bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cloudiness and water from cloud seeding in connection with plants distribution in the Republic of Moldova
Authors: Valjarević Aleksandar , Popovici Cristina , Štilić Anđelka , Radojković Milan 
Published: November 2022

AbstractClouds play an important role in maintaining the balance in the environment. Cloudiness can influence numerous important ecological drivers, including growth, survival and distribution of plants. The cloud cover dynamics and distribution may provide key information for delineating various types of plant and species distribution. During a year, different types of clouds on a territory can influence the distribution of plants and potential of cloud seeding. New properties of cloudiness may be important for new ecological patterns and new adapted values of ecological drivers. In the territory of the Republic of Moldova, 30 years of cloudiness were taken to show new distribution of clouds properties and their influence on sunflower (Helianthus annus L.) and grapes (Vitis vinifera). The main result of this research is the first established map with the location of water derived from cloud seeding. In the last decade (2010–2020), the cloudiness increased by more than 15%. Another important focus of this research is the possibility of cloud seeding in line with recent deficit of precipitation, especially in dry areas. The territory of Moldova has 37% of semiarid and 6% of arid zones. The distribution and the potential of cloud seeding has been analyzed by means of Remote Sensing and GIS techniques and methodology, along with the Moderate Resolution Imaging Spectroradiometer. Satellite data in the resolution of 1 km2 were downloaded for the period between 1990 and 2020. The exact distribution of clouds helped to determine the belts for the potential irrigation of crops. Extremely low cloudiness and a long period of drought may jeopardize the agricultural sector in the Republic of Moldova, and therefore, the irrigation from alternative water sources is of utmost importance.
Optimizing pump-and-treat method by considering important remediation objectives
Authors: Zeynali Mohammad Javad , Pourreza-Bilondi Mohsen , Akbarpour Abolfazl , Yazdi Jafar , Zekri Slim 
Published: November 2022

AbstractThe efficiency of groundwater remediation by pump-and-treat (PAT) method is affected by several components. The most important of these components include the pumping wells location, pumping rate, and remediation period. In this research, hybrid optimization-simulation models were developed to find the appropriate groundwater remediation strategy by PAT method. The GA-FEM and NSGA-II-FEM models were used to solve four optimization problems for a hypothetical and real aquifer. These optimization problems were investigated from one objective problem to a four-objectives problem. In the multi-objective problems, in each step, one objective function is added to the previous set of objective functions. In the one-objective case, the objective function was defined as minimizing the contaminant concentration by pumping at a constant rate, while in the two-objectives problem, minimizing the drawdown of groundwater head by pumping at a constant rate was added. In the three-objectives problem, the pumping rate was variable and the mean pumping rate from all the wells is minimized. Finally, minimizing the remediation period is added in the four-objective case. The results indicated that locating the pumping wells in the path of the contaminant flow and close to the source improves the efficiency of the PAT system. The wells with higher pumping rates would be in the path of contamination flow and the wells with lower pumping rates should be located in nodes near the Dirichlet boundary. It is concluded that the remediation period in the hypothetical and real aquifer cannot be less than almost 3000 and 760 days, respectively. Finally, it can be said, the most important component in choosing the proper PAT strategy is the proper location of pumping wells.
The application of the hybrid copula-GARCH approach in the simulation of extreme discharge values
Authors: Nazeri Tahroudi Mohammad , Mohammadi Mirali , Khalili Keivan 
Published: November 2022

