bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic population structure of introduced and native lineages of kokanee in a large impounded watershed
Authors: Breault Paige W. , Wetklo Mike , Langston Arne R. , Zemlak Randy J. , Withler Ruth E. , Shrimpton J. Mark 
Published: September 2023

Abstract

Objective
The kokanee Oncorhynchus nerka (nonanadromous life-history form of Sockeye Salmon) uses lacustrine habitat in watersheds draining into the north Pacific Ocean. Kokanee also have been widely introduced into reservoirs following impoundment of rivers consequent to the construction of hydroelectric dams. Genetically divergent subpopulations of kokanee should be identified and evaluated when implementing watershed-level fishery management strategies.

Methods
We analyzed the genetic population structure of kokanee in the Williston watershed, north-central British Columbia, where native populations were present in the reservoir and headwater lakes prior to stocking Columbia River-origin fish in the 1990s using microsatellite markers.

Result
We determined that native Williston Reservoir kokanee were divergent from only one of the headwater lake populations. Native populations in headwater lakes remain entirely separate from the reservoir populations, and there was no indication of past or current introgression with the introduced stock. We identified all fish collected from 2006 to 2019 as introduced Columbia River-origin genotypes, and there was no evidence of genetic divergence by spawning location.

Conclusion
As native Williston kokanee have not been sampled from the reservoir in survey efforts since 2000, it is likely that this population has been extirpated from the reservoir perhaps through competition with the introduced Columbia River-origin lineage.
Nuclear power and environmental injustice
Authors: Höffken Johanna , Ramana M. V. 
Published: September 2023

AbstractPolicy makers around the world have been advocating for an expansion of nuclear energy as a way to mitigate climate change, putting in place financial and political incentives for building new reactors and associated facilities. At the same time, policy makers have also been emphasizing the importance of incorporating justice considerations while decarbonizing. The two are not compatible because of the environmental injustices inflicted by the chain of processes required to generate electricity at nuclear power plants. These injustices are a result of the radioactive nature of the waste materials produced at each step of the nuclear fuel chain. Some of these materials remain hazardous for tens of thousands of years. In addition, nuclear facilities face the ever present risk of catastrophic accidents which can contaminate large tracts of land, rendering them uninhabitable for decades if not centuries. These consequences disproportionately fall on Indigenous Peoples and other disempowered communities, as well as non‐human entities. Such impacts are overlooked in our current socio‐political system committed to growth and a techno‐economic approach to dealing with any challenges to its continued existence.This article is categorized under:
Human and Social Dimensions > Energy and Climate Justice
Energy and Power Systems > Energy Infrastructure
BioPBS™ (Polybutylene Succinate)
Authors: Kato Satoshi , Ueda Tadashi , Aoshima Takayuki , Kosaka Naoyuki , Nitta Shigeki 
Published: September 2023

AbstractPBS (polybutylene succinate) is a biodegradable polymer that is spontaneously degraded into water and carbon dioxide due to the power of microorganisms under soil in the natural world. PBS has high heat resistance and good mechanical properties among general biodegradable polymers, is moldable into the molded articles such as films, sheets, and fibers, and has a characteristic feature such that compatibility with other biodegradable polymers is high.Mitsubishi Chemical Corporation (MCC) has successfully achieved commercialization of PBS without using a chain extender in a trademark name of “GS Pla™” and commenced the market development since 2003. “GS Pla™” is completely a biodegradable polymer made of fossil-based succinic acid and 1,4-butanediol as the main raw materials.Since 2017, PTTMCC Biochem, a joint venture established by Mitsubishi Chemical and PTT Global Chemical Public Company Limited in Thailand, has performed continuous commercial production of bio-based PBS (trademark: BioPBS™) made of bio-based succinic acid and fossil-based 1,4-butanediol.Mitsubishi Chemical has also been developing, manufacturing, and selling FORZEAS™ that is a compounding material giving new functions to BioPBS™ utilizing the excellent compatibility with various biodegradable materials and biodegradability of BioPBS™. In this article, basic physical properties, biodegradability, moldability, certification acquisition, and characteristic features of BioPBS™ and FORZEAS™ were reviewed.
Development of the National Gravimetric Geoid Model for the Kingdom of Saudi Arabia
Authors: Vergos Georgios S. , Grebenitcharsky Rossen S. , Al-Qahtani Abdullah , Al-Shahrani Sultan , Natsiopoulos Dimitrios A. , Al-Jubreen Suliman , Tziavos Ilias N. , Golubinka Juri 
Published: September 2023

