bluuebunny/crossref_metadata_2025_split huggingface dataset:
An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications
Authors: Hussien Abdelazim G. , Bouaouda Anas , Alzaqebah Abdullah , Kumar Sumit , Hu Gang , Jia Heming 
Published: August 2024

AbstractA recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for a wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from the behavior of Gorilla Troops in the natural world. To emulate the impact of gorillas at each stage of the search process, the GTO employs a flexible weighting mechanism rooted in its concept. Its exceptional qualities, including its independence from derivatives, lack of parameters, user-friendliness, adaptability, and simplicity, have resulted in its rapid adoption for addressing various optimization challenges. This review is dedicated to the examination and discussion of the foundational research that forms the basis of the GTO. It delves into the evolution of this algorithm, drawing insights from 112 research studies that highlight its effectiveness. Additionally, it explores proposed enhancements to the GTO’s behavior, with a specific focus on aligning the geometry of the search area with real-world optimization problems. The review also introduces the GTO solver, providing details about its identification and organization, and demonstrates its application in various optimization scenarios. Furthermore, it provides a critical assessment of the convergence behavior while addressing the primary limitation of the GTO. In conclusion, this review summarizes the key findings of the study and suggests potential avenues for future advancements and adaptations related to the GTO.
Spatiotemporal patterns of public attention to invasive species across an invasion front: a case study of lionfish (Pterois miles) from the Mediterranean Sea
Authors: Fazzari Lara , Vardi Reut , Jaric Ivan , Correia Ricardo A. , Coll Marta , Sbragaglia Valerio 
Published: August 2024

AbstractUnderstanding societal interest in invasive species is crucial as greater public attention can support the success of conservation efforts. One of the main challenges in monitoring societal interest to support conservation is the absence of near-real-time indicators to track large-scale spatiotemporal dynamics of public attention. However, the digital revolution has opened up new opportunities to develop such indicators in support of invasive species research and management. Here, we aim to evaluate public awareness and interest in the invasive lionfish (Pterois miles) in the Mediterranean Sea and investigate spatiotemporal patterns of public interest in the species along its invasion front by using Google search volumes as a proxy for public attention. We implemented topic searches for the lionfish to download Google search volumes from 2013 to 2022 and used these data to test two hypotheses: (1) countries experiencing lionfish invasion exhibit higher public attention than those without lionfish, and (2) in invaded countries, public attention peaks around the arrival year, followed by a decline to baseline levels. Our study confirmed the first hypothesis, showing higher public attention in invaded countries. However, temporal patterns of attention did not fully align with the year of lionfish arrival, suggesting that the socio-political complexity of the region (e.g., cultural trends or internet accessibility), may be a key aspect to consider in future studies. Overall our study shows that digital data sources provide a unique opportunity to monitor social aspects of biological invasions with low associated costs and quick access to a great amount of data generated by internet users.
Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds
Authors: Ryan Kevin A. , Garayburu-Caruso Vanessa A. , Crump Byron C. , Bambakidis Ted , Raymond Peter A. , Liu Shaoda , Stegen James C. 
Published: August 2024

AbstractQuantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km2) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.
Spotting Portfolio Greenwashing in Environmental Funds
Authors: Abouarab Rabab , Mishra Tapas , Wolfe Simon 
Published: August 2024

AbstractThis paper examines greenwashing practices in environmental funds. We utilize a unique data set of US equity mutual fund holdings between 2012 and 2021 to calculate the funds’ carbon footprints. Using a difference-in-differences analysis, we find that, following their commitments to sustainability, environmental funds fail to reduce their carbon footprints relative to a matched group of conventional funds. We also find, using an event study, a significant increase in the flows of environmental funds in response to these commitments. The combination of the failure to reduce carbon footprints and the surge in inflows provides evidence of greenwashing by environmental funds, raising concerns about their fiduciary duty. Our findings also show that greenwashers tend to initially have low flows and high portfolio carbon emissions suggesting that they announce their commitments to sustainability just to attract investors.
Dynamics in Realized Volatility Forecasting: Evaluating GARCH Models and Deep Learning Algorithms Across Parameter Variations
Authors: Akgun Omer Burak , Gulay Emrah 
Published: August 2024

