bluuebunny/crossref_metadata_2025_split huggingface dataset:
A continental approach to estimate the area required for proposed wind-power parks and their overlap with protected areas in Africa
Authors: Mayer Jana , Peters Rebecca , Bange Jens , Mauz Moritz , Platis Andreas , Zarfl Christiane 
Published: January 2025

Abstract
Wind energy is one of the key renewable energy technologies required to transform the energy sector to reduce climate change. In the global expansion of wind energy, one main concern is that wind-power parks (WPPs) take up large areas of land, causing conflicts with other uses such as nature conservation. Existing impact analyses for WPPs are mostly restricted to case studies, and it lacks studies that investigate potential impacts at a larger scale because no scientific consensus on the area associated to a WPP exists. This study proposes a continental, GIS-based approach to estimate the area required for proposed onshore WPPs and to estimate their potential overlap with protected areas on the African continent. The results of the spatial analysis show that, in total, the currently proposed 149 WPPs would require 852 km2 of land on the African continent, thereof 11 would overlap with protected areas. The overlaps sum up to an area of 42 km2, which corresponds to an affected nominal power of 834 MW (5% of the total projected wind power capacity). These findings reveal the need for further purposefully local in-depth analyses to investigate if the WPP can be operated in accordance with the conservation of the protected area. This work provides new data and a transferable methodological approach on the expansion of wind energy and its potential space requirement and contributes to the investigation of potential land-use conflicts of proposed onshore WPPs on a broader scale.
Understanding the influence of hydrogen on BCC iron grain boundaries using the kinetic activation relaxation technique (k-ART)
Authors: Khosravi Aynour , Song Jun , Mousseau Normand 
Published: January 2025

Abstract
Hydrogen embrittlement (HE) poses a significant challenge to the mechanical integrity of iron and its alloys. This study explores the influence of hydrogen atoms on two distinct grain boundaries (GBs), $\Sigma37$ and $\Sigma3$, in body-centered-cubic (BCC) iron. Using the kinetic activation relaxation technique (k-ART), an off-lattice kinetic Monte Carlo approach with an EAM-based potential, extensive catalogs of activated events for atoms in both H-free and H-saturated GBs were generated.&#xD;Studying the diffusion of H, we find that, for these systems, while GB is energetically favorable for H, this element diffuses more slowly at the GBs than in the bulk. The results further indicate that the $\Sigma 3$ GB exhibits higher stability in its pure form compared to the $\Sigma 37$ GB, with notable differences in energy barriers and diffusion behaviors. Moreover, with detailed information about the evolution landscape around the GB, we find that the saturation of a GB with hydrogen both stabilizes the GB by shifting barriers associated with Fe diffusion to higher energies and reducing the number of diffusion events. For the $\Sigma 37$ GB, the presence of hydrogen causes elastic deformation, affecting the diffusion of Fe atoms both at the GB and in adjacent positions. This results in new diffusion pathways but with higher diffusion barriers, unlike for the $\Sigma 3$ GB. These results indicate that the presence of hydrogen rigidifies the direct GB interface layers while allowing more atoms to be active for the $\Sigma 37$ GB. This provides a microscopic basis to support the existence of competing mechanisms compatible with either plasticity (such as hydrogen enhanced localized plasticity --- HELP) or energy-dominated (hydrogen enhanced decohesion mechanism ---HEDE) embrittlement, with the relative importance of these mechanisms determined by the local geometry of the GBs.
A design method to optimize GaN-based class E resonant inductive Wireless Power Transfer systems – application to non-mating connectors
Authors: CÔTE Nathis , GARRAUD Nicolas , Sterna Léo , Périchon Pierre , Frassati François , Boisseau Sebastien 
Published: January 2025

Abstract
Wireless Power Transfer (WPT) systems have demonstrated their efficiency over a broad frequency spectrum. However, the design and the optimization of multi-MHz frequency WPT systems remain complex due to parasitic components specifically. This paper outlines a methodology for designing and optimizing a compact 13.56 MHz resonant inductive coupling WPT systems based on a GaN Class E inverter. The proposed design approach combines analytical modeling and advanced numerical simulations taking into account parasitic elements. The effectiveness of this approach is experimentally confirmed through a prototype, which shows an efficiency of 78% for a transmitted power of 43 W to a 5.54 cm³ coupler. The advanced modeling results perfectly match with the experimental ones (mismatch of only 2% for the evaluation of efficiency). The WPT system developed thanks to this method shows a power density among the highest ones in the non-mating connectors’ state of the art.
Underwater target detection algorithm based on feature enhancement and feature fusion
Authors: Liu Qinxiao , Ji Longlong , Zhao Fen 
Published: January 2025

