bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evapotranspiration increment was underestimated in China due to underrepresented land cover changes
Authors: Wu Xinyue , Han Wangya , Yu Zhen 
Published: May 2024

Abstract
Numerous evapotranspiration (ET) products have been produced using various approaches and diverse forcing data even as the magnitude and trends of ET show divergence. We simulated ET using updated land use and cover change (LUCC) data in China from 1900 to 2020. We found that China’s ET increased slightly from 1900 to 1980, but it increased rapidly after 1980 due to LUCC characterized by forest expansion (2.05 mm yr−1, P < 0.01). We also found that the ET trends derived from our simulation were significantly higher than other ET products (−0.70–1.47 mm yr−1, P < 0.01), implying that existing, long-term ET products might have underestimated ET trends in China during the post-1980 period because of underrepresented LUCC. These underestimated ET trends could introduce biases in the regional water budget and water resources management. We advocate for future studies to take into account the impacts of LUCC in global ET simulations.
Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis
Authors: Wang Yumiao , Yuan Xing , Liu Yuxiu , Wang Wenyan 
Published: May 2024

Abstract
An unprecedented soil moisture drought broke out over the Yangtze River basin (YRB) in the summer of 2022 and lasted until the spring of 2023, caused great economic losses and serious environmental issues. With the rapid onset and long-lasting duration, the mega soil drought challenges the current seasonal prediction capacity. Whether the state-of-the-art climate models provide skillful predictions of the onset, persistence and recovery of the 2022–23 mega soil drought needs to be assessed. Identified by the drought area percentage, here we show that the mega soil drought over the YRB started from July, 2022, reached the peak in August, and diminished in April, 2023. Combined with real-time predictions of monthly precipitation released by three climate models participating in the North American multi-model ensemble (NMME) project, we predict the monthly evolution of the 2022–23 soil drought through a joint distribution between precipitation and soil moisture established by the copula method. The results indicate that the NMME/copula prediction well reproduced the spatiotemporal evolution of the mega soil drought at 1 month lead. Using the climatological prediction that relies on the information of initial soil moisture conditions as the reference forecast, the Brier skill score (BSS) values for NMME multi-model ensemble are 0.26, 0.23 and 0.2 for the forecast lead times increased from 1 to 3 months during the entire soil drought period. Specifically, the BSS is 0.14 at 2 months lead during drought onset stage, and 0.26 at 3 months lead during persistence stage, while it is close to zero at all leads during the recovery stage. Our study implies that climate models have great potential in probabilistic seasonal prediction of the onset and persistency of mega soil drought through combining with the copula method.
Integrable systems in magnetic fields: the generalized parabolic cylindrical case
Authors: Kubů O , Marchesiello A , Šnobl L 
Published: May 2024

Abstract
This article is a contribution to the classification of quadratically integrable systems with vector potentials whose integrals are of the nonstandard, nonseparable type. We focus on generalized parabolic cylindrical case, related to non-subgroup-type coordinates. We find three new systems, two with magnetic fields polynomial in Cartesian coordinates and one with unbounded exponential terms. The limit in the parameters of the integrals yields a new parabolic cylindrical system; the limit of vanishing magnetic fields leads to the free motion. This confirms the conjecture that non-subgroup type integrals can be related to separable systems only in a trivial manner.
Effect of temperature on the ability to synthesize SiC from rice husks
Authors: Kieu Do Trung Kien , Nguyen Cam Thuy , Huynh Ngoc Minh 
Published: May 2024

Abstract
Agricultural production in Vietnam annually generates a substantial volume of by-products and waste, with rice husks constituting the predominant fraction. Due to their meager economic value, rice husks are typically deemed agricultural waste and are commonly disposed of through incineration or discharge into rivers, contributing significantly to environmental pollution. In this investigation, rice husks were employed as the principal raw material for synthesizing silicon carbide. A blend of rice husks and silica gel in a ratio of 1.4/1 was subjected to sintering in a CO2 environment within the temperature range of 800 °C–1300 °C for 30 min. The chemical composition of the resultant product post-pyrolysis was ascertained in accordance with the ISO 21068–2:2008 standard. The capacity for SiC formation was further assessed utilizing Fourier transform infrared spectroscopy and x-ray diffraction. The outcomes revealed that the optimal temperature for SiC synthesis was 1200 °C. The composition of the sample post-pyrolysis was determined as 20.4% SiC, 51.2% SiO2, and 26.4% C (%wt). The primary phase constituents encompass amorphous carbon, cristobalite, α-SiC, and β-SiC. Scanning Electron Microscopy/ Energy Dispersive x-ray imaging of the product at 1200 °C exhibited dispersed SiC crystals on a SiO2-C substrate. The presence of SiC suggests the potential application of the product as a wear-resistant material.
Sketching phase diagrams using low-depth variational quantum algorithms
Authors: Lukas Bosse Jan , Santos Raul A , Montanaro Ashley 
Published: May 2024

Abstract
Mapping out phase diagrams of quantum systems using classical simulations can be challenging or intractable due to the computational resources required to simulate even small quantum systems far away from the thermodynamic limit. We investigate using quantum computers and the variational quantum eigensolver (VQE) for this task. In contrast to the task of preparing the exact ground state using VQE, sketching phase diagrams might require less quantum resources and accuracy, because low fidelity approximations to the ground state may be enough to correctly identify different phases. We used classical numerical simulations of low-depth VQE circuits to compute order parameters for four well-studied spin and fermion models which represent a mix of 1D and 2D, and exactly-solvable and classically hard systems. We find that it is possible to predict the location of phase transitions up to reasonable accuracy using states produced by VQE even when their overlap with the true ground state is small. Further, we introduce a model-agnostic predictor of phase transitions based on the speed with which the VQE energy improves with respect to the circuit depth, and find that in some cases this is also able to predict phase transitions.
CSIU-Net+: Pepper and corn leaves classification and severity identification using hybrid optimization
Authors: Begum Shaik Salma Asiya , Syed Hussain 
Published: May 2024

Abstract
An agricultural product plays a major role in the economical growth of developing countries. Agricultural products like pepper and corn are the essential crops with respect to human health food security. But, these two crops are prone to different diseases such as gray leaf spot, common rust and fruit rot which affects the productivity of crops. Further, the severity identification is also a challenging one. To address these limitations, this work presents different approaches for identifying the crop lesions and predicting the severity and thereby increasing the productivity of crops. The development of the proposed model includes steps such as dataset collection, noise removal, segmentation, feature extraction, classification and severity prediction. Initially, the crop images are pre-processed by the median filter and the pre-processed images are processed are segmented, extracted and classified by the optimized U-Net model. Moreover, hybrid optimizer which is the integration of GJA (Golden jackal algorithm) and RDA (Red deer algorithm) are utilized for precise segmentation and classification. Finally, the severity prediction is computed for the diseased leaves by the measuring the size of image pixels. The experimentation is carried out on the benchmark PlantVillage dataset; the accuracy and precision values achieved are 99.2% and 99.1%. Thus, the experimental outcomes show the effectiveness and stability of the model.
Insights to the water balance of a Boreal watershed using a SWAT model
Authors: Islam Kamal , Daraio Joseph , Sabau Gabriela , Cheema Mumtaz , Galagedara Lakshman 
Published: May 2024

Abstract
The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal ecozone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHRW includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model’s capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model’s performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal ecozone watersheds.
Surveying the landscape of environmental social science: a bibliometric and network analysis
Authors: Theis Nicholas G , Light Ryan 
Published: May 2024

Abstract
Social science research on the environment has grown exponentially in the past four decades alongside increasing awareness that environmental risks, such as climate change, cannot be understood by natural science interventions alone. While prior research examines how specific disciplines, like sociology, have attended to the environment or how specific problems have been engaged across disciplines, less attention has been offered to the entire landscape of research on the environment in the social sciences. In this article, we ask: What is the landscape of environmental social science? Focusing on 124,906 social scientific articles from the Web of Science, we analyze the relationship between journals publishing research on the environment. Specifically, we construct journal citation networks and topic models on bibliographic records from 1990 to 2022. Results suggest that journals form coherent communities associated with both disciplinary and interdisciplinary research topics, indicating that journal communities may be a central organizing feature of ESS. Moreover, research topics prevalent in this field have changed over time, potentially in relation to the ongoing neoliberalization of climate change politics. As scholars want to influence policy and industrial practice, their research implicitly reflects values consistent with the current political economic order. We conclude by highlighting the implications of these findings for the field of environmental social science.
Predictors of solar water heater purchasing intention: an empirical analysis of residential behaviors in Hanoi city, Vietnam
Authors: Truong Dinh Duc 
Published: May 2024

