Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of homogenous territorial units: a methodological proposal

One of the main questions to solve when analysing geographically added information consists of the design of territorial units adjusted to the objectives of the study. This is related with the reduction of the effects of the Modifiable Areal Unit Problem (MAUP). In this paper an optimisation model to solve regionalisation problems is proposed. This model seeks to reduce some disadvantages found in previous works about automated regionalisation tools.
Associations of oxidative balance score with total abdominal fat mass and visceral adipose tissue mass percentages among young and middle-aged adults: findings from NHANES 2011–2018

Objective This study aimed to explore the association of the oxidative balance score (OBS) with total abdominal fat mass (TAFM) and visceral adipose tissue mass (VATM) percentages among young and middle-aged U.S. adults. Methods Young and middle-aged adults in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018 were included. Analysis of variance and Rao-Scott adjusted chi-square tests were used to compare the characteristics across quartiles of OBS. Univariate and multivariate weighted logistic regression models were employed to explore the relationship between OBS and the risks of high TAFM or high VATM percentage in the general population and subgroups, while the interaction effects were tested with a likelihood test. Weighted restricted cubic spline analyses were utilized to assess the non-linear association of OBS with TAFM and VATM percentages. Results The final sample included 8,734 young and middle-aged non-institutionalized U.S. adults representing 134.7 million adults. Compared with adults in the first quartile of OBS, those with higher OBS were less likely to have a high TAFM percentage; the ORs and 95% CI for adults in the second, third, and highest quartiles of OBS were 0.70 (0.53–0.94), 0.49 (0.36–0.60), and 0.25 (0.18–0.36), respectively. Similar trends were observed in the association between OBS and VATM percentages. Moreover, similar effects were confirmed in the sensitivity analyses and subgroup analyses according to demographic characteristics. Regarding the OBS subclass, higher dietary OBS and lifestyle OBS were also correlated with decreased ORs of high TAFM and VATM percentages. Conclusion This study strongly suggests that higher OBS, as well as higher dietary OBS and lifestyle OBS, are significantly correlated with lower risks of abdominal obesity and visceral fat accumulation. The findings highlight the importance of an antioxidant-rich diet and maintaining a healthy lifestyle in reducing the risks.
Optical Appearance of Eccentric Tidal Disruption Events

Stars approaching supermassive black holes can be tidally disrupted. Despite being expected to emit X-rays, tidal disruption events (TDEs) have been largely observed in optical bands, which is poorly understood. In this Letter, we simulate the tidal disruption of a 1 M ⊙ main-sequence star on an eccentric (e = 0.95) orbit with a periapsis distance 1 or 5 times smaller than the tidal radius (β = 1 or 5) using general relativistic smoothed particle hydrodynamics. We follow the simulation for up to 1 yr postdisruption. We show that accretion disks in eccentric TDEs are masked by unbound material outflowing at ∼10,000 km s −1. Assuming electron scattering opacity, this material would be visible as a ∼100 au photosphere at ∼104 K, in line with observations of candidate TDEs.
Visual Hindsight Self-Imitation Learning for Interactive Navigation

Interactive visual navigation tasks, which involve following instructions to reach and interact with specific targets, are challenging not only because successful experiences are very rare but also because the complex visual inputs require a substantial number of samples. Previous methods for these tasks often rely on intricately designed dense rewards or the use of expensive expert data for imitation learning. To tackle these challenges, we propose a novel approach, Visual Hindsight Self-Imitation Learning (VHS) for enhancing sample efficiency through hindsight goal re-labeling and self-imitation. We also introduce a prototypical goal embedding method derived from experienced goal observations, that is particularly effective in vision-based and partially observable environments. This embedding technique allows the agent to visually reinterpret its unsuccessful attempts, enabling vision-based goal re-labeling and self-imitation from enhanced successful experiences. Experimental results show that VHS outperforms existing techniques in interactive visual navigation tasks, confirming its superior performance and sample efficiency.
On the Role of Edge Dependency in Graph Generative Models

