bluuebunny/crossref_metadata_2025 huggingface dataset:
Promoting diagnostic reasoning in teacher education: the role of case format and perceived authenticity
Authors: Bichler Sarah , Sailer Michael , Bauer Elisabeth , Kiesewetter Jan , Härtl Hanna , Fischer Martin R. , Fischer Frank
Published: July 2024

Teachers routinely observe and interpret student behavior to make judgements about whether and how to support their students’ learning. Simulated cases can help pre-service teachers to gain this skill of diagnostic reasoning. With 118 pre-service teachers, we tested whether participants rate simulated cases presented in a serial-cue case format as more authentic and become more involved with the materials compared to cases presented in a whole case format. We further investigated whether participants with varying prior conceptual knowledge (what are symptoms of ADHD and dyslexia) gain more strategic knowledge (how to detect ADHD and dyslexia) with a serial-cue versus whole case format. We found that the case format did not impact authenticity ratings but that learners reported higher involvement in the serial-cue case format condition. Bayes factors provide moderate evidence for the absence of a case format effect on strategic knowledge and strong evidence for the absence of an interaction of case format and prior knowledge. We recommend using serial-cue case formats in simulations as they are a more authentic representation of the diagnostic reasoning process and cognitively involve learners. We call for replications to gather more evidence for the impact of case format on knowledge acquisition. We suggest a further inquiry into the relationship of case format, involvement, and authenticity but think that a productive way forward for designing authentic simulations is attention to aspects that make serial-cue cases effective for diverse learners. For example, adaptive feedback or targeted practice of specific parts of diagnostic reasoning such as weighing evidence.
An Extended Neck Position is Likely to Produce Cervical Spine Injuries Through Buckling in Accidental Head-First Impacts During Rugby Tackling
Authors: Silvestros Pavlos , Quarrington Ryan D. , Preatoni Ezio , Gill Harinderjit S. , Jones Claire F. , Cazzola Dario
Published: July 2024

Catastrophic cervical spine injuries in rugby often occur during tackling. The underlying mechanisms leading to these injuries remain unclear, with neck hyperflexion and buckling both proposed as the causative factor in the injury prevention literature. The aim of this study was to evaluate the effect of pre-impact head–neck posture on intervertebral neck loads and motions during a head-first rugby tackle. Using a validated, subject-specific musculoskeletal model of a rugby player, and computer simulations driven by in vivo and in vitro data, we examined the dynamic response of the cervical spine under such impact conditions. The simulations demonstrated that the initial head–neck sagittal-plane posture affected intervertebral loads and kinematics, with an extended neck resulting in buckling and supraphysiologic intervertebral shear and flexion loads and motions, typical of bilateral facet dislocation injuries. In contrast, an initially flexed neck increased axial compression forces and flexion angles without exceeding intervertebral physiological limits. These findings provide objective evidence that can inform injury prevention strategies or rugby law changes to improve the safety of the game of rugby.
Predicting the Average Charring Rate of Mass Timber Using Data-Driven Methods for Structural Calculations
Authors: Amin Rikesh , Mo Yaxin , Richter Franz , Kurzer Christoph , Werther Norman , Rein Guillermo
Published: July 2024

Engineered timber is increasingly in demand for tall buildings due to its positive impact on building sustainability. However, quick adoption raises fire engineering questions regarding flammability and structural performance. Understanding the behaviour of timber in fire is crucial, particularly for structural calculations of tall buildings. The charring rate of timber plays a significant role in its structural performance because the loss of cross section reduces the load bearing capacity of the element. Eurocode-5 (EC5) provides a simple method to calculate the charring rate and it is widely adopted for design in many countries while more complex physics-based models exist but are rarely used for design. For example, we want to know when EC5 underpredicts or overpredicts and by how much. This paper compares different data-driven methods, including statistical and artificial intelligence algorithms, for predicting the average charring rate of timber in fire. A new database of charring rates, VAQT, was created comprised of 231 furnace tests of timber products found in the scientific and technical literature. Statistical methods such as ridge regression (λ = 0.001) predict the charring rate with a minimum 11% error whilst EC5 predicts with 27% error. A trained neural network predicts the charring rate with minimum 9% error. This paper presents a novel database of timber charring experiments and provides a set of data-driven predictive models, all of which calculate the average charring rate with a significantly higher accuracy than EC5 for a wide range of mass timber products.
Educator Knowledge of Childhood Conduct Problems and Callous-Unemotional Traits
Authors: Fleming Georgette E. , Boulton Antonia L. , Prasad Ashneeta H. , Kershaw Kelly A. , Kimonis Eva R.
Published: July 2024

Research evaluating mental health literacy (MHL) of adults who support children with mental health difficulties is relatively scarce. To date, no studies have investigated educator knowledge of conduct problems and callous-unemotional (CU) traits. This is a significant gap in the literature since conduct problems are among the most prevalent childhood mental disorders, while CU traits are associated with poor academic, behavioral, and social outcomes in school settings. In the current study, we assessed educators’ knowledge of the characteristics and management of conduct problems and CU traits. Participants were N = 390 preschool and primary/elementary school educators (Mage = 38.62 years, SD = 11.66; 91% woman-identifying; 71% White) who completed a Knowledge Test and survey assessing educator characteristics and various student-educator outcomes. Averaged across items, educators scored 57.1% on the Knowledge Test. We identified gaps in educator knowledge with respect to identifying characteristics associated with distinct domains of externalizing difficulties and evidence-based management strategies. Educators’ years of experience and accreditation status were not associated with knowledge. Paraeducators had significantly lower knowledge scores than teachers and leadership. Unexpectedly, greater knowledge was not associated with better student-teacher relationship quality or more positive perceptions of students with conduct problems. Findings support the need for universal MHL programs focused on conduct problems and CU traits, especially among paraeducators, while also suggesting that more intensive interventions may be required to improve educator-student relationship quality.
Multi-channel anomaly detection using graphical models
Authors: Namoano Bernadin , Latsou Christina , Erkoyuncu John Ahmet
Published: July 2024

Anomaly detection in multivariate time-series data is critical for monitoring asset conditions, enabling prompt fault detection and diagnosis to mitigate damage, reduce downtime and enhance safety. Existing literature predominately emphasises temporal dependencies in single-channel data, often overlooking interrelations between features in multivariate time-series data and across multiple channels. This paper introduces G-BOCPD, a novel graphical model-based annotation method designed to automatically detect anomalies in multi-channel multivariate time-series data. To address internal and external dependencies, G-BOCPD proposes a hybridisation of the graphical lasso and expectation maximisation algorithms. This approach detects anomalies in multi-channel multivariate time-series by identifying segments with diverse behaviours and patterns, which are then annotated to highlight variations. The method alternates between estimating the concentration matrix, which represents dependencies between variables, using the graphical lasso algorithm, and annotating segments through a minimal path clustering method for a comprehensive understanding of variations. To demonstrate its effectiveness, G-BOCPD is applied to multichannel time-series obtained from: (i) Diesel Multiple Unit train engines exhibiting faulty behaviours; and (ii) a group of train doors at various degradation stages. Empirical evidence highlights G-BOCPD's superior performance compared to previous approaches in terms of precision, recall and F1-score.
Religious Belief Among Women in Australia: Characteristics and Role in Influencing Children’s Health-Related Quality of Life and Lifestyle
Authors: Bahrampour Abbas , Scuffham Paul , Cross Megan , Ng Shu-Kay
Published: July 2024

Religiosity can be an important factor in women’s health-related behaviour, attitudes, and decision-making. Evidence however, regarding the religiosity of mothers and its influence on child health, is scarce. Based on a large population-based cohort in Australia, we aim to examine the religiosity of women in Australia and the association of maternal religiosity with children’s health-related quality of life (HRQOL) and lifestyle. Our findings indicate that (1) maternal religious involvement was higher for women with higher education levels, ascertained religious values in decision-making, and abstinence from binge drinking in the household, (2) maternal religiosity positively influenced their children’s HRQOL, (3) children of mothers who were more religious had less worries or fewer school-work problems, but the children of mothers with stronger religious beliefs used more internet/computer during the week but had less time playing games on weekends. This study provides additional specificity to inform future health interventions in religious community contexts to enhance the positive influence of maternal religious belief for better development of their children.
Counting meromorphic differentials on $${\mathbb {C}\mathbb {P}}^1$$
Authors: Buryak Alexandr , Rossi Paolo
Published: July 2024

We give explicit formulas for the number of meromorphic differentials on $$\mathbb{C}\mathbb{P}^1$$

C

P

1

with two zeros and any number of residueless poles and for the number of meromorphic differentials on $$\mathbb{C}\mathbb{P}^1$$

C

P

1

with one zero, two poles with unconstrained residue and any number of residueless poles, in terms of the orders of their zeros and poles. These are the only two finite families of differentials on $$\mathbb{C}\mathbb{P}^1$$

C

P

1

with vanishing residue conditions at a subset of poles, up to the action of $$\textrm{PGL}(2,\mathbb {C})$$

PGL
(
2
,
C
)

. The first family of numbers is related to triple Hurwitz numbers by simple integration and we show its connection with the representation theory of $$\textrm{SL}_2(\mathbb {C})$$

SL
2

(
C
)

and the equations of the dispersionless KP hierarchy. The second family has a very simple generating series, and we recover it through surprisingly involved computations using intersection theory of moduli spaces of curves and differentials.
Enhancing flood wave modelling of reservoir failure: a comparative study of structure-from-motion based 2D and 3D methodologies
Authors: Lee Jong-hyuk , Lee Sang-ik , Jeong Youngjoon , Seo Byung-hun , Kim Dong-su , Seo Ye-jin , Her Younggu , Choi Won
Published: July 2024

Predicting flood wave propagation from reservoir failures is critical to practical flood hazard assessment and risk management. Flood waves are sensitive to topography, channel geometry, structures, and natural features along floodplain paths. Thus, the accuracy of flood wave modelling depends on how precisely those features are represented. This study introduces an enhancing approach to flood wave modelling by accurately representing three-dimensional objects in floodplains using the structure-from-motion (SfM). This method uses an unmanned aerial vehicle to capture topographic complexities and account for ground objects that impact flood propagation. Using the three-dimensional volume of fluid numerical approach significantly improves an enhanced representation of turbulent flow dynamics and computational efficiency, especially in handling large topography datasets. Reproductions from this enhanced three-dimensional approach were validated against recent reservoir failure observations and contrasted with traditional two-dimensional models. The results revealed that the suggested three-dimensional methodology achieved a significant 84.4% reproducibility when juxtaposed with actual inundation traces. It was 35.5%p more accurate than the two-dimensional diffusion wave equation (DWE) and 17.1%p more than the shallow water equation (SWE) methods in predicting flood waves. This suggests that the reproducibility of the DWE and SWE decreases compared to the three-dimensional approach when considering more complex floodplains. These results demonstrate that three-dimensional flood wave analysis with the SfM methodology is optimal for effectively minimising topographic and flood wave reproduction errors across extensive areas. This dual reduction in errors significantly enhances the reliability of flood hazard assessments and improves risk management by providing more precise and realistic predictions of flood waves.
Co-seismic and rainfall-triggered landslide hazard susceptibility assessment for Uganda derived using fuzzy logic and geospatial modelling techniques
Authors: Oleng Morris , Ozdemir Zuhal , Pilakoutas Kypros
Published: July 2024

Uganda has suffered from many damaging landslides like the 1966 Rwenzori, 1994 Kisomoro and 2010 Bududa events. Despite escalating landslide risks exacerbated by rapid deforestation, urbanization and population growth coupled with a substandard building stock, comprehensive national co-seismic and rainfall-induced landslide hazard and risk maps for Uganda do not exist. This study therefore aims to conduct landslide hazard assessment and zonation for Uganda using a geospatial-based fuzzy logic methodology. In this methodology, landslide frequency ratios obtained for the 1966 Toro and 1994 Kisomoro earthquakes are assigned to the stochastic event-based probabilistic seismic hazard map derived using OpenQuake-engine. The available co-seismic and rainfall-induced landslide inventory datasets are used to derive the distribution of landslide frequency ratios based on geology, topographic slope position index, slope aspect, slope angle, distance from streams, and proximity to major active faults. The spatial distribution of fuzzy membership functions obtained from frequency ratios are overlaid and aggregated to produce landslide susceptibility maps showing relative probabilities of landslide occurrences across Uganda. Results indicate that the highest overall landslide hazard susceptibility is expected in areas comprising highly weathered outcropping rocks of precambrian granites, dominantly metasedimentary, and granulites and gneisses geologies within 40 km from major active faults; where the bedrock peak ground acceleration ≥ 0.1 g, topographic position index ≥ 3.8, slope gradient ≥ 10°, and the distance from streams ≤ 1.25 km. These findings can inform Uganda’s directorate of disaster preparedness and management towards pioneering the development of co-seismic landslide risk mitigation measures for the country.
Statistical modeling for long-term meteorological forecasting: a case study in Van Lake Basin
Authors: Pala Zeydin , Şevgin Fatih
Published: July 2024

Predicting 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

In 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

Efficient 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

Human 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

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

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.

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

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

The 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

Predicting 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

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

Climate 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

Currently 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

The 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.
Efficacy of Interval Training in Improving Body Composition and Adiposity in Apparently Healthy Adults: An Umbrella Review with Meta-Analysis
Authors: Poon Eric Tsz-Chun , Li Hong-Yat , Little Jonathan Peter , Wong Stephen Heung-Sang , Ho Robin Sze-Tak
Published: July 2024

Background

Although the efficacy of interval training for improving body composition has been summarized in an increasing number of systematic reviews in recent years, discrepancies in review findings and conclusions have been observed.

Objective

This study aims to synthesize the available evidence on the efficacy of interval training compared with moderate-intensity continuous training (MICT) and nonexercise control (CON) in reducing body adiposity in apparently healthy adults.

Methods

An umbrella review with meta-analysis was performed. A systematic search was conducted in seven databases (MEDLINE, EMBASE, Cochrane Database, CINAHL, Scopus, SPORTDiscus, and Web of Science) up to October 2023. Systematic reviews with meta-analyses of randomized controlled trials (RCTs) comparing interval training and MICT/CON were included. Literature selection, data extraction, and methodological quality assessment (AMSTAR-2) were conducted independently by two reviewers. Meta-analyses were performed using a random-effects model. Subgroup analyses were conducted based on the type of interval training [high-intensity interval training (HIIT) and sprint interval training (SIT)], intervention duration, body mass index, exercise modality, and volume of HIIT protocols.

Results

Sixteen systematic reviews, including 79 RCTs and 2474 unique participants, met the inclusion criteria. Most systematic reviews had a critically low (n = 6) or low (n = 6) AMSTAR-2 score. Interval training demonstrated significantly greater reductions in total body fat percent (BF%) compared with MICT [weighted mean difference (WMD) of − 0.77%; 95% confidence interval (CI) − 1.12 to − 0.32%] and CON (WMD of − 1.50%; 95% CI − 2.40 to − 0.58%). Significant reductions in fat mass, visceral adipose tissue, subcutaneous abdominal fat, and android abdominal fat were also observed following interval training compared to CON. Subgroup analyses indicated that both HIIT and SIT resulted in superior BF% loss than MICT. These benefits appeared to be more prominent in individuals with overweight/obesity and longer duration interventions (≥ 12 weeks), as well as in protocols using cycling as a modality and low-volume HIIT (i.e., < 15 min of high-intensity exercise per session).

Conclusions

This novel umbrella review with large-scale meta-analysis provides an updated synthesis of evidence with implications for physical activity guideline recommendations. The findings support interval training as a viable exercise strategy for reducing adiposity in the general population.
Development and validation of a risk prediction model for motoric cognitive risk syndrome in older adults
Authors: Li Yaqin , Huang Yuting , Wei Fangxin , Li Tanjian , Wang Yu
Published: July 2024

Objective

The objective of this study was to develop a risk prediction model for motoric cognitive risk syndrome (MCR) in older adults.

Methods

Participants were selected from the 2015 China Health and Retirement Longitudinal Study database and randomly assigned to the training group and the validation group, with proportions of 70% and 30%, respectively. LASSO regression analysis was used to screen the predictors. Then, identified predictors were included in multivariate logistic regression analysis and used to construct model nomogram. The performance of the model was evaluated by area under the receiver operating characteristic (ROC) curve (AUC), calibration curves and decision curve analysis (DCA).

Results

528 out of 3962 participants (13.3%) developed MCR. Multivariate logistic regression analysis showed that weakness, chronic pain, limb dysfunction score, visual acuity score and Five-Times-Sit-To-Stand test were predictors of MCR in older adults. Using these factors, a nomogram model was constructed. The AUC values for the training and validation sets of the predictive model were 0.735 (95% CI = 0.708–0.763) and 0.745 (95% CI = 0.705–0.785), respectively.

Conclusion

The nomogram constructed in this study is a useful tool for assessing the risk of MCR in older adults, which can help clinicians identify individuals at high risk.
Impact of Covid-19 on the Economic Growth of ASEAN Countries: Convergence or Divergence?
Authors: D’Aloia Federico , Gugler Philippe
Published: July 2024

This contribution addresses the question of whether economic growth among ASEAN member countries is converging or diverging within the context of the COVID-19 pandemic. ASEAN has significantly developed economic and social mechanisms aimed at achieving greater integration among member countries, with the goal of enhancing economic growth and especially accelerating the catch-up of the poorer member countries. How resilient have the member countries been? Has the COVID-19 pandemic more severely affected the poorer member countries? To answer these questions, our contribution addresses this issue based on economic growth theories, particularly drawing on Solow’s theory regarding beta and gamma convergence. The study covers the period from 2000 to 2022. In the pre-pandemic period from 2000 to 2019, there is a general phenomenon of absolute convergence, demonstrating a reduction in the disparity in absolute values between less advanced and more modern countries, which also leads to a reduction in inequality as shown by the confirmation of the σ-convergence hypothesis. This is true for both GDPC and GDPW variables. The catch-up effect of absolute β-convergence is not evident when looking at the period from 2020 to 2022. During this timeframe, both the absolute β-convergence and σ-convergence hypotheses are rejected, which slows down the economic growth of poorer economies relative to the more advanced ASEAN countries. Overall, the entire 2000–2022 period confirms the unconditional β-convergence and σ-convergence assumptions of the pre-pandemic period.
Criterion for ellipticity on Heisenberg group
Authors: Zanin Dmitriy
Published: July 2024

We provide a semi-constructive criterion for ellipticity of the differential operator on the Heisenberg group $$\mathbb {H}^1.$$

H

1

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Tectonic, Topographic, Geologic, and Hydroclimatic Influence on Crack Formation During the 2021 Haiti Earthquake
Authors: Saint Fleur Newdeskarl , Dessable Joseph E. , Saint‐Preux Germain , Calais Éric , Feuillet Nathalie , Boisson Dominique , de Chabalier Jean‐Bernard , Klinger Yann
Published: July 2024

The 14 August 2021 Haiti earthquake mainly portrayed reverse motion to the east near L’Asile town and left‐lateral strike‐slip motion to the west near Camp‐Perrin town. To map the rupture and infer its segmentation, we conducted the first post‐seismic field reconnaissance along the left‐lateral strike‐slip Enriquillo fault from L’Asile to Macaya mountain. We identified 98 linear, minor cracks that are not representative of primary fault surface rupture. Analyzing the topographic slope distribution, we detected that the cracks were often located in areas that are prone to topographic instability. About 60% of the cracks are located in Quaternary alluvium and Middle‐Miocene continental marls, indicating a preference for soft sediments. The rivers also have an impact, as crack lengths and openings negatively correlate with their distance to neighboring rivers. In addition, the earthquake occurred in a rainy region with up to 2,479.34 mm of rainfall in 2021, increasing soil instability. Above all, we found a contrast and asymmetry between the eastern and the western parts of the rupture. By dividing the 60‐km long rupture into two equal parts, we observed 57 cracks to the west against 41 to the east. The longest and the widest cracks are to the west. Analyzing their orientation, the cracks mainly oriented as left‐lateral strike‐slip faults to the west and mainly thrusts to the east. This configuration appears to be influenced by the slip pattern of the 2021 Haiti earthquake and consistent with the regional stress field.