bluuebunny/crossref_metadata_2025 huggingface dataset:
Recharge Estimation Approach in a Data-Scarce Semi-Arid Region, Northern Ethiopian Rift Valley
Authors: Mekonen Sisay S. , Boyce Scott E. , Mohammed Abdella K. , Flint Lorraine , Flint Alan , Disse Markus
Published: November 2023

Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.
A Bibliometric Analysis of Circular Economies through Sustainable Smart Cities
Authors: Santibanez Gonzalez Ernesto D. R. , Kandpal Vinay , Machado Marcio , Martens Mauro Luiz , Majumdar Sushobhan
Published: November 2023

The rapid pace of urbanization has presented cities worldwide with a range of complex issues pertaining to the management of resources, reduction of waste, and promotion of sustainable practices. The concepts of circular economies and sustainable smart cities have arisen as viable solutions, converging to establish a revolutionary framework for the future of urban living. This study conducts a bibliometric analysis using literature focusing on the past ten years (2013–2022) of research on the circular economy and smart cities using VOSviewer. The most frequently used Scopus database was used to extract bibliometric data. 163 articles were considered for the analysis. This study utilizes co-authorship, co-occurrence, citation analysis and bibliographic coupling of author keywords while grap0hically mapping the bibliographic material using VOS viewer software Version 1.6.19. The bibliographic analysis reveals that the significant themes published in journals revolve around “circular economy”, “Sustainable development”, “sustainability”, “smart city”, “waste management”, “recycling”, “Sustainability”, “climate change”, “smart technologies”, “municipal solid waste”, “renewable energy”, and “planning”. The results would provide a robust base for more research in this area. The research work paves the way for future research in the related areas and issues of the domain, as it is an emerging issue in research, and many problems are untapped.
Urban–Rural Integration Empowers High-Quality Development of Tourism Economy: Mechanism and Empirical Evidence
Authors: Wang Ruigong , Gao Li , Zhu Yaling , Liu Ying , Li Yangyan , He Jiachen
Published: November 2023

Urban–rural integration has become an effective way to promote the high-quality development of the tourism economy in China’s policy evolution. Based on provincial panel data for China for the last ten years, this paper measured the high-quality-development level of the tourism economy based on multi-dimensional indicators, including by empirically analyzing the influence mechanisms in urban–rural integration. We found that urban–rural integration has a positive empowering effect on the high-quality development of the tourism economy as a whole, with the integration of urban and rural areas in the central and eastern regions playing a considerable role; however, its role in the western region is not obvious. This was shown through an analysis of the mediating effect by which urban–rural integration empowers the high-quality development of the tourism economy by improving residents’ income levels. However, urban–rural integration does not empower the high-quality development of the tourism economy through technological innovation, which instead has a covering effect. This paper makes suggestions on three levels: country, region, and enterprise. Firstly, at the national level, we suggest strengthening the overall planning for the high-quality development of the tourism economy, as well as improving its construction system. Secondly, at the regional level, governments at all levels should emphasize the functional differences between urban and rural areas and regional characteristics. Finally, at the enterprise level, enterprises in the western region should seize the opportunities provided by urban–rural integration.
Origin of Giant Rashba Effect in Graphene on Pt/SiC
Authors: Rybkina Anna , Gogina Alevtina , Tarasov Artem , Xin Ye , Voroshnin Vladimir , Pudikov Dmitrii , Klimovskikh Ilya , Petukhov Anatoly , Bokai Kirill , Yuan Chengxun , Zhou Zhongxiang , Shikin Alexander , Rybkin Artem
Published: November 2023

Intercalation of noble metals can produce giant Rashba-type spin–orbit splittings in graphene. The spin–orbit splitting of more than 100 meV has yet to be achieved in graphene on metal or semiconductor substrates. Here, we report the p-type graphene obtained by Pt intercalation of zero-layer graphene on SiC substrate. The spin splitting of ∼200 meV was observed at a wide range of binding energies. Comparing the results of theoretical studies of different models with the experimental ones measured by spin-ARPES, XPS and STM methods, we concluded that inducing giant spin–orbit splitting requires not only a relatively close distance between graphene and Pt layer but also the presence of graphene corrugation caused by a non-flat Pt layer. This makes it possible to find a compromise between strong hybridization and increased spin–orbit interaction. In our case, the Pt submonolayer possesses nanometer-scale lateral ordering under graphene.
Statistical Fuzzy Reliability Analysis: An Explanation with Generalized Intuitionistic Fuzzy Lomax Distribution
Authors: Kalam Abdul , Cheng Weihu , Du Yang , Zhao Xu
Published: November 2023