Abstract
In the field of target detection, identifying underwater targets remains a formidable challenge due to the complexity of the underwater environment and the substantial variations in target scale. To tackle the difficulties of extracting complex features in such conditions, this study introduces a novel feature extraction module built upon the YOLOv7 model. The module incorporates a custom-designed information attention mechanism and deformable convolution, enhancing the network’s ability to capture and process intricate underwater features. Additionally, a feature enhancement and interaction module was developed further to strengthen the extraction and utilization of key features. An adaptive feature fusion module was also introduced, allowing for dynamic adjustment of fusion weights based on the characteristics of the input features, thereby enabling more efficient and accurate multi-scale feature fusion. Experimental results reveal that, compared to the original YOLOv7 model on the URPC2020 dataset, the proposed method achieves improvements of 3.3% and 2.7% in mAP50 and mAP50:95 metrics, respectively. These significant performance gains highlight the effectiveness of the proposed approach in underwater target detection and provide valuable new directions for research and applications in this domain.
Advancing haptic realism: modelling grasp contact vibrations for enhanced virtual environment interaction
Authors: Najdovski Zoran , Pedrammehr Siamak , Qazani Mohammad Reza Chalak , Abdi Hamid , Asadi Houshyar 
Published: January 2025

Abstract
In haptic technology, achieving realistic tactile feedback is crucial for enhancing the user experience in virtual environments. Previous studies lack effective methods for transmitting high-frequency vibrations crucial for realistic tactile feedback in haptic interfaces, highlighting the need for our research to address this gap. This paper explores the application of a tactile gripping interface to transmit the high-frequency vibrations produced when contacting a hard object's surface. These short vibrations improve the tactile sensation of hard virtual surfaces when overlaid on traditional position-based force feedback within a haptic environment. The enhanced realism of virtual objects is achieved by effectively estimating the vibration composition from user-induced parameters. This work presents a prototype grasping interface and empirically demonstrates this device's utility. We examine empirical grasp contact data, recorded and interpreted, to recognise the relationship between dynamic user-controlled parameters and the resulting vibration transients. This relationship effectively incorporates these changing dynamics to model the grasp impact and estimate the essential system parameters to understand the influence of the user's grasp force. Through our multi-point grasping interface design, this work demonstrates a mathematical relationship between the user's grasp force and the high-frequency vibrations from contact with hard surfaces. The study found that the proposed haptic interface had an RMSE of 0.05, demonstrating the system's high level of accuracy. This low RMSE value indicates that the predicted vibrations closely matched the actual measured vibrations, proving the effectiveness of our methods through objective validation.&#xD;
Interface characterization of additively manufactured Inconel 718 heterogeneous products: Microstructural and surface characteristics
Authors: Shrivastava Abhishek , S Anand Kumar , Rao Samrat 
Published: January 2025

Abstract
The present work analyses the effect of laser powder bed fusion (LPBF) remanufacturing of wrought Inconel 718 on the metallurgical and surface characteristics of the part. The remanufactured part experienced geometrical mismatch due to computer-aided design (CAD) misalignment with the substrate. As a result, a ~200 μm shift in a material deposition is observed, leading to a material deposition without support from the substrate on one edge. The microstructural analysis of the remanufactured part showed an interface between the wrought substrate and LPBF processed layers. The substrate showed an equiaxed grain structure. However, a strongly textured columnar grain structure was observed in the remanufactured region. The remanufactured Inconel 718 showed lower micro-hardness, inhibiting the precipitation of strengthening precipitates during the LPBF process. The surface characterization showed a higher surface roughness and anisotropy in material distribution for overhanging edges due to layer deformation and lack of support from the substrate. It was observed that due to geometry mismatch, the overhanging side experienced layer deformation, thus leading to the formation of large undulations on the surface. A higher area fraction of fused powder particles on the overhanging side shows ineffective heat transfer due to the region's lack of efficient support.
A Method for Mitigating NLOS Error for UWB Based on CNN-Stacked-LSTM Model
Authors: Zhang Lieping , Liu Jinming , Zheng Xinpeng , Zou Jianchu 
Published: January 2025

Abstract
To address the shortcomings of traditional Long Short-Term Memory (LSTM) network in Non-Line-of-Sight (NLOS) mitigation, such as the large amount of training data required and the lengthy training times, in order to enhance the model's ability to process spatial features and multi-level features, this paper proposes an NLOS mitigation method based on Stacked Long Short-Term Memory (Stacked-LSTM) network and Convolutional Neural Network (CNN). This method combines CNN and Stacked-LSTM models to efficiently extract spatial and higher-level temporal features from the Channel Impulse Response (CIR) signal, reducing the input dimension and improving the performance of the model. The constructed CNN-Stacked-LSTM model is used to mitigate NLOS errors and reduce the impact of NLOS in the original ranging data. In the model performance validation experiment, the accuracy of the CNN-Stacked-LSTM model was improved by 4%-14% compared with the CNN-LSTM, Transformer, Attention-LSTM and LSTM models, and the training time was reduced by 0.07h compared with the traditional LSTM model. The experimental results in the two actual Ultra-WideBand (UWB) environments show that compared with the other four models, the RMSE value of the CNN-Stacked-LSTM model proposed in this paper is reduced by 19.55%-58.96% and 8.64%-45.52%, respectively. It has the best mitigation effect on NLOS and the highest positioning accuracy.
Slax: a composable JAX library for rapid and flexible prototyping of spiking neural networks
Authors: Summe Thomas Manuel , Joshi Siddharth 
Published: January 2025

