bluuebunny/crossref_metadata_2025_split huggingface dataset:
"And BAM. You Have a Connection”: Blind/Partially Blind Students and the Belonging in Academia Model
Authors: Bulk Laura Yvonne , Jarus Tal , Nimmon Laura 
Published: February 2024

Belonging has significant impacts on success in postsecondary. Blind people are underrepresented in postsecondary and lack equitable opportunities to develop a sense of belonging. To build a better understanding of this underrepresented experience, this study shares narratives of 28 Blind students from across Turtle Island (and what is colonially called Canada) using Teng et al.’s (2020) Belonging in Academia Model (BAM) as a conceptual framework. Thematically analyzed findings suggest that blind students’ perspectives offer nuance to the BAM’s conceptualization of how belonging develops. For blind students, external factors such as class size are especially important in determining affiliation with an institution. Blind students elucidated the importance of familiarity with different ways of being in the world, including blindness. Third, acceptance involved having their blind embraced in postsecondary contexts. Forth, interdependence was key to building trusting connections for blind students. Fifth, blind participants discussed equity at length related to access, the added work of trying to obtain access, and the emotional work involved. This study helps fill a gap regarding the experiences of these traditionally underrepresented postsecondary students in Canada. The BAM may raise the consciousness of stakeholders in considering the unique factors impacting belonging for blind people. By attending to these perspectives, stakeholders can become more responsive to the experiences of people from equity-deserving groups. Understanding facilitators and barriers to belonging could result in culturally safer practices and inclusive pedagogical practices and system policies. Only when we create a space where everyone can belong will higher education be truly inclusive.
CORRELATION OF DIAGNOSTIC RADIOGRAPHERS’ KNOWLEDGE AND AWARENESS OF THEIR EMOTIONAL INTELLIGENCE WITH THEIR SOCIO-DEMOGRAPHIC VARIABLES IN NNEWI METROPOLIS, NIGERIA
Authors: Ogolodom Michael , Mgbeadichie Uchechukwu Blessing , Zira Joseph Dlama , Chiegwu Hyacienth Uche , Egop Egop Brownson , Maduka Beatrice Ukamaka , Ezugwu Emmanuel Emeka 
Published: February 2024

Emotional intelligence (EI) is the ability to understand, use, and manage one’s own emotions in positive ways so as to avoid regrets after. This ability is seriously needed by diagnostic radiographers so as to promote interpersonal relationships in healthcare settings. This study aimed at determining the correlation between emotional intelligence and diagnostic radiographers’ socio-demographic variables. This was a cross-sectional questionnaire-based survey conducted in healthcare facilities in Nnewi metropolis to include 30 diagnostic radiographers. The completed questionnaires were retrieved by the researchers. Both descriptive and inferential statistics were used for statistical analysis with confidence level set at p < 0.05. Out of the 30 participants, 19(63.3%) were males while females accounted for the remaining 11(36.7%). Sixty percent (n=18) of the participants had good knowledge of their emotional intelligence. There was a negative and non-statistically significant correlation between age and level of awareness of the participants’ emotional intelligence (r = -.011, p=.953). There was a positive and non-statistically significant correlation between work experience and emotional intelligence (r = 0.346, p = 0.061) There was weak, positive and statistically significant correlation between work experience and level of awareness of the participants’ emotional feeling and why (r =0.602, p =0.000). Being that a good number of the population of this study had good knowledge of their emotional strength and weaknesses, it will not be very difficult to create an atmosphere where patients and staff enjoy a care environment that is both supportive and beneficial.
A REVIEW OF PREDICTIVE ANALYTICS IN THE EXPLORATION AND MANAGEMENT OF U.S. GEOLOGICAL RESOURCES
Authors: Michael Ayorinde Dada  , Johnson Sunday Oliha  , Michael Tega Majemite  , Alexander Obaigbena  , Preye Winston Biu  
Published: February 2024

