Miracle-dz/UniFAR-FTUs huggingface dataset:
OPTIMUM DESIGN FOR TOURISM LINE BASED ON IMPROVED ANT COLONY SYSTEM ALGORITHM

This paper proposed an available measure to find the shortest line of the tourism. Subjecting a lot of conditions, we aim at selecting to the best tourism line which will spend the less time in an economic way. To the practice travel problem, using K-means algorithm and ant colony optimization algorithm (ACO), we make a framework to solve the multi constraint combination optimization problem and design a real schedule to every feasible tourism line. In the end, we analyze the result and it is available to a traveler.
HISTORIC LAND COVER CHANGE ASSESSMENT OF CHILEAN MEDITERRANEAN COAST: DID FOREST PLANTATIONS REALLY CAUSED FRAGMENTATION?

Abstract. Currently, the Mediterranean coast of Chile has large areas of commercial pine and eucalyptus plantations. Several authors have reported habitat loss and fragmentation of the original “Maulino” native forest due to the expansion of plantations. However, in the Maule coast took place a previous process that affected the native forest. This is the extensive use of forestlands with a moderate slope for the cultivation of wheat. The peak of this process occurred in the second half of the 19th century and meant a great degradation of the soil in the area. In this work, we first look for whether the overexploitation of wheat is the primary cause of the fragmentation of the Maulino forest in the study area. Secondly, we wanted to determine what proportion of the area currently occupied by plantations was previously used for growing wheat. First, using the community of Constitución as a representative area of the central coast we use Species Distribution Modelling to identify the potential original extent of the Maulino forest. Secondly, we created a spatial model to estimate the extension of wheat production using historical data. Finally, we used Landsat-8 images to assess the actual extension of forest plantation. By comparing the three former results, we were able to quantify the effects of wheat production and forest plantation separately, for the first time. The results indicate that 41% of the original native forest was first affected by wheat production and that only 34 % was later replaced by forest plantations. Therefore, the rest of the plantations were installed in places where the forest had previously been cut down for use in wheat production. The main cause of deforestation and forest fragmentation in this landscape was originally the wheat extensive production boosted by the gold rush in California and Australia. Forest commercial plantations further augmented the Maulino forest fragmentation process by significantly reducing its mean patch size and increasing the number of patches.
Circulating Soluble ACE2 Plays an Independent Role to Protect against Vascular Damage in Diabetic Mice

Circulating levels of soluble ACE2 are increased by diabetes. Although this increase is associated with the presence and severity of cardiovascular disease, the specific role of soluble ACE2 in atherogenesis is unclear. Previous studies suggested that, like circulating ACE, soluble ACE2 plays a limited role in vascular homeostasis. To challenge this hypothesis, we aimed to selectively increase circulating ACE2 and measure its effects on angiotensin II dependent atherogenesis. Firstly, in Ace2/ApoE DKO mice, restoration of circulating ACE2 with recombinant murine soluble (rmACE219-613; 1 mg/kg/alternate day IP) reduced plaque accumulation in the aortic arch, suggesting that the phenotype may be driven as much by loss of soluble ACE2 as the reduction in local ACE2. Secondly, in diabetic ApoE KO mice, where activation of the renin angiotensin system drives accelerated atherosclerosis, rmACE219-613 also reduced plaque accumulation in the aorta after 6 weeks. Thirdly, to ensure consistent long-term delivery of soluble ACE2, an intramuscular injection was used to deliver a DNA minicircle encoding ACE219-613. This strategy efficiently increased circulating soluble ACE2 and reduced atherogenesis and albuminuria in diabetic ApoE KO mice followed for 10 weeks. We propose that soluble ACE2 has independent vasculoprotective effects. Future strategies that increase soluble ACE2 may reduce accelerated atherosclerosis in diabetes and other states in which the renin angiotensin system is upregulated.
EFFECT OF LABEL NOISE IN SEMANTIC SEGMENTATION OF HIGH RESOLUTION AERIAL IMAGES AND HEIGHT DATA

