Miracle-dz/UniFAR-FTUs huggingface dataset:
Market Segmentation Based on Ecotourism Motivations in Marine Protected Areas and National Parks in the Galapagos Islands, Ecuador

ABSTRACT Carvache-Franco, M. ; Carvache-Franco, W. , and Manner-Baldeon, F. , 2021. Market segmentation based on ecotourism motivations in marine protected areas and national parks in the Galapagos Islands, Ecuador. Journal of Coastal Research, 37(3), 620–633. Coconut Creek (Florida), ISSN 0749-0208. Demand segmentation in ecotourism is a strategy that supports efficient planning for the conservation of resources. This study aimed to create a market segmentation based on ecotourism motivations for protected areas on islands. Data were collected through a questionnaire given to 822 tourists on the Galapagos Islands, a province of Ecuador. The Galapagos Archipelago is located in the Pacific Ocean and has been declared a World Heritage Site by the United Nations Educational, Scientific and Cultural Organization (2019). It is of volcanic origin surrounded by marine currents. It is made up of two protected areas: Galapagos National Park, which covers the land surface, and the Galapagos Marine Reserve, which protects the marine environment. The findings were later evaluated with a factor analysis and a K-means clustering method. The results showed that there are six motivational dimensions, labeled: self-development, interpersonal relationships and ego-defensive function, escape, building personal relationships, nature appreciation, and reward. Three groups of tourists were identified. The first, the reward and escape group, had high motivations related to nature, experiencing new things, exploring the unknown, fun, escaping the routine, and destressing. The second, multiple motives tourists, scored highly on all motivational variables. Finally, nature tourists had high motivation in aspects related to nature. These results offer a basis for tourism service providers to develop specific products adapted to various tourist motivations, to improve offers adapted to tourist expectations, to increase satisfaction and tourist return rate, and ultimately to benefit the economy of marine protected areas and national parks with higher employment and income. This study also can help public institutions design appropriate plans for visitors to these island protected areas.
A Histopathological–Biochemical Health Assessment of Blue Mussel Mytilus edulis

ABSTRACT Aquaculture of the blue mussel Mytilus edulis in the Gulf of Maine is a growing industry at a time when wild mussel populations are threatened by a rapidly changing ocean. Intertidal mussel beds have largely disappeared in the region raising concerns over the long-term viability of mussel farming. Histology and lipid fatty acid analysis were used to assess gametogenesis, energy investment, and pathology of farmed mussels collected twice monthly for three years in Casco Bay, ME. Energy investment in reproduction and storage differed significantly between years, suggesting interannual variability. Wet weight of fatty acids such as DHA and EPA corresponded to pre-spawning periods, when gonad tissue was most abundant. Overall, pathology assessment showed low levels of common pathogens, parasites, and cellular abnormalities. The survey did, however, reveal high levels of oocyte atresia, a probable indicator of physiological or environmental stress from unfavorable spawning conditions. In addition, the presence of the potentially damaging digenetic trematode Proctoeces maculatus was documented using histology, marking the northernmost detection in the Northwest Atlantic and a likely climate-driven range expansion. These trends may signal a challenging future for blue mussels in the Gulf of Maine. Forward-looking farm mitigation practices informed by these results should be developed to ensure future sustainability of this industry.
Effect of Temperature on the Development and Settlement of the Abalone Larvae Haliotis midae: Considerations for Abalone Hatchery Management and Stock Enhancement

ABSTRACT Abalones are poikilotherms, and their rate of development is intrinsically linked with temperature. Understanding the effect of temperature on development is key for the successful production planning in abalone hatcheries and the reseeding of cultured abalone larvae for stock enhancement. This study investigates the effect of temperature on the development of the abalone larvae Haliotis midae. Newly hatched trochophore larvae were raised at temperatures of 14°C, 16°C, 18°C, 20°C, and 22°C until settlement. The temperature had a significant effect on the larval development—most rapid at 22°C [3 days and 65 degree-days (DD), °C day] and slowest at 14°C (6.7 days and 95 DD). Larval settlement was highest at 22°C (43.44% ± 3.73%, larvae). Larvae should preferably be seeded at temperatures between 20°C and 22°C as this will potentially increase settlement, thereby reducing larval dispersion outside of the seeding area.
Potential Distribution and Environmental Niche of the Black Corals Antipathes galapagensis and Myriopathes panamensis in the Eastern Tropical Pacific1