In an era where technological advancements are reshaping the landscape of resource management, this paper delves into the transformative role of predictive analytics in the exploration and management of geological resources in the United States. The study's backdrop is set against the burgeoning need for innovative approaches in geological exploration, driven by environmental, economic, and technological imperatives. The primary aim of this comprehensive review is to dissect the multifaceted contributions of predictive analytics in enhancing the efficiency, accuracy and sustainability of geological resource exploration. The scope of the paper encompasses a detailed examination of the integration of predictive analytics in geological exploration, focusing on its economic benefits, environmental sustainability implications and adherence to legal and ethical standards. Methodologically, the study employs a qualitative analysis of existing literature and case studies, juxtaposing traditional exploration methods with modern predictive analytic techniques. It further investigates the economic impact of predictive analytics implementation, the role of predictive models in identifying new geological resources and the strategic implications for sustainable resource management. The findings reveal that predictive analytics significantly enhances risk assessment, resource identification, and environmental management in geological exploration. The economic analysis underscores the cost-effectiveness and efficiency of predictive analytics, while strategic implications highlight the need for an integrated approach to sustainable resource management. Conclusively, the study recommends policy reforms and regulatory frameworks that align with technological advancements to ensure sustainable and prosperous geological resource management. It advocates for embracing advanced tools to navigate the complexities of resource exploration and management, ensuring a balance between economic viability and environmental sustainability.
Keywords: Predictive Analytics, Geological Exploration, Resource Management, Sustainability, Technological Advancements, Policy Reforms.
Characterization of the benthic biogeochemical dynamics after flood events in the Rhône River prodelta: a data–model approach
Authors: Ferreira Eva , Nmor Stanley , Viollier Eric , Lansard Bruno , Bombled Bruno , Regnier Edouard , Monvoisin Gaël , Grenz Christian , van Beek Pieter , Rabouille Christophe 
Published: February 2024

Abstract. At the land–sea interface, the benthic carbon cycle is strongly influenced by the export of terrigenous particulate material across the river–ocean continuum. Episodic flood events delivering massive sedimentary materials can occur, but their short-term impact on carbon cycling is poorly understood. In this paper, we use a coupled data–model approach to estimate the temporal variations in sediment–water fluxes, biogeochemical pathways and their reaction rates during these abrupt phenomena. We studied one episodic depositional event in the vicinity of the Rhône River mouth (NW Mediterranean Sea) during the fall–winter of 2021/22. The distributions of dissolved inorganic carbon (DIC), sulfate (SO42-) and methane (CH4) were measured in sediment porewaters collected every 2 weeks before and after the deposition of a 25 cm sediment layer during the main winter flood event. Significant changes in the distribution of DIC, SO42- and CH4 concentrations were observed in the sediment porewaters. The use of an early diagenetic model (FESDIA) to calculate biogeochemical reaction rates and fluxes revealed that this type of flood event can increase the total organic carbon mineralization rate in the sediment by 75 % a few days after deposition. In this period, sulfate reduction is the main process contributing to the increase in total mineralization relative to non-flood deposition. The model predicts a short-term decrease in the DIC flux out of the sediment from 100 to 55 mmolm-2d-1 after the deposition of the new sediment layer with a longer-term increase by 4 %, therefore implying an initial internal storage of DIC in the newly deposited layer and a slow release over relaxation of the system. Furthermore, examination of the stoichiometric ratios of DIC and SO42- as well as model output over this 5-month window shows a decoupling between the two modes of sulfate reduction following the deposition – organoclastic sulfate reduction (OSR) intensified in the newly deposited layer below the sediment surface, whereas anaerobic oxidation of methane (AOM) intensified at depth below the former buried surface. The bifurcation depth of sulfate reduction pathways, i.e., the sulfate–methane transition zone (SMTZ), is shifted deeper by 25 cm in the sediment column following the flood deposition. Our findings highlight the significance of short-term transient biogeochemical processes at the seafloor and provide new insights into the benthic carbon cycle in the coastal ocean.
Investigating ecosystem connections in the shelf sea environment using complex networks
Authors: Higgs Ieuan , Skákala Jozef , Bannister Ross , Carrassi Alberto , Ciavatta Stefano 
Published: February 2024

Abstract. We use complex network theory to better represent and understand the ecosystem connectivity in a shelf sea environment. The baseline data used for the analysis are obtained from a state-of-the-art coupled marine physics–biogeochemistry model simulating the North West European Shelf (NWES). The complex network built on model outputs is used to identify the functional groups of variables behind the biogeochemistry dynamics, suggesting how to simplify our understanding of the complex web of interactions within the shelf sea ecosystem. We demonstrate that complex networks can also be used to understand spatial ecosystem connectivity, identifying both the (geographically varying) connectivity length-scales and the clusters of spatial locations that are connected. We show that the biogeochemical length-scales vary significantly between variables and are not directly transferable. We also find that the spatial pattern of length-scales is similar across each variable, as long as a specific scaling factor for each variable is taken into account. The clusters indicate geographical regions within which there is a large exchange of information within the ecosystem, while information exchange across the boundaries between these regions is limited. The results of this study describe how information is expected to propagate through the shelf sea ecosystem, and how it can be used in multiple future applications such as stochastic noise modelling, data assimilation, or machine learning.
Leveraging gauge networks and strategic discharge measurements to aid the development of continuous streamflow records
Authors: Vlah Michael J. , Ross Matthew R. V. , Rhea Spencer , Bernhardt Emily S. 
Published: February 2024

Abstract. Quantifying continuous discharge can be difficult, especially for nascent monitoring efforts, due to the challenges of establishing gauging locations, sensor protocols, and installations. Some continuous discharge series generated by the National Ecological Observatory Network (NEON) during its pre- and early-operational phases (2015–present) are marked by anomalies related to sensor drift, gauge movement, and incomplete rating curves. Here, we investigate the potential to estimate continuous discharge when discrete streamflow measurements are available at the site of interest. Using field-measured discharge as truth, we reconstructed continuous discharge for all 27 NEON stream gauges via linear regression on nearby donor gauges and/or prediction from neural networks trained on a large corpus of established gauge data. Reconstructions achieved median efficiencies of 0.83 (Nash–Sutcliffe, or NSE) and 0.81 (Kling–Gupta, or KGE) across all sites and improved KGE at 11 sites versus published data, with linear regression generally outperforming deep learning approaches due to the use of target site data for model fitting rather than evaluation only. Estimates from this analysis inform ∼199 site-months of missing data in the official record, and can be used jointly with NEON data to enhance the descriptive and predictive value of NEON's stream data products. We provide 5 min composite discharge series for each site that combine the best estimates across modeling approaches and NEON's published data. The success of this effort demonstrates the potential to establish “virtual gauges”, sites at which continuous streamflow can be accurately estimated from discrete measurements, by transferring information from nearby donor gauges and/or large collections of training data.
Pionieren tussen straf en zorg
Authors: Oosterhuis Vera , Burger Andreas , van der Wolf Michiel , de Kogel Katy 
Published: February 2024

‘Pioneering between care and punishment’: the consideration and use of article 2.3 of the Dutch Forensic Care Act in case of violent offences
The enactment of article 2.3 of the Dutch Forensic Care Act allows criminal courts to issue a care authorization (CA) for compulsory care within the regular mental health care system. Before the enactment, scholars and professionals already stated their worries in the literature. The security levels of regular mental health care facilities would be insufficient and the treatment would lack forensic focus, while persons who would enter through this new article might suffer from complex disorders and/or exhibit violent behavior.This research therefore focusses on the (severity of) possible violent offences of those for whom a CA was considered. Case law from the first one-and-a-half year since the enactment was analysed quantitatively and qualitatively. Looking at violent offences it appeared that those without a CA were convicted for more severe violent offences than those with one. This was reflected in the number of convictions for (attempted) homicide, while the group with a CA was more often convicted for major physical assault. Furthermore, the use of weapons was important: for those for whom a CA was not issued, weapon use was more often mentioned, especially the use of sharp weapons. In neither one of the groups the use of a gun or another shooting weapon was mentioned. These results are partially in line with the existing literature, since criminal courts seemed less inclined to issue a CA when a severe violent offence was committed. However, a substantial part of the group with a CA committed some sort of violent offence. So, although conviction for a violent offence is not in itself a contra-indication for a CA, the specificities of the offence, like severity and weapon-use, can possibly be considered as such.
Klare taal?
Authors: van den Broek Ellen , Uzieblo Kasia , de Vogel Vivienne 
Published: February 2024