AbstractThe modeling and forecasting of return volatility for the top three cryptocurrencies, which are identified by the highest trading volumes, is the main focus of the study. Eleven different GARCH-type models were analyzed using a comprehensive methodology in six different distributions, and deep learning algorithms were used to rigorously assess each model’s forecasting performance. Additionally, the study investigates the impact of selecting dynamic parameters for the forecasting performance of these models. This study investigates if there are any appreciable differences in forecast outcomes between the two different realized variance calculations and variations in training size. Further investigation focuses on how the use of expanding and rolling windows affects the optimal window type for forecasting. Finally, the importance of choosing different error measurements is emphasized in the framework of comparing forecasting performances. Our results indicate that in GARCH-type models, 5-minute realized variance shows the best forecasting performance, while in deep learning models, median realized variance (MedRV) has the best performance. Moreover, it has been determined that an increase in the training/test ratio and the selection of the rolling window approach both play important roles in achieving better forecast accuracy. Finally, our results show that deep learning models outperform GARCH-type models in volatility forecasts.
Static analysis driven enhancements for comprehension in machine learning notebooks
Authors: Venkatesh Ashwin Prasad Shivarpatna , Sabu Samkutty , Chekkapalli Mouli , Wang Jiawei , Li Li , Bodden Eric 
Published: August 2024

AbstractJupyter notebooks have emerged as the predominant tool for data scientists to develop and share machine learning solutions, primarily using Python as the programming language. Despite their widespread adoption, a significant fraction of these notebooks, when shared on public repositories, suffer from insufficient documentation and a lack of coherent narrative. Such shortcomings compromise the readability and understandability of the notebook. Addressing this shortcoming, this paper introduces HeaderGen, a tool-based approach that automatically augments code cells in these notebooks with descriptive markdown headers, derived from a predefined taxonomy of machine learning operations. Additionally, it systematically classifies and displays function calls in line with this taxonomy. The mechanism that powers HeaderGen is an enhanced call graph analysis technique, building upon the foundational analysis available in PyCG. To improve precision, HeaderGen extends PyCG’s analysis with return-type resolution of external function calls, type inference, and flow-sensitivity. Furthermore, leveraging type information, HeaderGen employs pattern matching techniques on the code syntax to annotate code cells. We conducted an empirical evaluation on 15 real-world Jupyter notebooks sourced from Kaggle. The results indicate a high accuracy in call graph analysis, with precision at 95.6% and recall at 95.3%. The header generation has a precision of 85.7% and a recall rate of 92.8% with regard to headers created manually by experts. A user study corroborated the practical utility of HeaderGen, revealing that users found HeaderGen useful in tasks related to comprehension and navigation. To further evaluate the type inference capability of static analysis tools, we introduce TypeEvalPy, a framework for evaluating type inference tools for Python with an in-built micro-benchmark containing 154 code snippets and 845 type annotations in the ground truth. Our comparative analysis on four tools revealed that HeaderGen outperforms other tools in exact matches with the ground truth.
A Graph Multi-separator Problem for Image Segmentation
Authors: Irmai Jannik , Zhao Shengxian , Schöne Mark , Presberger Jannik , Andres Bjoern 
Published: August 2024

AbstractWe propose a novel abstraction of the image segmentation task in the form of a combinatorial optimization problem that we call the multi-separator problem. Feasible solutions indicate for every pixel whether it belongs to a segment or a segment separator, and indicate for pairs of pixels whether or not the pixels belong to the same segment. This is in contrast to the closely related lifted multicut problem, where every pixel is associated with a segment and no pixel explicitly represents a separating structure. While the multi-separator problem is np-hard, we identify two special cases for which it can be solved efficiently. Moreover, we define two local search algorithms for the general case and demonstrate their effectiveness in segmenting simulated volume images of foam cells and filaments.
A two-phase sequential algorithm for global optimization of the standard quadratic programming problem
Authors: Júdice Joaquim , Sessa Valentina , Fukushima Masao 
Published: August 2024