In this work, we introduce a novel evaluation framework for generative models of graphs, emphasizing the importance of model-generated graph overlap (Chanpuriya et al., 2021) to ensure both accuracy and edge-diversity. We delineate a hierarchy of graph generative models categorized into three levels of complexity: edge independent, node independent, and fully dependent models. This hierarchy encapsulates a wide range of prevalent methods. We derive theoretical bounds on the number of triangles and other short-length cycles producible by each level of the hierarchy, contingent on the model overlap. We provide instances demonstrating the asymptotic optimality of our bounds. Furthermore, we introduce new generative models for each of the three hierarchical levels, leveraging dense subgraph discovery (Gionis&Tsourakakis, 2015). Our evaluation, conducted on real-world datasets, focuses on assessing the output quality and overlap of our proposed models in comparison to other popular models. Our results indicate that our simple, interpretable models provide competitive baselines to popular generative models. Through this investigation, we aim to propel the advancement of graph generative models by offering a structured framework and robust evaluation metrics, thereby facilitating the development of models capable of generating accurate and edge-diverse graphs.
The Belledonne Border Fault: identification of an active seismic strike‐slip fault in the western Alps

SUMMARY In the French western Alps, east of Grenoble, we identify the Belledonne Border Fault as an active seismic fault. This identification is based on the seismic monitoring of the Grenoble area by the Sismalp seismic network over the past 12 yr (1989–2000). We located a set of earthquakes with magnitudes ranging from 0 to 3.5 along a ∼50 km long alignment which runs in a N30°E direction on the western flank of the Belledonne crystalline massif. Available focal solutions for these events are consistent with this direction (N36°E strike-slip fault with right-lateral displacement). These events along the Belledonne Border Fault have a mean focal depth of ∼7 km (in the crystalline basement), with a probably very low slip rate. The Belledonne Border Fault has never been mapped at the surface, where the otherwise heavily folded and faulted Mesozoic cover makes this identification difficult. Historical seismicity also shows that, over the past two and a half centuries, a few events located mainly along the southern part of the Belledonne Border Fault caused damage, with the magnitude 4.9 1963 Monteynard earthquake reaching intensity VII. The most recent damaging event in the study area is the magnitude 3.5 1999 Laffrey earthquake (intensity V–VI). Although its epicentre lies at the southern tip of the Belledonne Border Fault, there is clear evidence that aftershocks were activated by the left-lateral slip of a N122°E-striking fault. The length of the Belledonne Border Fault, which could easily accommodate a magnitude 6 event, as well as the proximity to the Isere valley with its unlithified Quaternary deposits up to 500 m thick known to generate marked site effects, make the identification of the Belledonne Border Fault an important step in the evaluation of seismic risk in the Grenoble area. Besides, the activity observed on the fault will now have to be taken into account in future geodynamic models of the western Alps.
Automated Multimodal Data Annotation via Calibration With Indoor Positioning System

Learned object detection methods based on fusion of LiDAR and camera data require labeled training samples, but niche applications, such as warehouse robotics or automated infrastructure, require semantic classes not available in large existing datasets. Therefore, to facilitate the rapid creation of multimodal object detection datasets and alleviate the burden of human labeling, we propose a novel automated annotation pipeline. Our method uses an indoor positioning system (IPS) to produce accurate detection labels for both point clouds and images and eliminates manual annotation entirely. In an experiment, the system annotates objects of interest 261.8 times faster than a human baseline and speeds up end-to-end dataset creation by 61.5%.
Gas Turbine Compressor Washing Economics and Optimisation Using Genetic Algorithm

