bluuebunny/crossref_metadata_2025_split huggingface dataset:
Statistical modeling for long-term meteorological forecasting: a case study in Van Lake Basin
Authors: Pala Zeydin , Şevgin Fatih 
Published: July 2024

AbstractPredicting environmental variables for a sustainable environment is vital for effective resource management and regional development, especially in sensitive regions such as the Lake Van basin in eastern Türkiye. This study focuses on long-term annual forecasts of important meteorological variables such as mean annual atmospheric pressure, wind speed and surface evaporation in the Van Lake basin. Long-term forecasts made using R-based statistical models such as AUTO.ARIMA, TBATS, EST, NAIVE, THETAF and HOLT-WINTERS are evaluated using mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Here, it has been observed that the AUTO.ARIMA model consistently stands out with better performance than its counterparts in the field of time series analysis when predicting the variables mentioned above. Such scientific studies, which are of great importance especially for the regional structure, add valuable information to the literature by determining a superior prediction model for meteorological events in the specific geographical context of the Lake Van basin. The results of the study have far-reaching implications for further improving predictive modeling techniques, improving the reliability of long-term meteorological forecasts, and decision-making in climate-related research and applications.
An integrated approach to the spatial distribution of the coastal infrastructure vulnerability by using coastal vulnerability index and hot spot analysis: a case study of Kusadasi-Selcuk
Authors: Kuleli Tuncay , Bayazit Seyma 
Published: July 2024

AbstractIn this study, a new methodology integrating spatial coastal vulnerability index (CVI) and hot spot analysis was proposed for a comprehensive and holistic coastal infrastructure (natural, built-up and socioeconomic) vulnerability assessment and determination of its spatial distribution. The Selçuk-Kuşadası coastal region, which is located on the Aegean coast of Turkey, was chosen as the case study area. For calculation spatial CVI, eight physical variables (geomorphology, coastal slope, relief, mean seal level, mean tide range, mean wave high, shoreline erosion and accretion) were used. Spatial CVI analyses were performed using basic methods of geographical information systems. CVI results were classified as 1 to 5 across the study area. The spatial distribution of vulnerabilities of coastal infrastructures has statistically tested with hotspot analysis. In conclusion; over 55% of infrastructure falls within high or very high vulnerability classes. It is seen that 57% of the infrastructure in the build-up category, 59% of the natural environment, and more than 42% of the socio-economic infrastructure are located in areas falling into the high or very high vulnerability classes. Additionally, $1.1 billion worth of land assets and a population of approximately 143.000 people are located in areas that fall into the high or very high vulnerability classes. Statistically significant clusters of high vulnerability for historical sites, tourism, roads, land value, population density, and urban areas were determined by hot spot analysis. According to the results of the developed integrated method, suggestions were presented for the preparation of coastal infrastructure vulnerability adaptation strategies.
GSNR-aware resource re-provisioning for C to C+L-bands upgrade in optical backbone networks
Authors: Kalkunte Ramanuja , Jana Rana Kumar , Ferdousi Sifat , Srivastava Anand , Mitra Abhijit , Tornatore Massimo , Lord Andrew , Mukherjee Biswanath 
Published: July 2024

AbstractEfficient network management in optical backbone networks is essential to manage continuous traffic growth. To accommodate this growth, network operators need to upgrade their infrastructure at appropriate times. Given the cost constraint of upgrading the entire network at once, upgrading the network periodically in multiple batches is a more pragmatic approach to meet the growing demands. While multi-period, batch-upgrade strategies to increase network capacity from the conventional C band to C+L bands have been proposed, they did not consider so far the possibility to re-provision existing traffic. In this work, we investigate how to selectively re-provision connections from C band to L band during a batch upgrade. This is to ensure greater availability of C-band resources which can help to delay network upgrade and hence reduce upgrade cost, while limiting the number of disrupted connections in the network. This study proposes two re-provisioning strategies, namely, Budget-Based (BB) and Margin-Aware (MA) re-provisioning, which rely on the Quality of Transmission (QoT) of lightpaths. These strategies leverage the knowledge of Generalized Signal-to-Noise Ratio (GSNR) to choose which lightpaths to re-provision. We compare these strategies with a baseline distance-based strategy that uses path length to select and re-provision lightpaths. We also incorporate Machine Learning techniques for QoT estimation of lightpaths to reduce the computational time required for optical-path feasibility check. Numerical results show that, compared to distance-based strategy, BB and MA strategies reduce disruption by about 22% and 27%, respectively, in representative network topologies.
Modelling Role of Protective and Nonprotective HLA Allele Inducing Different HIV Infection Outcomes
Authors: Xu Shilian 
Published: July 2024

AbstractHuman immunodeficiency virus (HIV) infects CD4+ cells and causes progressive immune function failure, and CD8+ cells lyse infected CD4+ cell via recognising peptide presented by human leukocyte antigens (HLA). Variations in HLA allele lead to observed different HIV infection outcomes. Within-host HIV dynamics involves virus replication within infected cells and lysing of infected cells by CD8+ cells, but how variations in HLA alleles determine different infection outcomes was far from clear. Here, we used mathematical modelling and parameter inference with a new analysis of published virus inhibition assay data to estimate CD8+ cell lysing efficiency, and found that lysing efficiency fall in the gap between low bound (0.1–0.2 day−1 (Elemans et al. in PLoS Comput Biol 8(2):e1002381, 2012)) and upper boundary (6.5–8.4 day−1 (Wick et al. in J Virol 79(21):13579–13586, 2005)). Our outcomes indicate that both lysing efficiency and viral inoculum size jointly determine observed different infection outcomes. Low lysing rate associated with non-protective HLA alleles leads to monostable viral kinetic to high viral titre and oscillatory viral kinetics. High lysing rate associated with protective HLA alleles leads monostable viral kinetic to low viral titre and bistable viral kinetics; at a specific interval of CD8+ cell counts, small viral inoculum sizes are inhibited but not large viral inoculum sizes remain infectious. Further, with CD8+ cell recruitment, HIV kinetics always exhibit oscillatory kinetics, but lysing rate is negatively correlated with range of CD8+ cell count. Our finding highlights role of HLA allele determining different infection outcomes, thereby providing a potential mechanistic explanation for observed good and bad HIV infection outcomes induced by protective HLA allele.
Needle tracking in low-resolution ultrasound volumes using deep learning
Authors: Grube Sarah , Latus Sarah , Behrendt Finn , Riabova Oleksandra , Neidhardt Maximilian , Schlaefer Alexander 
Published: July 2024

Abstract
Purpose
Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes.

Methods
We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 $$\times $$
×
16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation.

Results
Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning.

Conclusion
Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.
Statins for the Primary Prevention of Anthracycline Cardiotoxicity: A Comprehensive Review
Authors: Bhasin Varun , Vakilpour Azin , Scherrer-Crosbie Marielle 
Published: July 2024

Abstract
Purpose of Review
The aim of this review is two-fold: (1) To examine the mechanisms by which statins may protect from anthracycline-induced cardiotoxicity and (2) To provide a comprehensive overview of the existing clinical literature investigating the role of statins for the primary prevention of anthracycline-induced cardiotoxicity.

Recent Findings
The underlying cardioprotective mechanisms associated with statins have not been fully elucidated. Key mechanisms related to the inhibition of Ras homologous (Rho) GTPases have been proposed.
Data from observational studies has supported the beneficial role of statins for the primary prevention of anthracycline-induced cardiotoxicity. Recently, several randomized controlled trials investigating the role of statins for the primary prevention of anthracycline-induced cardiotoxicity have produced contrasting results.

Summary
Statins have been associated with a lower risk of cardiac dysfunction in cancer patients receiving anthracyclines. Further investigation with larger randomized control trials and longer follow-up periods are needed to better evaluate the long-term role of statin therapy and identify the subgroups who benefit most from statin therapy.
Impact and Interrelationships of Striatal Proteins, EPHB2, OPRM1, and PER2 on Mild Cognitive Impairment
Authors: Campomayor Nicole Bon , Kim Hee Jin , Lee Hyun Jun , Sayson Leandro Val , Ortiz Darlene Mae D. , Cho Eunbi , Kim Dong Hyun , Jeon Se Jin , Kim Bung-Nyun , Cheong Jae Hoon , Kim Mikyung 
Published: July 2024

Abstract
With the global increase in life expectancy, there has been a rise in the incidence of cognitive impairments attributed to diverse etiologies. Notably, approximately 50% of individuals diagnosed with mild cognitive impairment (MCI) progress to dementia within 3 years. However, the precise mechanisms underlying MCI remain elusive. Therefore, this study aimed to elucidate potential mechanisms implicated in MCI utilizing Per2 knockout (KO) mice, which have previously been shown to have cognitive deficits. Behavioral (Y-maze, Barnes maze) and molecular (electrophysiology, RNA sequencing, western blot, and immunofluorescence) experiments were conducted in Per2 KO and wild-type (WT) mice. Per2 KO mice exhibited impaired spatial working memory in the Y-maze and Barnes maze. However, there were no significant group differences in hippocampal long-term potentiation (LTP) between Per2 KO and WT mice, whereas striatal LTP in Per2 KO mice was lower compared to WT mice. In RNA sequencing analysis, 58 genes were downregulated and 64 genes were upregulated in the striatum of Per2 KO mice compared to WT mice. Among the differentially expressed genes, four genes (Chrm2, EphB2, Htr1b, Oprm1) were identified. Optimal expression levels of EPHB2 and OPRM1 were found to significantly enhance cognitive performance in mice. Additionally, Per2 KO mice exhibited reduced EPHB2-NMDAR-LTP and OPRM-mTOR signaling, along with elevated amyloid beta (Aβ) levels, when compared to WT mice. However, these alterations were reversed upon administration of morphine treatment. Striatal OPRM1-mTOR signaling, EPHB2-NMDAR-LTP signaling, and Aβ expression levels may exert a combined effect on MCI under the control of Per2 expression.
The relational correlates of sexual desire and distress in Turkish women: a structural equation model
Authors: Tetik Sinan , Alkar Özden Yalçınkaya 
Published: July 2024

AbstractThe objective of this study is to examine the relationship between relational factors and sexual desire and sexual distress. In order to achieve these goals, a sample of 788 married Turkish women was recruited through social media platforms between June and July 2022. The study employed three instruments to assess the relevant variables: the Sexual Satisfaction Scale for Women to measure sexual distress, the Female Sexual Function Index to assess sexual desire, and the Couple Burnout Measure Short Form to evaluate couple burnout. Structural equation modeling was utilized to analyze the data and test the proposed models. The initial model indicated a negative correlation between marriage length, parenthood, couple burnout, and sexual desire. The employment status of the participants was not found to be a significant factor. The second model revealed that marriage length, parenthood, and sexual desire were negatively correlated with sexual distress, whereas couple burnout was positively associated. In conclusion, our study highlights the intricate interplay between various factors and their impact on sexual desire and distress among married women. Specifically, marriage length, parenthood, and couple burnout were identified as significant determinants of sexual desire and distress. These findings underscore the importance of addressing relationship dynamics, parental responsibilities, and couple well-being in order to promote sexual well-being within committed relationships.
Enhancing Robotic Collaborative Tasks Through Contextual Human Motion Prediction and Intention Inference
Authors: Laplaza Javier , Moreno Francesc , Sanfeliu Alberto 
Published: July 2024

AbstractPredicting human motion based on a sequence of past observations is crucial for various applications in robotics and computer vision. Currently, this problem is typically addressed by training deep learning models using some of the most well-known 3D human motion datasets widely used in the community. However, these datasets generally do not consider how humans behave and move when a robot is nearby, leading to a data distribution different from the real distribution of motion that robots will encounter when collaborating with humans. Additionally, incorporating contextual information related to the interactive task between the human and the robot, as well as information on the human willingness to collaborate with the robot, can improve not only the accuracy of the predicted sequence but also serve as a useful tool for robots to navigate through collaborative tasks successfully. In this research, we propose a deep learning architecture that predicts both 3D human body motion and human intention for collaborative tasks. The model employs a multi-head attention mechanism, taking into account human motion and task context as inputs. The resulting outputs include the predicted motion of the human body and the inferred human intention. We have validated this architecture in two different tasks: collaborative object handover and collaborative grape harvesting. While the architecture remains the same for both tasks, the inputs differ. In the handover task, the architecture considers human motion, robot end effector, and obstacle positions as inputs. Additionally, the model can be conditioned on the desired intention to tailor the output motion accordingly. To assess the performance of the collaborative handover task, we conducted a user study to evaluate human perception of the robot’s sociability, naturalness, security, and comfort. This evaluation was conducted by comparing the robot’s behavior when it utilized the prediction in its planner versus when it did not. Furthermore, we also applied the model to a collaborative grape harvesting task. By integrating human motion prediction and human intention inference, our architecture shows promising results in enhancing the capabilities of robots in collaborative scenarios. The model’s flexibility allows it to handle various tasks with different inputs, making it adaptable to real-world applications.
Two-dimensional Jacobians $$\textrm{det}\hspace{0.56905pt}$$ and $$\textrm{Det}\hspace{0.56905pt}$$ for bounded variation functions and applications
Authors: Briane Marc , Casado-Díaz Juan 
Published: July 2024

Abstract
The paper deals with the comparison in dimension two between the strong Jacobian determinant

$$\textrm{det}\hspace{0.56905pt}$$

det

and the weak (or distributional) Jacobian determinant

$$\textrm{Det}\hspace{0.56905pt}$$

Det

. Restricting ourselves to dimension two, we extend the classical results of Ball and Müller as well as more recent ones to bounded variation vector-valued functions, providing a sufficient condition on a vector-valued U in

$$BV(\Omega )^2$$

B
V

(
Ω
)

2

such that the equality

$$\textrm{det}\hspace{0.56905pt}(\nabla U)=\textrm{Det}\hspace{0.56905pt}(\nabla U)$$

det

(
∇
U
)
=
Det

(
∇
U
)

holds either in the distributional sense on

$$\Omega $$

Ω

, or almost-everywhere in

$$\Omega $$

Ω

when U is in

$$W^{1,1}(\Omega )^2$$

W

1
,
1

(
Ω
)

2

. The key-assumption of the result is the regularity of the Jacobian matrix-valued

$$\nabla U$$

∇
U

along the direction of a given non vanishing vector field

$$b\in C^1(\Omega )^2$$

b
∈

C
1

(
Ω
)

2

, i.e.

$$\nabla U\, b$$

∇
U

b

is assumed either to belong to

$$C^0(\Omega )^2$$

C
0

(
Ω
)

2

with one of its coordinates in

$$C^1(\Omega )$$

C
1

(
Ω
)

, or to belong to

$$C^1(\Omega )^2$$

C
1

(
Ω
)

2

. Two examples illustrate this new notion of two-dimensional distributional determinant. Finally, we prove the lower semicontinuity of a polyconvex energy defined for vector-valued functions U in

$$BV(\Omega )^2$$

B
V

(
Ω
)

2

, assuming that the vector field b and one of the coordinates of

$$\nabla U\, b$$

∇
U

b

lie in a compact set of regular vector-valued functions.
Assessing climate-induced risks to urban railway infrastructure
Authors: Garmabaki A. H. S. , Naseri Masoud , Odelius Johan , Famurewa Stephen , Asplund Matthias , Strandberg Gustav 
Published: July 2024

AbstractClimate change and its severe impacts pose a number of challenges to transport infrastructure, particularly railway infrastructure, requiring immediate action. A railway system is a linear distributed asset passing different geographical locations and exposed to heterogeneous vulnerabilities under diverse environmental conditions. Furthermore, most of the railway infrastructure assets were designed and built without in-depth analysis of future climate impacts. This paper considers the effects of extreme temperatures on urban railway infrastructure assets, including rail, “switches and crossings”. The data for this study were gathered by exploring various railway infrastructure and meteorological databases over 19 years. In addition, a comprehensive nationwide questionnaire survey of Swedish railway infrastructure, railway maintenance companies, and municipalities has been conducted to assess the risks posed by climate change. A risk and vulnerability assessment framework for railway infrastructure assets is developed. The study shows that track buckling and vegetation fires due to the effect of hot temperatures and rail defects and breakage due to the effect of cold temperatures pose a medium risk. On the other hand, supportability losses due to cold temperatures are classified as high risk. The impact analysis helps infrastructure managers systematically identify and prioritize climate risks and develop appropriate climate adaptation measures and actions to cope with future climate change impacts.
Supporting mid-career students’ psychological needs to improve motivation and retention in post-graduate courses
Authors: Kirk Gillian , Adam Helen , Boylan Fiona , Sanbrook Carly 
Published: July 2024

AbstractCurrently Australia is experiencing an unprecedented teacher shortage. Increasing and retaining the number of mid-career Initial Teacher Education entrants has been identified as one strategy to combat the shortage. This study examines the psychological needs (autonomy, competence, and relatedness) innate to autonomous forms of motivation to support retention. While there is much research investigating how universities support students’ psychological needs using questionnaires, few have gathered data examining how students articulate their lived experiences, and fewer have examined mid-career students’ perspectives. This paper contributes to the literature by sharing 26 Master of Teaching (Early Childhood, Primary, Secondary) mid-career student perspectives on how a university promoted or hindered their psychological needs. The data, analysed using Critical Discourse Analysis, highlighted moments that students identified as maximising or minimising their autonomy and motivation. Similarities were found with previous research on supporting students’ psychological needs. Additional findings indicated mid-career students desired more control over the pace of their course, and workload issues in some schools during professional experience eroded their teacher-efficacy. Recommendations on how to support mid-career retention are suggested.
Teaching and learning about race, culture, and environment in a predominately white institution
Authors: Strong A. M. , Vea M. C. , Ginger C. , Blouin M. R. , Edling L. , Barrios-Garcia M. N. , McDonald M. J. , Ispa-Landa Z. 
Published: July 2024

AbstractThe environment and natural resource fields have traditionally centered western science, the scholarship of white men, and land conservation strategies that neglect historical inhabitants. These tenets have led to a narrow view of how conservation is defined and created challenges for BIPOC students and professionals to see themselves as full and equal participants in the environmental sciences. The Rubenstein School of Environment and Natural Resources has worked to address these shortcomings through courses designed to address issues of systemic racism and exclusion in the environmental field. In our student’s first year, we pair a fall course focused on communication skills with a spring course that addresses issues of racism and social justice in the environmental fields. We use the fall semester to create a learning community where students build relationships of trust, mutual regard, and care and develop a deeper understanding of their relationship with the environment. In the spring, we present students with a variety of frameworks to think critically about equity, inclusion, positionality, privilege, racism, and diversity. A key learning outcome is to help students consider how historical and present-day dynamics of race and racism have shaped the environmental field. Importantly, we focus on the voices and messages of environmental leaders who have historically been left out of popular environmental narratives. We outline lessons learned in the integration of diversity, equity, and inclusion into our environment and natural resources curriculum and ways to further enhance our centering of equity and inclusion in the curriculum.