Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Approach for Developing “Implementation Plans” for Cognitive Stimulation Therapy (CST) in Low and Middle-Income Countries: Results From the CST-International Study

Background: Even with a strong evidence base, many healthcare interventions fail to be translated to clinical practice due to the absence of robust implementation strategies. For disorders such as Alzheimer's disease and other dementias, access to evidence-based interventions beyond research settings is of great importance. Cognitive Stimulation Therapy (CST) is a brief, group-based intervention, with consistent evidence of effectiveness. Methods: An implementation focused, three-phase methodology was developed using extensive stakeholder engagement. The methods resulted in a standardized Implementation Plan for the successful translation of CST from research to practice. The methodology was developed using the Consolidated Framework for Implementation Research (CFIR) and refined in three countries that vary in levels of economic development and healthcare systems (Brazil, India and Tanzania). Results: Five Implemention Plans for CST were produced. Each plan contained implementation strategies and action plans devised in conjunction with policy professionals, healthcare professionals, people with dementia and family carers, and an international team of researchers and clinicians. Conclusion: This novel methodology can act as a template for implementation studies in diverse healthcare systems across the world. It is an effective means of devising socio-culturally informed Implementation Plans that account for economic realities, health equity and healthcare access.
Identifying Hydro-Geomorphological Conditions for State Shifts from Bare Tidal Flats to Vegetated Tidal Marshes

High-lying vegetated marshes and low-lying bare mudflats have been suggested to be two stable states in intertidal ecosystems. Being able to identify the conditions enabling the shifts between these two stable states is of great importance for ecosystem management in general and the restoration of tidal marsh ecosystems in particular. However, the number of studies investigating the conditions for state shifts from bare mudflats to vegetated marshes remains relatively low. We developed a GIS approach to identify the locations of expected shifts from bare intertidal flats to vegetated marshes along a large estuary (Western Scheldt estuary, SW Netherlands), by analyzing the interactions between spatial patterns of vegetation biomass, elevation, tidal currents, and wind waves. We analyzed false-color aerial images for locating marshes, LIDAR-based digital elevation models, and spatial model simulations of tidal currents and wind waves at the whole estuary scale (~326 km²). Our results demonstrate that: (1) Bimodality in vegetation biomass and intertidal elevation co-occur; (2) the tidal currents and wind waves change abruptly at the transitions between the low-elevation bare state and high-elevation vegetated state. These findings suggest that biogeomorphic feedback between vegetation growth, currents, waves, and sediment dynamics causes the state shifts from bare mudflats to vegetated marshes. Our findings are translated into a GIS approach (logistic regression) to identify the locations of shifts from bare to vegetated states during the studied period based on spatial patterns of elevation, current, and wave orbital velocities. This GIS approach can provide a scientific basis for the management and restoration of tidal marshes.
Ionospheric Responses to the June 2015 Geomagnetic Storm from Ground and LEO GNSS Observations

Geomagnetic storms are extreme space weather events, which have considerable impacts on the ionosphere and power transmission systems. In this paper, the ionospheric responses to the geomagnetic storm on 22 June 2015, are analyzed from ground-based and satellite-based Global Navigation Satellite System (GNSS) observations as well as observational data of digital ionosondes, and the main physical mechanisms of the ionospheric disturbances observed during the geomagnetic storm are discussed. Salient positive and negative storms are observed from vertical total electron content (VTEC) based on ground-based GNSS observations at different stages of the storm. Combining topside observations of Low-Earth-Orbit (LEO) satellites (GRACE and MetOp satellites) with different orbital altitudes and corresponding ground-based observations, the ionospheric responses above and below the orbits are studied during the storm. To obtain VTEC from the slant TEC between Global Positioning System (GPS) and LEO satellites, we employ a multi-layer mapping function, which can effectively reduce the overall error caused by the single-layer geometric assumption where the horizontal gradient of the ionosphere is not considered. The results show that the topside observations of the GRACE satellite with a lower orbit can intuitively detect the impact caused by the fluctuation of the F2 peak height (hmF2). At the same time, the latitude range corresponding to the peak value of the up-looking VTEC on the event day becomes wider, which is the precursor of the Equatorial Ionization Anomaly (EIA). However, no obvious response is observed in the up-looking VTEC from MetOp satellites with higher orbits, which indicates that the VTEC responses to the geomagnetic storm mainly take place below the orbit of MetOp satellites.
Efficient Paradigm to Measure Street-Crossing Onset Time of Pedestrians in Video-Based Interactions with Vehicles