AbstractStatistical analysis and simulation of annual maximum discharge values, while considering the corresponding maximum daily rainfall, provide a comprehensive view of flood management. This research presents the application of copula functions for simulating and modeling two variables of annual maximum discharge and corresponding precipitation. In this research, the performance of copula-based models and ARCH-based models including VAR-GARCH, copula, and copula-GARCH models was then evaluated to simulate the annual maximum discharge values. The simulation results of all three models were evaluated using NSE and NRMSE statistics. According to the 95% confidence intervals, the accuracy of all three models was confirmed. The correlation results of the studied pair variables confirmed the possibility of using copula-based models. The results of simulations revealed that a higher accuracy of the copula-GARCH approach compared with two models copula and VAR-GARCH. Considering 76% efficiency (NSE = 0.76) of the copula-GARCH approach, the results indicated 20 and 2.7% improvements in the performance of the proposed approach compared to both VAR-GARCH and copula models. The results also illustrated that by combining nonlinear ARCH models with copula-based simulations, the reliability of simulation results increased. The results obtained in this study suggest that the proposed method is very effective for increasing the certainty of frequency analysis of two variables. Because the copula-GARCH approach simulates the average values, the first and third quarters, as well as the amplitude of changes of 5 and 95% of the data better than the other two models.Graphical abstractViolin plot of AMD series in copula scale
Use of two hybrid algorithms in the investigation and prediction of the values of five quantitative traits of guar beans in different deficit irrigation methods
Authors: Mirhashemi Seyed Hassan 
Published: November 2022

AbstractNowadays, deficit irrigation is of particular importance in areas facing the water shortage and drought. This study focused on the investigation and prediction of the values of five quantitative traits of guar beans under different deficit irrigation methods. Deficit irrigation methods were carried out at the initial, development, mid, and late plant growth stages. The experiment was carried out in 25 treatments each with four replications in 2018 and 2019. Initially, the values of five quantitative traits of guar beans were divided into three categories, the values of which were clustered using the K-means algorithm. Then, clusters were predicted using a combination of K-means and CART algorithms. Finally, the relationship between different deficit irrigation methods and clusters was investigated by a combination of K-means and Apriori algorithms. The results of two hybrid algorithms determined that the amount of irrigation in the mid-stage of plant growth significantly affected the five quantitative traits of guar beans. After the mid-stage of the plant growth, the amount of irrigation in the development, initial, and late growth stages had the greatest effect on the quantitative traits of guar beans. Among the deficit irrigation methods, irrigation rates of 60% in the primary stage, 80% in the development stage, 100% in the mid-stage, and 40% in the late stage of the plant growth were the best deficit irrigation methods in the four stages of growth.
Groundwater budgeting of Nari and Gaj formations and groundwater mapping of Karachi, Pakistan
Authors: Sohail Muhammad Tayyab , Hussan Asrar , Ehsan Muhsan , Al-Ansari Nadhir , Akhter Malik Muhammad , Manzoor Zaira , Elbeltagi Ahmed 
Published: November 2022

AbstractGroundwater depletion is an emerging problem worldwide due to changes in climate and an increase in urbanization. Two significant water-bearing formations, the Oligocene-aged Nari and the Miocene-aged Gaj, were utilized as a case study exposed near Karachi, Pakistan. Groundwater budgeting was performed through a classical equation. The inflow of groundwater in the formations was calculated by thermo-pluviometric data and water loss of Hub Dam. The potential of evapotranspiration (PET) was calculated by the Thornthwaite method. The groundwater inflow from Hub Dam was estimated by using 20 years of annual water loss data by removing PET. The total mean annual inflow of groundwater in the formations was 2414.12 US Gallons per Second (gps). The annual mean outflow was estimated by calculation of groundwater usage for industries and domestic purposes and the mean annual groundwater outflow was 5562.61 US gps and an annual deficit of groundwater was 3148.5 US gps. The research is composed of validating the groundwater budget. Direct Current Electrical Resistivity (DCER) and static water level data from existing industrial wells were used for groundwater maps. The DCER data indicates A-Type and K-Type sub-surface with high resistivity in the three-layer model. The average water table of residential areas in 2019 was 60 m and in industrial areas was 130 m. The oscillation of the groundwater table over the last 20 years and the deficit of the groundwater budget shows an alarming condition for the future. If the same scenario persists, then by 2025, the water table will decline up to 140 m.
Utilizing deep learning machine for inflow forecasting in two different environment regions: a case study of a tropical and semi-arid region
Authors: Saab Saad Mawlood , Othman Faridah , Tan Chee Ghuan , Allawi Mohammed Falah , Sherif Mohsen , El-Shafie Ahmed 
Published: November 2022

