bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental investigation of the radial structure of energetic particle driven GAM in TCV
Authors: Dreval M.B. , Sharapov S.E. , Jansen van Vuuren A. , Karpushov A.N. , Mazzi S. , Podesta M. , Sheikh U.A. , Simons L. , Ochoukov R. , Poley J. , Testa D. ,   the TCV Team,   the EUROfusion Tokamak Exploitation Team
Published: November 2024

Abstract
High amplitude energetic particle geodesic acoustic mode (EGAM) oscillations driven by Neutral Beam (NBI) injected in the direction counter to the toroidal plasma current are observed in the TCV tokamak. The modes appear at frequencies close to the geodesic acoustic mode (GAM) frequency, corresponding to the mode radii calculated without plasma elongation corrections. The spatial structure of the EGAM density oscillation is analyzed using multichannel soft x-ray (SXR) and broadband light emission diagnostics. The analysis of spatiotemporal emission data shows a non-rotating structure of the density oscillations. The non-rotating mode discrimination is the additional new capability of the multichannel spatiotemporal SXR data analysis technique in TCV. We present the discrimination method of assessing the standing character of the EGAM wave. The structure of EGAM density oscillations in TCV is consistent with the theoretical GAM poloidal structure, namely a m= 1 standing wave with density oscillations amplitude proportional to sin(θ), where the poloidal angle θ is measured from the equatorial plane. The poloidal structure of magnetic the field oscillations is analyzed using a poloidal array of magnetic coils. The structure of the EGAM-induced magnetic field oscillations in TCV is a m = 2 standing wave. Time evolution of EGAMs suggests that a nonlinear EGAM chirping is observed. The chirping depends on the EGAM radial location, which varies as the NBI deposition is varied through a vertical shift of the plasma magnetic axis. The chirping disappears at the plasma periphery. A fast periodic radial shift of the EGAMs radial location is also observed to occur during the single chirp.
Identifying drivers of storage dynamics of lakes and reservoirs in the arid Central Asia
Authors: Zhao Gang , Liu Suxia , Deng Haoxin , Tang Qiuhong , Zhang Chi , Su Yanan , Chen Fahu 
Published: November 2024

Abstract
Knowing the storage variations in lakes and reservoirs are essential for water resources and environmental management, especially in the regions facing water scarcity. However, the quantification of the storage changes is limited by sparse in-situ observations and spatial coverage of space (air)-borne altimetric sensors that have been used conventionally in storage retrieval. This hampers the attribution analysis of lake storage changes. Here, we combined long-term optical remote sensing and multi-source terrain elevation data to derive the monthly storage time series from 1990 to 2020 for 8544 lakes and reservoirs in Central Asia, where water scarcity has been bottle-necking local socioeconomic sustainability. The regional total storage has been decreasing with a rate of −4.78 ± 0.88 km3 yr−1 mainly owing to the desiccation of the Aral Sea. For other lakes, 26% of them show decreasing while 22% show increasing trends. At a watershed-scale, the long-term changes in small to medium-sized (<5000 km2) lakes are primarily caused by the changes in surface runoff, jointly affected by precipitation and temperature changes. We also found that 29% lakes in Central Asia experienced frequent seasonal dry out in the past decades. Such seasonal dry out is mainly caused by fast evaporation losses during the summer months. For the majority (63 ± 8%) of these lakes, their evaporation water losses are larger than the seasonal storage drawdown. Our analysis highlights the co-regulation of surface runoff and lake evaporation in the storage losses in arid and semi-arid regions.
Burst flooding in Singapore: an emerging urban flooding type revealed by high-temporal-resolution observations
Authors: Lommen Dave , Jingyu Wang , Su Hui , Xiefei Zhi , Xianfeng Wang , Park Edward , Zhang Hugh , Lee Joshua , Chyi Wong Meei 
Published: November 2024

Abstract
Urbanisation significantly alters the interaction between land surface and the lower troposphere, impacting occurrences of natural hazards. The influence of urbanisation on natural hazards like heatwaves, hailstorms, and flooding remains debated. However, it is well established that impervious surfaces in urban areas can lead to flooding amplification. Singapore, amidst rapid urbanisation, experiences frequent flooding, exacerbated by its tropical-monsoon climate and climate change. Utilising high-temporal-resolution rainfall data from 2017 onwards, we examined the dynamics of urban flooding in Singapore. In total, 108 flooding events were reported for the period 2017–2023, all of a transient nature, primarily linked to cloudbursts. Based on the unique precipitation characteristics associated with urban flash flooding, the term ‘burst flooding’ is introduced to refer to urban floods caused by intense, short-duration rainfall events. A notable increase in cloudburst occurrences in November and December during La Niña years emphasises the role of global climate phenomena in local weather.
Some non-algebraic forms of exp

(

A
^

+

B
^

)
Authors: Tapia-Valerdi M A , Ramos-Prieto I , Soto-Eguibar F , Moya-Cessa H M 
Published: November 2024

Abstract
We present examples where expressions for

exp
⁡
(

A
^

+

B
^

)

can be derived even though operators (or superoperators)

A
^

and

B
^

do not commute in a manner that leads to known factorizations. We apply our factorization to the case of a Lindblad operator modeling single-photon decay and to a binary Glauber–Fock photonic lattice.
Casting more light in the shadows: dual Somos-5 sequences
Authors: Harrow J W E , Hone A N W 
Published: November 2024

Abstract
Motivated by the search for an appropriate notion of a cluster superalgebra, incorporating Grassmann variables, Ovsienko and Tabachnikov considered the extension of various recurrence relations with the Laurent phenomenon to the ring of dual numbers. Furthermore, by iterating recurrences with specific numerical values, some particular well-known integer sequences, such as the Fibonacci sequence, Markoff numbers, and Somos sequences, were shown to produce associated ‘shadow’ sequences when they were extended to the dual numbers. Here we consider the most general version of the Somos-5 recurrence defined over the ring of dual numbers

D

with complex coefficients, that is the ring

C

[
ε
]

modulo the relation ɛ
2 = 0. We present three different ways to present the general solution of the initial value problem for Somos-5 and its shadow part: in analytic form, using the Weierstrass sigma function with arguments in

D

; in terms of the solution of a linear difference equation; and using Hankel determinants constructed from

D

-valued moments, via a connection with a Quispel–Roberts–Thompson map over the dual numbers.
Effect of tissue viscoelasticity on delivered mechanical power in a physical respiratory system model: distinguishing between airway and tissue resistance
Authors: Walzel Simon , Roubik Karel 
Published: November 2024

Abstract
Understanding the mechanics of the respiratory system is crucial for optimizing ventilator settings and ensuring patient safety. While simple models of the respiratory system typically consider only flow resistance and lung compliance, lung tissue resistance is usually neglected. This study investigated the effect of lung tissue viscoelasticity on delivered mechanical power in a physical model of the respiratory system and the possibility of distinguishing tissue resistance from airway resistance using proximal pressure measured at the airway opening. Three different configurations of a passive physical model of the respiratory system representing different mechanical properties (Tissue resistance model, Airway resistance model, and No-resistance model) were tested. The same volume-controlled ventilation and parameters were set for each configuration, with only the inspiratory flow rates being adjusted. Pressure and flow were measured with a Datex-Ohmeda S/5 vital signs monitor (Datex-Ohmeda, Madison, WI, USA). Tissue resistance was intentionally tuned so that peak pressures and delivered mechanical energy measured at airway opening were similar in Tissue and Airway Resistance models. However, measurements inside the artificial lung revealed significant differences, with Tissue resistance model yielding up to 20% higher values for delivered mechanical energy. The results indicate the need to revise current methods of calculating mechanical power delivery, which do not distinguish between tissue resistance and airway flow resistance, making it difficult to evaluate and interpret the significance of mechanical power delivery in terms of lung ventilation protectivity.
Influence of physical and material parameters on switching current density in perpendicular STT-MTJ: a micromagnetic study
Authors: Shaw Abhishree , Kumar Joshi Vinod 
Published: November 2024

