Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding tourists' travel behavior before, during, and after the trip with data from social media platforms

Analyzing tourists' travel behavior at different stages of a trip can help to better understand their travel demands, providing valuable suggestions for destination management. In this article, we propose a research framework that can help in comprehensively analyzing tourists' travel behavior from the perspective of the three main stages: before, during, and after the trip. A case study was conducted based on user‐generated content data from the Old Town of Lijiang to verify the feasibility of the proposed research framework. The results show that: (1) before the trip, tourists inquire about destinations and optimal stay; (2) during the trip, tourists are attracted to natural sceneries; (3) after the trip, tourists discuss experience and services. Travel behavior in stages provide detailed (inter‐, intra‐, and individual attractions) guidance (from tourists) for destinations. The proposed framework provides a novel perspective for exploring the spatiotemporal behavioral features of tourists at different stages of their trips.
Limit distributions for compounded sums of extreme order statistics

Let X (1) ≦ X (2) ≦ ·· ·≦ X (N(t)) be the order statistics of the first N(t) elements from a sequence of independent identically distributed random variables, where {N(t); t ≧ 0} is a renewal counting process independent of the sequence of X's. We give a complete description of the asymptotic distribution of sums made from the top kt extreme values, for any sequence kt such that kt → ∞, kt /t → 0 as t → ∞. We discuss applications to reinsurance policies based on large claims.
Optimal portfolio problems under model ambiguity

The topic of this thesis is portfolio optimization under model ambiguity, i.e. a situation when the probability distribution of the events in the sample space is not known. The financial market studied is driven by a Brownian motion: a continuous driving element, and a doubly stochastic Poisson random process: a discontinuous driving element. What separates the doubly stochastic Poisson random process from the standard Poisson case, is that the jump intensity is a stochastic process. From a modeling point of view, this adds more flexibility in capturing hidden random effects. Models of this type appear in the literature of credit risk and in financial price modeling in the class of stochastic volatility models. See e.g. [9] and [4], respectively. In this thesis we assume that the investing agent is ambiguity averse, i.e. the agent does not take any risk with respect to the uncertainty of the probability distribution, and thus relates to the worst case probability distribution. A dynamic risk measure that respects the agents ambiguity aversion is applied to quantify the risk of a hedging strategy, and the agent wishes to make the risk vanish at all times. The optimization problem takes the form of a stochastic differential game, in which the agent minimizes the risk of the hedging strategy, while the opponent drives in the opposite way proposing a probability distribution yielding the worst case scenario. This thesis entails two methods of solving this stochastic differential game. First, through backward stochastic differential equations (BSDEs), and then using the maximum principle. In this thesis, the approach with BSDEs will give a solution of a price process at all times in the give horizon, while the approach using the maximum principle will give a solution at the initial time. This approach of pricing is of great interest to insurers, as it gives a supplement to Value-at-Risk and Estimated Shortfall calculations, and gives a benchmark for capital needed to withstand extreme scenarios.
Streamable Generalized Voronoi Tessellation Model for Tomographic Images

Assessing the mechanical behaviour of foams is far from being trivial [1, 2]. The two main classes of existing numerical models are deterministic models [1, 3] and stochastic models [2, 4]. They generally require extensive image analyses from foam samples and/or statistical estimations of their most significant geometrical parameters[5, 6]. The present contribution is a tool to generate deterministic tessellations based on ellipsoidal approximations of foam cells. The conventional image analysis steps [6] are partially replaced by a Hessian-based removal and by a clustering of local maxima of the distance transform. Clusters are then approximated by ellipsoids which are expanded in order to match cell walls. This method allows processing of very large 3D-images by slicing them and possibly distribute them on a cluster of computers. Then, a generalized Voronoi diagram using the ellipsoids as seeds is generated and allows the idealized geometric model to take into account the anisotropy of the cells. Finally, a mesh is generated from this model using conventional mesh generation algorithms. Figure 1 shows a tessellation applied on a 670x670x670 3D-image: (a) X-ray tomographic images as provided by E. Plougonven (Department of Applied Chemistry, University of Liège), (b) 3-dimensional rendering and (c) 3D tetrahedral mesh of the idealized geometry. Finite element simulations based on the obtained result will be validated through experimental measures.
Fully Anisotropic Solution of the Three Dimensional Boltzmann Transport Equation