AbstractReservoir inflow (Qflow) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consuming learning process. Hence, the recent alternative technology is the development of the machine learning models and deep learning neural network (DLNN) is the recent promising methodology explored in the field of water resources. The current research was adopted to forecast Qflow at two different catchment areas characterized with different type of inflow stochasticity, (semi-arid and topical). Validation against two classical algorithms of neural network including multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) was elaborated and discussed. The research was further investigated the potential of the feature selection algorithm “genetic algorithm (GA)”, for identifying the appropriate predictors. The research finding confirmed the feasibility of the developed DLNN model for the investigated two case studies. In addition, the DLNN model confirmed its capability in solving daily scale Q more accurately in comparison with the monthly scale. The applied GA as feature selection algorithm was reduced the dimension and complexity of the learning process of the applied predictive model. Further, the research finding approved the adequacy of the data span used in the current investigation development of computerized ML algorithm.
The complexity analysis of cerebral oxygen saturation during pneumoperitoneum and Trendelenburg position: a retrospective cohort study
Authors: Wang Xiaoxiao , Liu Chang , Zhang Kai , Zhang Yunliang , Yu Yao , Mi Weidong , Li Hao 
Published: November 2022

Abstract
Background
The human brain is a highly complex and nonlinear system, nonlinear complexity measures such as approximate entropy (ApEn) and sample entropy (SampEn) can better reveal characteristics of brain dynamics. However, no studies report complexity of perioperative physiological signals to reveal how brain complexity associates with age, varies along with the development of surgery and postoperative neurological complications.

Aim
This study examined the complexity of intraoperative regional cerebral oxygen saturation (rSO2), aiming to reveal brain dynamics during surgery.

Methods
This retrospective cohort study enrolled patients who scheduled for robot-assisted urological surgery. Intraoperative rSO2 was continuously monitored throughout the surgery. Postoperative delirium (POD) was diagnosed by the Confusion Assessment Method. ApEn and SampEn were used to characterize the complexity of rSO2. Pearson correlation coefficients were used to measure the correlation between complexity of rSO2 and age. The association between complexity of rSO2 and POD was examined using T tests.

Results
A total of 68 patients (mean [SD] age, 63.0 (12.0) years; 47 (69.1%) males) were include in this analysis. There was a significant reverse relationship between the complexity of rSO2 and age (The correlation coefficients range between − 0.32 and − 0.28, all p < 0.05). Patients  ≥ 75 years showed significantly lower complexity of rSO2 than the other two groups. Older age remained an independent factor influencing complexity of rSO2 after adjusting for a number of covariates. Six patients (8.8%) developed POD, and POD patients had lower complexity of rSO2 compared with non-POD patients.

Conclusions
The complexity of rSO2 may serve as a new candidate marker of aging and POD prediction.
Vagheit als Chance verstehen
Authors: Felder Ekkehard 
Published: November 2022

ZusammenfassungVagheit wird in der öffentlichen Debatte über angemessenen Sprachgebrauch häufig als ein Problem dargestellt. Die Diagnose sprachlicher Vagheit in Bezug auf ein Lexem oder eine Äußerung unterstellt in der Regel eine Absicht – so auch der Titel dieses Themenheftes »Veruneindeutigungen« mit dem Verweis auf »Uneindeutigkeit als Strategie« (Call for Paper, S. 1). Veruneindeutigung präsupponiert einen (aktiven) Agens, der veruneindeutigt – vermutlich intentional. Hierbei wird das Eindeutige als Standard-/default-Wert sprachlicher Kommunikation insinuiert. Pragmalinguistisch gewendet fokussiert dies strukturell paradigmatische und mentalistisch kontrastive Aspekte von Bestimmtheit und Nicht-Bestimmtheit beim Sprachhandeln. Gegenstand dieses Beitrages ist die in Sprache angelegte Eigenschaft der Vagheit, die durch die Arbitrarität von Ausdrucksseite und Inhaltsseite gegeben ist und durch Konventionalisierung des Sprachgebrauchs entschärft wird. Es werden daher zwei Seiten der Vereindeutigung, der pragma-semiotischen Erdung aufgezeigt. Dazu wird die aktive Rolle der rezipierenden Individuen beim Verstehen von uneindeutigen bzw. veruneindeutigten Kommunikationsprozessen fokussiert.
“Xylella is the Enemy that Must be Fought”: Representations of the X. Fastidiosa Bacterium in the Media Discourse
Authors: Pavlović Tijana Vesić , Đorđević Danijela 
Published: November 2022