To illustrate data uncertainty, intuitionistic fuzzy sets simply use membership and non-membership degrees. However, in some cases, a more complex strategy is required to deal with imprecise data. One of these techniques is generalized intuitionistic fuzzy sets (GIFSs), which provide a comprehensive framework by adding extra factors that provide a more realistic explanation for uncertainty. GIFSs contain generalized membership, non-membership, and hesitation degrees for establishing symmetry around a reference point. In this paper, we applied a generalized intuitionistic fuzzy set approach to investigate ambiguity in the parameter of the Lomax life distribution, seeking a more symmetric assessment of the reliability measurements. Several reliability measurements and associated cut sets for a novel L-R type fuzzy sets are derived after establishing the scale parameter as a generalized intuitionistic fuzzy number. Additionally, the study includes a range of reliability measurements, such as odds, hazards, reliability functions, etc., that are designed for the Lomax distribution within the framework of generalized intuitionistic fuzzy sets. These reliability measurements are an essential tool for evaluating the reliability characteristics of various types of complex systems. For the purpose of interpretation and application, the results are visually displayed and compared across different cut set values using a numerical example.
N-Acetyl-L-Cysteine Ameliorates BPAF-Induced Porcine Sertoli Cell Apoptosis and Cell Cycle Arrest via Inhibiting the ROS Level
Authors: Feng Yue , Wu Junjing , Lei Runyu , Zhang Yu , Qiao Mu , Zhou Jiawei , Xu Zhong , Li Zipeng , Sun Hua , Peng Xianwen , Mei Shuqi
Published: November 2023

Bisphenol AF (BPAF) is a newly identified contaminant in the environment that has been linked to impairment of the male reproductive system. However, only a few studies have systematically studied the mechanisms underlying BPAF-induced toxicity in testicular Sertoli cells. Hence, this study primarily aims to explore the toxic mechanism of BPAF on the porcine Sertoli cell line (ST cells). The effects of various concentrations of BPAF on ST cell viability and cytotoxicity were evaluated using the Counting Kit-8 (CCK-8) assay. The results demonstrated that exposure to a high concentration of BPAF (above 50 μM) significantly inhibited ST cell viability due to marked cytotoxicity. Flow cytometry analysis further confirmed that BPAF facilitated apoptosis and induced cell cycle arrest in the G2/M phase. Moreover, BPAF exposure upregulated the expression of pro-apoptotic markers BAD and BAX while downregulating anti-apoptotic and cell proliferation markers BCL-2, PCNA, CDK2, and CDK4. BPAF exposure also resulted in elevated intracellular levels of reactive oxygen species (ROS) and malondialdehyde (MDA), alongside reduced activities of the antioxidants glutathione (GSH), catalase (CAT), and superoxide dismutase (SOD). Furthermore, the ROS scavenger N-acetyl-L-cysteine (NAC) effectively blocked BPAF-triggered apoptosis and cell cycle arrest. Therefore, this study suggests that BPAF induces apoptosis and cell cycle arrest in ST cells by activating ROS-mediated pathways. These findings enhance our understanding of BPAF’s role in male reproductive toxicity and provide a foundation for future toxicological assessments.
Simulating Microscale Urban Airflow and Pollutant Distributions Based on Computational Fluid Dynamics Model: A Review
Authors: Liang Qian , Miao Yucong , Zhang Gen , Liu Shuhua
Published: November 2023

Urban surfaces exert profound influences on local wind patterns, turbulence dynamics, and the dispersion of air pollutants, underscoring the critical need for a thorough understanding of these processes in the realms of urban planning, design, construction, and air quality management. The advent of advanced computational capabilities has propelled the computational fluid dynamics model (CFD) into becoming a mature and widely adopted tool to investigate microscale meteorological phenomena in urban settings. This review provides a comprehensive overview of the current state of CFD-based microscale meteorological simulations, offering insights into their applications, influential factors, and challenges. Significant variables such as the aspect ratio of street canyons, building geometries, ambient wind directions, atmospheric boundary layer stabilities, and street tree configurations play crucial roles in influencing microscale physical processes and the dispersion of air pollutants. The integration of CFD with mesoscale meteorological models and cutting-edge machine learning techniques empowers high-resolution, precise simulations of urban meteorology, establishing a robust scientific basis for sustainable urban development, the mitigation of air pollution, and emergency response planning for hazardous substances. Nonetheless, the broader application of CFD in this domain introduces challenges in grid optimization, enhancing integration with mesoscale models, addressing data limitations, and simulating diverse weather conditions.
Suppressed Histone H3 Lysine 18 Acetylation Is Involved in Arsenic-Induced Liver Fibrosis in Rats by Triggering the Dedifferentiation of Liver Sinusoidal Endothelial Cells
Authors: Hu Fang , Zhou Xingcheng , Peng Qianqian , Ma Lu
Published: November 2023

