bluuebunny/crossref_metadata_2025_split huggingface dataset:
The North China record-breaking rainfall in July 2021: the atmospheric influential factors and precursory signal
Authors: Ye Jiaxin , Zhao Ping , Wen Zhiping , Li Jiao 
Published: November 2024

Abstract
In July 2021, the southeastern part of North China (SENC) suffered a record-breaking extreme rainfall event that caused devastating flooding and enormous losses. In this study, the major atmospheric influential factors and the precursory signal of heavy rainfall in 2021 are investigated using the correlation, regression, power spectrum, and filtering methods, the quasi-geostrophic velocity equation, observational data and numerical simulation of a linear baroclinic model (LBM). The results show that the extremity of a quasi-barotropic high anomaly over Northeast Asia (NEA) contributes to the deep anomalous upward motion within SENC by inducing positive vorticity and temperature advections. On the other hand, the anomalous southeasterly flow at the southwestern flank of the NEA high anomaly transports sufficient moisture to SENC in the lower troposphere. The local deep upward motion combined with the lower-tropospheric moisture convergence directly leads to the occurrence of this extreme rainfall event. Further analysis shows that the intensification of the NEA high in July 2021 is closely tied to the westward migration of atmospheric disturbance originating from the vicinity of Northeast Pacific-North America, which could be supported by numerical simulation in LBM. The variation of the geopotential height anomaly over Northeast Pacific-North America precedes that of the NEA high by two weeks, which is likely to provide a potential source of predictability for the extreme rainfall in SENC.
Contrasting impact of single-year and multi-year El Niño on the Pacific-North American teleconnection pattern
Authors: Liu Yixuan , Tao Weichen , Huang Gang , Wang Ya , Hu Kaiming , Liu Yong 
Published: November 2024

Abstract
Distinct Pacific-North American (PNA) teleconnection patterns and their associated climate impacts in North America have been observed during single-year El Niño events, as well as during both the first and second years of multi-year El Niño events. The research highlights the critical role of PNA-related North Pacific circulation anomalies, which shift northwestward during multi-year events and reach their peak intensity in the second year. Multiple dynamical diagnostic methods are employed to elucidate the reasons behind the diverse subtropical circulation responses. Differences in the anomalous tropical heat sources influence the formation of Rossby wave sources through adjustments in divergent wind anomalies, subsequently modulating the position and intensity of the PNA patterns. Additionally, variations in the meridional range of sea surface temperature anomalies affect the edge of tropospheric temperature through moist-adiabatic adjustment, leading to distinct subtropical jet stream responses. This, in turn, modifies the position of North Pacific circulation anomalies through the advection of anomalous kinetic energy. Furthermore, synoptic-scale transient eddies act as a feedback mechanism, helping to maintain and intensify these diverse atmospheric anomalies.
Minimum time connection between non-equilibrium steady states: the Brownian gyrator
Authors: Patrón A , Plata C A , Prados A 
Published: November 2024

Abstract
We study the problem of minimising the connection time between non-equilibrium steady states of the Brownian gyrator. This is a paradigmatic model in non-equilibrium statistical mechanics, an overdamped Brownian particle trapped in a two-dimensional elliptical potential, with the two degrees of freedom (x, y) coupled to two, in principle different, thermal baths with temperatures Tx
and Ty
, respectively. Application of Pontryagin’s Maximum Principle reveals that shortest protocols belong to the boundaries of the control set defined by the limiting values of the parameters (k, u) characterising the elliptical potential. We identify two classes of optimal minimum time protocols, i.e. brachistochrones: (i) regular bang–bang protocols, for which (k, u) alternatively take their minimum and maximum values allowed, and (ii) infinitely degenerate singular protocols. We thoroughly investigate the minimum connection time over the brachistochrones in the limit of having infinite capacity for compression. A plethora of striking phenomena emerge: sets of states attained at null connection times, discontinuities in the connection time along adjacent target states, and the fact that, starting from a state in which the oscillators are coupled, uncoupled states are impossible to reach in a finite time.
A model of random sequential adsorption on a ladder graph
Authors: Došlić Tomislav , Puljiz Mate , Šebek Stjepan , Žubrinić Josip 
Published: November 2024