AbstractThe development of a high-resolution and high accuracy geoid model is becoming nowadays a fundamental component of any modern geodetic infrastructure. The Kingdom of Saudi Arabia (KSA) has devoted the last decade a significant number of resources and manpower to collect high-quality land and airborne gravity data as well as GNSS/Levelling observations to create a state-of-the-art geoid model as a fundamental part of the Saudi Arabia National Spatial Reference System (SANSRS). In that frame, this work focuses on the collected gravity, terrain, and GNSS/Levelling data for the area under study, and their pre-processing in terms of horizontal, vertical and gravity reference system homogenization, blunder detection and removal. Given the availability of these data the latest gravimetric geoid model for the KSA is developed.The gravity data pre-processing relied on the available metadata to collect information about the horizontal, vertical and gravity reference system. Hence, all this information has been homogenized to KSA-GRF17, tied to ITRF2014 at epoch 2017.0, and KSA-VRF14 which is tied to the geopotential number above the MSL of the Jeddah TGBM-B. Given that several data holdings of land gravity where either in the form of Bouguer anomalies or referred to some unknown horizontal datum, several tests have been carried out to identify the proper choices. Then, a least-squares collocation-based blunder detection and removal procedure has been conducted to identify blunders in the land data and possible biases between the various campaigns and the high-quality airborne gravity observations. The geoid prediction was carried out by the well-known remove-compute-restore technique evaluating Stokes’ integral in the frequency domain via a 2D spherical Fast Fourier Transform and the Wang-Gore modification. After several tests with the latest GOCE/GRACE-based and combined Global Geopotential Models, XGM2019e has been used as a reference, while the residual terrain model correction was employed for the treatment of the topography. The validation of such a developed gravimetric geoid model has been performed for a set of ~4,500 GNSS/Leveling benchmarks reaching external absolute accuracies at the 10–11 cm level and relative accuracies at the 1–5 ppm over distances ranging from 10 to 2,000 km.
Investigating Transparency Dimensions for Internet Voting
Authors: Agbesi Samuel , Budurushi Jurlind , Dalela Asmita , Kulyk Oksana 
Published: September 2023

AbstractWhile Internet voting is argued to have the potential to improve election processes, concerns about security risks remain one of its main adoption barriers. These concerns are furthermore aggravated by the lack oftransparencyof Internet voting systems that are often perceived as a “black box”. Moreover, there is a research gap in conceptualizing the idea of transparency and in studying voters’ attitudes towards transparency in Internet voting. In this work, we aim to address this gap by (1) Conducting a systematic literature review, from which we identified five dimensions of transparency; (2) Developing a questionnaire (Transparency Dimensions of Internet Voting, TDIV) to assess voters’ attitudes regarding the correlation of these dimensions with transparency; and (3) Conducting an online study (N = 500) to investigate voters’ attitudes towards transparency in Internet voting. We conclude that providing information about the security of the Internet voting system; testing it by independent experts for security vulnerabilities prior to the election; monitoring the election process and verifying its integrity; and providing a remedy for security breaches while holding the responsible parties accountable, are perceived by voters as important, and enhance transparency in Internet voting systems.
Verifiability Experiences in Ontario’s 2022 Online Elections
Authors: Goodman Nicole , Spycher-Krivonosova Iuliia , Essex Aleksander , Brunet James 
Published: September 2023