Arsenic pollution is a global environmental concern. Arsenic-induced chronic liver injury and its irreversible outcomes, including liver cirrhosis and liver cancer, threaten the health of residents in arsenic-contaminated areas. Liver fibrosis is a reversible pathological stage in the progression of arsenic-induced chronic liver injury to cirrhosis and liver cancer. The aim of this study is to identify the epigenetic mechanism of arsenic-induced liver fibrosis based on the dedifferentiation of liver sinusoidal endothelial cells (LSECs). Rats were treated with 0.0, 2.5, 5.0, or 10.0 mg/kg sodium arsenite for 36 weeks. Marked fibrotic phenotypes were observed in the rat livers, manifested by hepatic stellate cell activation and an increased extracellular matrix, as well as the deposition of collagen fibers. The reduced fenestrations on the cells’ surface and the increased expression of the dedifferentiation marker CD31 corroborated the LSECs’ dedifferentiation in the liver tissue, which was also found to be significantly associated with fibrotic phenotypes. We further revealed that arsenic exposure could inhibit the enrichment of histone H3 lysine 18 acetylation (H3K18ac) in the promoters of Fcgr2b and Lyve1, two key genes responsible for maintaining the differentiation phenotype of LSECs. This inhibition subsequently suppressed the genes’ expression, promoting LSEC dedifferentiation and subsequent liver fibrosis. In conclusion, arsenic can trigger liver fibrosis by inhibiting H3K18ac-dependent maintenance of LSEC differentiation. These findings uncover a novel mechanism of arsenic-induced liver fibrosis based on a new insight into epigenetically dependent LSEC dedifferentiation.
Preliminary Insights of Brazilian Snake Venom Metalloproteomics
Authors: Cavecci-Mendonça Bruna , Luciano Karen Monique , Vaccas Tauane , de Oliveira Laudicéia Alves , Clemente Eloisa Fornaro , Rossini Bruno Cesar , Vieira José Cavalcante Souza , de Barros Luciana Curtolo , Biondi Ilka , de Magalhães Padilha Pedro , Santos Lucilene Delazari dos
Published: November 2023

Snakebite envenoming is one of the most significantly neglected tropical diseases in the world. The lack of diagnosis/prognosis methods for snakebite is one of our motivations to develop innovative technological solutions for Brazilian health. The objective of this work was to evaluate the protein and metallic ion composition of Crotalus durissus terrificus, Bothrops jararaca, B. alternatus, B. jararacussu, B. moojeni, B. pauloensis, and Lachesis muta muta snake venoms. Brazilian snake venoms were subjected to the shotgun proteomic approach using mass spectrometry, and metal ion analysis was performed by atomic spectrometry. Shotgun proteomics has shown three abundant toxin classes (PLA2, serine proteases, and metalloproteinases) in all snake venoms, and metallic ions analysis has evidenced that the Cu2+ ion is present exclusively in the L. m. muta venom; Ca2+ and Mg2+ ions have shown a statistical difference between the species of Bothrops and Crotalus genus, whereas the Zn2+ ion presented a statistical difference among all species studied in this work. In addition, Mg2+ ions have shown 42 times more in the C. d. terrificus venom when compared to the average concentration in the other genera. Though metal ions are a minor fraction of snake venoms, several venom toxins depend on them. We believe that these non-protein fractions are capable of assisting in the development of unprecedented diagnostic devices for Brazilian snakebites.
Renocardiac Effects of p-Cresyl Sulfate Administration in Acute Kidney Injury Induced by Unilateral Ischemia and Reperfusion Injury In Vivo
Authors: Falconi Carlos Alexandre , Fogaça-Ruiz Fernanda , da Silva Jéssica Verônica , Neres-Santos Raquel Silva , Sanz Carmen Lucía , Nakao Lia Sumie , Stinghen Andréa Emília Marques , Junho Carolina Victoria Cruz , Carneiro-Ramos Marcela Sorelli
Published: November 2023

The precise mechanisms underlying the cardiovascular complications due to acute kidney injury (AKI) and the retention of uremic toxins like p-cresyl sulfate (PCS) remain incompletely understood. The objective of this study was to evaluate the renocardiac effects of PCS administration in animals subjected to AKI induced by ischemia and reperfusion (IR) injury. C57BL6 mice were subjected to distinct protocols: (i) administration with PCS (20, 40, or 60 mg/L/day) for 15 days and (ii) AKI due to unilateral IR injury associated with PCS administration for 15 days. The 20 mg/L dose of PCS led to a decrease in renal mass, an increase in the gene expression of Cystatin C and kidney injury molecule 1 (KIM-1), and a decrease in the α-actin in the heart. During AKI, PCS increased the renal injury biomarkers compared to control; however, it did not exacerbate these markers. Furthermore, PCS did not enhance the cardiac hypertrophy observed after 15 days of IR. An increase, but not potentialized, in the cardiac levels of interleukin (IL)-1β and IL-6 in the IR group treated with PCS, as well as in the injured kidney, was also noticed. In short, PCS administration did not intensify kidney injury, inflammation, and cardiac outcomes after AKI.
Lamium album Flower Extracts: A Novel Approach for Controlling Fusarium Growth and Mycotoxin Biosynthesis
Authors: Uwineza Pascaline Aimee , Urbaniak Monika , Stępień Łukasz , Gramza-Michałowska Anna , Waśkiewicz Agnieszka
Published: November 2023