Abstract
Spiking Neural Networks (SNNs) offer rich temporal dynamics and unique capabilities, but their training presents challenges. While backpropagation through time (BPTT) with surrogate gradients is the defacto standard for training SNNs, it scales poorly with long time sequences. Alternative learning rules and algorithms could help further develop models and systems across the spectrum of performance, bio-plausibility, and complexity. However, implementing and evaluating these alternatives at scale is cumbersome and error-prone, requiring repeated reimplementations. To address this, we introduce Slax, a JAX-based library designed to accelerate SNN algorithm design and evaluation. Slax is compatible with the broader JAX and Flax ecosystem and provides optimized implementations of diverse training algorithms, enabling direct performance comparisons. Its toolkit includes methods to visualize and debug algorithms through loss landscapes, gradient similarities, and other metrics of model behavior during training. By streamlining the implementation and evaluation of novel SNN learning algorithms, Slax aims to facilitate research and development in this promising field.
Evaluating the effectiveness of cost-minimal planning of decarbonized electricity systems in reducing life cycle greenhouse gas emissions
Authors: Tarroja Brian , Mulvaney Dustin , Peer Rebecca A M , Grubert Emily 
Published: January 2025

Abstract
The decarbonization of regional electricity systems is a critical enabler of broader economy-wide decarbonization strategies and has motivated robust research on how to decarbonize electricity systems at minimum monetary cost. Since these efforts often focus on the operation of electricity systems, however, they do not account for emissions of the full life cycle associated with different resources. Different resource mixes can achieve a similar level of decarbonization apparent decarbonization through operational emissions, while achieving very different levels of life cycle greenhouse gas (GHG) emissions reductions. To explore these differences, we model the expansion of the California electricity system from 2030 to 2045 using a simplified electricity dispatch model to investigate how the planning of future electricity systems may differ between prioritizing minimum cost versus minimum life cycle GHG emissions under a common target for operational GHG reductions. We find that explicitly planning for minimum life cycle GHG emissions yields an additional 1.6-2.0% reduction in annual life cycle GHG emissions at a cost penalty of 3.2–9.6% and that electricity resource mixes that minimize life cycle GHG emissions tend to favor high capacity factor zero-carbon resources and long-duration storage compared to a minimum cost approach. Comparatively, we find that aggressive supply chain decarbonization of generation and storage technologies reduces life cycle GHG emissions of electricity supplies by 3.0% to 14% and combining both increases these reductions to 5.0% to 16%. Further, we find that applying a carbon tax to minimum cost-based capacity expansion can incentivize planning to account for life cycle GHG emissions. Our results indicate that a planning approach focused on minimizing life cycle GHG emissions may not be palatable, but significant reductions in life cycle emissions can be realized through conventional mechanisms like carbon taxes, import standards, and targeted supply chain decarbonization.
An Individualist Bias
Authors: Zahavi Dan 
Published: January 2025

Abstract
Standard accounts of collective intentionality typically see the main challenge to be that of explaining how collective intentionality can grow out of individual intentionality. But why assume that the first-person singular is more self-explanatory than the first-person plural? What is ultimately the relation between self-identity and self-experience on the one hand and collective identities and we-experiences on the other? Might the former ultimately be enabled by the latter? Engaging with discussions in the philosophical, sociological, and anthropological literature, this chapter considers a variety of attempts to assign primacy to the we and argues that they all fall short by operating with too undifferentiated conceptions of selfhood.
Local Elections and Ranked Choice
Authors: Endersby James W , Towle Michael J 
Published: January 2025

Abstract
This chapter provides historical context and electoral experience with preferential voting in nonpartisan elections for local governments throughout North America. Early adoptions of ranked choice voting (RCV) and single transferable vote (STV) occurred during the Progressive Era in the early twentieth century in locations ranging from across the US to provinces in western Canada. Emphasis was often placed on proportional representation and the STV. Retrenchment occurred as most communities abandoned preferential voting. A subsequent wave occurred in the late twentieth century and early twenty-first century as local communities adopted, and sometimes repealed, instant runoff voting. A renewal in election policy adoption followed as the system was retitled RCV and the number of municipal governments adopting the system expanded from the Bay Area in California to communities across the United States. Other aligned systems used for local elections include Bucklin voting, the limited vote, Nanson’s rule, cumulative voting, and approval voting.
Model selection and model robustness for population size estimation in two-sample capture-recapture studies
Authors: Dong Gracia Y , McNichol Jennifer , Cowen Laura L E 
Published: January 2025