Studies have shown that online compressor washing of gas turbine engines slows down the rate of fouling deterioration during operation. However, for most operators, there is a balancing between the performance improvements obtained and the investment (capital and recurring cost). Washing the engine more frequently to keep the capacity high is a consideration. However, this needs to be addressed with expenditure over the life of the washing equipment rather than a simple cost-benefit analysis. The work presented here is a viability study of online compressor washing for 17 gas turbine engines ranging from 5.3 to 307MW. It considers the nonlinear cost of the washing equipment related to size categories, as well as nonlinear washing liquid consumption related to the variations in engine mass flows. Importantly, the respective electricity break-even selling price of the respective engines was considered. The results show that for the largest engine, the return of investment is 520% and the dynamic payback time of 0.19 years when washing every 72 hours. When this is less frequent at a 480-hour interval, the investment return and payback are 462% and 0.22 years. The optimisation study using a multi-objective genetic algorithm shows that the optimal washing is rather a 95-hour interval. For the smallest engine, the investment was the least viable for this type of application.
Exploring the Factors behind Nurses’ Decision to Leave Clinical Practice: Revealing Causes for Leaving and Approaches for Enhanced Retention

Nursing turnover has emerged as an urgent concern with a substantial influence on the financial efficiency and quality of care in healthcare frameworks worldwide. This study determined important factors associated with nurses’ intentions to leave and what would bring them back. This was a cross-sectional, multisite study of nurses in three public hospitals. Convenience sampling was used to recruit 205 nurses from the selected hospitals. A questionnaire was used to measure demographic and professional background information, current job satisfaction, and the intention to leave work and return. Bivariate and multivariate analyses were run using SPSS. This study revealed that both job satisfaction (M = 47.26, SD = 11.59, range: 19–76) and intent to leave a current job (M = 14.18, SD = 4.36, range: 4–20) were reported at moderate levels. There were significant differences reported between the scores of nationality and job satisfaction (p < 0.05) and between the means of income level and intention to leave (p < 0.05). There was also a significant, negative association between satisfaction and intention to leave (r = −0.551, p < 0.05). In regression, income level (β = 0.159, p = 0.021), incentives (β = 0.186, p = 0.002), hospital type (β = 0.189, p = 0.005), and intention to leave (β = −0.454, p < 0.001) significantly influenced satisfaction. Gender (β = −0.122, p = 0.037) and nationality (β = −0.210, p = 0.007) were found to influence the intention to leave among participants significantly. In conclusion, this study indicated that job satisfaction and intention to leave are important factors affecting nurses’ enthusiasm. Incentives also had a positive impact on increasing nurses’ satisfaction levels. Future research studies should investigate what factors might lead to improved monthly salaries and provide more incentives among nurses.
The bigger picture: developing a graphical user interface to process UAV imagery of tidal stream environments

— Unmanned Aerial Vehicles (UAVs), or drones, offer the ability to collect cost-effective fine-scale imagery that is suitable for the capture of concurrent turbulence and faunal data within tidal stream environments. This is a necessary stage of information gathering to inform tidal energy device design, advise control and maintenance strategies and better inform environmental consenting processes. For this study a total of sixty-three UAV surveys were undertaken within the Inner Sound of the Pentland Firth, Scotland, UK, over two 4-day periods in 2016 and 2018. The aims of this data collection effort were to characterise bathymetrically driven turbulence features, including distribution, presence, and area, as well as marine life such as seabird distributions, presence, and orientation relative to the flow. To achieve this, a method to extract quantifiable metrics from UAV imagery was required. This paper details the processes and methodology to create a graphical user interface (GUI) to provide these outputs rather than examining specific results. It includes an explanation of the criteria that the GUI needed to meet to be able to process the imagery, a description of the workflow and an explanation of the sub-routines required such as image registration and calibration. The outputs of the GUI, and their relevance to tidal energy developments, are also discussed. Finally, this paper details future work incorporating machine learning techniques to improve the accuracy, reliability, and processing speed of the GUI.
A probabilistic assessment of resolution in the SASW test and its impact on the prediction of ground vibrations