Abstract
In random sequential adsorption (RSA), objects are deposited on a substrate randomly, irreversibly, and sequentially. Attempts of deposition that lead to an overlap with previously deposited objects are discarded. The process continues until the system reaches a jammed state when no further additions are possible. We analyze a class of RSA models on a two-row square ladder graph in which landing on an empty site in a graph is allowed when at least b neighboring sites in the graph are unoccupied (

b
∈

N

). In this paper we complement this typical way of studying RSA models by analyzing also the structure of the set of all jammed states in a static way, disregarding the dynamics that led to a particular jammed state. In both considered settings (dynamic and static) we provide explicit expressions for key statistics that describe the average proportion of the substrate covered by deposited objects, and then we comment on significant differences between the two settings. We illustrate all of our findings through a toy model for ensembles of trapped Rydberg atoms with blockade range b.
Orthogonal flatbands in Hamiltonians with local symmetry
Authors: Ryu Jung-Wan , Andreanov Alexei , Chul Park Hee , Han Jae-Ho 
Published: November 2024

Abstract
We derive symmetry-based conditions for tight-binding Hamiltonians with flatbands to have compact localized eigenstates (CLS) occupying a single unit cell. The conditions rely on unitary operators commuting with the Hamiltonian and are associated with local symmetries that ensure the existence of orthogonal compact localized states and a flatband. We also demonstrate the inverse: orthogonal flatbands, whose CLS can be made to occupy a single unit cell, always have associated local symmetries that satisfy our conditions.We illustrate our analytical result with several model Hamiltonians with given local symmetries, including examples with finite-range hopping and higher-dimensional ones.
Reducing measurement costs by recycling the Hessian in adaptive variational quantum algorithms
Authors: Ramôa Mafalda , Paulo Santos Luis , Mayhall Nicholas J , Barnes Edwin , Economou Sophia E 
Published: November 2024

Abstract
Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with Adaptive Derivative-Assembled Problem-Tailored variational quantum eigensolver (ADAPT-VQE), an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.
Adoption of solar home lighting system: a behavioral paradigm shift from consumers to prosumers in the urban household energy transition of Punjab, India
Authors: Kaur Navdeep , Sharma Anupam 
Published: November 2024

Abstract
This research explores the question of what inspires households to adopt a solar home lighting system (SHS) in Punjab, one of the wealthiest states of India with adequate solar potential. This research leverages the framework of the extended unified theory of acceptance and use of technology (UTAUT2) to identify adoption drivers and their impact on behavioral intentions moderated by age, socioeconomic status, and usage, with a particular emphasis on urban households, as they have been the subject of very few studies. The proposed research model was tested using structural equation modeling (SEM) in Smart PLS4. The study verified that the UTAUT2 constructs are the drivers of SHS adoption and significantly impact behavioral intention (R2 = 0.730). The findings suggested that for fiscally conservative prosumers, reduced monthly electricity bills were the greatest motivator for adopting SHSs, and our assumption that all prosumers are earth-loving environmentalists was not the case, suggesting that environmental concerns are not enough to explain SHS adoption. Age (β = 0.137, p < 0.01) was found to have a significant moderation effect on the behavioral intention to adopt SHS, while usage (β = −0.115; p = 0.21) and socioeconomic status (β = 0.006; p value = 0.888) were found to have no moderating effect on the behavioral intentions of adoption drivers. The results could serve as a roadmap for policymakers while framing government policies to improve the penetration of this new and promising green technology for the coherent domestic energy transition of consumers to prosumers.
MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy
Authors: Luo Entao , Shi Li , Liu Jiyan , Wu Zheng , Duan Guoyun , Zeng Lingxuan , Huang Tangsen 
Published: November 2024

Abstract
The rapid development of the global economy and society relies on continuous energy demand, while the severe impact of carbon emissions on the ecological environment has garnered significant international attention. Accurately forecasting carbon emission trends is crucial for developing effective reduction strategies and ensuring sustainable green economic development. In this paper, we propose a Metabolic Neural Network Prediction Model (MNPM) for carbon emissions. This model comprehensively considers the impact of various carbon emission-related factors across different regions in China. By employing one-hot encoding, we address the difficulties traditional classifiers face when handling categorical data, enabling predictions under more realistic conditions. To optimize the nonlinear characteristics of the data and overcome the limitations of grey models, we utilize dynamic iterative time series training to address errors due to data inaccuracies effectively. Experimental results demonstrate that our approach outperforms other methods in filtering out irrelevant data, achieving an average relative residual of 0.055 and an average ratio bias of 0.049, translating to more precise prediction accuracy.
Decentralization of renewable energy sources based optimum scheduling and management through a virtual power plant
Authors: Pandey Anubhav Kumar , Jadoun Vinay Kumar , N S Jayalakshmi , Singh Manohar 
Published: November 2024

Abstract
In this paper, renewable resources and co-generation units are grouped together to perform optimized operational scheduling in a virtual power plant (VPP) system. Renewable sources consist of solar photovoltaic, wind, and fuel cell units that are equipped together with a cogenerating unit to intensify the system's dependency in case of power shortage from renewable sources. The uncertainty aspect is investigated for the developed system as renewables are associated, and its impact on network operation is discussed. Storage provision is facilitated in the form of flexible and spinning reserves in which electric vehicle (EV) and energy storage systems (ESS) increase the system reliability by ensuring continuity of power supply. Optimal scheduling is carried out from both economic and environmental perspectives in which the net profit of the system is improved and the generated emissions are reduced by considering dual scheduling i.e., day-ahead (DA) and proposed 15 min interval. Moreover, a nature-inspired metaheuristic red fox optimization (RFO) is employed to handle the VPP problem and the results with the context to net profit and emission are also compared with work available in the literature. Finally, the proposed approach is promising in terms of higher computational efficacy and reveals the suitability of the VPP system.
Global floating PV status and potential
Authors: Rodríguez-Gallegos Carlos D , Gandhi Oktoviano , Sun Huixuan , Paton Céline , Zhang Ji , Moideen Yacob Ali Jaffar , Alvarez-Alvarado Manuel S , Zhang Wenjie , Rodríguez-Gallegos César A , Chua Lloyd H C , Reindl Thomas 
Published: November 2024

Abstract
Floating photovoltaics (FPV) has been a rapidly growing source of renewable electricity for the past 15 years since first commercial systems were installed. In this work, the insights from SERIS FPV database are shared. This is likely the largest database of its kind and contains the information from 1142 FPV systems in operation, totalling 5.9 GWp, by the end of 2022. Mainland China has been leading FPV installation capacities since 2017 and comprises almost half of the cumulative installed capacity. Similar to land-based PV, FPV installation size has been increasing; the median size has grown from 0.09 MWp in 2013 to 1.40 MWp in 2022, while the median power density has increased from 82 Wp m−2 to 123 Wp m−2 in the same timeframe. The installation cost has fallen simultaneously – the lowest reported was 0.41 USD/Wp in India in 2021. Other pertinent insights from SERIS FPV database include float supplier market share, reported electricity price, water body types and characteristics, as well as its coverage ratio. Finally, the global FPV potential (capacity, energy production, and water saving), for different tilt angles, tracking configurations, and solar panel types are explored. By installing FPV on 10% of the area of 249 717 inland reservoirs, FPV capacity could reach up to 22 TWp and could fulfil the whole global electricity consumption and up to 5% of the world’s water demand. The use of trackers and bifacial panels are advantageous for energy generation in all locations, with trackers increasing specific energy yield of a typical fixed 10° tilt FPV by up to 50% for reservoirs within

±

40

∘

latitude, while the bifacial gains reach up to 4.5% for all analysed configurations within

±

40

∘

latitude. The insights from this global FPV market and potential analysis can serve as a reliable reference for FPV stakeholders, researchers, and regulators alike.
Putting the costs and benefits of carbon capture and storage into perspective: a multi-sector to multi-product analysis
Authors: Roussanaly Simon , Gundersen Truls , Ramirez Andrea 
Published: November 2024

Abstract
Carbon dioxide capture, transport, and storage (CCS) is essential in achieving the net-zero target. Despite this increasing recognition, current CCS deployments are far behind targeted ambitions. A key reason is that CCS is often perceived as too expensive. While assessments of the costs of CCS have traditionally looked at impact at the plant level, the present study seeks to understand the costs and environmental benefits that will be passed to consumers via end-products and services. In particular, nine end-products/services (bridge construction, electricity from onshore wind power, electricity from offshore wind power, transport of a container via ship, a magazine, the production and transport of an avocado, a beer can, waste treatment via waste-to-energy, and long-distance air travel) connected to ten potential areas of application for CCS (cement production, iron and steel production, oil and gas production, natural gas processing, refining, ship propulsion engines, pulp and paper production, urea production, waste-to-energy, and direct air capture). The evaluations highlight that significant emission reductions (beyond 50%) could be achieved at marginal costs for end-users in six end-products/services: bridge construction, electricity from onshore wind power, electricity from offshore wind power, transport by ship, magazine, and waste treatment. Moderate emission reductions (between 11 and 37%) could be achieved in two cases at virtually no cost (increase below 1%): beer can and avocado production. Finally, only the case of using direct air capture to compensate for emissions from air travel was found to raise the cost for end-users significantly. Although more research is still needed in this area, this work broadens our understanding of the real cost and benefits of CCS and provides useful insights for decision-makers and society.
Bearing fault diagnosis based on sparsity structure pruning graph attention network
Authors: Zhang Chenye , Shi Hui , Song Renwang , Yao Chenghao , Chen Linying 
Published: November 2024