Abstract: Antipathes galapagensis and Myriopathes panamensis are black coral species (Order Antipatharia) distributed in the Eastern Pacific from the Gulf of California to the coasts of Peru, at depths from -10 m to -200 m for A. galapagensis and to -50 m for M. panamensis. Antipatharians have great ecological importance as engineer species and major constituents of “coral forests,” which are recognized as vulnerable marine ecosystems. Antipathes galapagensis has been a target of fisheries in several countries and has frequently been overexploited. Thus, the aim of this study was to compile a database of georeferenced records of A. galapagensis and M. panamensis, and use distribution data and environmental variables to construct habitat suitability models with Maxent software, in order to better understand black coral environmental requirements and potential distributions in the Eastern Tropical Pacific. The models showed good fit and performance (AUC > 0.9); key variables that drive the potential distribution of both species were chlorophyll a, nutrient concentration, and seawater temperature. The models did not predict occurrences beyond the recognized distribution range limits of the species; notwithstanding, they indicated that the Pacific coast of the Baja California Peninsula and the central and southern coasts of the Mexican Pacific have areas of high suitability for the presence of these corals. To our knowledge, no black coral records from these sites exist in the literature to date; thus, field validations are needed to verify the model predictions. With these results, it is possible to offer suggestions of new conservation sites for black corals based on the areas with the highest probability of occurrence of the species.
Dispersive stresses in turbulent flow over riblets

Abstract We carry out direct numerical simulations of turbulent flow over riblets, streamwise- aligned grooves that are designed to reduce drag by modifying the near-wall flow. Twenty riblet geometries and sizes are considered, namely symmetric triangular with tip angle $30^\circ$, $60^\circ$ and $90^\circ$, asymmetric triangular, blade and trapezoidal. To save on computational cost, simulations are performed using the minimal-channel flow configuration. With this unprecedented breadth of high-fidelity flow data near the wall, we are able to obtain more general insights into the flow physics of riblets. As observed by García-Mayoral & Jiménez (J. Fluid Mech., vol. 678, 2011, pp. 317–347), we confirm that the drag-change curves of all the present groove geometries better collapse when reported with the viscous-scaled square root of the groove area $\ell _g^+$, rather than the riblet spacing $s^+$. Using a two-dimensional generalization of the Fukagata–Iwamoto–Kasagi identity in difference form we isolate the different drag-change contributions. We show that the drag increase associated with dispersive stresses carried by secondary flows can be as important as the one associated with the turbulent stresses and the pre-eminence of dispersive stresses can be estimated by the groove width at the riblet mean height.
Two-layer fluid flows on inclined surfaces

Abstract We present a theoretical and experimental study of the dynamics of two-layer viscous fluid flows on inclined surfaces, motivated by natural and industrial phenomena involving the interactions between two fluid layers. A general model describing the evolution of two fluids on an inclined substrate is developed and explored to reveal a rich variety of flow regimes for different modes of release. The asymptotic reduction of this problem due to the dominance of the along-slope component of gravity is shown to yield considerable analytical inroads compared with previous studies of multi-layer flow configurations, which have focused exclusively on the case of horizontal beds. For the canonical example in which two fluids are introduced at a constant flux, the flow forms two regions: an upstream region containing both fluids, and a downstream region comprised purely of the lighter fluid, with a sharp intervening jump in thicknesses between the two. By constructing similarity solutions, we establish a full regime diagram of the possible configurations over all asymptotic limits of the viscosity, flux and density ratios. For the release of two fixed volumes of fluid, the layers separate completely into two disjoint but connected regions, contrasting in essential structure from the constant flux case. Even a small volume of the heavier fluid is able to significantly accelerate the propagation of the lighter fluid in front of it. Excellent agreement is found between our theoretical predictions and the results of a series of laboratory experiments.
Investigation of the factors influencing the efficiency of a solar still combined with a solar collector