SUMMARY The Spectral Analysis of Surface Waves (SASW) method is a technique for the identification of the thickness, dynamic shear modulus, and damping ratio of shallow soil layers. The method consists of an in situ experiment to determine the dispersion curve of the soil and the solution of an inverse problem where the corresponding soil profile is identified. The SASW method has been used to investigate pavement systems, to assess the quality of ground improvement, to determine the thickness of waste deposits, and to identify the dynamic soil properties for the prediction of ground vibrations. In this paper, the focus is on the last application. The information on the dynamic soil properties provided by the dispersion curve is limited. The dispersion curve is insensitive to variations of the soil properties on a small spatial scale and at a large depth. As a result, the solution of the inverse problem in the SASW method is non-unique and hence uncertain. The prediction of ground vibrations is therefore based on a soil model with uncertain properties. In this study, a Bayesian approach is followed to solve the inverse problem in the SASW method. A prior stochastic soil model is first formulated using the information that is available before the SASW test is performed. A Markov chain Monte Carlo method is used to transform the prior model into a posterior stochastic soil model that accounts for the SASW test results. Finally, the prediction of ground vibrations is addressed. The posterior soil model is used to assess the robustness of the predicted vibrations, accounting for the uncertainty on the results of the SASW test. As an example, the free field vibrations due to a hammer impact on a concrete foundation are considered. More complicated problems, such as the prediction of road and rail traffic induced vibrations, can be addressed in a similar way.
Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and MFO Algorithm.

The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (MFO) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.
Atmosphere‐ocean ozone fluxes during the TexAQS 2006, STRATUS 2006, GOMECC 2007, GasEx 2008, and AMMA 2008 cruises

A ship-based eddy covariance ozone flux system was deployed to investigate the magnitude and variability of ozone surface fluxes over the open ocean. The flux experiments were conducted on five cruises on board the NOAA research vessel Ronald Brown during 2006-2008. The cruises covered the Gulf of Mexico, the southern as well as northern Atlantic, the Southern Ocean, and the persistent stratus cloud region off Chile in the eastern Pacific Ocean. These experiments resulted in the first ship-borne open-ocean ozone flux measurement records. The median of 10 min oceanic ozone deposition velocity (v(d)) results from a combined similar to 1700 h of observations ranged from 0.009 to 0.034 cm s(-1). For the Gulf of Mexico cruise (Texas Air Quality Study (TexAQS)) the median v(d) (interquartile range) was 0.034 (0.009-0.065) cm s(-1) (total number of 10 min measurement intervals, N-f = 1953). For the STRATUS cruise off the Chilean coast, the median v(d) was 0.009 (0.004-0.037) cm s(-1) (N-f = 1336). For the cruise from the Gulf of Mexico and up the eastern U. S. coast (Gulf of Mexico and East Coast Carbon cruise (GOMECC)) a combined value of 0.018 (0.006-0.045) cm s(-1) (N-f = 1784) was obtained (from 0.019 (-0.014-0.043) cm s(-1), N-f = 663 in the Gulf of Mexico, and 0.018 (-0.004-0.045) cm s(-1), N-f = 1121 in the North Atlantic region). The Southern Ocean Gas Exchange Experiment (GasEx) and African Monsoon Multidisciplinary Analysis (AMMA), the Southern Ocean and northeastern Atlantic cruises, respectively, resulted in median ozone v(d) of 0.009 (-0.005-0.026) cm s(-1) (N-f = 2745) and 0.020 (-0.003-0.044) cms(-1) (N-f = 1147). These directly measured ozone deposition values are at the lower end of previously reported data in the literature (0.01-0.12 cm s(-1)) for ocean water. Data illustrate a positive correlation (increase) of the oceanic ozone uptake rate with wind speed, albeit the behavior of the relationship appears to differ during these cruises. The encountered wide range of meteorological and ocean biogeochemical conditions is used to investigate fundamental drivers of oceanic O-3 deposition and for the evaluation of a recently developed global oceanic O-3 deposition modeling system.
An Inertia-Based Upper Limb Motion Capture Model for Accurate Position Estimation in Magnetic Field Disturbances

