bluuebunny/crossref_metadata_2025_split huggingface dataset:
Log‐location‐scale increment degradation model: A Bayesian perspective
Authors: Yu I‐Tang , Wang Kuei‐Mao 
Published: March 2024

AbstractDegradation modeling serves as a valuable tool for assessing the lifetime information of highly reliable products. One frequently employed approach for describing the degradation phenomenon involves the use of a degradation model that relies on stochastic processes. In a stochastic‐process‐based degradation model, it is assumed that the increments follow a distribution with the additivity property. This property makes the further inferences mathematically and statistically tractable. However, it limits the choices of the distributions. This paper aims to use those distributions without the additivity property to model the increments and explores distributions from the log‐location‐scale family. Under the frame of Bayesian analysis, Markov Chain Monte Carlo algorithms are developed for executing the necessary computations. Given that the proposed degradation models do not adhere to the additivity property, this paper tackles the challenges involved in predicting the lifetime of both on‐line and off‐line products. Two illustrative examples are subsequently analyzed to demonstrate the procedural steps outlined. The suitability of the proposed model is finally validated through a simulation study.
The finite mixture model for the tails of distribution: Monte Carlo experiment and empirical applications
Authors: Furno Marilena , Caracciolo Francesco 
Published: March 2024

AbstractThe finite mixture model estimates regression coefficients distinct in each of the different groups of the dataset endogenously determined by this estimator. In what follows the analysis is extended beyond the mean, estimating the model in the tails of the conditional distribution of the dependent variable within each group. While the clustering reduces the overall heterogeneity, since the model is estimated for groups of similar observations, the analysis in the tails uncovers within groups heterogeneity and/or skewness. By integrating the endogenously determined clustering with the quantile regression analysis within each group, enhances the finite mixture models and focuses on the tail behavior of the conditional distribution of the dependent variable. A Monte Carlo experiment and two empirical applications conclude the analysis. In the well‐known birthweight dataset, the finite mixture model identifies and computes the regression coefficients of different groups, each one with its own characteristics, both at the mean and in the tails. In the family expenditure data, the analysis of within and between groups heterogeneity provides interesting economic insights on price elasticities. The analysis in classes proves to be more efficient than the model estimated without clustering. By extending the finite mixture approach to the tails provides a more accurate investigation of the data, introducing a robust tool to unveil sources of within groups heterogeneity and asymmetry otherwise left undetected. It improves efficiency and explanatory power with respect to the standard OLS‐based FMM.
Wetlands contribution and linkage to support SDGs, its indicators and targets‐ A critical review
Authors: Mohanty Smrutisikha , Pandey Prem Chandra , Pandey Manish , Srivastava Prashant K. , Dwivedi Chandra Shekhar 
Published: March 2024

AbstractThis study marks one of the pioneering efforts to compile comprehensive information on Ramsar sites globally. It delves into the significance of wetlands and the designation of Ramsar sites across various countries, incorporating a concise exploration of the utilization of Unmanned Aerial Vehicles (UAVs) for wetland monitoring and assessment. Additionally, the study conducts a comparative evaluation of Ramsar sites, analyzing their percentage area and overall coverage worldwide. Incorporating a Scientometric analysis utilizing the Scopus database, the study features a co‐occurrence map, thematic map, thematic evolution trend, and country collaboration map. Emphasizing the interconnection between wetlands and Sustainable Development Goals (SDGs), particularly SDG6 (Clean Water & Sanitation), SDG12 (Responsible Consumption & Production), SDG13 (Climate‐Action), SDG14 (Life Below Water) and SDG15 (Life on Land), the study delves into associated targets and indicators. Targets such as 6.1, 6.2, 6.3, 6.4, 6.5, 6a, 6b of SDG‐6, 12.1, 12.2, 12.4 of SDG‐12, and 13.2, 13.3 of SDG‐13 align with wetland management and conservation. Moreover, it affirms the role of wetlands in supporting targets 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14a‐c of SDG‐14, and 15.1, 15.5, 15.6, 15.7, 15.8, and 15.8 of SDG‐15. Policies, regulations and management plans of different countries relevant for supporting and establishing relationship with SDGs are discussed in details. The study offers a detailed exploration of these targets, elucidating indicator types associated with each SDG target. By doing so, it provides valuable insights for future researchers and policymakers, underlining the indispensable contribution of wetlands to the direct and indirect fulfillment of SDGs 6,12,13,14,15 and 17.
A nonparametric relative treatment effect for direct comparisons of censored paired survival outcomes
Authors: Dobler Dennis , Möllenhoff Kathrin 
Published: March 2024