With self-driving vehicles (SDVs), pedestrians can no longer rely on a human driver. Previous research suggests that pedestrians may benefit from an external Human–Machine Interface (eHMI) displaying information to surrounding traffic participants. This paper introduces a natural methodology to compare eHMI concepts from a pedestrian’s viewpoint. To measure eHMI effects on traffic flow, previous video-based studies instructed participants to indicate their crossing decision with interfering data collection devices, such as pressing a button or slider. We developed a quantifiable concept that allows participants to naturally step off a sidewalk to cross the street. Hidden force-sensitive resistor sensors recorded their crossing onset time (COT) in response to real-life videos of approaching vehicles in an immersive crosswalk simulation environment. We validated our method with an initial study of N = 34 pedestrians by showing (1) that it is able to detect significant eHMI effects on COT as well as subjective measures of perceived safety and user experience. The approach is further validated by (2) replicating the findings of a test track study and (3) participants’ reports that it felt natural to take a step forward to indicate their street crossing decision. We discuss the benefits and limitations of our method with regard to related approaches.
15N NATURAL ABUNDANCE IN WOODY PLANTS AND SOILS OF CENTRAL BRAZILIAN SAVANNAS (CERRADO)

We measured the 15N natural abundance values of 320 individuals belonging to 45 different plant species of the Brazilian cerrado. We also determined δ15N of soil nitrogen as a function of soil depth. Our purpose was to test the hypothesis that N-limited cerrado would have a large range of plant δ15N values, similar to patterns seen in northern high-latitude ecosystems. Foliar δ15N values did demonstrate the large range found in some other N-limited ecosystems, varying from −5‰ to +7.9‰. Significant variability within individual species was also seen across cerrado community types. Several factors contributed to this variability, including the presence of N-fixing legumes, associations with mycorrhizal fungi, δ15N variability of soil organic matter with depth, fire events, and the seasonality of N-immobilization and mineralization processes in cerrado soils.
Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery

Shrub-dominated ecosystems support biodiversity and play an important storage role in the global carbon cycle. However, it is challenging to characterize biophysical properties of low-stature vegetation like shrubs from conventional ground-based or remotely sensed data. We used spectral and structural variables derived from high-resolution unmanned aerial system (UAS) imagery to estimate the aboveground biomass of shrubs in the Betula and Salix genera in a montane meadow in Banff National Park, Canada using an area-based approach. In single-variable linear regression models, visible light (RGB) indices outperformed multispectral or structural data. A linear model based on the red ratio vegetation index (VI) accumulated over shrub area could model biomass (calibration R2 = 0.888; validation R2 = 0.774) nearly as well as the top multivariate linear regression models (calibration R2 = 0.896; validation R2 > 0.750), which combined an accumulated RGB VI with a multispectral metric. The excellent performance of accumulated RGB VIs represents a novel approach to fine-scale vegetation biomass estimation, fusing spectral and spatial information into a single parsimonious metric that rivals the performance of more complex multivariate models. Methods developed in this study will be relevant to researchers interested in estimating fine-scale shrub aboveground biomass within a range of ecosystems.
How Much Space Is Required? Effect of Distance, Content, and Color on External Human-Machine Interface Size

The communication of an automated vehicle (AV) with human road users can be realized by means of an external human–machine interface (eHMI), such as displays mounted on the AV’s surface. For this purpose, the amount of time needed for a human interaction partner to perceive the AV’s message and to act accordingly has to be taken into account. Any message displayed by an AV must satisfy minimum size requirements based on the dynamics of the road traffic and the time required by the human. This paper examines the size requirements of displayed text or symbols for ensuring the legibility of a message. Based on the limitations of available package space in current vehicle models and the ergonomic requirements of the interface design, an eHMI prototype was developed. A study involving 30 participants varied the content type (text and symbols) and content color (white, red, green) in a repeated measures design. We investigated the influence of content type on content size to ensure legibility from a constant distance. We also analyzed the influence of content type and content color on the human detection range. The results show that, at a fixed distance, text has to be larger than symbols in order to maintain legibility. Moreover, symbols can be discerned from a greater distance than text. Color had no content overlapping effect on the human detection range. In order to ensure the maximum possible detection range among human road users, an AV should display symbols rather than text. Additionally, the symbols could be color-coded for better message comprehension without affecting the human detection range.
Towards Geometric Understanding of Motion

The motion of the world is inherently dependent on the spatial structure of the world and its geometry. Therefore, classical optical flow methods try to model this geometry to solve for the motion. However, recent deep learning methods take a completely different approach. They try to predict optical flow by learning from labelled data. Although deep networks have shown state-of-the-art performance on classification problems in computer vision, they have not been as effective in solving optical flow. The key reason is that deep learning methods do not explicitly model the structure of the world in a neural network, and instead expect the network to learn about the structure from data. We hypothesize that it is difficult for a network to learn about motion without any constraint on the structure of the world. Therefore, we explore several approaches to explicitly model the geometry of the world and its spatial structure in deep neural networks. The spatial structure in images can be captured by representing it at multiple scales. To represent multiple scales of images in deep neural nets, we introduce a Spatial Pyramid Network (SpyNet). Such a network can leverage global information for estimating large motions and local information for estimating small motions. We show that SpyNet significantly improves over previous optical flow networks while also being the smallest and fastest neural network for motion estimation. SPyNet achieves a 97% reduction in model parameters over previous methods and is more accurate. The spatial structure of the world extends to people and their motion. Humans have a very well-defined structure, and this information is useful in estimating optical flow for humans. To leverage this information, we create a synthetic dataset for human optical flow using a statistical human body model and motion capture sequences. We use this dataset to train deep networks and see significant improvement in the ability of the networks to estimate human optical flow. The structure and geometry of the world affects the motion. Therefore, learning about the structure of the scene together with the motion can benefit both problems. To facilitate this, we introduce Competitive Collaboration, where several neural networks are constrained by geometry and can jointly learn about structure and motion in the scene without any labels. To this end, we show that jointly learning single view depth prediction, camera motion, optical flow and motion segmentation using Competitive Collaboration achieves state-of-the-art results among unsupervised approaches. Our findings provide support for our hypothesis that explicit constraints on structure and geometry of the world lead to better methods for motion estimation.
Precursors of Role-Based Access Control Design in KMS: A Conceptual Framework