Abstract. The performance of deep learning models in semantic segmentation is dependent on the availability of a large amount of labeled data. However, the influence of label noise, in the form of incorrect annotations, on the performance is significant and mostly ignored. This is a big concern in remote sensing applications, wherein acquired datasets are spatially limited, labeling is done by domain experts with possible sources of high inter-and intra-observer variability leading to erroneous predictions. In this paper, we first simulate the label noise while conducting experiments on two different datasets with very high-resolution aerial images, height data, and inaccurate labels, responsible for the training of deep learning models. We then focus on the effect of these noises on the model performance. Different classes respond differently to the label noise. The typical size of an object belonging to a class is a crucial factor regarding the class-specific performance of the model trained with erroneous labels. Errors caused by relative shifts of labels are the most influential label errors. The model is generally more tolerant of the random label noise than other label errors. It has been observed that the accuracy gets reduced by at least 3% while 5% of label pixels are erroneous. In this regard, our study provides a new perspective of evaluating and quantifying the propagation of label noise in the model performance that is indeed important for adopting reliable semantic segmentation practices.
Transcriptome and Physiological Analyses of a Navel Orange Mutant with Improved Drought Tolerance and Water Use Efficiency Caused by Increases of Cuticular Wax Accumulation and ROS Scavenging Capacity

Drought is one of the main abiotic stresses limiting the quality and yield of citrus. Cuticular waxes play an important role in regulating plant drought tolerance and water use efficiency (WUE). However, the contribution of cuticular waxes to drought tolerance, WUE and the underlying molecular mechanism is still largely unknown in citrus. ‘Longhuihong’ (MT) is a bud mutant of ‘Newhall’ navel orange with curly and bright leaves. In this study, significant increases in the amounts of total waxes and aliphatic wax compounds, including n-alkanes, n-primary alcohols and n-aldehydes, were overserved in MT leaves, which led to the decrease in cuticular permeability and finally resulted in the improvements in drought tolerance and WUE. Compared to WT leaves, MT leaves possessed much lower contents of malondialdehyde (MDA) and hydrogen peroxide (H2O2), significantly higher levels of proline and soluble sugar, and enhanced superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) activities under drought stress, which might reduce reactive oxygen species (ROS) damage, improve osmotic regulation and cell membrane stability, and finally, enhance MT tolerance to drought stress. Transcriptome sequencing results showed that seven structural genes were involved in wax biosynthesis and export, MAPK cascade, and ROS scavenging, and seven genes encoding transcription factors might play an important role in promoting cuticular wax accumulation, improving drought tolerance and WUE in MT plants. Our results not only confirmed the important role of cuticular waxes in regulating citrus drought resistance and WUE but also provided various candidate genes for improving citrus drought tolerance and WUE.
A new hybrid analytical model for electromagnetic analysis of wound rotor induction motors

Accurate modeling of slotting and magnetic saturation effects has been two main challenges for analytical modeling of electric machines. In this article, first, different techniques such as sub‐domain (S‐D) model, winding function theory, magnetic equivalent circuit (MEC) model, and conformal mapping (CM) method are introduced to analytical model and analysis of electric machines. These techniques are also compared in terms of their accuracy, and computational complexity. Second, with respect to the comparison results, a new hybrid analytical model is presented for electromagnetic modeling of wound rotor induction motors, which consider the modeling accuracy of slotting and magnetic saturation effect, simultaneously. In this way, the air‐gap region is modeled by using S‐D model, and the iron parts are considered by using MEC model assisted by CMs. In final, the analytical results are verified by comparing with corresponding results obtained through finite element method and experiment set‐up.
Predicting Academic Performance with an Assessment of Students’ Knowledge of the Benefits of High-Level and Low-Level Construal

Metamotivation research suggests that people understand the benefits of engaging in high-level versus low-level construal (i.e., orienting toward the abstract, essential versus concrete, idiosyncratic features of events) in goal-directed behavior. The current research examines the psychometric properties of one assessment of this knowledge and tests whether it predicts consequential outcomes (academic performance). Exploratory and confirmatory factor analyses revealed a two-factor structure, whereby knowledge of the benefits of high-level construal (i.e., high-level knowledge) and low-level construal (i.e., low-level knowledge) were distinct constructs. Participants on average evidenced beliefs about the normative benefits of high-level and low-level knowledge that accord with published research. Critically, individual differences in high-level and low-level knowledge independently predicted grades, controlling for traditional correlates of grades. These findings suggest metamotivational knowledge may be a key antecedent to goal success and lead to novel diagnostic assessments and interventions.
MATCHING PERSISTENT SCATTERER CLUSTERS TO BUILDING ELEMENTS IN MESH REPRESENTATION