A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy‐to‐interpret parameter. Hence, we seek to develop a new estimand‐driven method to compare the effectiveness of two treatments in the context of right‐censored survival data with matched pairs. With the help of competing risks techniques, the so‐called relative treatment effect is estimated. This estimand describes the probability that individuals under Treatment 1 have a longer lifetime than comparable individuals under Treatment 2. We derive hypothesis tests and confidence intervals based on a studentized version of the estimator, where resampling‐based inference is established by means of a randomization method. In a simulation study, we demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well‐known benchmark data set from a trial with patients suffering from diabetic retinopathy.
Getting heard? How employees learn to gain senior management attention in inclusive strategy processes
Authors: Splitter Violetta , Seidl David , Whittington Richard 
Published: March 2024

AbstractResearch SummaryRecent trends toward inclusive strategy processes raise the issue of how employees acquire the discursive competence necessary to gain senior management attention. Building on the emergent dynamic attention‐based view's (DABV) emphasis on communicative interaction, we ethnographically track an inclusive strategy process in a large insurance company. We find that employees typically failed to gain CEO attention because they lacked the discursive competence to integrate their operational knowledge with the CEO's corporate themes. Employees acquired this competence by both experiential and vicarious learning. The CEO promoted employee learning more effectively by specific coaching than by generic coaching. We contribute primarily to the DABV by showing how interactions are sites for learning as well as communications and that communication channels can be both expandable and transparent.Managerial SummaryThe benefits of increased employee inclusion in strategy processes depend upon participants being truly heard. This study of an inclusive strategy process in a large insurance company shows that top management attention to employee contributions cannot be assumed. Employees often fail to pitch ideas in a manner that top managers can work with. Employees learn to pitch ideas effectively both by receiving direct feedback from top management and by observing feedback on other employees' contributions. Top managers must also learn how to coach effectively, engaging with the specifics of employees' contributions rather than offering general advice. Designs for new inclusive strategy processes should include opportunities for top managers to improve their coaching and for employees to learn from both direct feedback and indirect observation.
Engineering Nitrogen‐Doped Carbon Quantum Dots: Tailoring Optical and Chemical Properties through Selection of Nitrogen Precursors
Authors: Nguyen Kiem G. , Huš Matej , Baragau Ioan‐Alexandru , Bowen James , Heil Tobias , Nicolaev Adela , Abramiuc Laura Elena , Sapelkin Andrei , Sajjad Muhammad Tariq , Kellici Suela 
Published: March 2024

AbstractThe process of N‐doping is frequently employed to enhance the properties of carbon quantum dots. However, the precise requirements for nitrogen precursors in producing high‐quality N‐doped carbon quantum dots (NCQDs) remain undefined. This research systematically examines the influence of various nitrogen dopants on the morphology, optical features, and band structure of NCQDs. The dots are synthesized using an efficient, eco‐ friendly, and rapid continuous hydrothermal flow technique. This method offers unparalleled control over synthesis and doping, while also eliminating convention‐related issues. Citric acid is used as the carbon source, and urea, trizma base, beta‐alanine, L‐arginine, and EDTA are used as nitrogen sources. Notably, urea and trizma produced NCQDs with excitation‐independent fluorescence, high quantum yields (up to 40%), and uniform dots with narrow particle size distributions. Density functional theory (DFT) and time‐dependent DFT modelling established that defects and substituents within the graphitic structure have a more significant impact on the NCQDs’ electronic structure than nitrogen‐containing functional groups. Importantly, for the first time, this work demonstrates that the conventional approach of modelling single‐layer structures is insufficient, but two layers suffice for replicating experimental data. This study, therefore, provides essential guidance on the selection of nitrogen precursors for NCQD customization for diverse applications.
Balancing Flexible Side Chains on 2D Conjugated Acceptors Enables High‐Performance Organic Solar Cell
Authors: Bi Xingqi , Li Shitong , He Tengfei , Chen Hongbin , Li Yu , Jia Xinyuan , Cao Xiangjian , Guo Yaxiao , Yang Yang , Ma Wei , Yao Zhaoyang , Kan Bin , Li Chenxi , Wan Xiangjian , Chen Yongsheng 
Published: March 2024

AbstractBalancing the rigid backbones and flexible side chains of light‐harvesting materials is crucially important to reach optimized intermolecular packing, micromorphology, and thus photovoltaic performance of organic solar cells (OSCs). Herein, based on a distinctive CH‐series acceptor platform with 2D conjugation extended backbones, a series of nonfullerene acceptors (CH‐6F‐Cn) are synthesized by delicately tuning the lengths of flexible side chains from n‐octyl to n‐amyl. A systemic investigation has revealed that the variation of the side chain's length can not only modulate intermolecular packing modes and crystallinity but also dramatically improve the micromorphology of the active layer and eventual photovoltaic parameters of OSCs. Consequently, the highest PCE of 18.73% can be achieved by OSCs employing D18:PM6:CH‐6F‐C8 as light‐harvesting materials.
Pinpointing the Cl Coordination Effect on Mn‐N3‐Cl Moiety Toward Boosting Reaction Kinetics and Suppressing Shuttle Effect in Li–S Batteries
Authors: Yan Yurong , Fu Ning , Shao Wei , Wang Tiantian , Liu Ying , Niu Yongsheng , Zhang Yanwei , Peng Mao , Yang Zhenglong 
Published: March 2024

AbstractSingle atom catalysts (SACs) are highly favored in Li–S batteries due to their excellent performance in promoting the conversion of lithium polysulfides (LiPSs) and inhibiting their shuttling. However, the intricate and interrelated microstructures pose a challenge in deciphering the correlation between the chemical environment surrounding the active site and its catalytic activity. Here, a novel SAC featuring a distinctive Mn‐N3‐Cl moiety anchored on B, N co‐doped carbon nanotubes (MnN3Cl@BNC) is synthesized. Subsequently, the selective removal of the Cl ligands while inheriting other microstructures is performed to elucidate the effect of Cl coordination on catalytic activity. The Cl coordination effectively enhances the electron cloud density of the Mn‐N3‐Cl moiety, reducing the band gap and increasing the adsorption capacity and redox kinetics of LiPSs. As a modified separator for Li–S batteries, MnN3Cl@BNC exhibits high capacities of 1384.1 and 743 mAh g−1 at 0.1 and 3C, with a decay rate of only 0.06% per cycle over 700 cycles at 1 C, which is much better than that of MnN3OH@BNC. This study reveals that Cl coordination positively contributes to improving the catalytic activity of the Mn‐N3‐Cl moiety, providing a fresh perspective for the design of high‐performance SACs.
Acidity‐Aided Surface Modification Strategy to Enhance In Situ MnO2 Deposition for High Performance Zn‐MnO2 Battery Prototypes
Authors: Panda Manas Ranjan , El Meragawi Sally , Mirshekarloo Meysam Sharifzadeh , Chen Wanqing , Shaibani Mahdokht , Majumder Mainak 
Published: March 2024