Abstract
Two-sample capture-recapture studies are commonly used in the epidemiological and ecological literature. Most of these studies have been limited to analysis using the Lincoln-Petersen estimator, especially in epidemiological studies. We examine the use of the Lincoln-Petersen estimator and two alternative closed-population methods: Huggins’ conditional likelihood method and Pledger’s likelihood method with mixtures. We assessed the ability of aic to perform model selection when time, behavioural effects, or heterogeneity are present in capture probabilities. In addition, we examined the effects of model misspecification on estimates of population size. An extensive simulation study found that these models are not robust to misspecification and that aic was not capable of selecting the correct model with two capture occasions.
Decoding Monogenic Hypertension: A Review of Rare Hypertension Disorders
Authors: Gallegos Flora R , Delahunty Meaghan P , Hu Jieji  , Yerigeri Shivani B , Dev Vishnu , Bhatt Girish , Raina Rupesh  
Published: January 2025

Abstract

BACKGROUND
Hypertension is a growing concern worldwide, with increasing prevalence rates in both children and adults. Most cases of hypertension are multifactorial, with various genetic, environmental, socioeconomic, and lifestyle influences. However, monogenic hypertension, a blanket term for a group of rare hypertensive disorders, is caused by single-gene mutations that are typically inherited in an autosomal dominant fashion, and ultimately disrupt normal blood pressure regulation in the kidney or adrenal gland. Being able to recognize and understand the pathophysiology of these rare disorders is critical for properly diagnosing hypertension, particularly in children and young adults, as treating each form of monogenic hypertension requires specific and targeted treatment approaches.

METHODS
A scoping literature review was conducted on the available knowledge regarding each of the disorders currently categorized as forms of monogenic hypertension.

RESULTS
This narrative review serves to highlight the epidemiology, pathophysiology, clinical presentation, recent case reports, and most current methods of evaluation and treatment for familial hyperaldosteronism types 1–4, Gordon syndrome. Liddle syndrome, syndrome of apparent mineralocorticoid excess, congenital adrenal hyperplasia, Geller syndrome, hereditary syndromes related to pheochromocytomas and paragangliomas, and brachydactyly type E.

CONCLUSIONS
Recent and future advances in genetic analysis techniques will further enhance the diagnosis and early management of these disorders, preventing the consequences of uncontrolled hypertension.
Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks
Authors: Cai Lingsheng , Ma Xiuli , Ma Jianzhu 
Published: January 2025

Abstract
Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships. To address these limitations, we introduce single-cell Multi-omics Integration (scMI), a heterogeneous graph embedding method that encodes both cells and modality features from single-cell RNA-seq and ATAC-seq data into a shared latent space by learning cross-modality relationships. By modeling cells and modality features as distinct node types, we design an inter-type attention mechanism to effectively capture long-range cross-modality interactions between genes and peaks. Benchmark results demonstrate that embeddings learned by scMI preserve more biological information and achieve comparable or superior performance in downstream tasks including modality prediction, cell clustering, and gene regulatory network inference compared to methods that rely on databases. Furthermore, scMI significantly improves the alignment and integration of unmatched multi-omics data, enabling more accurate embedding and improved outcomes in downstream tasks.
DD-PRiSM: a deep learning framework for decomposition and prediction of synergistic drug combinations
Authors: Jin Iljung , Lee Songyeon , Schmuhalek Martin , Nam Hojung 
Published: January 2025

Abstract
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy. DD-PRiSM consists of two predictive models. The first is the Monotherapy model, which predicts parameters of the drug response curve based on drug structure and cell line gene expression. This reconstructed curve is then used to predict cell viability at the given drug dosage. The second is the Combination therapy model, which predicts the efficacy of drug combinations by analyzing individual drug effects and their synergistic interactions with a specific dosage level of individual drugs. The efficacy of DD-PRiSM is demonstrated through its performance metrics, achieving a root mean square error of 0.0854, a Pearson correlation coefficient of 0.9063, and an R2 of 0.8209 for unseen pairs. Furthermore, DD-PRiSM distinguishes itself by its capability to decompose combination therapy efficacy, successfully identifying synergistic drug pairs. We demonstrated synergistic responses vary across cancer types and identified hub drugs that trigger synergistic effects. Finally, we suggested a promising drug pair through our case study.