Lamium album is a medicinal flowering plant that is rich in bioactive compounds with various biological properties. Fusarium species, known for causing significant crop losses and mycotoxin contamination, pose threats to food safety and human health. While synthetic fungicides are commonly employed for fungal management, their environmental impact prompts the ongoing development of alternative methods. This study aimed to evaluate the efficacy of L. album flower extracts in inhibiting the in vitro growth and biosynthesis of mycotoxins by Fusarium culmorum and F. proliferatum strains. The extracts were obtained by supercritical fluid extraction using CO2 (SC-CO2). The effects of various concentrations (2.5, 5, 7.5, and 10%) were assessed on a potato dextrose agar (PDA) medium using the “poisoning” technique. L. album flower extracts reduced mycelium growth by 0 to 30.59% for F. culmorum and 27.71 to 42.97% for F. proliferatum. Ergosterol content was reduced by up to 88.87% for F. culmorum and 93.17% for F. proliferatum. Similarly, the amounts of synthesized mycotoxins produced by both strains were also lower compared to control cultures. These findings are a preliminary phase for further in vivo tests planned to determine the fungistatic effect of L. album flower extracts on cereal substrates as seedlings incubated in controlled environments and under field conditions. Their phytotoxicity and biological stability, as well as the possibility of formulating a bio-preparation to protect cereals against Fusarium infections, will be evaluated.
Neutralizing Antibody Response to Genotypically Diverse Measles Viruses in Clinically Suspected Measles Cases
Authors: Vaidya Sunil R. , Kumbhar Neelakshi S. , Andhare Gargi K. , Pawar Nilesh , Walimbe Atul M. , Kinikar Meenal , Kasibhatla Sunitha M. , Kulkarni-Kale Urmila
Published: November 2023

The neutralizing antibody (Nt-Ab) response to vaccine and wild-type measles viruses (MeV) was studied in suspected measles cases reported during the years 2012–2016. The neutralization activity against MeV A, D4 and D8 genotypes was studied on sera (Panel A; n = 68 (measles-immunized) and Panel B; n = 50 (unvaccinated)) that were either laboratory confirmed or not confirmed by the presence of IgM antibodies. Additionally, the Nt-Ab response in Panel A was measured against the MeV vaccine and four wild-type viruses. Neutralization results were compared using homology modeling and molecular dynamics simulation (MDS) of MeV-hemagglutinin (H) and fusion (F) proteins. Overall, the Nt-Ab titres for MeV-A were found to be significantly lower than MeV-D4 and MeV-D8 viruses for Panel A. No major difference was noted in Nt-Ab titres between MeV-D8 viruses (Jamnagar and New Delhi), whereas MeV-D4 (Sindhudurg and Bagalkot (BGK) viruses) showed significant differences between Nt-Ab titres for Panel B. Interestingly, the substitutions observed in epitopes of H-protein, L249P and G316A are observed to be unique to MeV-BGK. MDS of H-protein revealed significant fluctuations in neutralizing epitopes due to L249P substitution. The majority of the clinically suspected cases showed Nt-Abs to MeV wild-types. Higher IgG antibody avidity and Nt-Ab titres were noted in IgM-negatives than in IgM-positives cases, indicating reinfection or breakthrough. MDS revealed reduced neutralization due to decreased conformational flexibility in the H-epitope.
Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets
Authors: Paremskaia Anastasiia Iu. , Rudik Anastassia V. , Filimonov Dmitry A. , Lagunin Alexey A. , Poroikov Vladimir V. , Tarasova Olga A.
Published: November 2023

Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.
A Re-Evaluation of African Swine Fever Genotypes Based on p72 Sequences Reveals the Existence of Only Six Distinct p72 Groups
Authors: Spinard Edward , Dinhobl Mark , Tesler Nicolas , Birtley Hillary , Signore Anthony V. , Ambagala Aruna , Masembe Charles , Borca Manuel V. , Gladue Douglas P.
Published: November 2023