Abstract
Graph neural networks have been widely used in the field of bearing fault diagnosis, which can deal with non-Euclidean space data and dig deep the relationship between signals. However, most graph neural networks do not distinguish the importance of nodes in information aggregation, and do not take edge noise and data redundancy into account when constructing the graph structure, which affects the diagnostic accuracy. To solve these problems, a fault diagnosis method of graph attention network based on sparsity structure pruning is proposed. Firstly, a sparsity coefficient is introduced to construct the graph structure, and pruning operations are carried out according to the coefficient and the weight of the edges to avoid invalid fusion of information. Then, a graph attention network model based on sparsity structure pruning is constructed, and features of different scales are aggregated into new node representations through multi-head attention mechanism. Finally, the fault diagnosis of bearing is carried out according to the extracted signal discrimination characteristics. To verify the effectiveness of the proposed method, experiments are performed on two different fault diagnosis datasets and compared with other graph neural network methods. The results show that the accuracy and stability of the proposed method are superior to other methods even under the condition of low signal to noise ratio (SNR).
Effects of overloading commercial vehicles on vehicle damage factor for the National Highways in Bihar
Authors: Kumar Aditya , Suman Sanjeev Kumar 
Published: November 2024

Abstract
Overloaded vehicles and traffic growth have a significant impact on the pavement’s performance and service life. As a result, pavement construction and rehabilitation costs have drastically increased. The paper presents the results of the research, including analysis of axle-wise commercial vehicles with 24 h axle-load survey data over a one-year period. The information was collected from two sections of National Highway Road using a weigh-in-motion machine. The fourth power rule is used to calculate the VDF value of over-loaded, legal-loaded, and actual-loaded vehicles by converting the all-vehicle load into a standard axle load. This paper presents the axle-wise impacts of overloaded vehicles on the calculated VDF value. The analysis has shown that when only over-loaded traffic is considered, the class-2 VDF value is higher than the other classes of vehicles, and the class-5 VDF value is higher when all types of collected traffic—legal-loaded and over-loaded traffic are considered. The study concluded that 20% overloaded weight of vehicles increases the VDF value by about 3–4 times. The study reveals that the actual loaded VDF value should be considered during the design of new pavement instead of the VDF value of over-loaded and legal-loaded vehicles. This result may assist the pavement designer in calculating the thickness of the pavement layer and the construction cost of the pavement.
Design and modeling of three-mode nonlinear hybrid piezoelectric-electromagnetic vibration energy harvester for harvesting multi-band vibration energy on the freight wagon axle box
Authors: Dong Wentao , Pan Xianghui , Huang Bo 
Published: November 2024

Abstract
With the rapid development of rail transit system, it is becoming more demanding for structural health monitoring (SHM) of the train. It is crucial to supply power for sensing devices on the freight wagon to ensure the safe operation of rail transit systems. Three-mode nonlinear hybrid piezoelectric-electromagnetic vibration energy harvester (TNHVEH) with a three-layer pickup system has been designed and applied to efficiently harvest vibration energy on the axle box of freight wagons for providing the power of the sensors for online SHM. Dynamic coupling model of the vehicle-harvester system is built to devote the relationship between the operation bandwidth and the nonlinear degree of TNHVEH for broadening the operation frequency band. Simulation and experimental results demonstrate that the resonant frequencies of the three pickup systems of TNHVEH are concentrated around 27, 70, and 120 Hz, matching the representative frequencies of axle box vibrations. A maximum output voltage of 2.97 V and output power of 29.4 μW under an optimal load of 300 kΩ at 0.5 g acceleration is achieved. It successfully lights up 53 LEDs with ‘ECJTU’ patterns, providing a solution to the power supply problem of microelectronic devices on freight wagons.