AbstractThe paper explores media representations of Xylella fastidiosa, the bacterium that causes severe plant diseases, using data from online sources in English which reported on the developments in the bacterium spreading and treatment from 2015 to 2020. Two directions of analysis are pursued, the quantitative and qualitative one. Quantitative analysis reveals that the bacterium and its effects are described via the terms belonging to the lexical fields of fear, diseases, change, the supernatural, hostility, destruction, killing and war, with the latter three being the most dominant statistically speaking. Further, qualitative analysis attests that some of these terms are used metaphorically as instantiations of the war metaphor, which is generally effective in communicating the severity of the X. fastidiosa induced diseases and mobilising the necessary support. Based on the results, it may be argued that the language used in the media for describing X. fastidiosa fits into the prevalent “catastrophe discourse”, with the purpose of raising awareness of the gravity of the threat the bacterium poses, as well as justifying the severe measures undertaken to contain it.
Carbon and Nitrogen Mineralisation in Soils and Nutrient Efficiency of Digestates from Fruit and Vegetable Wastes
Authors: Álvarez-Alonso Cristina , Clemente Rafael , Bernal María Pilar 
Published: November 2022

AbstractA significant amount of fruit and vegetable wastes is generated annually in markets, supermarkets, restaurants and households. Anaerobic digestion allows their appropriate management and helps to complete the cycle of the circular economy as it converts wastes into resources: biogas, a renewable source of energy, and digestate, rich in nutrients of interest for agriculture. The aim of this study was to define the agronomic value of digestate from fruit and vegetable wastes. Two digestates from fruit and vegetable residues were used in incubation experiments for C and N mineralisation in the soil, and in a pot experiment with crops (cardoon and maize), to calculate their fertiliser potential in comparison with a mineral fertiliser. The digestate quickly mineralised in the soil and nitrification processes led to fast formation of NO3−-N. However, increasing the digestate application rate enhanced N-immobilisation and reduced N-mineralisation in the soil. The addition of digestates to the soil resulted in adequate plant growth and nutrient composition, without any negative effects on the plants or soil. However, special attention should be paid to the salt accumulation in the soil for long-term digestate application. The nutrient recovery efficiency indicated that digestate could replace mineral fertiliser completely in cardoon crops and partially (44.5–82.6%) for maize, with an associated economic benefit. The salinity of the digestates limits their quality and their agricultural use to salt-sensitive crops should be limited.
Seeing Through Each Other’s Hearts: Inferring Others’ Heart Rate as a Function of Own Heart Rate Perception and Perceived Social Intelligence
Authors: Arslanova Irena , Galvez-Pol Alejandro , Kilner James , Finotti Gianluca , Tsakiris Manos 
Published: November 2022

AbstractSuccessful social interactions require a good understanding of the emotional states of other people. This information is often not directly communicated but must be inferred. As all emotional experiences are also imbedded in the visceral or interoceptive state of the body (i.e., accelerating heart rate during arousal), successfully inferring the interoceptive states of others may open a window into their emotional state. But how well can people do that? Here, we replicate recent results showing that people can discriminate between the cardiac states (i.e., the resting heartrate) of other people by simply looking at them. We further tested whether the ability to infer the interoceptive states of others depends on one’s own interoceptive abilities. We measured people’s performance in a cardioception task and their self-reported interoceptive accuracy. Whilst neither was directly associated to their ability to infer the heartrate of another person, we found a significant interaction. Specifically, overestimating one’s own interoceptive capacities was associated with a worse performance at inferring the heartrate of others. In contrast, underestimating one’s own interoceptive capacities did not have such influence. This pattern suggests that deficient beliefs about own interoceptive capacities can have detrimental effects on inferring the interoceptive states of other people.
Functional Fibers and Functional Fiber-Based Components for High-Power Lasers
Authors: Chen Xiao , Yao Tianfu , Huang Liangjin , An Yi , Wu Hanshuo , Pan Zhiyong , Zhou Pu 
Published: November 2022

AbstractThe success of high-power fiber lasers is fueled by maturation of active and passive fibers, combined with the availability of high-power fiber-based components. In this contribution, we first overview the enormous potential of rare-earth doped fibers in spectral coverage and recent developments of key fiber-based components employed in high-power laser systems. Subsequently, the emerging functional active and passive fibers in recent years, which exhibit tremendous advantages in balancing or mitigating parasitic nonlinearities hindering high-power transmission, are outlined from the perspectives of geometric and material engineering. Finally, novel functional applications of conventional fiber-based components for nonlinear suppression or spatial mode selection, and correspondingly, the high-power progress of function fiber-based components in power handling are introduced, which suggest more flexible controllability on high-power laser operations.
Graphical abstract
Reducing potassium deficiency by using sodium fertilisation
Authors: Thorne Sarah J. , Maathuis Frans J. M. 
Published: November 2022

AbstractPotassium (K) is the most abundant cation in the vast majority of plants. It is required in large quantities which, in an agronomic context, typically necessitates application of K in the form of potash or other K fertilisers. Recently, the price of K fertiliser has risen dramatically, a situation that is paralleled by increasing K deficiency of soils around the globe. A potential solution to this problem is to reduce crop K fertiliser dependency by replacing it with sodium (Na) fertiliser which carries a much smaller price tag. In this paper we discuss the physiological roles of K and Na and the implications of Na fertilisation for crop cultivation and soil management. By using greenhouse growth assays we show distinct growth promotion after Na fertilisation in wheat, tomato, oilseed and sorghum. Our results also show that up to 60% of tissue K can be substituted by Na without growth penalty. Based on these data, simple economic models suggest that (part) replacement of K fertiliser with Na fertiliser leads to considerable savings.
Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning
Authors: Kreitmair Monika J. , Makasis Nikolas , Menberg Kathrin , Bidarmaghz Asal , Farr Gareth J. , Boon David P. , Choudhary Ruchi 
Published: November 2022

Abstract
Understanding the subsurface is crucial in building a sustainable future, particularly for urban centers. Importantly, the thermal effects that anthropogenic infrastructure, such as buildings, tunnels, and ground heat exchangers, can have on this shared resource need to be well understood to avoid issues, such as overheating the ground, and to identify opportunities, such as extracting and utilizing excess heat. However, obtaining data for the subsurface can be costly, typically requiring the drilling of boreholes. Bayesian statistical methodologies can be used towards overcoming this, by inferring information about the ground by combining field data and numerical modeling, while quantifying associated uncertainties. This work utilizes data obtained in the city of Cardiff, UK, to evaluate the applicability of a Bayesian calibration (using GP surrogates) approach to measured data and associated challenges (previously not tested) and to obtain insights on the subsurface of the area. The importance of the data set size is analyzed, showing that more data are required in realistic (field data), compared to controlled conditions (numerically-generated data), highlighting the importance of identifying data points that contain the most information. Heterogeneity of the ground (i.e., input parameters), which can be particularly prominent in large-scale subsurface domains, is also investigated, showing that the calibration methodology can still yield reasonably accurate results under heterogeneous conditions. Finally, the impact of considering uncertainty in subsurface properties is demonstrated in an existing shallow geothermal system in the area, showing a higher than utilized ground capacity, and the potential for a larger scale system given sufficient demand.