AbstractDespite being one of the biggest international users of online voting with two decades of use, Canada has tended to use non-verifiable online voting systems. This has prompted concern about the verification of election results and potential impacts on public and administrator confidence in elections and democracy. In the 2022 Ontario municipal elections, however, about 9% of municipalities offered the option of individual verifiability to online voters. This article draws upon the experiences of two local governments of different sizes, resources, capacity, and online voting histories - Ignace and Markham - and their vendors to understand the considerations and challenges that come with the introduction of verifiability mechanisms in local elections. We identify deterrents to implementation and possible solutions to see an increase in uptake and improve the integrity of local elections.
Estimating Carbon Footprint of Paper and Internet Voting
Authors: Willemson Jan , Krips Kristjan 
Published: September 2023

AbstractThis paper compares the carbon footprint of paper voting in polling stations with the emissions of remote vote casting via the Internet. We identify the process steps with the most significant emissions in terms of $$\textrm{CO}_2$$

CO
2

equivalent, design a methodology to quantify these emissions and give a comparative analysis based on the example of the Estonian parliamentary elections of 2023. Our results show that paper voting has about 180 times higher carbon footprint, owing largely to the need to transport the voters to the polling stations and back.
A Graph Neural Network Approach for Evaluating Correctness of Groups of Duplicates
Authors: De Bonis Michele , Minutella Filippo , Falchi Fabrizio , Manghi Paolo 
Published: September 2023

AbstractUnlabeled entity deduplication is a relevant task already studied in the recent literature. Most methods can be traced back to the following workflow: entity blocking phase, in-block pairwise comparisons between entities to draw similarity relations, closure of the resulting meshes to create groups of duplicate entities, and merging group entities to remove disambiguation. Such methods are effective but still not good enough whenever a very low false positive rate is required. In this paper, we present an approach for evaluating the correctness of “groups of duplicates”, which can be used to measure the group’s accuracy hence its likelihood of false-positiveness. Our novel approach is based on a Graph Neural Network that exploits and combines the concept of Graph Attention and Long Short Term Memory (LSTM). The accuracy of the proposed approach is verified in the context of Author Name Disambiguation applied to a curated dataset obtained as a subset of the OpenAIRE Graph that includes PubMed publications with at least one ORCID identifier.
Detecting the Pre-impact of Falls in the Elderly, Along with the Use of an Airbag Belt for Protection Against Femoral Neck Fractures
Authors: Seddar Mohand O. , Rao Guillaume , Fleury Anthony , Kahn Maurice 
Published: September 2023

AbstractFalls are a significant health risk for older adults, and fall-related injuries are a leading cause of morbidity and mortality in this population. Elderlies are particularly vulnerable to falls due to age-related declines in mobility, balance, and muscle strength, as well as chronic medical conditions with use of certain medications. These injuries can range from minor bruises and scrapes to more severe like fractures, head trauma, or internal bleeding. To prevent falls in older adults, some solutions propose to ensure a safe living environment, others to maintain physical activity, and others to manage chronic medical conditions. This article presents the implementation and test of a system preventing hip fractures resulting from falls using a fall detection and prediction system designed to protect and alert individuals during falls.
Towards a Medical Waste Classification System Based on Blockchain, Smart Contracts, and NFT Technologies
Authors: Nguyen Hien Q. , Trong Nguyen D. P. , Huong Luong H. , Dang Khoa T. , Gia Khiem H. , Trong Phuc N. , Van Hieu L. , Phu Loc V. C. , Quoc Duy N. T. , Tran Nguyen H. , The Anh N. , Nghia Huynh T. , Khanh Bang L. , Tuan Kiet L. , Kim Ngan N. T. , Tran Bao Q. , Doan Hieu M. , Vo Hong K. 
Published: September 2023