AbstractZn–MnO2 batteries offer cost‐effective, eco‐friendly, and efficient solutions for large‐scale energy storage applications. However, challenges, like irreversible cathode reactions, prolonged cyclability, and electrolyte stability during high‐voltage operations limit their broader application. This study provides insight into the charge–discharge process through in situ deposition of active β‐MnO2 nanoflakes on a carbon‐based current collector. The study elucidates the effect of pH and electrolyte concentration on chemical conversion reactions with Zn, in particular focus on their impact on the two‐electron MnO2/Mn2+ reaction crucial for high voltage operation. The electrolyte, characterized by being relatively lean in Mn2+ and with a targeted low pH, enables extended cycling. This research achieves greater cycling durability by integrating a carbon‐based cathode current collector with high density of structural defects in combination with cell architectures suitable for large‐scale energy storage. A flooded stack‐type Zn–MnO2 battery prototype employing the optimized electrolyte demonstrates a high discharge voltage (≈2 V) at a substantial discharge current rate of 10 mA cm−2. The battery exhibits an impressive areal capacity of ≈2 mAh cm−2, maintaining ≈100% capacity retention over 400 cycles. This research establishes a promising practical, and cost‐effective cathode‐free design for Zn–MnO2 batteries, that minimizes additional processing and assembly costs.
Accelerating the Development of Thin Film Photovoltaic Technologies: An Artificial Intelligence Assisted Methodology Using Spectroscopic and Optoelectronic Techniques
Authors: Grau‐Luque Enric , Becerril‐Romero Ignacio , Atlan Fabien , Huber Daniel , Harnisch Martina , Zimmermann Andreas , Pérez‐Rodríguez Alejandro , Guc Maxim , Izquierdo‐Roca Victor 
Published: March 2024

AbstractThin film photovoltaic (TFPV) materials and devices present a high complexity with multiscale, multilayer, and multielement structures and with complex fabrication procedures. To deal with this complexity, the evaluation of their physicochemical properties is critical for generating a model that proposes strategies for their development and optimization. However, this process is time‐consuming and requires high expertise. In this context, the adoption of combinatorial analysis (CA) and artificial intelligence (AI) strategies represents a powerful asset for accelerating the development of these complex materials and devices. This work introduces a methodology to facilitate the adoption of AI and CA for the development of TFPV technologies. The methodology covers all the necessary steps from the synthesis of samples for CA to data acquisition, AI‐assisted data analysis, and the extraction of relevant information for research acceleration. Each step provides details on the necessary concepts, requirements, and procedures and are illustrated with examples from the literature. Then, the application of the methodology to a complex set of samples from a TFPV production line highlights its ability to rapidly glean significant insights even in intricate scenarios. The proposed methodology can be applied to other types of materials and devices beyond PV and using different characterization techniques.
“Are you Gonna Help us with Writing?” Empowering Writers though Dialogue Journaling
Authors: Bryars Leah , Pletcher Bethanie 
Published: March 2024

AbstractBefore ever entering a formal educational setting, children use writing to make themselves known. Yet by the time students reach the fourth grade, only a small minority of students demonstrate writing skills considered competent by national standards. By this time in their school career, many have begun to internalize that they are poor writers and have become apprehensive toward writing tasks and tests. Using an approach that applies the well‐established literacy practice of dialogue journaling, Leah, a teacher, facilitated an after‐school book club for fourth‐grade students who experienced writing apprehension. The outcomes of the book club were that students began to build confidence and develop more positive attitudes toward writing.
Basic Properties of Plasma in Fluid Model
Authors: Takabe Hideaki 
Published: March 2024