Abstract. The deformation time series of Persistent Scatterer points show a correlated behavior if they lay on the same rigid structure and therefore undergo the same deformation process. By clustering, such groups of Persistent Scatterer (PS) points can be identified. We use segmented mesh representations of single buildings, to find the optimal assignment of such clusters to parts of the structure. By applying a quality metric, the assignment is judged quantitatively. The proposed method is useful for the integration of PSInSAR into building information modeling (BIM) and aims at a cost-effective city-wide per-building monitoring.
Prosodic prominence and clefting in L2 focus interpretation

Speech cues available in the utterance can guide the listener to the most important information (focus) and thus facilitate discourse comprehension. It has long been established that prosodic prominence plays an important role in the search for focus in a variety of languages including Mandarin and English; however, it is not yet clear how prosodic prominence interacts with other cues, such as clefting, available in the utterance, especially how L2 learners integrate multiple cues in processing information structure. This paper investigates the relative roles of prosodic and clefting cues in the interpretation of focus position in English utterances by moderate or high proficiency Mandarin listeners of English. It was found that both cues played an important role, with clefting weighting slightly higher. This study contributes significantly to our limited knowledge of how information structure is processed in L2 and has implications for speech perception, language learning and prosodic typology.
Transcriptomic profiling of the extremophile eutrema salsugineum response to environmental stressors

I thank my supervisor, Dr. Ahmed Moustafa, for introducing me to my newly found passion, bioinformatics. I thank him for his support and care throughout my undergraduate and graduate years at AUC. I thank him for being there for me through my last year in the biotechnology program even though he was in another continent. I thank the American University in Cairo for awarding me the lab instruction fellowship for my three years as an undergraduate student; without it, I would not have been able to finish the MSc degree. I thank all my professors from the biology department at AUC; Dr. Rania Siam, Dr. Hamza El Dory, Dr. Suher Zada, and Dr. Walid Fouad. Thank you for providing guidance through my undergraduate and graduate years at AUC. I thank my one and only true love, Yousef Hamed, for supporting me and pushing me forward whenever I was ready to quit. I thank him for believing in me and forcing me to believe in myself. I thank my parents for believing in me and encouraging me to pursue my dreams. I thank my best friends and work collegues Yara El Shorafa and Rafif Kassab for providing me with emotional and physical support whenever I needed it. I thank my friend Omar Sheikha for his endless help with this project, and for offering his office and headphones when I needed a quiet place to write. I thank my friend Mohamed Ibrahim for his endless help with this project. Excel was a piece of cake because of you.
Charging infrastructure planning for ride-sourcing electric vehicles considering drivers’ value of time

ABSTRACT A reliable-charging network is urgently demanded to support electrified ride-sourcing services due to their shorter dwell time, longer daily vehicle miles traveled, and concerns of sacrificing revenue for charging activities. We developed an integer programming (IP) model for the optimal allocation of charging stations and charging plugs to minimize the total investment costs and spatio-temporal varying drivers’ value of time (VOT) for charging activities. The trip chain data of the RideAustin ride-sourcing services have been used as a test case, based on which we estimated the charging needs of ride-sourcing EVs and identified candidate charging locations to fulfill the daily travel needs of ride-sourcing drivers. Through numerical study and sensitivity analyses, we analyze the impacts of different charger types, fleet sizes, government incentives, and VOT considerations on the optimal investment plans and system costs, and show the importance of considering ride-sourcing drivers’ VOT into charging infrastructure planning.
Association between Stroke and Abdominal Obesity in the Middle-Aged and Elderly Korean Population: KNHANES Data from 2011–2019

Obesity and overweight status are primary risk factors for stroke. A relative small number of studies has analyzed the association of abdominal obesity, a crucial indicator for insulin resistance with stroke, compared to general obesity. We aimed to reveal 31,490 records from the Korea National Health and Nutrition Examination Survey (KNHANES). Logistic regression was used to identify the association of abdominal obesity with the risk of stroke. For the multivariate model, covariates were determined based on the cardio-cerebro vascular prediction models. In the sex-specific multivariate logistic regression analysis (including age, antihypertensive drug, diabetes, current smoking, and systolic blood pressure as confounders), the elevated waist circumference (WC) in women was significantly associated with the increased risk for stroke. In case of the categorized form of WC, we discerned the non-linear relationships between WC and the stroke status. The sex-specific associations between the abdominal obesity and stroke status were shown and their relationship pattern exhibited non-linear relationships.
A communication and identity process that mediates parents’ nonaccommodation and sexual minorities’ mental well-being