The African swine fever virus (ASFV) is currently causing a world-wide pandemic of a highly lethal disease in domestic swine and wild boar. Currently, recombinant ASF live-attenuated vaccines based on a genotype II virus strain are commercially available in Vietnam. With 25 reported ASFV genotypes in the literature, it is important to understand the molecular basis and usefulness of ASFV genotyping, as well as the true significance of genotypes in the epidemiology, transmission, evolution, control, and prevention of ASFV. Historically, genotyping of ASFV was used for the epidemiological tracking of the disease and was based on the analysis of small fragments that represent less than 1% of the viral genome. The predominant method for genotyping ASFV relies on the sequencing of a fragment within the gene encoding the structural p72 protein. Genotype assignment has been accomplished through automated phylogenetic trees or by comparing the target sequence to the most closely related genotyped p72 gene. To evaluate its appropriateness for the classification of genotypes by p72, we reanalyzed all available genomic data for ASFV. We conclude that the majority of p72-based genotypes, when initially created, were neither identified under any specific methodological criteria nor correctly compared with the already existing ASFV genotypes. Based on our analysis of the p72 protein sequences, we propose that the current twenty-five genotypes, created exclusively based on the p72 sequence, should be reduced to only six genotypes. To help differentiate between the new and old genotype classification systems, we propose that Arabic numerals (1, 2, 8, 9, 15, and 23) be used instead of the previously used Roman numerals. Furthermore, we discuss the usefulness of genotyping ASFV isolates based only on the p72 gene sequence.
Comparison of Feedback Field-Weakening Techniques for Synchronous Machines with Permanent Magnets
Authors: Dianov Anton
Published: November 2023

In recent decades the market share of electrical cars has increased significantly, which has paved the way for the development of automotive electronics. Some of the most important parts of modern electrical vehicles are motor drives, which are used in car training and mechanization. Electrical drives are used in powertrains for traction, in air conditioning systems to cool cars and their parts, in doors for opening/closing as well as window movements, etc. The most popular motor type in electrical vehicles is synchronous motors with permanent magnets, which are compact and provide high torque. However, these motors require the development of control systems for proper operation. This system has to have the capacity to implement several state-of-the-art techniques, which can fully utilize motor potential, increase its efficiency, and decrease battery usage. One of these techniques is field-weakening, which overcomes speed limitations due to a lack of supply voltage and increases the motor’s speed operation range. This paper discusses the most popular approaches to field-weakening, including a new method proposed by the author. It considers both the pros and cons of each approach and provides recommendations for their usage. After that, this manuscript demonstrates the experimental results of each field-weakening technique obtained in the same motor drive, compares their performance, and discusses their strengths and weaknesses. Finally, the experimental part demonstrates that the proposed field-weakening approach demonstrates similar dynamics in load transients but provides 10 times less load to the microcontroller.
Comprehensive Regulation of Water–Nitrogen Coupling in Hybrid Seed Maize in the Hexi Oasis Irrigation Area Based on the Synergy of Multiple Indicators
Authors: Deng Haoliang , Pan Xiaofan , Zhang Hengjia , Xiao Zhanwen , Xiao Rang , Zhao Zhixi , Chen Tao
Published: November 2023

Water scarcity and the excessive application of nitrogen fertilizer are key factors limiting the sustainable development of the hybrid seed maize industry in the oasis agricultural areas of the Hexi Corridor in China. To determine the optimal water–nitrogen management regime of hybrid seed maize, we established a field experiment in 2020–2021 with three irrigation quotas (W1, W2, and W3 were 60, 80, and 100% of the local conventional irrigation quota, respectively) and four nitrogen application levels (N0, N1, N2, and N3 were 0, 190, 285, 380 kg·hm−2). We analysed the influence of different water–nitrogen combinations on indices of seed vigour, yield, water use efficiency (WUE), irrigation water use efficiency (IUE), the partial productivity of nitrogen fertilizer (NFP), and the nitrogen fertilizer agronomic use efficiency (NFA) of hybrid seed maize. A comprehensive growth evaluation system for hybrid seed maize was established based on the AHP, entropy weight, and TOPSIS methods, and a coupled water–nitrogen response model for hybrid seed maize was established with the objectives of obtaining high-yield, efficient, and high-seed vigour. The results showed that the yield of hybrid seed maize, NFP, and NFA gradually increased with the increase in the irrigation amount, while IUE continuously decreased; the yield of hybrid seed maize, WUE, and NFA increased and then decreased, while NFP continuously decreased with an increase in the amount of nitrogen application. Further, treatment N2W3 had higher water and nitrogen use efficiency and the highest yield and seed viability with a yield of 9209.11 kg·hm−2 and germination percentage, germination index, and vigour index of 97.22, 58.91, and 1.55%, respectively. The model of the integrated growth response of hybrid seed maize to water–nitrogen showed that the combined benefits of the hybrid seed maize yield, WUE, and seed viability could be maximised in conjunction with the irrigation rate ranging from 3558.90 to 3971.64 m3·hm−2 and the fertiliser application rate of 262.20 to 320.53 kg·hm−2. This study can provide scientific guidance and act as a decision-making reference for the productive, efficient, and sustainable development of hybrid seed maize in the oasis agricultural area of the Hexi Corridor.
Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin
Authors: Wang Yiyang , Wang Wenchuan , Zang Hongfei , Xu Dongmei
Published: November 2023

The long short-term memory network (LSTM) model alleviates the gradient vanishing or exploding problem of the recurrent neural network (RNN) model with gated unit architecture. It has been applied to flood forecasting work. However, flood data have the characteristic of unidirectional sequence transmission, and the gated unit architecture of the LSTM model establishes connections across different time steps which may not capture the physical mechanisms or be easily interpreted for this kind of data. Therefore, this paper investigates whether the gated unit architecture has a positive impact and whether LSTM is still better than RNN in flood forecasting work. We establish LSTM and RNN models, analyze the structural differences and impacts of the two models in transmitting flood data, and compare their performance in flood forecasting work. We also apply hyperparameter optimization and attention mechanism coupling techniques to improve the models, and establish an RNN model for optimizing hyperparameters using BOA (BOA-RNN), an LSTM model for optimizing hyperparameters using BOA (BOA-LSTM), an RNN model with MHAM in the hidden layer (MHAM-RNN), and an LSTM model with MHAM in the hidden layer (MHAM-LSTM) using the Bayesian optimization algorithm (BOA) and the multi-head attention mechanism (MHAM), respectively, to further examine the effects of RNN and LSTM as the underlying models and of cross-time scale bridging for flood forecasting. We use the measured flood process data of LouDe and HuaYuankou stations in the Yellow River basin to evaluate the models. The results show that compared with the LSTM model, under the 1 h forecast period of the LouDe station, the RNN model with the same structure and hyperparameters improves the four performance indicators of the Nash–Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency coefficient (KGE), the mean absolute error (MAE), and the root mean square error (RMSE) by 1.72%, 4.43%, 35.52% and 25.34%, respectively, and the model performance of the HuaYuankou station also improves significantly. In addition, under different situations, the RNN model outperforms the LSTM model in most cases. The experimental results suggest that the simple internal structure of the RNN model is more suitable for flood forecasting work, while the cross-time bridging methods such as gated unit architecture may not match well with the flood propagation process and may have a negative impact on the flood forecasting accuracy. Overall, the paper analyzes the impact of model architecture on flood forecasting from multiple perspectives and provides a reference for subsequent flood forecasting modeling.
Prediction Model of Residual Soil Shear Strength under Dry–Wet Cycles and Its Uncertainty
Authors: Ding Jiefa , Wang Shijun , Huang Haoran , Pan Fengqian , Wu Yunxing , Gu Yanchang , Zhang Yan
Published: November 2023

Granite residual soil is widely distributed in Southeast Fujian. Large-scale engineering construction leads to the exposure of residual soil slopes to the natural environment. Affected by seasonal climate factors, the soil of slopes experiences a dry–wet cycle for a long time. The repeated changes in water content seriously affect the shear strength of soil, and then affect the stability of the slope. In order to explore the influence of the dry–wet cycle on the shear strength of granite residual soil in Fujian, an indoor dry–wet cycle simulation test was carried out for shallow granite residual soil on a slope in Fuzhou, and the relationship between water content, dry–wet cycle times, and the shear strength index, including the cohesion and internal friction angle of the granite residual soil, was discussed. The results show that when the number of dry–wet cycles is constant, the cohesion and internal friction angle of the granite residual soil decrease with an increase in water content. The relationship between the cohesion, internal friction angle, and water content can be described using a power function. Meanwhile, the fitting parameters of the power function are also a function of the number of wet and dry cycles. The prediction formulas of the cohesion and internal friction angle considering the number of dry–wet cycles and water content are established, and then the prediction formula of shear strength is obtained. The ratio of the predicted value of shear strength to the test value shall be within ±15%. An error transfer analysis based on the point estimation method shows that the overall uncertainty of the predicted value of shear strength caused by the combined uncertainty of the predicted value of cohesion and the internal friction angle and the single-variable uncertainty of the predicted value of shear strength caused only by the uncertainty of the predicted value of either the cohesion or internal friction angle increases first and then decreases with an increase in the number of dry–wet cycles. All increase with an increasing water content. The maximum standard deviation of the proposed shear strength prediction model of granite residual soil is less than 9%.
Spatial Distribution and Factors Influencing the Various Forms of Iron in Alluvial–Lacustrine Clayey Aquitard
Authors: Chen Juan , Liu Rui , Jian Yantao , Ma Teng
Published: November 2023

The compression release of pore water in clayey aquitards has a significant impact on groundwater quality. Iron is an active variable element that mediates biochemical reactions in groundwater systems, but its transformation mechanisms in clayey aquitards remain unclear. The sediment and pore water samples from the shallow clayey aquitard (thickness = ~20 m) in the Chen Lake area of China were collected in three boreholes. The spatial distribution and influencing factors of Fe occurrence in the aquitard were revealed using hierarchical extraction, statistical analysis, and simulation calculations. The results indicate that the background value of alluvial–lacustrine sediments primarily affects the Fe concentration of clayey sediments. The dissimilatory reduction in free Fe oxide was the main source of Fe ions in pore water, resulting in a major percentage of Fe2+ in the total Fe concentration (0.07−5.91 mg/L). The abundant organic matter in organic-rich clay promoted a dissimilatory reduction in Fe (III) oxides, while the Fe concentrations of sediment and pore water were lower in the sand-rich stratum because of its weak adsorption capacity. The impact of human reclamation activities on the aquitard was mainly concentrated in the shallow layer (>−3 m), resulting in water drainage and O2 and CO2 input, which induced the crystallization of poorly crystalline Fe oxides. The input of reactive organic matter from reclaimed crops promoted the dissimilatory reduction in Fe oxides and the enrichment of Fe in deep pore water. The copious Fe2+ in deep stratum pore water tended to interact with CO32− and S2− to form coprecipitation with Fe (II). The concentrations of As, Cr, Sr, Zn, and Mn in pore water followed a similar variation trend to the Fe ion concentration, and their release could be attributed to the reduction dissolution of sediment Fe (III) oxides.
Small Island City Flood Risk Assessment: The Case of Kingston, Jamaica
Authors: Rivosecchi Andrea , Singh Minerva
Published: November 2023

Jamaica has had over 200 floods in the past 50 years, causing significant human and economic losses. Kingston has often caused the most damage due to its high population density and capital exposure. Kingston is crucial to the country’s socio-economic stability, and climate change is increasing flood risk, but a local-scale assessment of its flood risk is unavailable. This study fills this gap in the literature by using two models of the integrated Valuation of Ecosystem Services and Tradeoffs (inVEST) suite to qualitatively assess Kingston metropolitan pluvial and coastal flood risk. Key locations like Kingston Container Terminal and downtown Kingston are at high coastal flood risk, according to the results. The study also shows that sea level rise (117%) and habitat loss (104%) will increase the highly exposed area. Instead of hard-engineering coastal protection, this study suggests investing in nature-based and ecoengineering solutions to improve coastal resilience and ecosystem services. The urban flood assessment finds downtown, particularly the Mountview and Minor catchments, at high risk due to poor runoff retention and high population density. To fully address downtown pluvial flood risk, structural social reforms are needed. To reduce short-term flood risk, local authorities should consider targeted adaptation measures. These may include maintaining the drainage gully system and reducing surface runoff in uphill downtown areas. Thus, this study seeks to inform Kingston urban planners about risk distribution and suggest adaptation measures to improve flood resilience.
Computational Analysis of the Kinetic Processes of Microbial Electrolysis Cell-Assisted Anaerobic Digestion Using the ADM1
Authors: Kanellos Gerasimos , Tremouli Asimina , Arvanitakis Georgios , Lyberatos Gerasimos
Published: November 2023

This study deals with the computational analysis of the kinetic processes of microbial electrolysis cell-assisted anaerobic digestion (MEC-AD) for treating raw-waste-activated sludge (WAS), compared to conventional AD, as well as investigating the effect of the organic loading rate (OLR) on the system’s performance. The aim was to derive a mathematical model for the study of MEC-AD using the ADM1 framework, which can be utilized to extract the effect of an applied potential on the kinetics of AD. The experimental data were obtained from the operation of two identical reactors (an AD reactor and an MEC-AD reactor), which were operated at different OLRs. The kinetic parameters extracted from the ADM1 showed that the MEC-AD reactor yielded improved biomass yields, substrate consumption, and first-order disintegration rates, with a predominant contribution to the disintegration of complex particulates, which increased fourfold compared to the AD reactor. Moreover, it enabled operation at higher OLRs (achieving the highest divergence from the AD reactor at the OLR of 4.14 gCOD/(L × d)), therefore accelerating WAS treatment, as well as showing an improved performance at increased solids retention time (SRT). The ADM1 exhibited efficient adaptability and predictability of the kinetic processes and can be effectively used for the optimization of the MEC-AD operation.
A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction
Authors: Ehteram Mohammad , Banadkooki Fatemeh Barzegari
Published: November 2023

Groundwater level (GLW) prediction is essential for monitoring water resources. Our study introduces a novel model called convolutional neural network (CNN)–long short-term memory neural network (LSTM)–Multiple linear regression (MLR) for groundwater level prediction. We combine two deep learning models with the MLR model to predict GWL and overcome the limitations of the MLR model. The current paper has several innovations. Our study develops an advanced hybrid model for predicting groundwater levels (GWLs). The study also presents a novel feature selection method for selecting optimal input scenarios. Finally, an advanced method is developed to examine the impact of inputs and model parameters on output uncertainty. The current paper introduces the gannet optimization algorithm (GOA) for choosing the optimal input scenario. A CNN-LSTM-MLR model (CLM), CNN, LSTM, MLR model, CNN-MLR model (CNM), LSTM-MLR model (LSM), and CNN-LSTM model (CNL) were built to predict one-month-ahead GWLs using climate data and lagged GWL data. Output uncertainty was also decomposed into parameter uncertainty (PU) and input uncertainty (IU) using the analysis of variance (ANOVA) method. Based on our findings, the CLM model can successfully predict GWLs, reduce the uncertainty of CNN, LSTM, and MLR models, and extract spatial and temporal features. Based on the study’s findings, the combination of linear models and deep learning models can improve the performance of linear models in predicting outcomes. The GOA method can also contribute to feature selection and input selection. The study findings indicated that the CLM model improved the training Nash–Sutcliffe efficiency coefficient (NSE) of the CNL, LSM, CNM, LSTM, CNN, and MLR models by 6.12%, 9.12%, 12%, 18%, 22%, and 30%, respectively. The width intervals (WIs) of the CLM, CNL, LSM, and CNM models were 0.03, 0.04, 0.07, and, 0.12, respectively, based on IU. The WIs of the CLM, CNL, LSM, and CNM models were 0.05, 0.06, 0.09, and 0.14, respectively, based on PU. Our study proposes the CLM model as a reliable model for predicting GWLs in different basins.
Deformation Characteristics and Stability Prediction of Mala Landslide at Miaowei Hydropower Station under Hydrodynamic Action
Authors: Lv Jingqing , Shan Zhigang , Yin Fei , Chen Liang , Dong Menglong , Zhang Faming
Published: November 2023