Role-based access control (RBAC) continues to gain popularity in the management of authorization concerning access to knowledge assets in organizations. As a socio-technical concept, the notion of role in RBAC has been overemphasized, while very little attention is given to the precursors: role strain, role ambiguity, and role conflict. These constructs provide more significant insights into RBAC design in Knowledge Management Systems (KMS). KMS is the technology-based knowledge management tool used to acquire, store, share, and apply knowledge for improved collaboration and knowledge-value creation. In this paper, we propose eight propositions that require future research concerning the RBAC system for knowledge security. In addition, we propose a model that integrates these precursors and RBAC to deepen the understanding of these constructs. Further, we examine these precursory constructs in a socio-technical fashion relative to RBAC in the organizational context and the status–role relationship effects. We carried out conceptual analysis and synthesis of the relevant literature, and present a model that involves the three essential precursors that play crucial roles in role mining and engineering in RBAC design. Using an illustrative case study of two companies where 63 IT professionals participated in the study, the study established that the precursors positively and significantly increase the intractability of the RBAC system design. Our framework draws attention to both the management of organizations and RBAC system developers about the need to consider and analyze the precursors thoroughly before initiating the processes of policy engineering, role mining, and role engineering. The propositions stated in this study are important considerations for future work.
Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.
Testing the "(Neo-)Darwinian" Principles against Reticulate Evolution: How Variation, Adaptation, Heredity and Fitness, Constraints and Affordances, Speciation, and Extinction Surpass Organisms and Species

Variation, adaptation, heredity and fitness, constraints and affordances, speciation, and extinction form the building blocks of the (Neo-)Darwinian research program, and several of these have been called “Darwinian principles”. Here, we suggest that caution should be taken in calling these principles Darwinian because of the important role played by reticulate evolutionary mechanisms and processes in also bringing about these phenomena. Reticulate mechanisms and processes include symbiosis, symbiogenesis, lateral gene transfer, infective heredity mediated by genetic and organismal mobility, and hybridization. Because the “Darwinian principles” are brought about by both vertical and reticulate evolutionary mechanisms and processes, they should be understood as foundational for a more pluralistic theory of evolution, one that surpasses the classic scope of the Modern and the Neo-Darwinian Synthesis. Reticulate evolution moreover demonstrates that what conventional (Neo-)Darwinian theories treat as intra-species features of evolution frequently involve reticulate interactions between organisms from very different taxonomic categories. Variation, adaptation, heredity and fitness, constraints and affordances, speciation, and extinction therefore cannot be understood as “traits” or “properties” of genes, organisms, species, or ecosystems because the phenomena are irreducible to specific units and levels of an evolutionary hierarchy. Instead, these general principles of evolution need to be understood as common goods that come about through interactions between different units and levels of evolutionary hierarchies, and they are exherent rather than inherent properties of individuals.
Archival Aerial Images Georeferencing: A Geostatistically-Based Approach for Improving Orthophoto Accuracy with Minimal Number of Ground Control Points

Georeferenced archival aerial images are key elements for the study of landscape evolution in the scope of territorial planning and management. The georeferencing process proceeds by applying to photographs advanced digital photogrammetric techniques integrated along with a set of ground truths termed ground control points (GCPs). At the end of that stage, the accuracy of the final orthomosaic is assessed by means of root mean square error (RMSE) computation. If the value of that index is deemed to be unsatisfactory, the process is re-run after increasing the GCP number. Unfortunately, the search for GCPs is a costly operation, even when it is visually carried out from recent digital images. Therefore, an open issue is that of achieving the desired accuracy of the orthomosaic with a minimal number of GCPs. The present paper proposes a geostatistically-based methodology that involves performing the spatialization of the GCP errors obtained from a first gross version of the georeferenced orthomosaic in order to produce an error map. Then, the placement of a small number of new GCPs within the sub-areas characterized by the highest local errors enables a finer georeferencing to be achieved. The proposed methodology was applied to 67 historical photographs taken on a geo-morphologically complex study area, located in Southern Italy, which covers a total surface of approximately 55,000 ha. The case study showed that 75 GCPs were sufficient to garner an orthomosaic with coordinate errors below the chosen threshold of 10 m. The study results were compared with similar works on georeferenced images and demonstrated better performance for achieving a final orthomosaic with the same RMSE at a lower information rate expressed in terms of nGCPs/km2.
Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain

Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.