Abstract
Switching in magnetic tunnel junctions (MTJs) is considered to be coherent according to the macrospin model but above a critical characteristic length (R
c
) this process becomes incoherent. As a result, switching becomes a complex process and affects the switching current density (J
c
). We designed a spin transfer torque (STT) based single barrier perpendicular MTJ (SMTJ) and observed the influence of the junction size and exchange stiffness constant (Aex) on the switching process through micromagnetic simulations performed on Object Oriented Micromagnetic Framework (OOMMF). It was found that coherent switching occurred only for junction diameter ≤20nm and showed dependence on Aex as well. The influence of damping constant and anisotropy on J
c
is studied and the mechanism of magnetic reversal through domain formation is revisited in this work. Furthermore, Double barrier MTJ (DBMTJ) stack was designed, which showed increased STT efficiency in switching time with a requirement of J
c
lower by 42.86% compared to SMTJ.
Relationships between microbial characteristics and the arsenic cycle in high-arsenic groundwater
Authors: Yang Jingru , Zhang Yanyan , Li Qiao , Su Yanpeng , Tao Hongfei , Jiang Youwei , Mahemujiang Aihemaiti 
Published: November 2024

Abstract
Microbial activities play a pivotal role in the migration and transformation of arsenic (As) within groundwater systems, particularly at high concentrations. Focusing on the Kuitun River Basin, this study used high-throughput sequencing to unravel the intricate diversity of microbial communities and their influence on As enrichment. Groundwater with elevated As hosted richer and more diverse microbial populations compared to that with lower As levels. The Sobs and Shannon diversity indices for the high-As group initially declined before rising with increasing As concentrations. Key microbial taxa dominating these ecosystems included Pseudomonas, Acinetobacter, Aquabacterium, Novosphingobium, and Sphingobium, with Pseudomonas showing substantial variation in abundance across different As concentrations. Distance-based redundancy analysis emphasized the importance of dissolved oxygen in shaping the microbial landscape, followed closely by the interplay between iron and As concentrations. Flavobacteriaceae and Burkholderiaceae displayed inverse correlations with As levels, highlighting the multifaceted relationship between microbial communities and groundwater chemistry. This investigation underscores the profound impact of As concentration gradients on microbial biodiversity and elucidates a complex regulatory framework where environmental parameters and microorganisms collaborate in governing As accumulation in groundwater. The results enrich our understanding of the biogeochemical dynamics underlying arsenic behavior in subterranean aquatic habitats.
The major role of riverine outflows in shaping the current and future habitats of Harmful Algal Blooms: the case of the North Sea
Authors: Duteil Olaf , Macias Diego , Stips Adolf , Polimene Luca 
Published: November 2024

Abstract
This study investigates the extension of the potential habitat of Harmful Algal Blooms (HABs) in the North Sea using observational data and model experiments under current and future climate scenarios. We assess the combined effects of temperature, salinity, and nutrient availability, particularly the nitrogen-to-phosphorus ratio, on HABs in the region. Climate change projections indicate a decrease in salinity concomitant with an increase in surface temperature, potentially leading to an offshore extension of HAB habitat. Reducing nitrogen and phosphorus loads in rivers differentially affects dissolved inorganic nitrogen (DIN) and phosphorus (DIP) levels, with DIN being more sensitive to load reduction, thereby constraining HAB habitat extension. We underscore the importance of considering both physical and biogeochemical factors in assessing HAB habitat dynamics and the potential impacts of climate change and nutrient reduction measures on HAB expansion in the North Sea. These findings have significant implications for environmental policy and management.
Advancing life cycle assessment of bioenergy crops with global land use models
Authors: Arvesen Anders , Humpenöder Florian , Navarrete Gutierrez Tomás , Gibon Thomas , Baustert Paul , Dietrich Jan Philipp , Stadler Konstantin , Iordan Cristina-Maria , Luderer Gunnar , Popp Alexander , Cherubini Francesco 
Published: November 2024