AbstractWe introduce a new sequential algorithm for the Standard Quadratic Programming Problem (StQP), which exploits a formulation of StQP as a Linear Program with Linear Complementarity Constraints (LPLCC). The algorithm is finite and guarantees at least in theory a $$\delta $$
δ
-approximate global minimum for an arbitrary small $$\delta $$
δ
, which is a global minimum in practice. The sequential algorithm has two phases. In Phase 1, Stationary Points (SP) with strictly decreasing objective function values are computed. Phase 2 is designed for giving a certificate of global optimality for the last SP computed in Phase 1. Two different Nonlinear Programming Formulations for LPLCC are proposed for each one of these phases, which are solved by efficient enumerative algorithms. New procedures for computing a lower bound for StQP are also proposed, which are easy to implement and give tight bounds in general. Computational experiments with a number of test problems from known sources indicate that the two-phase sequential algorithm is, in general, efficient in practice. Furthermore, the algorithm seems to be an efficient way to study the copositivity of a matrix by exploiting an StQP with this matrix.
Managing the threat of decoupling in the U.S. affordable housing industry
Authors: Read Dustin C. , Sedgwick Donna 
Published: August 2024

AbstractDecoupling, or the practice of communicating different organizational values and goals than those rigorously pursued, is a concept discussed in the organization theory literature. Organizations engage in it when their legitimacy hinges on obtaining the support of stakeholders with disparate interests, and they attempt to appease some stakeholders with words and others with actions. One problem with decoupling is that it can lead organizations to operate in ways that are not reflective of their stated missions. With this concern in mind, the analysis presented here explores the prevalence of decoupling in the U.S. affordable housing industry and considers how, when, and why it occurs, and to what effect. Thematic analysis of interview data collected from industry practitioners suggests decoupling is common, comes in multiple forms, and must be effectively managed if municipal governments hope to partner with for-profit and nonprofit affordable housing providers to achieve their social policy goals.
Berry–Esseen-Type Estimates for Random Variables with a Sparse Dependency Graph
Authors: Janisch Maximilian , Lehéricy Thomas 
Published: August 2024

AbstractWe obtain Berry–Esseen-type bounds for the sum of random variables with a dependency graph and uniformly bounded moments of order $$\delta \in (2,\infty ]$$

δ
∈
(
2
,
∞
]

using a Fourier transform approach. Our bounds improve the state-of-the-art obtained by Stein’s method in the regime where the degree of the dependency graph is large.
Isothermal crystallization kinetics of commercial PA66 and PA11
Authors: Vázquez Laura S. , Pereira Mercedes , Díaz-Díaz Ana-María , López-Beceiro Jorge , Artiaga Ramón 
Published: August 2024

AbstractThis study is aimed at investigating the crystallization kinetics of two structurally related polymers, Nylon 6,6 (PA66) and Nylon 11 (PA11), by differential scanning calorimetry (DSC) in the scope of a logistic-based model using a model fitting approach. By this method, the values of the rate parameters for each specific temperature are obtained from fitting all points of the crystallization exotherm that were accurately recorded at that temperature. This method differs from Arrhenius-based model fitting approaches, in which the initial and final parts of the exotherm do not usually match the shape of Arrhenius-based models and are therefore discarded for fitting. Furthermore, in other kinetic approaches that fall outside the scope of this article, kinetic parameters are typically obtained from specific points in the crystallization exotherm, and good fits cannot generally be obtained nor is that the goal of those approaches. The DSC curves of both polymers obtained at different temperatures are analysed to determine the crystallization kinetics. One of the most insightful parameters of the model is the crystallization rate. Its dependence on temperature is analysed for both polymers and compared to others. The other parameters can also help to better understand some of the crystallization features of these polymers. In addition, the information retrieved from this study can be useful to adjust processing conditions.
Balancing riderless electric scooters at zero speed in the presence of a feedback delay
Authors: Horvath Hanna Zsofia , Takacs Denes 
Published: August 2024

AbstractThe nonlinear dynamics of electric scooters are investigated using a spatial mechanical model. The equations of motion are derived with the help of Kane’s method. Two control algorithms are designed in order to balance the e-scooter in a vertical position at zero forward speed. Hierarchical, linear state feedback controllers with feedback delay are considered. In the case of a delay-free controller, the linear stability properties are analyzed analytically, with the help of the Routh–Hurwitz criteria. The linear stability charts of the delayed controllers are constructed with the help of the D-subdivision method and semi-discretization. The control gains of the controllers are optimized with respect to the robustness against perturbations. The effects of the feedback delay of the controllers, the rake angle, the trail, and the center of gravity of the handlebar on the linear stability are shown. The performance of the control algorithms is verified by means of numerical simulations.
Correlation between shear wave velocity (Vs) and penetration resistance along with the stress condition of Eskisehir(Turkey) case
Authors: Civelekler Ebru , Afacan Kamil B. 
Published: August 2024