In recent years, with the completion of the construction of large-scale hydropower projects in China, a series of engineering geological problems that occurred during the operation of the hydropower station have become an important issue affecting the normal operation of hydropower stations. Landslides on reservoir slopes triggered especially by water storage and other factors related to the construction of hydropower stations seriously affect the normal operation of the hydropower station and lead to other geological disasters. Research indicates that many reservoir-area landslides are triggered by hydrodynamic forces resulting from water level fluctuations in hydroelectric power stations. The Mala landslide of Miaowei Hydropower Station in the Lancang River Basin of China is taken as the engineering example to study the influence of hydrodynamic forces on the deformation characteristics and stability trends of the landslide. This paper explores the formation mechanism and influencing factors of the Mala landslide by conducting a field investigation of the Mala landslide and analyzing the monitoring data. Additionally, this paper also discusses the impacts of water storage, rainfall, and engineering construction on landslide induction. It is considered that the evolution of the Mala landslide from the initial stage of water storage to the current state mainly includes four stages: small-scale bank collapse stage, creep deformation stage, accelerated sliding stage, and uniform sliding stage. Moreover, the changes in the trend of landslide stability are analyzed using the two-dimensional finite element method. The research results provide a valuable reference for understanding the formation mechanism and predicting the deformation of reservoir landslides, which has considerable engineering practical significance.
Texas Well User Stewardship Practices Three Years after Hurricane Harvey
Authors: Gitter Anna C. , Boellstorff Diane E. , Gholson Drew M. , Pieper Kelsey J. , Mena Kristina D. , Mendez Karla S. , Gentry Terry J.
Published: November 2023

Private wells are susceptible to contamination from flooding and are exempt from the federal requirements of the Safe Drinking Water Act. Consequently, well users must manage (e.g., disinfect) and maintain (e.g., regularly test) their own wells to ensure safe drinking water. However, well user practices and perceptions of well water quality in the years following a natural disaster are poorly characterized. An online follow-up survey was administered in October 2020 to private well users who had previously experienced Hurricane Harvey in 2017. The survey was successfully sent to 436 participants, and 69 surveys were returned (15.8% return rate). The survey results indicate that well users who had previously experienced wellhead submersion or a positive bacteria test were more likely to implement well stewardship practices (testing and disinfection) and to report the feeling that their well water was safe. While the majority of well users believed that their water was safe (77.6%), there was a significant decrease in well water being used for drinking, cooking, and for their pets after Hurricane Harvey. Generally, these well users tend to maintain their wells at higher rates than those reported in other communities, but there continues to be a critical need to provide outreach regarding well maintenance practices, especially before natural disaster events occur.
Modelling Flash Floods Driven by Rain-on-Snow Events Using Rain-on-Grid Technique in the Hydrodynamic Model TELEMAC-2D
Authors: Godara Nitesh , Bruland Oddbjørn , Alfredsen Knut
Published: November 2023

Due to the changing climate, flash floods have been increasing recently and are expected to further increase in the future. Flash floods caused by heavy rainfall with snowmelt contribution due to sudden rises in temperature or rain-on-snow events have become common in autumn and winter in Norway. These events have caused widespread damage, closure of roads and bridges, and landslides, leading to evacuations in the affected areas. Hence, it is important to analyze such events. In this study, the rain-on-grid technique in the TELEMAC-2D hydrodynamic model was used for runoff modelling and routing using input of snowmelt, and precipitation partitioned on snow and rain was calculated via the hydrological model HBV. The results show the importance of including snowmelt for distributed runoff generation and how the rain-on-grid technique enables extracting flow hydrographs anywhere in the catchment. It is also possible to extract the flow velocities and water depth at each time step, revealing the critical locations in the catchment in terms of flooding and shear stresses. The rain-on-grid model works particularly well for single peak events, but the results indicate the need for a time-varying curve number for multiple peak flood events or the implementation of another infiltration model.