Title of dissertation: Fully Anisotropic Solution of the Three Dimensional Boltzmann Transport Equation Francis G. VanGessel, Masters of Science, 2016 Thesis directed by: Professor Peter W. Chung Department of Mechanical Engineering The development of accurate modeling techniques for nanoscale thermal transport is an active area of research. Modern day nanoscale devices have length scales of tens of nanometers and are prone to overheating, which reduces device performance and lifetime. Therefore, accurate temperature profiles are needed to predict the reliability of nanoscale devices. The majority of models that appear in the literature obtain temperature profiles through the solution of the Boltzmann transport equation (BTE). These models often make simplifying assumptions about the nature of the quantized energy carriers (phonons). Additionally, most previous work has focused on simulation of planar two dimensional structures. This thesis presents a method which captures the full anisotropy of the Brillouin zone within a three dimensional solution to the BTE. The anisotropy of the Brillouin zone is captured by solving the BTE for all vibrational modes allowed by the Born Von-Karman boundary conditions.
EliCoDe at MultiGED2023: fine-tuning XLM-RoBERTa for multilingual grammatical error detection

In this paper we describe the participation of our team, E LI C O D E , to the first shared task on Multilingual Grammatical Error Detection, MultiGED, organised within the work-shop series on Natural Language Processing for Computer-Assisted Language Learning (NLP4CALL). The multilingual shared task includes five languages: Czech, English, German, Italian and Swedish. The shared task is tackled as a binary classification task at token level aiming at identifying correct or incorrect tokens in the provided sentences. The submitted system is a token classifier based on XLM-RoBERTa language model. We fine-tuned five different models—one per each language in the shared task. We devised two different experimental settings: first, we trained the models only on the provided training set, using the development set to select the model achieving the best performance across the training epochs; second, we trained each model jointly on training and development sets for 10 epochs, retaining the 10-epoch fine-tuned model. Our submitted systems, evaluated us-ing F 0 . 5 score, achieved the best performance in all evaluated test sets, except for the English REALEC data set (second classified). Code and models are publicly available at https: //github.com/davidecolla/EliCo De .
Chatbot Development using APIs and Integration into the MOOC

In recent years, chatbot technologies have evolved into modern information and communication technology applications that perform many virtual tasks, including learning. One of the challenges in improving the chatbot is the insufficient knowledge base of chatbots, including education-oriented conversational agents, the challenges in connecting the chatbot with course content on Massive Open Online Course platforms. In this study, a chatbot was developed to answer questions using publicly available technologies, specifically Application Program Interfaces (APIs) that promise convenient user accessibility via APIs, such as the Facebook Messenger platform along with wit.ai API, Canvas MOOC API, and Wikipedia API. API technologies were used to connect the chatbot to selected course content on the MOOC platform as well as to large knowledge bases such as Wikipedia to expand the knowledge base of the Conversational Agent. The course selected for the chatbot integration was on general informatics topics. Most course participants interacted with the chatbot via the Facebook Messenger platform using their handheld devices. Thus, integrating the chatbot into a widely used platform such as Facebook Messenger is a convenient and effective way for reaching learners. The API technology enabled an efficient connection between the chatbot and third-party apps, including the Messenger app, wit.ai, Canvas MOOC, and Wikipedia. This was due to the variety, richness, manipulation capabilities, and format of data that an API can transfer. In addition, the Wikipedia API seemed to be a vast source of information for expanding the chatbot's knowledge base. Not all of the queries posed to the chatbot were part of the course content. Some participants questioned the personality of the chatbot and were curious about the persona of the conversational agent. This suggests that a chatbot that has been endowed with some personality traits is stimulating and more likely to be accepted by learners.
Production and Characterization of ACE Inhibitory and Anti-Diabetic Peptides from Buffalo and Camel Milk Fermented with Lactobacillus and Yeast: A Comparative Analysis with In Vitro, In Silico, and Molecular Interaction Study

The investigation aimed at assessing a comparative study on the production and characterization of ACE inhibitory, anti-diabetic, and anti-inflammatory activities, along with the production of ACE inhibitory and anti-diabetic peptides through the fermentation of buffalo and camel milk by Limosilactobacillus fermentum (KGL4) and Saccharomyces cerevisiae (WBS2A). The angiotensin-converting enzyme (ACE) inhibitory and anti-diabetic properties were evaluated at particular time intervals (12, 24, 36, and 48 h) at 37 °C, and we discovered maximum activity at 37 °C after 48 h of incubation. The maximum ACE inhibitory, lipase inhibitory activities, alpha-glucosidase inhibitory, and alpha-amylase inhibitory activities were found in the fermented camel milk (77.96 ± 2.61, 73.85 ± 1.19, 85.37 ± 2.15, and 70.86 ± 1.02), as compared to the fermented buffalo milk (FBM) (75.25 ± 1.72, 61.79 ± 2.14, 80.09 ± 0.51, and 67.29 ± 1.75). Proteolytic activity was measured with different inoculation rates (1.5%, 2.0%, and 2.5%) and incubation times (12, 24, 36, and 48 h) to optimize the growth conditions. Maximum proteolysis was found at a 2.5% inoculation rate and at a 48 h incubation period in both fermented buffalo (9.14 ± 0.06) and camel milk (9.10 ± 0.17). SDS-PAGE and 2D gel electrophoresis were conducted for protein purification. The camel and buffalo milk that had not been fermented revealed protein bands ranging from 10 to 100 kDa and 10 to 75 kDa, respectively, whereas all the fermented samples showed bands ranging from 10 to 75 kDa. There were no visible protein bands in the permeates on SDS-PAGE. When fermented buffalo and camel milk were electrophoresed in 2D gel, 15 and 20 protein spots were detected, respectively. The protein spots in the 2D gel electrophoresis ranged in size from 20 to 75 kDa. To distinguish between different peptide fractions, water-soluble extract (WSE) fractions of ultrafiltration (3 and 10 kDa retentate and permeate) of fermented camel and buffalo milk were employed in RP-HPLC (reversed-phase high-performance liquid chromatography). The impact of fermented buffalo and camel milk on inflammation induced by LPS (lipopolysaccharide) was also investigated in the RAW 264.7 cell line. Novel peptide sequences with ACE inhibitory and anti-diabetic properties were also analyzed on the anti-hypertensive database (AHTDB) and bioactive peptide (BIOPEP) database. We found the sequences SCQAQPTTMTR, EMPFPK, TTMPLW, HPHPHLSFMAIPPK, FFNDKIAK, ALPMHIR, IPAVFK, LDQWLCEK, and AVPYPQR from the fermented buffalo milk and the sequences TDVMPQWW, EKTFLLYSCPHR, SSHPYLEQLY, IDSGLYLGSNYITAIR, and FDEFLSQSCAPGSDPR from the fermented camel milk.
A decomposition algorithm for Af-player games

An Af-player game can be decomposed by adding a coordinator who interacts bilaterally with each player. The coordinator proposes profiles of strategies to the players, and his payoff is maximized when players' optimal replies agree with his proposal. When the feasible set of proposals is finite, a solution of an associated linear complementarity problem yields an equilibrium of the approximate game and thus an approximate equilibrium of the original game. Computational efficiency is improved by using vertices of a triangulation of the players' strategy space for the coordinator's pure strategies. Computational experience is reported.
A Situation-Based Analysis of Flash Flood Fatalities in the United States

AbstractThis paper investigates the circumstances of 1,075 fatalities from flash flooding recorded from 1996 to 2014 across the United States. This study provides insights into the situations of the fatality events as determined by the victims’ profile and activity and the spatiotemporal context of the flooding. A reclassification of the individual fatality circumstance (i.e., location and/or activity) is performed to explore statistically the timing, the duration, and location of the flash flood event and the age and gender of the victims. In agreement with other studies, more than 60% of the reported fatalities were related to vehicles involving mainly males. A geospatial analysis indicated these were most common in southern states. Further, 21% of fatalities occurred outdoors, typically in neighborhoods near streams, where the victims were exhibiting high-risk-taking behavior, such as cleaning out drains and even playing in the floodwaters. Human vulnerability varies dynamically on a subdaily basis and.
Spatial Resolution in Inverse Problems: The EZIE Satellite Mission

Inverse modeling has become one of the primary methods for studying ionospheric electrodynamics, especially when using magnetic field measurements from below the ionosphere. We present a method for quantifying the spatial resolution in an inverse model for non‐uniformly sampled spatial data. This method provides a tool for assessing if a model can resolve the physical phenomena of interest. We quantify the spatial resolution for the Spherical Elementary Current System basis functions to model the ionospheric dynamics. Our results apply to models with spatially confined model parameters, unlike spherical harmonics where the model parameters describe the amplitude of global surface functions. The method is demonstrated for the upcoming Electrojet Zeeman Imaging Explorer cubesat mission which will provide spatially distributed remote sensing measurements of the magnetic field in the mesosphere. We show that, including measurements from a single ground magnetometer can significantly improve the spatial resolution. However, the impact of including a ground magnetometer depends on the relative position of the station with respect to the mesospheric measurements. In addition, a method for reducing two regularization parameters to one is presented. Reducing the amount of regularization parameters simplifies the optimization problem and facilitates a fair comparison between the models with and without a ground magnetometer.
Prediction of Student Graduation Predicts using Hybrid 2D Convolutional Neural Network and Synthetic Minority Over-Sampling Technique

Abstract. With the rapid growth of technology, educational institutions are constantly looking for ways to improve their services and enhance student performance. One of the significant challenges in higher education is predicting the graduation outcome of students. Predicting student graduation can help educators and academic advisors to provide early intervention and support to students who may be at risk of not graduating on time. In this paper, we propose a hybrid 2D convolutional neural network (CNN) and synthetic minority over-sampling technique (SMOTE) to predict the graduation outcome of students. Purpose: Knowing the results and how the Hybrid 2D Convolutional Neural Network (CNN) and Synthetic Minority Over-sampling Technique (SMOTE) algorithms work in predicting student graduation predicates. This algorithm uses a dataset based on family background variables and academic data. Methods/Study design/approach: This study uses the Hybrid 2D CNN algorithm for the classification process and SMOTE for the minority class over-sampling. Result/Findings: The prediction accuracy of the model using SMOTE is 96.31%. Meanwhile, the model that does not use SMOTE obtains an accuracy of 95.32%. Novelty/Originality/Value: This research shows that the use of a Hybrid 2D CNN algorithm with SMOTE gives better accuracy than without using SMOTE. The dataset used also proves that family background and student academic data can be used as a reference for predicting student graduation predicates.
Assessing the Spontaneous Spread of Climate-Adapted Woody Plants in an Extensively Maintained Collection Garden

Climate change may strongly modify the habitat conditions for many woody plant species. Some species could disappear from their natural habitats and become endangered, while others could adapt well to the changed environmental conditions and continue to survive successfully or even proliferate more easily. A similar process can occur within the artificial urban environment as the hitherto popularly planted urban trees may suffer from the extremities of the urban climate. However, among the planted taxa, there are species that spread spontaneously and appear as weeds in extensively managed gardens. In our study, we evaluated the native and non-native species involved in spontaneous spreading in the institutional garden of Buda Arboretum (Budapest) during the COVID-19 period in 2020–2021 when entry was prohibited, and maintenance went on in a restricted, minimal level. We investigated the correlation between spontaneously settling and planted individuals, and then performed multivariate analyses for native and non-native spreading plants for spatial and quantitative data. During our studies, we observed the spontaneous spreading of 114 woody species, of which 38 are native and 76 are non-native. Taking the total number of individuals into account, we found that, in addition to the 2653 woody species planted, a further 7087 spontaneously emerged weeds developed, which creates an additional task in the maintenance.
Generalized Differential Evolution for Constrained Multi-Objective Optimization

Multi-objective optimization with Evolutionary Algorithms has been gaining popularity recently because its applicability in practical problems. Many practical problems contain also constraints, which must be taken care of during optimization process. This chapter is about Generalized Differential Evolution, which is a general-purpose optimizer. It is based on a relatively recent Evolutionary Algorithm, Differential Evolution, which has been gaining popularity because of its simplicity and good observed performance. Generalized Differential Evolution extends Differential Evolution for problems with several objectives and constraints. The chapter concentrates on describing different development phases and performance of Generalized Differential Evolution but it also contains a brief review of other multi-objective DE approaches. Ability to solve multi-objective problems is mainly discussed, but constraint handling and the effect of control parameters are also covered. It is found that GDE versions, in particular the latest version, are effective and efficient for solving constrained multi-objective problems.
Hybrid-Type Dual Active Bridge DC–DC Converter for Ultrawide Input-Voltage Range

This article proposes a hybrid-type dual active bridge (H-DAB) topology and extended gain (EG) control method applied to the ultrawide input-voltage range. The H-DAB is obtained by adding an auxiliary half-bridge to the conventional current-fed dual active bridge (CF-DAB) topology. The auxiliary half-bridge can assist the CF-DAB to achieve zero voltage switching (ZVS) on the one hand and further expand the voltage gain range on the other. The proposed EG control expands the input-voltage range of ZVS for all switches by calculating the optimal intermediate bus voltage. In addition, the backflow power has also been drastically reduced. The proposed piecewise control strategy simplifies the complexity of control. The proposed topology and the control method are verified by the experimental results of a 500 W prototype. The input-voltage range is 27–240 V and the output voltage is 375 V. The experimental results proved that the proposed scheme achieves ZVS of all switches in a wide input-voltage range and power range, and the efficiency is improved.
Effect of two diets on the fecundity of ablated and non-ablated female of Macrobrachium amazonicum

Macrobrachium amazonicum is a species of freshwater shrimp of great importance for aquaculture and to know its reproductive potential, through the study of its fertility is necessary. Therefore, this study aimed to evaluate the effect of two different diets on the reproductive performance of the species, whether or not adopting the ablation procedure. The experiment was carried out in a completely randomized design in a 2 x 2 factorial arrangement, with two types of feed (inert and fresh) and two conditions (ablated and non-ablated females), using six replicates per treatment. After 10 days of spawning, the eggs were removed and counted in their entirety. For each female, three spawns were obtained, totaling 72 samples. The fertility rate, the interval between spawning and the weight gain of the females were analyzed. There was no interaction between the studied factors, except for weight gain, in which ablated females that received fresh feed gained up to three times more weight than non-ablated females. Females that received fresh feed obtained higher fertility (1,373.9 eggs) than those that received only inert feed (1,084.1 eggs). The interval between spawns was 1.6 days shorter in ablated females.
Supply Chain Network Design in Determining Location of Solar Powered Pest Control to Increase Harvesting Effectiveness of Rojolele Rice in Delanggu

Pest problems are the most frightening scourge for farmers in the process of cultivating Rojolele rice which has high rice characteristics and fragrant aroma, so it is necessary to have environmentally friendly technology assistance to assist farmers in controlling pests such as sparrows and leafhoppers. Startup Ecokarsa's solar-powered pest control technology by utilizing ultrasonic waves to make sparrows uncomfortable and ultraviolet to collect leafhoppers at one point is expected to be a solution for the Sanggar Rojolele farmer group to help control pests in the rice fields of Delanggu without damaging the environment. Because this technology is considered quite expensive for the Sanggar Rojolele farmer group, an appropriate location and allocation system is needed to determine how many points are efficient and where these points are located so that the rice fields in Delanggu are safe from attacks by sparrows and leafhoppers.