AbstractThe end of the covid-19 epidemic has revealed many weaknesses in the health system and the medical waste treatment process. In particular, the ineffective treatment of medical waste has also contributed to the explosion in the number of infections in some countries (i.e., India, Brazil, and Vietnam). Several studies have found that even developed countries (i.e., with better infrastructure and health services than the world average) have to face emergencies during this time. epidemic. Therefore, the amount of medical waste dumped into the environment is extremely terrible. The waste generated suddenly during this period includes protective gear, masks, and vaccines that burden the waste treatment process. There are several approaches to exploiting Blockchain technologies to solve the problem of direct contact between the stages: medical staff - transportation staff - waste disposal staff to minimize unintended spread. However, to thoroughly solve the current waste classification and treatment processes, a more reward/punishment solution is needed. Specifically, we propose a model to assess the compliance/violation level of waste sorting and treatment in medical centers and isolation areas based on current popular technologies: blockchain, smart contracts, and NFTs.
Enhancing the Measurement of the Range of Motion Using Multi-camera Learning Approaches
Authors: Noh Giseop , Ahn Juyoung , Jeoung Bogja 
Published: September 2023

AbstractMeasuring the range of motion (ROM) is one of the important tasks in medical or healthcare sectors. However, person-to-person measurement is time-consuming and requires resources. In this paper, we propose an approach to estimate ROM using machine learning algorithm equipped with computer vision technology based on data-driven experiments. We describe the setup to gather experimental dataset to learn the angle of human joints in 2D space. From the extensive experiments and multi-linear regression learning approach, our method can reduce estimation error by 11.1% in average. We draw conclusions that machine learning-based, data-driven approaches could predict ROM better than using vision camera only.
Towards a Personalized Business Services Recommendation System Dedicated to Preventing Frailty in Elderly People
Authors: Frikha Ghassen , Lorca Xavier , Pingaud Hervé , Bortolaso Christophe , Borgiel Katarzyna , Lamine Elyes 
Published: September 2023

AbstractFrailty is a clinical syndrome associated with ageing that characterizes an intermediate state between robust health and loss of autonomy. To preserve the abilities of older adults and prevent dependency, it is important to identify and evaluate their frailty. This approach is part of a dependency prevention strategy, based on a thorough understanding of their medical, social, and living environment. This understanding is usually acquired through significant data collection using standardized evaluation surveys. The obtained data is then analyzed to provide personalized recommendations for the beneficiaries’ lifestyles. Our article presents the concept of frailty and a personalized recommendation system aimed at helping citizens prevent frailty. This system uses an innovative self-assessment approach designed for older adults, without necessarily involving healthcare professionals.
Situation-Aware Framework for Energy Saving in Unattended Activities of Daily Living in Smart Home Environments
Authors: Oyeleke Richard O. , Parekh Pranav , DiCuffa Sophia , Quinlan Andrew 
Published: September 2023

AbstractActivities of daily living (ADLs) are basic self-care tasks that are necessary for independent living. It is also used to assess aging adults’ functional ability. As people age, they experience impaired awareness, which affects their reasoning, cognitive ability, and consequently impedes their ability to manage or supervise ADLs successfully. Impaired awareness has a potential economic impact on aging adults. For example, recent studies have shown that aging adults with impaired awareness use more energy in homes due to deviation from normal execution of ADLs. This often results in incompletely executed ADLs which are left unattended or unsupervised. Unsupervised ADLs involving appliances with high wattage ratings (e.g., TV, air conditioner) can lead to an increase in the cost of home energy services used by aging adults who desire to age-in-place. Thus, in this paper, we propose a situation-aware framework that leverages a smart home resident's context information with respect to unattended ADLs in mitigating increased energy consumptions in smart home environments.
Drug Intervention Follow up with Internet of Things: A Case Study
Authors: Ahmed Hassan M. , Maraoui Souhail , Abdulrazak Bessam , Cossette Benoît , Blanchet F. Guillaume 
Published: September 2023