Abstract
Solar energy is a field that has grown rapidly in the world in recent years to solve the energy crisis and respond to climate change. Vietnam is a country with renewables energy potential including solar power and is pursuing net zero emissions target by 2050. This study explores factors affecting the residents’ intention to buy solar water heater in Hanoi, Vietnam. We used theory of planned behaviors and technology acceptance model to build research model and hypotheses. A community survey was conducted with 450 households in 3 districts using cluster sampling method to collect information. We conducted descriptive statistical analysis, Cronbach’s Alpha test, exploring factor analysis, conformity factor analysis and regression to identify and evaluate the impact of factors on solar water heater consumption intention. The results showed that there were significant factors affecting people’s intention to buy solar water heater including attitude, perceived usefulness, price perception, social influence and financial support in which attitude is the most influential factor. Regarding social characteristic of respondents, we also found positively significant relationship between income, education level and intention to use solar heater. Recommendations for government and business included increasing awareness of the benefits and economic feasibility of renewable energy devices to consumers through social media, implementing financial incentives to support clean energy consumption behavior, and strengtheing the emergence of local solar evangelist groups in the communities. Future research can expand to other cities and explore more indirect factors affecting the green energy consumption behavior of Vietnamese residents.
Paper mill wastes and biochar improve physiochemical properties and reduce heavy metals leaching risks in podzolic soils
Authors: Farhain Muhammad , Cheema Mumtaz , Nadeem Muhammad , Katanda Yeukai , Thomas Raymond , Javed Bilal , Galagedara Lakshman 
Published: May 2024

Abstract

Background: The incorporation of industrial wastes, such as wood ash and paper sludge, as soil amendments is vital for both environmental sustainability and agroecosystem productivity. Herein, we evaluated the effects of wood ash and paper sludge alone and in combination with biochar on the physicochemical properties and heavy metal leaching risks in podzolic soils. Methods: The treatments included limestone (control), wood ash, paper sludge, wood ash+paper sludge, limestone+biochar, wood ash+biochar, paper sludge+biochar and wood ash+paper sludge+biochar, arranged in a 4 × 2 factorial design with three replicates. The Hydrus-1D model was employed to simulate the water movement under these soil amendments using leaching colums. Results: Overall, wood ash, paper sludge and biochar application significantly increased the pH of amended soil compared to control. Paper sludge amended treatments alone or in combination with biochar significantly decreased bulk density (8%–17%) and increased the total porosity (14%–25%). While biochar addition to wood ash and paper sludge significantly reduced the concentrations of Cd (by 6.42%), Co (by 10.95%), Cu (by 11.76%), Pb (by 30%) and Ni (by 3.75%) in the collected leachates. The treatment paper sludge + biochar was found to be the most effective treatment to retain the heavy metals, with maximum plant available water (0.28 cm3 cm−3) and field capacity (0.36 cm3 cm−3) compared to control treatment. The predictions from Hydrus-1D showed that paper mill wastes with biochar has a significant potential to increase the volumetric moisture contents of amended podzolic soil, with the simulated leaching times and saturation levels closely aligning with the measured values. Conclusion: paper sludge + biochar treatment showed improved soil physicochemical properties and displayed lower heavy metals than allowed limits to be used in soil. Further, experiments are needed to assess the effects of papermill waste products on podzolic soil properties under variable field conditions.
Computation of the expectation value of the spin operator

S
^

2

for the spin-flip Bethe–Salpeter equation
Authors: Barker B A , Seshappan A , Strubbe D A 
Published: May 2024

Abstract
Spin-flip (SF) methods applied to excited-state approaches like the Bethe–Salpeter equation allow access to the excitation energies of open-shell systems, such as molecules and defects in solids. The eigenstates of these solutions, however, are generally not eigenstates of the spin operator

S
^

2

. Even for simple cases where the excitation vector is expected to be, for example, a triplet state, the value of

⟨

S
^

2

⟩

may be found to differ from 2.00; this difference is called ‘spin contamination’. The expectation values

⟨

S
^

2

⟩

must be computed for each excitation vector, to assist with the characterization of the particular excitation and to determine the amount of spin contamination of the state. Our aim is to provide for the first time in the SF methods literature a comprehensive resource on the derivation of the formulas for

⟨

S
^

2

⟩

as well as its computational implementation. After a brief discussion of the theory of the SF Bethe–Salpeter equation (BSE) and some examples further illustrating the need for calculating

⟨

S
^

2

⟩

, we present the derivation for the general equation for computing

⟨

S
^

2

⟩

with the eigenvectors from an SF-BSE calculation, how it is implemented in a Python script, and timing information on how this calculation scales with the size of the SF-BSE Hamiltonian.
A theoretical analysis to reveal the prospects of MoS2 as a back surface field layer in TiS3-based near infrared photodetector
Authors: Miah Md. Rashed , Ebon Md. Islahur Rahman , Abir Ahnaf Tahmid , Hossain Jaker 
Published: May 2024