Inertial-based upper limb motion capture technology is widely applied in directly controlling robots through human motions due to its low cost and portability. Most upper limb motion capture models typically focus solely on estimating the wrist position (WP) while neglecting the motion of the wrist. Moreover, these models struggle to precisely estimate joint positions, especially in the presence of magnetic disturbances. In this article, a two-layer upper limb motion capture model is proposed. In the upper layer of the model, a hand center position estimation method (HCPEM) is presented to estimate accurately hand center position (HCP) regardless of magnetic field interference. In the lower layer of the model, an optimized sensor-to-segment alignment algorithm based on physical constraints (OABPCs) is proposed to align sensors and segments with the aim of improving the precision of the estimated HCPs. The UR3 robotic experiment platform was designed to verify the accuracy of the HCPs. For different motion tasks and different mounting positions of the inertial measurement units (IMUs), the performance of the proposed two-layer model was further investigated through experiments. The results show that the HCP estimated by the proposed two-layer model achieves root-mean-square errors (RMSEs) below 2.5 cm for different motion tasks and various IMU mounting positions, even in the presence of magnetic field disturbances. Compared with the existing algorithm, the RMSE of the joint position measurements is improved by 1 cm. This study enables flexible, accurate, and convenient control of the robot through human motion.
Dendritic Cells: Features and Functions

Dendritic cells (DC) are irregularly shaped cells that were initially identified in the glass and plastic adherent population of mouse spleen. DC are la*, Ig", thy-1" bone marrow derived elements that show little or no endocytic activity for several tracers. DC occur in low frequency accounting for less than 1 *% of the cells in all organs we have studied. However, methods have been developed for their enrichment. DC in small numbers stimulate allogeneic and syngeneic mixed leukocyte reactions (MLR) and serve as accessory cells for the development of in viiro immune responses. This review will consider several topics: a) the principal features of DC that are useful in their identification, purification, and differentiation from mononuclear phagocytes the other cell type most often considered in studies of accessory cell functions; b) surface markers of DC including expression of la antigens; c) properties of DC in situ; and d) functional capacities of DC in vitro.
Dendritic cells: unique leukocyte populations which control the primary immune response.

The description of skin dendritic cells (DC) by Langerhans in 1868 was followed by prolonged speculation as to their function. Steinman and Cohn identified mouse spleen DC in 19731 and initiated a series of experiments that established lymphoid tissue–derived DC as potent stimulators of primary
Ból psychiczny — konceptualizacja i narzędzia pomiaru

W artykule omowiono ujecia bolu psychicznego, jego przyczyny oraz sposoby pomiaru tej zmiennej. Szczegolną uwage zwrocono na koncepcje Edwina Shneidmana, zgodnie z ktorą bol psychiczny jest podstawowym źrodlem myśli i zachowan samobojczych. Dokonano przeglądu badan wskazujących na związki bolu psychicznego z tendencjami suicydalnymi i zmiennymi osobowościowymi. W artykule zawarto rowniez postulaty dotyczące dalszych kierunkow badan nad bolem psychicznym
DEMO PAPER: Deep Reinforcement Learning for Real-Time Fear Induction in an SCP-087-Inspired Horror Game

This demo paper presents a novel approach to inducing fear in a horror game inspired by the SCP-087 creepypasta. The game features an infinite staircase that players must descend, with horror events triggered by real-time valence arousal values, as measured by facial expression detection. Deep reinforcement learning is used to train an AI agent to trigger these events and maintain players’ valence arousal values at a certain level. The approach uses recent valence arousal values and game environment features as inputs to a neural network that outputs actions for the AI agent. As time constraints limited our ability to conduct thorough experiments, we present a prototype of our approach in this paper. While the results are preliminary, we believe that our prototype demonstrates the potential of our approach to induce fear in players using real-time facial expression detection and deep reinforcement learning. Future work will involve more extensive testing and refinement of our approach.