AbstractAdvancements on the Internet of Things (IoT) have enabled the development of advanced monitoring systems that can track human behavior and vital signs in real-time, which can have a real impact in the way healthcare is provided. This paper presents a system implementation to monitor and analyze a subject's behavior changes over time using IoT, with the objective of detecting the impact of an inhibitor drug on the subject's activity levels. In this research we present a case study by which we showed it is possible to follow the effect of an anticholinergic drug by means of an unobtrusive IoT system. We have monitored the physical activity of a subject in his residence for seven consecutive months to study the effect of the inhibiting drug doses introduced at three known specific timestamps. Following, we compared our detection results for the subject’s physical activity change timestamps with the medical staff medication doses timestamps. Our results show that we can detect the physical activity change at close timestamps compared to those indicated by the medical staff.
Replay to Remember: Continual Layer-Specific Fine-Tuning for German Speech Recognition
Authors: Pekarek Rosin Theresa , Wermter Stefan 
Published: September 2023

AbstractWhile Automatic Speech Recognition (ASR) models have shown significant advances with the introduction of unsupervised or self-supervised training techniques, these improvements are still only limited to a subsection of languages and speakers. Transfer learning enables the adaptation of large-scale multilingual models to not only low-resource languages but also to more specific speaker groups. However, fine-tuning on data from new domains is usually accompanied by a decrease in performance on the original domain. Therefore, in our experiments, we examine how well the performance of large-scale ASR models can be approximated for smaller domains, with our own dataset of German Senior Voice Commands (SVC-de), and how much of the general speech recognition performance can be preserved by selectively freezing parts of the model during training. To further increase the robustness of the ASR model to vocabulary and speakers outside of the fine-tuned domain, we apply Experience Replay [20] for continual learning. By adding only a fraction of data from the original domain, we are able to reach Word-Error-Rates (WERs) below 5% on the new domain, while stabilizing performance for general speech recognition at acceptable WERs.
CycleIK: Neuro-inspired Inverse Kinematics
Authors: Habekost Jan-Gerrit , Strahl Erik , Allgeuer Philipp , Kerzel Matthias , Wermter Stefan 
Published: September 2023

AbstractThe paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task—a Generative Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These methods can be used in a standalone fashion, but we also show how embedding these into a hybrid neuro-genetic IK pipeline allows for further optimization via sequential least-squares programming (SLSQP) or a genetic algorithm (GA). The models are trained and tested on dense datasets that were collected from random robot configurations of the new Neuro-Inspired COLlaborator (NICOL), a semi-humanoid robot with two redundant 8-DoF manipulators. We utilize the weighted multi-objective function from the state-of-the-art BioIK method to support the training process and our hybrid neuro-genetic architecture. We show that the neural models can compete with state-of-the-art IK approaches, which allows for deployment directly to robotic hardware. Additionally, it is shown that the incorporation of the genetic algorithm improves the precision while simultaneously reducing the overall runtime.
Neural Field Conditioning Strategies for 2D Semantic Segmentation
Authors: Gromniak Martin , Magg Sven , Wermter Stefan 
Published: September 2023

AbstractNeural fields are neural networks which map coordinates to a desired signal. When a neural field should jointly model multiple signals, and not memorize only one, it needs to be conditioned on a latent code which describes the signal at hand. Despite being an important aspect, there has been little research on conditioning strategies for neural fields. In this work, we explore the use of neural fields as decoders for 2D semantic segmentation. For this task, we compare three conditioning methods, simple concatenation of the latent code, Feature-wise Linear Modulation (FiLM), and Cross-Attention, in conjunction with latent codes which either describe the full image or only a local region of the image. Our results show a considerable difference in performance between the examined conditioning strategies. Furthermore, we show that conditioning via Cross-Attention achieves the best results and is competitive with a CNN-based decoder for semantic segmentation.