AbstractIf the spatial variation of plasma is longer than the particle mean free path and the time variation is sufficiently longer than the plasma Coulomb collision time, the plasma can be approximated as being in local thermal equilibrium (LTE) at any point (t, r). Then the velocity distribution functions of the particles become Maxwellian. In addition, assuming Maxwellian is also a good assumption in many cases even for collisionless plasmas such as high-temperature fusion plasmas. In the fluid model of plasmas, The plasmas can be described in terms of five variables characterizing local Maxwellian: the density n(t, r), flow velocity vector u(t,r), and temperature T(t, r). So, the mathematics used in fluid physics is widely applicable to studying plasma phenomena.Although conventional fluids are neutral, plasma fluids of electrons and ions couple with electromagnetic fields. It is, therefore, necessary to solve Maxwell’s equations simultaneously. It is also possible to approximate electrons and ions as two different fluids or as a single fluid in case-by-case. This requires an insight into what kind of physics is important in our problem.After reviewing the basic equation of fluids, several fluid models for plasmas are shown. Especially, a variety of waves appears because of charged particle fluids are derived to know why waves are fundamental to knowing the plasma dynamics. The mathematical method to obtain the wave solutions as an initial value problem is explained as well as the meaning of the resultant dispersion relations.Magneto-hydrodynamic equations (MHD) are derived to explain the effects of the Biermann battery, magnetic dynamo, etc. The relationship of magnetic field and vortex flow is studied. Resistive MHD is derived including the Nernst effect, which becomes important for the magnetic field in strong electron heat flux.Finally, electromagnetic (EM) waves in magnetized plasmas are derived to see how to use for diagnostics in the laboratory and observation of wide range of electromagnetic waves from the Universe.
Shock Waves and Ablation Dynamics
Authors: Takabe Hideaki 
Published: March 2024

AbstractWhen an intense laser is irradiated on a solid target, the laser energy is absorbed on the surface so that the material becomes plasma to expand into the vacuum region. Through the laser-plasma interaction, the laser energy heats the expanding region spreading by its sound velocity. As the result the expanding region has the temperature ~ 1 keV and the pressure reaches 100 Mbar (10TPa). Since the laser is absorbed near relatively high density (~cut-off density), the plasma can be assumed to be in LTE and hydrodynamic description is acceptable.The surface pressure called ablation pressure drives strong shock waves in the solid material as if the solid is almost gas. The shock wave physics is briefly reviewed to use the Rankin-Hugoniot (RH) relation, although detail studied is needed for the equation of state of the compressed matter. By use of the ablation pressure, it is possible to accelerate a thin material to higher velocity like a rocket propulsion.One dimensional hydrodynamics is reviewed for steady state and time dependent dynamics within the ideal fluid assumption. Deflagration and detonation waves are also explained as jump condition with energy deposition. The laser implosion dynamics is compered between stationary solutions, computational results, and the experimental data. The importance of validation of simulation codes is discussed.
The Impact of Workforce Agile Behavior on Organizational Innovation Performance for Manufacturing Enterprises
Authors: Mohamad Amal Abdulrahim 
Published: March 2024

AbstractPurpose - This paper intends to study the relationship between workforce agile behavior and organizational innovation in manufacturing enterprises. The research focuses on the impact of three characteristics of workforce agility (proactivity, flexibility, and resilience) on organizational innovation performance using the planned behavior theory (PBT), with a particular emphasis on product and process innovation.Methodology - The research of this paper uses the theoretical background for the selected topic to propose a conceptual research framework to improve manufacturing enterprises’ innovation performance by understating the significance of workforce agility.Findings - The expected outcome of the paper is to understand the relationship between agility in the workforce and innovation performance in manufacturing organizations. It can show the positive impact of a proactive, flexible, and resilient workforce in manufacturing enterprises to guarantee continuous growth in the rapid market landscape.Implications - This study can support leadership and employees in the manufacturing sector to understand the importance of providing agile capabilities in the work environment to improve innovative organizational performance, sustain competitive market advantage, and respond to internal and external changes.Originality/value - The paper addresses the literature gap on how organizational innovation can be influenced by workforce agility behavior in the manufacturing sector and helps understand the significance of agility practices in sustaining competitive advantages.