Abstract
Shear wave velocity (Vs) is an important characteristic in geotechnical earthquake engineering practices for estimating site response. Seismic refraction and well seismicity are the common ways to determine the shear wave velocity. However, these methods require on-site land studies. For this reason, there are empirical approaches that have been proposed to calculate Vs depending on the number of SPT-N obtained from the Standard Penetration experiment data. Studies in the literature have different aspects and correspond to varied empirical approaches. The study aims to establish empirical correlations between the Shear Wave Velocity (Vs), Standard Penetration Test results (SPT-N), and stress conditions of soils considering the soil types of the local sites in Eskişehir, Turkey. The Vs values of the soil from the data set we used in this study were obtained from seismic refraction-reflection studies in 22 different locations in Eskişehir. SPT-N values obtained from 42 boreholes representing the Eskişehir ground were used. The Vs values calculated from the empirical approaches given depending on the SPT-N values and the Vs values measured on the site were compared. In addition, regression analyses were performed between SPT- N and Vs. As a result of the regression analyses performed, new empirical correlations were developed according to soil types. Finally, new empirical correlations are established that can predict Vs at different soil depths and conditions taking the soil type and overburden pressure into account and contributing valuable insights for geotechnical earthquake engineering purpose. The regressions show that there is a good correlation between the Vs-SPT-N along with the total stress with high R2s and soil types are effective on predicting the shear wave velocity.
(Un)usual advocacy coalitions in a multi-system setting: the case of hydrogen in Germany
Authors: Löhr Meike , Markard Jochen , Ohlendorf Nils 
Published: August 2024

AbstractGrand sustainability challenges span multiple sectors and fields of policymaking. Novel technologies that respond to these challenges may trigger the emergence of new policy subsystems at the intersection of established sectors. We develop a framework that addresses the complexities of ‘multi-system settings.’ Empirically, we explore belief and coalition formation in the nascent policy subsystem around hydrogen technologies in Germany, which emerges at the intersection of electricity, transport, heating, and industry and is characterised by a broad range of actors from different sectoral backgrounds. We find two coalitions: a rather unusual coalition of actors from industry, NGOs, and research institutes as well as an expectable coalition of gas and heat sector actors. Actors disagree over production, application, and import standards for hydrogen. However, there is widespread support for hydrogen and for a strong role of the state across almost all actors. We explain our findings by combining insights from the advocacy coalition framework and politics of transitions: Belief and coalition formation in a nascent subsystem are influenced by sectoral backgrounds of actors, technology characteristics, as well as trust and former contacts. Our study contributes to a better understanding of early stages of coalition formation in a multi-system setting.
Ride-pooling service assessment with heterogeneous travellers in non-deterministic setting
Authors: Bujak Michal , Kucharski Rafal 
Published: August 2024

AbstractRide-pooling remains a promising emerging mode with a potential to contribute towards urban sustainability and emission reductions. Recent studies revealed complexity and diversity among travellers’ ride-pooling attitudes. So far, ride-poling analyses assumed homogeneity of ride-pooling travellers. This, as we demonstrate, leads to a false assessment of ride-pooling system performance. We experiment with an actual NYC demand from 2016 and classify travellers into four groups of various ride-pooling behaviours (value of time and penalty for sharing), as reported in the recent SP study from Netherlands. We replicate their behavioural characteristics, according to the population distribution, to obtain meaningful performance estimations. Results vary significantly from the homogeneous benchmark: mileage savings were lower, while the utility gains for travellers were greater. Observing performance of heterogeneous travellers, we find that those with a low value of time are most beneficial travellers in the pooling system, while those with an average penalty for sharing benefit the most. Notably, despite the highly variable travellers’ behaviour, the confidence intervals for the key performance indicators are reasonably narrow and system-wide performance remains predictable. Our results show that the incorrect assumption of homogeneous traits leads to a high dissatisfaction of 18.5% and a cancellation rate of 36%. Such findings shed a new light on the expected performance of large scale ride-pooling systems.