Miracle-dz/UniFAR-FTUs huggingface dataset:
Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.
Spationomy Simulation Game - Playful Learning in Spatial Economy Higher Education

Simulation games, as a method of playful learning, have been used for more than 70 years in various disciplines with the economy as a leading application field. Their development has been tied with advances in computer science, and nowadays, hundreds of simulation games exist. However, simulation games are not just useful for encouraging disciplinary knowledge production; they also promise to be effective tools for interdisciplinary collaboration. To further explore these promises, we report on the design and playing of a simulation game on the boundary of geoinformatics and business and economics; an interdisciplinary field we have termed Spationomy. Within this game, students from different disciplinary (and cultural) backgrounds applied their knowledge and skills to tackle interdisciplinary problems. In this paper, we also analyze students’ feedback on the game to complement this aspect. The main goal is to discuss the design process that went into creating the game as well as experiences from play sessions in relation to this increase of interdisciplinary knowledge among students. In the end, we present a new gaming concept based on real-world data that can be played in other interdisciplinary situations. Here, students´ feedback on individual features of the game helped to identify future directions in the development of our simulation game.
A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.
Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.
Simultaneous Extraction of Road and Centerline from Aerial Images Using a Deep Convolutional Neural Network

The extraction of roads and centerlines from aerial imagery is considered an important topic because it contributes to different fields, such as urban planning, transportation engineering, and disaster mitigation. Many researchers have studied this topic as a two-separated task that affects the quality of extracted roads and centerlines because of the correlation between these two tasks. Accurate road extraction enhances accurate centerline extraction if these two tasks are processed simultaneously. This study proposes a multitask learning scheme using a gated deep convolutional neural network (DCNN) to extract roads and centerlines simultaneously. The DCNN is composed of one encoder and two decoders implemented on the U-Net backbone. The decoders are assigned to extract roads and centerlines from low-resolution feature maps. Before extraction, the images are processed within an encoder to extract the spatial information from a complex, high-resolution image. The encoder consists of the residual blocks (Res-Block) connected to a bridge represented by a Res-Block, and the bridge connects the two identical decoders, which consists of stacking convolutional layers (Conv.layer). Attention gates (AGs) are added to our model to enhance the selection process for the true pixels that represent road or centerline classes. Our model is trained on a dataset of high-resolution aerial images, which is open to the public. The model succeeds in efficiently extracting roads and centerlines compared with other multitask learning models.
Integrating a Three-Level GIS Framework and a Graph Model to Track, Represent, and Analyze the Dynamic Activities of Tidal Flats

Tidal flats (non-vegetated area) are soft-sediment habitats that are alternately submerged and exposed to the air by changeable tidal levels. The tidal flat dynamics research mainly utilizes the cell-level comparisons between the consecutive snapshots, but the in-depth study requires more detailed information of the dynamic activities. To better track, represent, and analyze tidal flats’ dynamic activities, this study proposes an integrated approach of a three-level Geographic Information Science (GIS) framework and a graph model. In the three-level GIS framework, the adjacent cells are assembled as the objects, and the objects on different time steps are linked as lifecycles by tracking the predecessor–successor relationships. Furthermore, eleven events are defined to describe the dynamic activities throughout the lifecycles. The graph model provides a better way to represent the lifecycles, and graph operators are utilized to facilitate the event analysis. The integrated approach is applied to tidal flats’ dynamic activities in the southwest tip of Florida Peninsula from 1984 to 2018. The results suggest that the integrated approach provides an effective way to track, represent, and analyze the dynamic activities of tidal flats, and it offers a novel perspective to examine other dynamic geographic phenomena with large spatiotemporal scales.
Transfer Learning of a Deep Learning Model for Exploring Tourists' Urban Image Using Geotagged Photos

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos.
Effects of Several Classes of Voltage-Gated Ion Channel Conductances on Gamma and Theta Oscillations in a Hippocampal Microcircuit Model

Gamma and theta oscillations have been functionally associated with cognitive processes, such as learning and memory. Synaptic conductances play an important role in the generation of intrinsic network rhythmicity, but few studies have examined the effects of voltage-gated ion channels (VGICs) on these rhythms. In this report, we have used a pyramidal-interneuron-gamma (PING) network consisting of excitatory pyramidal cells and two types of inhibitory interneurons. We have constructed a conductance-based neural network incorporating a persistent sodium current (INaP), a delayed rectifier potassium current (IKDR), a inactivating potassium current (IA) and a hyperpolarization-activated current (IH). We have investigated the effects of several conductances on network theta and gamma frequency oscillations. Variation of all conductances of interest changed network rhythmicity. Theta power was altered by all conductances tested. Gamma rhythmogenesis was dependent on IA and IH. The IKDR currents in excitatory pyramidal cells as well as both types of inhibitory interneurons were essential for theta rhythmogenesis and altered gamma rhythm properties. Increasing INaP suppressed both gamma and theta rhythms. Addition of noise did not alter these patterns. Our findings suggest that VGICs strongly affect brain network rhythms. Further investigations in vivo will be of great interest, including potential effects on neural function and cognition.
Discovering and Characterizing of Survivin Dominant Negative Mutants With Stronger Pro-apoptotic Activity on Cancer Cells and CSCs

Survivin as a member of the inhibitor of apoptosis proteins (IAPs) family is undetectable in normal cells, but highly expressed in cancer cells and cancer stem cells (CSCs) which makes it an attractive target in cancer therapy. Survivin dominant negative mutants have been reported as competitive inhibitors of endogenous survivin protein in cancer cells. However, there is a lack of systematic comparative studies on which mutants have stronger effect on promoting apoptosis in cancer cells, which will hinder the development of novel anti-cancer drugs. Here, based on the previous study of survivin and its analysis of the relationship between structure and function, we designed and constructed a series of different amino acid mutants from survivin (TmSm34, TmSm48, TmSm84, TmSm34/48, TmSm34/84, and TmSm34/48/84) fused cell-permeable peptide TATm at the N-terminus, and a dominant negative mutant TmSm34/84 with stronger pro-apoptotic activity was selected and evaluated systematically in vitro. The double-site mutant of survivin (TmSm34/84) showed more robust pro-apoptotic activity against A549 cells than others, and could reverse the resistance of A549 CSCs to adriamycin (ADM) (reversal index up to 7.01) by decreasing the expression levels of survivin, P-gp, and Bcl-2 while increasing cleaved caspase-3 in CSCs. This study indicated the selected survivin dominant negative mutant TmSm34/84 is promising to be an excellent candidate for recombinant anti-cancer protein by promoting apoptosis of cancer cells and their stem cells and sensitizing chemotherapeutic drugs.
Fungal Endophytes of Tahiti Lime (Citrus citrus × latifolia) and Their Potential for Control of Colletotrichum acutatum J. H. Simmonds Causing Anthracnose

Colletotrichum acutatum is one of the causal agents of anthracnose in several crops, and of post-flowering fruit drop (PFD) in citrus and key lime anthracnose (KLA). The pathogen normally attacks flowers, causing lesions only in open flowers. Under very favorable conditions, however, it can also affect flower buds and small fruits, causing complete rotting of the fruit and a premature fall, resulting in major economic crop losses. We isolated endophytic fungi from Tahiti lime to evaluate its diversity, verify its antagonistic capacity against the phytopathogen Colletotrichum acutatum C-100 in dual tests, and evaluate the ability of various endophytic agents to control flowers with induced anthracnose. 138 fungal isolates were obtained from 486 fragments of branches, leaves, and fruit; from which 15 species were identified morphologically. A higher isolation frequency was found in branches and leaves, with a normal level of diversity compared to other citrus species. Of the 15 morphospecies, 5 were trialed against C. acutatum in antagonism tests, resulting in a finding of positive inhibition. 2 endophytic fungi from the antagonism tests demonstrated high inhibition of the phytopathogen, and were thus used in in vivo tests with Tahiti lime flowers, applied in a spore solution. Spore solutions of two molecularly identified species, Xylaria adscendens, and Trichoderma atroviride, reduced the lesions caused by the phytopathogen in these in vivo tests. The finding that these endophytes react antagonistically against C. acutatum may make them good candidates for further biological control research in an agroindustry that requires environmental sustainability.
EEG Assessment in Patients With Disorders of Consciousness: Aims, Advantages, Limits, and Pitfalls

This study presents a brief review of literature exploring simple EEG-polygraphic examinations and procedures that can be carried out at a patient's bedside. These include EEG with a common electrode array and sleep evaluation. The review briefly discusses more complex analytical techniques, such as the application of advanced EEG signal processing methods developed by our research group, to define what type of consistent markers are suitable for clinical use or to better understand complex patient conditions. These advanced analytical techniques aim to detect relevant EEG-based markers that could be useful in evaluating patients and predicting outcomes. These data could contribute to future developments in research.
Effects of Adding Small Combat Games to Regular Taekwondo Training on Physiological and Performance Outcomes in Male Young Athletes

This study investigated the effect of area sizes (4 × 4, 6 × 6, and 8 × 8 m) and effort-pause ratios (free combat vs. 1:2) variation on the physiological and perceptive responses during taekwondo combats (Study 1). In a second study, the effects on physical performance of 8 weeks of small combat-based training added to regular taekwondo training were investigated (Study 2). In random order, 32 male taekwondo athletes performed six (i.e., two effort-to-pause ratios × three area sizes conditions) different 2-min taekwondo combats (Study 1). Thereafter (Study 2), they were randomly assigned to three experimental groups (4 × 4, 6 × 6, and 8 × 8 m) and an active control group (CG). Regarding Study 1, blood lactate concentration [La] before and after each combat, mean heart rate (HRmean) during each combat, and rating of perceived exertion (CR-10) immediately after each combat were assessed. Regarding Study 2, progressive specific taekwondo (PSTT) to estimate maximum oxygen consumption (VO2max), taekwondo-specific agility, and countermovement jump (CMJ) tests were administered before and after 8 weeks of training. Study 1 results showed that 4 × 4 m elicited lower HRmean values compared with 6 × 6 m (d = −0.42 [small], p = 0.030) and free combat induced higher values compared with the 1:2 ratio (d = 1.71 [large], p < 0.001). For [La]post, 4 × 4 m area size induced higher values than 6 × 6 m (d = 0.99 [moderate], p < 0.001) and 8 × 8 m (d = 0.89 [moderate], p < 0.001) and free combat induced higher values than 1:2 ratio (d = 0.69 [moderate], p < 0.001). Higher CR-10 scores were registered after free combat compared with 1:2 ratio (d = 0.44 [small], p = 0.007). For Study 2, VO2max increased after training [F (1, 56) =30.532, p < 0.001; post-hoc: d = 1.27 [large], p < 0.001] with higher values for 4 × 4 m compared with CG (d = 1.15 [moderate], p = 0.009). Agility performance improved after training [F (1, 56) = 4.419, p = 0.04; post-hoc: d = −0.46 [small], p = 0.04] and 4 × 4 m induced lower values in comparison with 6 × 6 m (d = −1.56 [large], p = 0.001) and CG (d = −0.77 [moderate], p = 0.049). No training type influenced CMJ performance. Smaller area size elicited contrasting results in terms of metabolic demand compared with larger sizes (i.e., lower HRmean but higher [La] and CR-10), whereas free combat induced variables' consistently higher values compared with imposed 1:2 ratio (Study 1). Taekwondo training is effective to improve VO2max and agility (Study 2), but small combat training modality should be investigated further.
Computational Analysis of Dynamic Light Exposure of Unicellular Algal Cells in a Flat-Panel Photobioreactor to Support Light-Induced CO2 Bioprocess Development

Cyanobacterial cell factories trace a vibrant pathway to climate change neutrality and sustainable development owing to their ability to turn carbon dioxide-rich waste into a broad portfolio of renewable compounds, which are deemed valuable in green chemistry cross-sectorial applications. Cell factory design requires to define the optimal operational and cultivation conditions. The paramount parameter in biomass cultivation in photobioreactors is the light intensity since it impacts cellular physiology and productivity. Our modeling framework provides a basis for the predictive control of light-limited, light-saturated, and light-inhibited growth of the Synechocystis sp. PCC 6803 model organism in a flat-panel photobioreactor. The model here presented couples computational fluid dynamics, light transmission, kinetic modeling, and the reconstruction of single cell trajectories in differently irradiated areas of the photobioreactor to relate key physiological parameters to the multi-faceted processes occurring in the cultivation environment. Furthermore, our analysis highlights the need for properly constraining the model with decisive qualitative and quantitative data related to light calibration and light measurements both at the inlet and outlet of the photobioreactor in order to boost the accuracy and extrapolation capabilities of the model.