This study tested sexual minorities’ (SM) surface acting with parents, identity gaps with family, and perceived stress as serial mediators of parents’ nonaccommodation and SM’s mental well-being (i.e., mental health and self-esteem). Participants included 205 sexual minorities who reported on their parents’ unwanted advice about their sexual identity and the degree to which they emphasized divergent values in conversations about LGBTQ+ issues. For both mental health and self-esteem, significant and negative indirect effects emerged for both mother’s and father’s nonaccommodation through a sequence of SM’s surface acting, to personal-enacted identity gap, to perceived stress. These indirect effects, however, were moderated by relational closeness with each parent. Consequently, unwanted advice about children’s SM identities and messages that emphasize divergent values may be harmful even if family relations do not appear overtly contentious or hostile.
Drivers of career success among the visually impaired: Improving career inclusivity and sustainability in a career ecosystem

Visual impairment, as a form of disability, remains understudied in the context of employment and careers. Drawing on career ecosystem and career sustainability theories, we explore factors that lead to career success and career sustainability of visually impaired individuals. We collected qualitative data from 66 visually impaired individuals from India who had experienced varying degrees of career attainment. We applied grounded theory to study their deep-seated attitudes and ingrained behaviors that help build successful and sustainable careers. High-career-attainment participants were extremely resilient, able to bounce back after rejection, and willing to adopt certain key psycho-social processes such as non-acceptance of rejection, relatability (forging positive relations with the sighted), family support, enabling self through technology, and influence mindset change, which led them to be ‘masters of circumstance. ’ Conversely, the characteristics of low-career-attainment participants included their unquestioning acceptance of fate, higher degrees of skepticism, and obligation to support the family, which led them to become ‘victims of circumstance. ’ We contribute to the career ecosystems and career sustainability literature by expanding it to wider populations and crystalizing processes that influence careers. The findings have policy implications for visually impaired individuals as well as for other people with disabilities, as well as for organizations and governments. Individuals should challenge conventional norms, be persistent and improve self-efficacy. Organization should think out of the box in order to win the war for talent by employing hidden talent.
COASTAL HABITAT MAPPING WITH UAV MULTI-SENSOR DATA: AN EXPERIMENT AMONG DCNN-BASED APPROACHES

Abstract. With recent abundant availability of high resolution multi-sensor UAV data and rapid development of deep learning models, efficient automatic mapping using deep neural network is becoming a common approach. However, with the ever-expanding inventories of both data and deep neural network models, it can be confusing to know how to choose. Most models expect input as conventional RGB format, but that can be extended to incorporate multi-sensor data. In this study, we re-implement and modify three deep neural network models of various complexities, namely UNET, DeepLabv3+ and Dense Dilated Convolutions Merging Network to use both RGB and near infrared (NIR) data from a multi-sensor UAV dataset over a Norwegian coastal area. The dataset has been carefully annotated by marine experts for coastal habitats. We find that the NIR channel increases UNET performance significantly but has mixed effects on DeepLabv3+ and DDCM. The latter two are capable of achieving best performance with RGB-only. The class-wise evaluation shows that the NIR channel greatly increases the performance in UNET for green, red algae, vegetation and rock. However, the purpose of the study is not to merely compare the models or to achieve the best performance, but to gain more insights on the compatibility between various models and data types. And as there is an ongoing effort in acquiring and annotating more data, we aim to include them in the coming year.
An effective detection of COVID‐19 using adaptive dual‐stage horse herd bidirectional long short‐term memory framework

COVID‐19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID‐19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID‐19 patients with chest x‐ray images. Chest x‐ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual‐stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID‐19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low‐resolution images into high‐resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high‐density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual‐stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID‐19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F‐measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID‐19 chest x‐ray database.
Motives, Frequency, and Consequences of Cannabis Use Among College Students

We investigated profiles of cannabis use motives among current cannabis-consuming college students. Then we assessed profile differences in demographic characteristics, social contexts of use, regulatory environment, alcohol use, negative affect, negative consequences, and cannabis use. Participants (N = 1, 213) were from three universities in states with different cannabis legislation. Six profiles emerged: Low Motives, Low to Moderate Enhance, High Enhance, High Enhance & Social + Moderate Expand, High Enhance & Cope, and High Motives. Profiles differed in social contexts of use, sex, alcohol use, negative affect, and regulatory environment. Profiles endorsing high and multiple motives had higher cannabis use and negative consequences, relative to profiles with low or fewer motives. Profiles characterized by high avoidance motives (i.e., coping) had the highest cannabis use and consequences. Interventions targeting types and intensity of motives for cannabis-use may help reduce use and related consequences among college students who use cannabis.