Abstract
In this article, a photodetector based on TiS3 absorber material, featuring a narrow direct bandgap of 1 eV, has been simulated. Throughout this research, the J-V (current–voltage) curves and spectral responses have been explored by systematically varying the thickness, doping concentration, and defect density of specific layers. The JSC (short circuit current) and VOC (open circuit voltage) of the heterostructure photodetector are found to be 45.77 mA cm−2 and 0.693 V respectively. Also, the responsivity (R) and detectivity (D*) are 0.81 A W−1 and 2.19 × 1014 Jones at a light wavelength of 1100 nm. The spectral response exhibits significantly elevated values within the wavelength range of 800 nm to 1200 nm, indicating the device’s ability to detect light in the near-infrared (NIR) region effectively. The novel research offers valuable insights, emphasizing the material’s suitability for photodetector (PD) applications and signaling a promising direction for further research interest in photodetector development.
A short time series rolling bearing fault diagnosis method based on FMTF-CNN
Authors: Chen Xuejiang , Zhang Yansong , Su Yang , Zhou Yan , Gong Wuqi 
Published: May 2024

Abstract
The signal characteristics are extracted directly from the convolutional level when the Convolutional Neural Network (CNN) is used as a fault diagnosis method in most instances. Some scholars have proposed to use the Markov Transition Field (MTF) to extract the signal characteristics before convolution. However, for time-domain signals, the fault characteristics extraction is difficult, and the training process of the neural network parameters is slow. Therefore, in this paper, based on Fast Fourier Transform (FFT) and MTF algorithm, the Fourier Markov Transition Field (FMTF) characteristics extraction method in frequency domain is proposed, and a large number of experiments are carried on the public bearing data set of Jiangnan University. The final verification shows that the FMTF-CNN method has higher accuracy, faster training process and more accurate characteristics extraction than the traditional MTF-CNN diagnosis method.
Digital banking fortification: a real-time isolation forest architecture for detecting online transaction fraud
Authors: Abbassi Hanae , E L Mendili Saida , Gahi Youssef 
Published: May 2024

Abstract
Since the use of the Internet has increased exponentially, numerous organizations, including the financial industry, offer services online. As a result, financial scams are expanding in quantity and complexity worldwide, resulting in massive revenue losses and making digital fraudulent transactions a severe issue. Abnormal attempts and illegal access are instances of these dangers that fraudulent activity detection systems must identify. Machine learning and data mining approaches have been extensively used to address this issue in recent years. However, these approaches must be enhanced regarding real-time detection speed, tackling enormous amounts of data, and finding undiscovered attack patterns. Consequently, the present study provides a real-time architecture for averting and identifying digital transaction fraud, which relies on the Isolation Forest (IForest) approach and big data analytic tools, including Spark Streaming, sparkling water, Kafka, and PostgreSQL. This architecture seeks to improve present detection strategies by increasing accuracy for detection when considering enormous amounts of data. Two real datasets of online transactional fraud are used to assess the proposed architecture, and the findings are compared to relevant studies. The investigation results showed that IForest performed flawlessly, achieving an accuracy of 0.99 in two datasets.
Kofi Annan and Global Leadership at the United Nations
Authors: Williams Abiodun 
Published: May 2024

AbstractKofi Annan was the most significant and influential Secretary-General of the United Nations. Kofi Annan and Global Leadership at the United Nations explores how Annan conceived his role as Secretary-General and exercised global leadership at a turbulent period in world affairs. It sheds light on the importance of leadership for the performance of a global institution. It discusses the challenges he faced during his tenure from 1997 to 2006 and how he dealt with them. It examines such issues as Conflict Prevention, Peacekeeping, Peacebuilding, the Millennium Development Goals, HIV/AIDS, the Responsibility to Protect, Human Rights, Climate Change, and Migration. It provides insight into how Annan led the UN during several international crises, including the terrorist attacks of 9/11, conflicts in Kosovo and East Timor, and the war in Iraq. It illustrates how he built partnerships with nonstate actors, including nongovernmental organizations, the private sector, universities, think tanks, and Nobel laureates in order to advance the UN’s mission without relying exclusively on state power and interstate cooperation. It recounts how he used the bully pulpit to articulate a global agenda, speak to a global public, and shape norms. The book charts Annan’s ambitious efforts to reform and adapt the UN to the needs of the twenty-first century.