Abstract
Bioenergy crops can cut greenhouse gas (GHG) emissions, yet often bring hard-to-quantify environmental impacts. We present an approach for integrating global land use modeling into life cycle assessment (LCA) to estimate effects of bioenergy crops. The approach involves methodological choices connected to time horizons, scenarios of GHG prices and socioeconomic pathways, and flexible data transfer between models. Land-use change emissions are treated as totals, avoiding uncertain separation into direct and indirect emissions. The land use model MAgPIE is used to generate scenarios up to 2070 of land use, GHG emissions, irrigation and fertilizer use with different scales of perennial grass bioenergy crop deployment. We find that land use-related CO2 emission for bioenergy range from 2 to 35 tonne TJ−1, depending on bioenergy demand, policy context, year and accounting method. GHG emissions per unit of bioenergy do not increase with bioenergy demand in presence of an emission tax. With a GHG price of 40 or 200 $ tonne−1 CO2, GHG per bioenergy remain similar if the demand is doubled. A carbon tax thus has a stronger effect on emissions than bioenergy demand. These findings suggest that even a relatively moderate GHG price (40 $ tonne−1 CO2) can prevent significant emissions, highlighting the critical role governance plays in securing the climate benefits of bioenergy. However, realizing these benefits in practice will depend on a coherent policy framework for pricing CO2 emissions from land-use change, which is currently absent. Overall, our approach addresses direct and indirect effects associated with irrigation, machinery fuel and fertilizer use as well as emissions. Thanks to a global spatial coverage and temporal dimension, it facilitates a systematic and consistent inclusion of indirect effects in a global analysis framework. Future research can build on our open-source data/software to study different regions, bioenergy products or impacts.
Optimizing wireless sensor network routing with Q-learning: enhancing energy efficiency and network longevity
Authors: Jain Amit Kumar , Jain Sushil , Mathur Garima 
Published: November 2024

Abstract
WSNs have gained importance in the recent years in the fields of industries, automobiles, military, agriculture and health care sectors, and others for data acquisition and monitoring. WSNs are made up of a large number of low-power nodes implemented with requisite sensing, computational, and wireless communication functionalities in relation to other nodes or a BS. LEACH is hierarchical routing protocol that adapts nodes into clusters, with an objective of achieving an equal distribution of energy load. However, the drawback of LEACH is that the selection of cluster head (CH) and the usage of single hop data transmission to the base station (BS) depends on the random probability, which is not efficient in large network. New additions involve the integration of a k-means clustering algorithm and reinforcement learning Q-learning for the selection of CHs and routing of data. For instance, Q-Learning improves the reliability and flexibility of a given network as it allows for acquisition of the best routes that involve multi-hope communication. An assessment of Q-Learning based routing is conducted on WSNs, based on energy depletion rate, node duration, and packet delivery ratio. Theoretical analysis shows that the Q-Learning based algorithm outperforms the traditional algorithms such as LEACH and k-means by adopting better energy utilization, reduced node mortality and high throughput. Overall, this study exposes the ability of Q-Learning algorithm in enhancing WSN life and efficiency in the modern world, and thus could be considered an optimal solution for changeability and limited resources in WSN networks.
Comparing advanced and traditional machine learning algorithms for construction duration prediction: a case study of Addis Ababa’s public sector
Authors: Liben Soreti M , Belachew Demiss A , Elsaigh Walied A 
Published: November 2024

Abstract
Accurate prediction of construction durations is crucial for effective project management, particularly in rapidly urbanizing areas such as Addis Ababa. However, there exists a notable research gap regarding the comparative analysis of advanced machine learning (ML) algorithms against traditional methods for this purpose. This study aims to develop and evaluate various advanced ML algorithms to predict construction completion times in Addis Ababa, with the goal of improving resource allocation and enhancing client satisfaction. Data were collected through surveys administered to multiple construction organizations within the city, which served as the foundation for training, validating, and comparing a range of ML models. The research utilized the caret package in R for model development and assessment, incorporating methodologies such as artificial neural networks (NN), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Classification and Regression Trees (CART). To evaluate variable importance, multivariate visualizations, including correlation and scatter plot matrices, were employed, while performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and R-squared (R2) were utilized for model comparison. The findings indicated that the RF model achieved an RMSE of 74 days and an R2 of 0.97, while the KNN model also demonstrated strong performance with an RMSE of 81 days and an R2 of 0.97, marking them as the most accurate models for predicting construction durations. In contrast, the NN model exhibited subpar performance, likely due to constraints related to training data and variable selection. As a result, the RF model was further optimized to improve its predictive accuracy. The study concludes that while the RF model proves to be highly effective for predicting construction durations in Addis Ababa, there is a critical need to expand the dataset and incorporate additional variables to enhance the performance of deep learning and other ML algorithms in this field.
A curvature and temperature simultaneous measurement sensor based on micro-taper formed MZI and an embedded FBG
Authors: Xuelan He , Li Jiahui , Liu Shuyu , Jian Xing , Yuan Libo 
Published: November 2024

Abstract
A compact fiber Bragg grating (FBG) and Mach-Zehnder interferometer (MZI) compound for simultaneous measurement of bending and temperature is described. The FBG and MZI are fabricated in fiber, forming a compact device that is different from the previously prepared axial cascade of multiple structures. Experimental results show that the FBG and MZI have different sensitivity to temperature and bending response, allowing this device to measure bending and temperature simultaneously. The proposed sensor has the advantages of simple preparation steps, small size, low cost, short preparation cycle, etc. Due to these advantages, it is competitive in the future sensing field where bending and temperature are measured simultaneously.
Investigation of tool wear behavior in CFRP drilling by using different tool materials
Authors: Sarfraz Sana , Husnain Naveed , Asif Tahir , Ashraf Ghulam Abbas , Sultan Tahir , Sahar Muhammad Sana Ullah , Imran Muhammad , Zaheer Mansoor Ali , Ahmad Rauf 
Published: November 2024

Abstract
The industrial usage of Carbon fiber reinforced polymers (CFRP) has increased significantly over the past few years due to its distinguished characteristics like great strength-to-weight ratio, fatigue resistance, superior damage tolerance, excellent corrosion resistance, etc Machining of CFRP differs from traditional machining due to its structure. This experimental study aims to investigate the effect of drill bit material on tool wear and to optimize the material selection process of twist drill bits during CNC drilling of CFRP regarding the output of tool wear, thrust forces, and torque generated during the drilling process. Four twist drill bits with dissimilar materials (i.e., HSS-4341, HSS-M2, HSS-Co, and Tungsten Carbide) having the same size and diameter (3 mm) were selected and used for CNC drilling on CFRP plates. During the drilling process, thrust forces and torque were calculated with the help of a dynamometer for each drilled hole. Moreover, the tool wear was seen and compared after the 1st hole and 20th hole for each drill bit to analyze the behavior of different tool materials toward CFRP machining. The results showed that Tungsten Carbide is the most suitable tool material for CFRP drilling as it yielded the lowest thrust forces and lowest torque for all 20 holes. Moreover, the Tungsten Carbide had the lowest tool wear among all the drill bits. These results imply that tool life can be significantly elevated if Tungsten Carbide is to be used for machining of CFRP.
A comprehensive review on sensor supported monitoring of machining processes
Authors: Javvadi Eswara Manikanta , S Santosh , Ambhore Nitin , Nalawade Dattatraya 
Published: November 2024

Abstract
Online monitoring of machining processes is revealed as a critical tool for detecting tool wear, influencing the determination of the remaining useful lifetime of cutting tools. Embracing the ethos of Industry 4.0, the study emphasizes the automatic monitoring of cutting forces, surface roughness, power consumption, tool wear, and tool life, citing their indispensable role in mitigating unfavourable machining conditions such as chatter vibrations, tool breakage, and compromised dimensional accuracy. The paper underscores the pivotal role played by advanced sensor systems in achieving enhanced machining characteristics, characterized by reduced human effort, minimized errors, and streamlined production times. The exploration extends to a comprehensive overview of online detection systems, encompassing sensors and signal processing software tailored for mechanical machining operations. Commencing with an up-to-date literature introduction, the paper systematically navigates through the types of sensors employed in machining, online detection methods, and addresses pertinent challenges while offering insightful suggestions. In a nutshell, the paper summarized its findings and provided future insights, particularly centered on the industry 4.0 theme. It is important to note that this review offers significant assistance to researchers and academics in the industrial sectors.