AbstractA solar still was designed for the evaporation of desalination brine. The influence of several factors, such as the basin heating, the material of the cover (glass or polycarbonate), the existence of a mirror, the activation of an air extractor, and the existence of a black painted floor in the solar still, was evaluated in terms of their contribution to brine evaporation. The experiments were conducted with a factorial design approach. The combination of the factors that produced the best results was used in a subsequent daily monitoring study for brine evaporation. The monitoring parameters were the hourly average incident radiation, the changes in the temperature, the brine mass, and the brine volume. The accumulated amounts of the solar energy were calculated, and the correlation relationship was assessed.
RICAPS: residual inception and cascaded capsule network for broadcast sports video classification

The field of broadcast sports video analysis requires attention from the research community. Identifying the semantic actions within a broadcast sports video aids better video analysis and highlight generation. One of the key challenges posed to sports video analysis is the availability of relevant datasets. In this paper, we introduce a new dataset SP-2 related to broadcast sports video (available at https://github.com/abdkhanstd/Sports2). SP-2 is a large dataset with several annotations such as sports category (class), playfield scenario, and game action. Along with the introduction of this dataset, we focus on accurately classifying the broadcast sports video category and propose a simple yet elegant method for the classification of broadcast sports video. Broadcast sports video classification plays an important role in sports video analysis as different sports games follow a different set of rules and situations. Our method exploits and explores the true potential of capsule network with dynamic routing, which was introduced recently. First, we extract features using a residual convolutional neural network and build temporal feature sequences. Further, a cascaded capsule network is trained using the extracted feature sequence. Residual inception cascaded capsule network (RICAPS) significantly improves the performance of broadcast sports video classification as deeper features are captured by the cascaded capsule network. We conduct extensive experiments on SP-2 dataset and compare the results with previously proposed methods, and the results show that RICAPS outperforms the previously proposed methods.
EvoGAN: an evolutionary GAN for face aging and rejuvenation

In biology, evolution is the gradual change in the characteristics of a species over several generations. It has two properties: 1) The change is gradual, and 2) long-term changes are relied on short-term changes. Face aging/rejuvenation, which renders younger or elder facial images, follows the principles of evolution. Inspired by this, we propose an Evolutionary GANs (EvoGAN) for face aging/rejuvenation by making each age transformation smooth and decomposing a long-term transformation into several short-terms. Specifically, since short-term facial changes are gradual and relatively easy to render, we first divide the ages into several groups (i.e., chronologically from child, adult to elder). Then, for each pair of adjacent groups, we design two age transforms for face aging and rejuvenation, which are supposed to preserve personal identify information and predict age-specific characteristics. Compared with the mainstream for face aging/rejuvenation, i.e., conditional GANs based methods utilizing one-hot age vector as an age transformation condition, our smooth EvoGAN abandons this condition and can better predict age-specific factors (e.g., the drastic shape and appearance change from an adult to a child). To evaluate our EvoGAN, we construct a challenging dataset FFHQ_Age. Extensive experiments conducted on the dataset demonstrate that our model is able to generate significantly better results than the state-of-the-art methods qualitatively and quantitatively.
On-the-fly, data-driven reachability analysis and control of unknown systems: an F-16 aircraft case study

We describe data-driven algorithms, DaTaReach and DaTaControl, for reachability analysis and control of systems with a priori unknown nonlinear dynamics. The resulting algorithms provide provable performance guarantees while satisfying real-time constraints. To this end, they merge data from a single finite-horizon trajectory and, if available, various forms of side information derived from laws of physics and qualitative properties of the system. Specifically, DaTaReach constructs a differential inclusion that contains the unknown vector field. Then, it over-approximates the reachable set through interval Taylor-based methods applied to systems with dynamics described as differential inclusions. DaTaControl achieves near-optimal and convex-optimization-based control of the system through the computed over-approximations and the receding horizon framework. We empirically demonstrate that DaTaControl outperforms, in terms of optimality of the control and computation time, state-of-the-art control approaches based on system identification and contextual optimization. Finally, using the scenario of an F-16 aircraft diving towards the ground, we show how DaTaControl prevents a ground collision using only the measurements obtained during the dive and elementary laws of physics as side information.
Overlap classification mechanism for skeletal bone age assessment

The bone development is a continuous process, however, discrete labels are usually used to represent bone ages. This inevitably causes a semantic gap between actual situation and label representation scope. In this paper, we present a novel method named as overlap classification network to narrow the semantic gap in bone age assessment. In the proposed network, discrete bone age labels (such as 0-228 month) are considered as a sequence that is used to generate a series of subsequences. Then the proposed network makes use of the overlapping information between adjacent subsequences and output several bone age ranges at the same time for one case. The overlapping part of these age ranges is considered as the final predicted bone age. The proposed method without any preprocessing can achieve a much smaller mean absolute error compared with state-of-the-art methods on a public dataset.
Neural Networks for Entity Matching: A Survey

Entity matching is the problem of identifying which records refer to the same real-world entity. It has been actively researched for decades, and a variety of different approaches have been developed. Even today, it remains a challenging problem, and there is still generous room for improvement. In recent years, we have seen new methods based upon deep learning techniques for natural language processing emerge. In this survey, we present how neural networks have been used for entity matching. Specifically, we identify which steps of the entity matching process existing work have targeted using neural networks, and provide an overview of the different techniques used at each step. We also discuss contributions from deep learning in entity matching compared to traditional methods, and propose a taxonomy of deep neural networks for entity matching.
Intracellular and Extracellular Roles of Granzyme K

Granzymes are a family of serine proteases stored in granules inside cytotoxic cells of the immune system. Granzyme K (GrK) has been only limitedly characterized and knowledge on its molecular functions is emerging. Traditionally GrK is described as a granule-secreted, pro-apoptotic serine protease. However, accumulating evidence is redefining the functions of GrK by the discovery of novel intracellular (e.g. cytotoxicity, inhibition of viral replication) and extracellular roles (e.g. endothelial activation and modulation of a pro-inflammatory immune cytokine response). Moreover, elevated GrK levels are associated with disease, including viral and bacterial infections, airway inflammation and thermal injury. This review aims to summarize and discuss the current knowledge of i) intracellular and extracellular GrK activity, ii) cytotoxic and non-cytotoxic GrK functioning, iii) the role of GrK in disease, and iv) GrK as a potential therapeutic target.
A Three-Gene Peripheral Blood Potential Diagnosis Signature for Acute Rejection in Renal Transplantation

Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood. Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation. Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells. Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.
Musical Hallucinations in Chronic Pain: The Anterior Cingulate Cortex Regulates Internally Generated Percepts

The anterior cingulate cortex (ACC) has been extensively implicated in the functional brain network underlying chronic pain. Electrical stimulation of the ACC has been proposed as a therapy for refractory chronic pain, although, mechanisms of therapeutic action are still unclear. As stimulation of the ACC has been reported to produce many different behavioral and perceptual responses, this region likely plays a varied role in sensory and emotional integration as well as modulating internally generated perceptual states. In this case series, we report the emergence of subjective musical hallucinations (MH) after electrical stimulation of the ACC in two patients with refractory chronic pain. In an N-of-1 analysis from one patient, we identified neural activity (local field potentials) that distinguish MH from both the non-MH condition and during a task involving music listening. Music hallucinations were associated with reduced alpha band activity and increased gamma band activity in the ACC. Listening to similar music was associated with different changes in ACC alpha and gamma power, extending prior results that internally generated perceptual phenomena are supported by circuits in the ACC. We discuss these findings in the context of phantom perceptual phenomena and posit a framework whereby chronic pain may be interpreted as a persistent internally generated percept.
Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence–based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives.