease-free survival (DFS) rate (OR: 1.96, 95% CI: 1.57-2.44, P &lt; .00001), 5-year DFS (OR: 1.73, 95% CI: 1.04-2.89, P = .04), and 10-year DFS (OR: 1.56, 95% CI: 1.17-2.09, P = .003) were higher in HT group. The recurrent (OR: 0.62, 95% CI: 0.45-0.83, P = .002), RAIT dosage (MD = −38.71, 95% CI: −60.86 to −16.56, P = .0006), and treatment (MD: −0.13, 95% CI: −0.22 to −0.03, P = .008) were less in HT group.

Conclusions
DTC coexisting with HT was associated with less invasion. DFS of HT group was higher than non-HT group after RAIT. Low-dose treatment did not impair the efficacy of RAIT in DTC with HT.

Advances in knowledge
Hashimoto’s thyroiditis is a risk for DTC, but it minimalizes the progression of cancer and enhance the efficacy of RAIT, which should be considered in personalizing RAIT.