Being clear?: A study on communicating violence risk
Within the legal, judicial and forensic field risk assessments play an important role. The reliability and validity of commonly used risk assessment instruments have been well researched. Relatively little is known however about how the outcomes of risk assessments are understood by decision-makers and whether the way of communicating these outcomes affects subsequent risk estimation and decision-making.This study aims to contribute to increasing knowledge on risk communication for Dutch forensic and judicial practice.A vignette study was conducted in a group of one hundred forensic experts reporting to the court to examine how the way in which violence risks are communicated affects recidivism risk assessment and decision-making. The study also examined how different communication formats were viewed in terms of clarity, usability and probative value. The (combinations of) risk communication formats most commonly used in the Netherlands were compared, namely 1) categorical communication (low - moderate - high), 2) numerical communication (percentages), 3) combined communication (categorical + numerical), and 4) categorical communication with an added descriptive risk scenario.Results. The study showed that the way of communicating the outcome of a risk assessment did influence the estimation of the likelihood of violent reoffending, but did not influence decision-making. Furthermore, it was found that formats in which the outcome was offered in combination were rated more positively than formats in which the outcome was communicated singularly (categorically or numerically). Finally, the study showed that participants tended to deviate from the information provided when asked for a free estimate of the likelihood of recidivism in percentages or in a categorical judgment.Depending on how the outcome of a risk assessment is communicated, there may be differences in interpretation among recipients of these assessments. It is recommended that the implications of this be further studied, as well as that research and practice pay more attention to risk communication.
Digital twins a digital transformation for management of the construction industry
Authors: Alvarenga Cristiane do Bom Conselho Sales , De Aguilar Maria Teresa Paulino , Sales Rosemary do Bom Conselho , Caldas Rodrigo Barreto 
Published: February 2024

In recent years technologies have been transforming working relationships in the construction industry. To dynamize the sector, new ways are sought to obtain better control over its lifecycle. The goals are to achieve cost savings, improvement in the quality of services, greater security and less environmental impact. Important information, to reach such goals, is generated during the project, the construction, the use/upkeep of assets. Nevertheless, this information is not used properly, because it requires participative management for the data to be kept updated and available in real time. A process that allows for this management right from the project-planning phase is Building Information Modeling (BIM). Its association with cloud-based communication platforms allows users to access all the data in real time. Allied to the resources of the Internet of Things (IoT), BIM makes it possible to transfer data to specific software that assist this information to remain alive during the whole lifecycle of the construction. The cloud-based platforms integrated to the BIM and the IoT, make it possible to digitalise and transfer the information related to the construction in real time, to create the Digital Twin (DT). With this DT all this information can be managed on one only digital platform which permits intelligent constructions and operational excellence in the sector. In this article, concepts relating to the creation of a DT are analysed by means of the Life Cycle Assessment (LCA) for buildings. A review of the literature was also carried out on the recent applications of the DT and, especially, in the construction industry. It became clear in the study that in recent years the DT is gradually being implemented in different sectors. Most of the research presents a review of the literature followed by small-scale experiments or case studies. It was found that the concept of DT has attracted the attention of the construction industry, and debate on the theme is developing rapidly in the academic sector, so as to establish the ways of effectively developing it in the construction industry.
Prevalence of canine leptospirosis in Tiruchirappalli, Tamil Nadu
Authors: Lakshmipriya C , Anandhagiri S , Nataerajaseenivasan K 
Published: February 2024

Canine leptospirosis is prevalent worldwide and generally associated with infection by Leptospira interrogans serovar Canicola and Icterohaemorrhagiae. Present study was aimed to determine the incidence and prevalence of canine leptospirosis and also to determine the utility of the 16S rRNA sequence based diagnosis of canine leptospirosis in comparison with the standard microscopic agglutination test (MAT). In total 135 blood samples were collected consisting of both domestic and stray dogs attending the veterinary polyclinic for the treatment of febrile illness. The leptospiral infection was determined by MAT, ELISA and nested PCR analysis. The present investigation showed an overall seroprevalence of 37.7% (51/135) with the predominant serovar to be Autumnalis 9.6% (13/135), followed by Icterohaemorrhagiae 8.14% (11/135), Canicola 8.14% (11/135) and Javanica 6.66% (9/135). Thus the surveillance of canine leptospirosis is an important tool for detecting the risk of exposure in human and it provide an insight about the important prevailing circulating serovars. In the present study 16S rRNA nested PCR for the diagnosis of canine leptospirosis showed a sensitivity 92%, specificity 73%, PPV 68%, NPV 93%, respectively on comparison with MAT which envisaged the detection of canine leptospirosis at the acute phase of the disease.
A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification
Authors: Meenalochini G. , Amutha Guka D. , Sivasakthivel Ramkumar , Rajagopal Manikandan 
Published: February 2024

According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the disease's fatality rate. Early cancer detection raises the chance of survival by up to 8 %. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy