Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the psycho-emotional state of the observed based on the analysis of video observations

This paper develops a system for determining the psycho-emotional state of the observed people based on the analysis of video surveillance with the application of artificial intelligence technology using hardware and software tools such as PoseNet, PyTorch, SQLite, FastAPI and Flask. In many areas of human endeavor, there is an urgent need for a surveillance system that can reliably function and detect suspicious activities. To solve this problem, this paper proposes a novel framework for a real-time surveillance system that automatically detects abnormal human activities. The system has been tested and validated in real environments. The results of testing artificial intelligence program models showed the best results (f1 score with values of 0.98–0.99). The weighted average value of the f1-score metric was 0.96, which is quite a high value. The use of PoseNet implemented with PyTorch allowed to accurately determine the pose of the person in the video and extract information about the position of different body parts. The peculiarity of this work lies in the development of artificial intelligence models for automatic detection of possible physical aggression in videos, in the methods of forming an optimal set of features for the development of AI models that identify the aggressor and the victim of bullying. The developed system has the potential to be a useful tool in various fields such as psychology, medicine, security and others where it is important to analyze the emotional state of people based on their physical manifestations. The obtained applied results can be used in educational institutions and in spheres where video analysis is necessary
Betaine Alleviates High-Fat Diet Induced Excessive Lipid Deposition in Gibel Carp Hepatopancreas and L8824 Cells by Enhancing VLDL Secretion through HNF4α/MTTP Pathway

Betaine, a methyl donor, plays a crucial role in lipid metabolism. Previous studies have shown that appropriate betaine supplementation in a high-fat diet reduces triglycerides (TG) of serum and hepatopancreas in fish. However, the underlying mechanism remains unclear. This study examined whether betaine can enhance the secretion of very low-density lipoprotein (VLDL) and sought to identify the specific mechanisms through which this enhancement occurs. A lipid accumulation model was established in gibel carp and L8824 cells using a high-fat diet and oleic acid, respectively. Different doses of betaine (1, 4, and 16 g/kg in the diet; 400 μmol in cell culture) were administered, and measurements were taken for lipid deposition, gene expression of HNF4α, MTTP, and ApoB, as well as the regulation of Mttp and Apob promoters by HNF4α. The results showed that betaine supplementation mitigated lipid droplet accumulation, TG levels, and VLDL production induced by the high-fat diet in gibel carp hepatopancreas and L8824 cells. Moreover, betaine not only increased VLDL content in the cell culture supernatant but also reversed the inhibitory effects of the high-fat diet on protein expression of MTTP, ApoB, and HNF4α in both gibel carp hepatopancreas and L8824 cells. Additionally, HNF4α exhibits transactivating activity on the promoter of Mttp in gibel carp. These findings suggest that betaine supplementation exerts its effects through the HNF4α/MTTP/ApoB pathway, promoting the assembly and secretion of VLDL and effectively reducing lipid accumulation in the hepatopancreas of farmed gibel carp fed a high-fat diet.
Quantification of Polystyrene Uptake by Different Cell Lines Using Fluorescence Microscopy and Label-Free Visualization of Intracellular Polystyrene Particles by Raman Microspectroscopic Imaging

Environmental pollution caused by plastic is a present problem. Polystyrene is a widely used packaging material (e.g., Styrofoam) that can be broken down into microplastics through abrasion. Once the plastic is released into the environment, it is dispersed by wind and atmospheric dust. In this study, we investigated the uptake of polystyrene particles into human cells using A549 cells as a model of the alveolar epithelial barrier, CaCo-2 cells as a model of the intestinal epithelial barrier, and THP-1 cells as a model of immune cells to simulate a possible uptake of microplastics by inhalation, oral uptake, and interaction with the cellular immune system, respectively. The uptake of fluorescence-labeled beads by the different cell types was investigated by confocal laser scanning microscopy in a semi-quantitative, concentration-dependent manner. Additionally, we used Raman spectroscopy as a complementary method for label-free qualitative detection and the visualization of polystyrene within cells. The uptake of polystyrene beads by all investigated cell types was detected, while the uptake behavior of professional phagocytes (THP-1) differed from that of adherent epithelial cells.
THICK-BILLED MURRE STUDIES IN DISKO BAY (RITENBENK) WEST GREENLAND

Studies of Thick-billed Murre were carried out in the colony at Ritenbenk/Innaq which has declined severely and is estimated to 2447 birds in 2006. This report includes studies of colony attendance, population estimate, population modelling and sustainable harvest modelling, chick feeding and foraging activities and migration based on ringing recoveries and satellite telemetry.
The Cognitive Perspective in Strategic Choice

This paper examines to what degree organizations use strategies that focus on maximizing shareholder value (Theory E) or if they use strategies emphasizing the development of organizational capability (Theory O). Applying a cognitive perspective in strategic choice, our main goal was to investigate to what extent cognitive biases influenced strategic choices. A survey was developed that measured different aspects of the cognitive perspective in strategic choice. It was distributed to managers of several medium-sized organizations in Scandinavia (n = 119). The results indicated that managers used mixed strategies (Theory E and O) contrary to recommendations. Results also revealed that illusions of control together with beliefs about change processes skewed the application of strategies towards Theory E. Theoretical and practical implications of the results were finally discussed enlightening the cognitive perspective in strategic choice.
Application of Automated Geospatial Watershed Assessment Tool to Watersheds in Arid Region

This study applied the AGWA ArcGIS tool to the 3.7 hectare Lucky Hills watershed, and three large watersheds (Yuma Desert, Coyote Wash, and Mohawk Wash) in the Barry Marsh Goldwater Range (BMGR) in Arizona. The Kineros model was applied to Lucky Hills watershed to calibrate modeling parameters. Events were grouped by similar precipitation patterns, runoff hydrographs, and precipitation volumes to optimize four key parameters for infiltration and surface runoff. These parameters include the soil suction parameter (G), saturated hydraulic conductivity (Ks), and Manning’s roughness for both the channels and planes. Model calibration was carried on event by event, with 10,000 simulations for each event. The set of parameters that yielded the minimum Root Mean Square Error was chosen as the best-fit parameters. Then, the SWAT and Kineros models were applied to three large watersheds in BMGR to determine possible causes of erosion in the range. Model calibration was attempted based on the observed bed elevation changes using the Digital Elevation Model (DEM) over a period of 13 years. Results for the Lucky Hills showed that grouping events based on precipitation volume yielded varying ranges of best-fit parameters. The six smallest volume precipitation events yielded parameter ranges for G, Ks, cn, and pn as 143.09-147.34 mm, 5.98-7.79 mm/hr, . 03-. 06, and . 03.04, for each parameter respectively. The six medium sized events yielded parameter ranges for G, Ks, cn, and pn as 145.61-147.34 mm, 5.98-11.32 mm/hr, . 03, and . 03-. 06, for each parameter respectively. The six largest volume precipitation events yielded parameter ranges for G, Ks, cn, and pn as 143.09-236.77 mm, 5.98-14.65 mm/hr, . 01-. 03, and . 03-. 07 for each parameter set
Genetic Algorithm for Arbitrage with More than Three Currencies

We develop a genetic algorithm that is able to find the optimal sequence of exchange rates that maximizes arbitrage profits with more than three currencies, being both the triangular arbitrage and the direct exchange rate two special cases of the proposed algorithm. Applying the algorithm to the most traded currencies, we find average profits ranking from 4.5083% to 0.3162% for changing 1 USD for EUR with respect to the direct exchange rate, for different transaction costs, during the period October 2000-April 2012. Our results also suggest that the arbitrage profits increased just after the subprime crisis in summer of 2007 and that they are higher when the market is less liquid.
EventMASK: A Frame-Free Rapid Human Instance Segmentation With Event Camera Through Constrained Mask Propagation

Human Instance Segmentation (HIS) is essential in robotics for applications such as autonomous driving and human-robot interaction, etc. Existing HIS solutions using conventional cameras are computationally expensive and slow. Benefits such as sparsity, high temporal resolution, etc. make the event camera a promising alternative. HIS with an event camera is not actively explored, though. Thus, we introduce EventMASK, a novel HIS solution that makes use of an event camera. EventMASK has been meticulously designed to process sparse raw events asynchronously, enabling low-latency processing. EventMASK employs simple statistical and probabilistic non-deep learning techniques for computational efficiency and adopts mask propagation for real-time performance. To curtail error accumulation, we present an innovative constrained likelihood-based mask updation method. EventMASK's semi-supervised approach circumvents the need for event-level instance labeling. The comprehensive analysis demonstrates EventMASK's robustness in a wide spectrum of scenarios, offering a low-cost and low-latent HIS solution for resource-constrained robotics.
Analysis of Flood Inundation Vulnerability to the Deli Watershed of North Sumatra Using Remote Sensing and GIS Techniques

Deli watersheds are listed as one of the most heavily damaged watersheds in Indonesia, and this causes many problems, such as floods, flash floods, and landslides. Studying this watershed becomes essential because the Deli watershed passes through a major city and has become the main source of flood and other natural disasters in the city such as Medan, The study aims to detect the vulnerability level of the areas around the watershed and the most rapid and possible way to study it is by using remote sensing and GIS, the overlay method by considering several parameters that affect the level of vulnerability to flooding in Deli Watershed, North Sumatra. Some of the parameters considered include the topographic wetness index, slope, land elevation, river flow density, land use cover, normalized difference vegetation index, distance to river, and rainfall. Each parameter is given a weight and value according to each classifier. The overlay process is carried out using ArcGIS 10.8. 1 software, results are divided into 5 levels of area vulnerabilities all around the area, the study shows that all parts of Medan City in the Deli watershed have a high level of flood vulnerability and some areas with higher ground elevation such as Tanah Karo have spots area that is vulnerable with floods such as Daulu District with 0.45 ha and Semangat Gunung District with 0.63 ha of its areas are vulnerable to floods. The Topographic wetness index, Normalized difference vegetation Index, slope, and land use are the main factors causing flooding with weighting values 16, 13, 11, and 14. The other parameters elevation, flow density, distance to the river, rainfall, and soil class each have weighting values 7, 10, 11, 8, and 10. Slope with a range of 0-8% in most areas of Medan City are classified as very prone to flood inundation. This is due to the area's flat topography and low river flow density, the existing 3.5 km old sewer in the Deli Tua area is unable to accommodate the flood inundation load in the city of Medan and it is necessary to build two new sewers which are more effective and efficient in diverting the flow of water, before entering the city of Medan which makes the area vulnerable to becoming a water catchment area when it rains, thereby increasing the risk of flooding. With this flood-prone map, efforts to manage and mitigate floods in Medan City can be carried out. Information on vulnerable locations can be used to take appropriate preventive and countermeasures, such as building adequate drainage, regulating land use, and controlling river flows.
Quantification of the Hydrological Components to Estimate Water Balance of Lake Badovc, Kosovo

This paper aims to quantify the hydrological (Rainfalls, surface runoff, stream inflow, evaporations, water level fluctuation and abstraction) components of Lake Badovc watershed for the period 2013-2015. For the scope of water administration of the lake, it is important to understand which factors are affecting the unexpected lowering of water level in Lake Badovc. The main hydrological components are quantified based on the annual (501.6, 871.36 and 626.0mm) rainfalls measured during the observation (year 2013, 2014 and 2015, respectively) period. The lowest (92.1 mm) runoff value was in 2013 which represents 18.3% of annual rainfalls, while the highest (250.1 mm) one was in 2015 representing 40.0% of annual rainfalls. Real evaporated rainfalls from catchment are almost equal for each monitored year. Contribution of groundwater discharge in the amount of water runoff for each monitored year is more than 50%. High (417.8mm) water infiltration rates were found in 2014 and they are considered to be caused by low soil moisture conditions and high amount of rainfall. Water loss from lake and low runoff rate from catchment basin are the main factors affecting the water level in Lake Badovc. An average annual deficit of 3,701,327 m3 was found in lake water balance calculations which was considered as water loss from the lake.
New theropod dinosaur teeth from the Middle Jurassic of the Isle of Skye, Scotland

The Middle Jurassic is a largely mysterious interval in dinosaur evolution, as few fossils of this age are known worldwide. In recent years, the Isle of Skye has yielded a substantial record of trackways, and a more limited inventory of body fossils, that indicate a diverse fauna of Middle Jurassic dinosaurs living in and around lagoons and deltas. Comparatively little is known about the predators in these faunas (particularly theropod dinosaurs), as their fossils are among the rarest discoveries. We here report two new isolated theropod teeth, from the Valtos Sandstone Formation and Lealt Shale Formation of Skye, which we visualized and measured using high-resolution x-ray computed microtomographic scanning (µCT) and identified via statistical and phylogenetic analyses of a large comparative dental dataset. We argue that these teeth most likely represent at least two theropod species – one small-bodied and the other large-bodied – which likely belonged to one or several clades of basal avetheropods (ceratosaurs, megalosauroids, or allosauroids). These groups, which were diversifying during the Middle Jurassic and would become dominant in the Late Jurassic, filled various niches in the food chain of Skye, probably both on land and in the lagoons. Supplementary material: Character lists, datasets, and measurements are available at https://doi.org/10.6084/m9.figshare. c.4452533
DEVELOPMENT FLOW DURATON CURVE FOR CRITICALITY ASSESMENT OF RIVER BASIN APPLIED AT THE UPPER CITARUM RIVER BASIN, INDONESIA

A river basin is a landscape unit in which a hydrologic cycle completely occurs in every respect. River basins, initially healthy and natural, are slipping into critical conditions, affected by urbanization processes. Hydrologic imbalance happens due to land-use change resulting in increased runoff and reduced soil infiltration capability. The change of a river basin from a fine healthy hydrologic condition into a critical state is shown by flow duration curve and its least square line. Having been applied at the Upper Citarum river basin using ArcSWAT hydrologic model and SWAT-CUP calibration program, the curves could map the health range of the river basin showing wheather the river basin was heading towards a critical condition. Based on modeling of FDC on natural and fully utilized condition, the criticality feature is the increase in high flow and decrease in low flow. The upper Citarum river basin was in criticality process which shown by high flow increases and low flow decreases over the period 1981-2010. Using model of FDC’s gradient, the criticality value of the period 1981-1986 was 3.15 and it increased to 3.35 in the period of 2005-2010. The increase is correlated with land use change in increasing settlements, dry crops, barrenland, and forest decline.
Tackling Infectious Diseases with Rapid Molecular Diagnosis and Innovative Prevention

Infectious diseases (IDs) are a leading cause of death. The diversity and adaptability of microbes represent a continuing risk to health. Combining vision with passion, our transdisciplinary medical research team has been focussing its work on the better management of infectious diseases for saving human lives over the past five decades through medical discoveries and innovations that helped change the practice of medicine. The team used a multiple-faceted and integrated approach to control infectious diseases through fundamental discoveries and by developing innovative prevention tools and rapid molecular diagnostic tests to fulfill the various unmet needs of patients and health professionals in the field of ID. In this article, as objectives, we put in context two main research areas of ID management: innovative infection prevention that is woman-controlled, and the rapid molecular diagnosis of infection and resistance. We also explain how our transdisciplinary approach encompassing specialists from diverse fields ranging from biology to engineering was instrumental in achieving success. Furthermore, we discuss our vision of the future for translational research to better tackle IDs.
Automated Detection of Keratorefractive Laser Surgeries on Optical Coherence Tomography using Deep Learning

Financial interest: Dr Reinstein is a consultant for Carl Zeiss Meditec (Carl Zeiss Meditec AG, Jena, Germany). Dr Reinstein is also a consultant for CSO Italia (Florence, Italy) and has a proprietary interest in the Artemis technology (ArcScan Inc, Golden, Colorado) through patents administered by the Cornell Center for Technology Enterprise and Commercialization (CCTEC), Ithaca, New York. Dr Awwad is a consultant for Carl Zeiss Meditec (Carl Zeiss Meditec AG, Jena, Germany). Drs Assaf and Awwad have financial interest in NeuralVision – FZCO (Dubai, UAE). In addition to the previously disclosed interests, a full patent has been filed by Drs Assaf, Awwad, and Yazbeck pertaining to the methodologies and technologies discussed in this study. The remaining authors have no proprietary or financial interest in the materials presented herein. Funding: This research was partially funded by the Suhail Muasher Endowed Medical Student Research Award.
TinyML Empowered Transfer Learning on the Edge

Tiny machine learning (TinyML) is a promising approach to enable intelligent applications relying on Human Activity Recognition (HAR) on resource-limited and low-power Internet of Things (IoT) edge devices. However, designing efficient TinyML models for these devices remains challenging due to computational resource constraints and the need for customisation to unique use cases. To address this, we propose a novel approach that utilises transfer learning (TL) techniques on edge micro-controller units (MCUs) to accelerate TinyML development. Our strategy involves pre-training generalised ML models on large-scale and varied datasets and fine-tuning them on-device for specific applications using TL. We demonstrate the effectiveness of our approach for HAR by experimenting with a convolutional neural network, long short-term memory TL (CNN-LSTM-TL) model architecture and visualisation techniques like t-distributed stochastic neighbour embedding (t-SNE) for dimensionality reduction. To further validate our model’s proficiency and adaptability, we conducted extensive testing using two distinct datasets: MotionSense and UCI. This dual-dataset approach allowed us to assess the robustness of our model across different data domains, showcasing its versatility and effectiveness in various HAR scenarios. Our results show significant model accuracy and reduced training time while maintaining high inference rates and low MCU memory footprint. We also provide insights into best practices for implementing TL on edge MCUs and evaluate classification performance metrics such as accuracy, precision, recall, F1 score, and categorical cross-entropy loss. Our work lays a solid foundation for faster and more efficient TinyML deployments through TL framework across different application domains and types of edge IoT devices.
Supplementation with Astragalus Root Powder Promotes Rumen Microbiota Density and Metabolome Interactions in Lambs

Simple Summary The gut microbiota plays an important role in animals. Metabolomics analysis was used in this study to investigate the rumen microbiota density and metabolome interactions in lambs supplemented with Astragalus root powder; the density of the rumen microbiota and its relationship with the metabolome in lambs supplemented with Astragalus root powder were evaluated. The results showed a significant correlation between the rumen microbiota and its metabolome in lambs. These findings have important implications for livestock nutrition and management practices, particularly in terms of improving overall productivity and profitability. Abstract The gut microbiota is highly symbiotic with the host, and the microbiota and its metabolites are essential for regulating host health and physiological functions. Astragalus, as a feed additive, can improve animal immunity. However, the effects of Astragalus root powder on the rumen microbiota and their metabolites in lambs are not apparent. In this study, thirty healthy Hu sheep lambs with similar body weights (17.42 ± 2.02 kg) were randomly selected for the feeding experiment. Lambs were fed diets supplemented with 0.3% Astragalus root powder, and the rumen microbiota density and metabolome were measured to determine the effects of Astragalus on the health of lambs in the rumen. The results showed that the relative abundance of Butyrivibrio fibrisolvens (Bf), Ruminococcus flavefaciens (Rf), Succiniclasticum (Su), and Prevotella (Pr) in the rumen was increased in the Astragalus group (p < 0.01), and metabolic profiling showed that the metabolites, such as L-lyrosine and L-leucine, were upregulated in the Astragalus group (p < 0.01). KEGG functional annotation revealed that upregulated metabolites were mainly enriched in the pathways of amino acid metabolism, lipid metabolism, fatty acid biosynthesis, and bile secretion in the Astragalus group, and downregulated metabolites were enriched in the pathways of methane metabolism and other pathways. Correlation analysis revealed that butyric acid was positively correlated with Roseburia and Blautia (p < 0.05) and negatively correlated with Desulfovibrio (p < 0.05). Thus, by analyzing the interactions of Astragalus root powder with the density of rumen microorganisms and their metabolites in lambs, it was shown that Astragalus root powder could improve the structure of rumen microbiota and their metabolites and then participate in the regulation of amino acid metabolism, lipid metabolism, immune metabolism, and other pathways to improve the efficiency of energy absorption of the lambs.