Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecast of International Tourist Arrivals Based on ARIMA Model

As the world's fastest-growing economic industry, the forecast of changes in the number of inbound tourists can promote the growth of local investment, economy and trade, foreign exchange and labor employment. In this paper, we want to study the impact of the epidemic on the number of international tourists arrivals in China. By observing the information displayed in ACF, PACF, EACF and other images, we build a suitable ARIMA model by using seasonal difference method on the data before the epidemic, and predict the number of international tourists arrivals if the epidemic does not occur. Finally, based on our forecasts, we observe that our projected trends differ significantly from actual visitor trends, suggesting that the impact of the coronavirus pandemic on international visitor arrivals is huge and difficult to recover. This model is no longer suitable for measuring post-pandemic international arrivals when a pandemic occurs.
Implementing a social innovation for community-based peer support for immigrant mothers in Sweden: a mixed-methods process evaluation

Introduction A South African social innovation based on peer support for mothers was contextualized in southern Sweden. The objective of the project was to support expectant women and mothers of young children in immigrant communities to access public services that would benefit maternal and child health. This study aimed to assess how the intervention was implemented, what the contextual barriers and facilitators were, and how the implementation was perceived by those who delivered and received it. Methods The study used mixed methods with a convergent parallel design and followed the Medical Research Council guidance on process evaluations of complex interventions. Semi-structured interviews (n = 19) were conducted with peer supporters, client mothers, and key stakeholders involved in the intervention. The qualitative data were analyzed using content analysis. Quantitative data on peer supporters' activities were collected during contacts with client mothers and were presented descriptively. Results The five peer supporters had 1,294 contacts with client mothers, of which 507 were first-time contacts. The reach was perceived as wide, and the dose of the intervention was tailored to individual needs. Barriers to implementation included community mistrust of social services, norms on gender roles and parenting, and funding challenges. The implementation was facilitated by the organization's reputation, network, experience, and third-sector affiliation. Peer supporters tended to prioritize linking clients to other services over the educational components of the intervention, sometimes doing more than what was originally planned. Implementation strategies used included building trust, using multiple outreach venues, using internal support structures, and providing practical assistance as an entry point to comprehensive psychosocial support. The personal connection between peer supporters and clients was highly valued, and the building of relationships enabled them to address sensitive topics. Peer supporters sometimes experienced a blurred line between professional and personal roles. Conclusions Peer supporters used a variety of strategies to navigate identified barriers and facilitators. Trust was central both as a contextual factor and a strategy for implementation. It is valuable to maintain a balance between flexibility and adherence to the function of peer supporters. Further research is needed to evaluate the effects of the intervention.
Research on Design Methods of Smart Home Products Based on Virtual Reality Technology

In recent years, the variety of smart home products has been continuously increasing, and smart home products are also moving towards rapid development towards intelligence. However, most enterprises do not attach importance to the improvement of product design methods, and there are still many problems to be solved for products in the market. They hope to improve the existing problems through research on design methods. The design process of smart home products based on virtual reality technology is divided into five stages: research stage, building user model stage, product positioning stage, building product module library and product system stage, and product development and testing stage. The design method of smart home products based on virtual reality technology can achieve innovation in design methods, reduce product development costs, and improve user experience. The experimental results indicate that the design method based on virtual reality is far superior to traditional methods.
Azolla Plant Production and Their Potential Applications

Globally, the human population is growing at an alarming rate, reducing land coverage over time. In the modern world, lifestyle changes, the nature of work, and food habits increase the incidence of serious diseases in animals, and human activity influences the environment, such as the use of chemical fertilizers for agriculture, large terrestrial ecosystems all over the biosphere, such as deforestation of plants, which could not sink from atmospheric CO2, and the production of fuels for energy, which would increase the need for fossil fuels but would also deliver low energy fuels at a high energy cost. To overcome the above problems, Azolla plants perform well since they can be grown at low labor costs, on small plots of land, and for a variety of purposes including animal feed/livestock, poultry and fish production, environmental remediation, biofertilizer and biocontrol for mosquito repellents, carbon sequestration of CO2, and bioenergy effectiveness all year. As a result of their low impact on the environment and human health, Azolla plants are becoming increasingly important. The purpose of this review is to provide evidence of Azolla plant production and its potential role in various applications for a greener, more sustainable approach. This review was progressive in that it assessed and produced peer-reviewed papers related to Azolla plant production and its potential role in different applications for a sustainable greener approach. Based on the findings of reputable educational journals, articles were divided into three categories: methods used to produce the nutritional composition of Azolla, environmental factors that affect the efficiency of Azolla plants, and strain improvement of Azolla for enhanced multipurposes and techniques that are currently being used to meet Azolla plants production and its prospective applications for different sustainable greener approaches. The present findings indicate that Azolla is a rich source of protein, which has a clear benefit in offsetting a portion of the nutritional needs of animal/livestock, poultry, and fish production with what is effectively a low-cost dietary supplement, biocontrol of mosquito repellent, environmental bioremediation, biofertilizer, carbon sequester of CO2, and bioenergy for the potential need of Azolla plant applications. Azolla plants have long been recognized for their benefits in greener and more sustainable lifestyles, as well as quality enhancement and bio-based economy over traditional approaches. Because it relied on natural resources and utility green production, this review’s recovery was chosen as an appropriate and environmentally sound solution for a long and healthy lifestyle.
Optimal Repair Algorithm of Single-Disk Failure for Array Codes with Local Properties

Distributed storage systems ensure data reliability and availability in the event of a disk failure by employing array codes. Generalized Expanded Blaum-Roth (GEBR) codes and Generalized Expanded Independent Parity(GEIP) codes are MDS array codes with local properties that can tolerate multiple disk failures while maintaining good repair performance. However, when repairing a single-disk failure, the repair bandwidth (defined as the total number of symbols downloaded from other surviving disks in repairing the single-disk failure) is not optimal. To address this issue, we propose a random search algorithm that uses a simulated annealing algorithm to optimize the repair strategy. It starts with a viable repair strategy and gradually replaces the current strategy with another that downloads less data. Experimental results demonstrate that our proposed algorithm can significantly reduce the data required to repair single-disk failure for both GEBR and GEIP codes by 25%-38%, under the selection of suitable parameters, when compared with traditional repair methods. Moreover, our repair algorithm can be done in polynomial time.
Antifungal activity of eumelanin-inspired indoylenepheyleneethynylene against Cryptococcus neoformans

Cryptococcus neoformans is an opportunistic fungal pathogen that causes meningitis in >152,000 immunocompromised individuals annually, leading to 112,000 yearly deaths. The four classes of existing antifungal agents target plasma membrane sterols (ergosterol), nucleic acid synthesis, and cell wall synthesis. Existing drugs are not highly effective against Cryptococcus, and antifungal drug resistance is an increasing problem. A novel antimicrobial compound, a eumelanin-inspired indoylenepheyleneethynylene, EIPE-1, was synthesized and has antimicrobial activity against Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus (MSRA), but not towards Gram-negative organisms. Based on EIPE-1’s antibacterial activity, we hypothesized that EIPE-1 could have antifungal activity. For these studies, we tested EIPE-1 against C. neoformans strain H99 and 6 additional cryptococcal clinical isolates. We examined antifungal activity, cytotoxicity, effects on fungal gene expression, and mechanism of action of EIPE-1. Results showed that EIPE-1 has fungicidal effects on seven cryptococcal strains with MICs ranging from 1.56 to 3.125 μg/mL depending on the strain, and it is non-toxic to mammalian cells. We conducted scanning and transmission electron microscopy on the exposed cells to examine structural changes to the organism following EIPE-1 treatment. Cells exposed displayed structural changes to their cell wall and membranes, with internal contents leaking out of the cells. To understand the effect of EIPE-1 on fungal gene expression, RNA sequencing was conducted. Results showed that EIPE-1 affects several processes involved stress response, ergosterol biosynthesis, capsule biosynthesis, and cell wall attachment and remodeling. Therefore, our studies demonstrate that EIPE-1 has antifungal activity against C. neoformans, which affects both cellular structure and gene expression of multiple fungal pathways involved in cell membrane stability and viability.
Discovering objects in images and videos

This thesis presents a novel way of scene analysis in images and videos. Traditional scene analysis using object detection involves a lot of human labor for labeling the images, and also has the difficulty of handling a large number of objects categories. Our approach to scene analysis is unsupervised in nature. Given a video, we want to “discover” the objects of interest. No single labeled image is used to pre-train or initialize the system. Still, the system is able to discover the objects of interest. It works on a wide variety of videos and it can discover objects belonging to a large set of different categories. It works in crowded scenes with distracting background pattern and motion. It works in partial occlusions and total removal. The probabilistic framework consists of an appearance model and a motion model. The appearance model exploits the consistency of object parts in appearance across frames. The motion model exploits the motion continuity across frames. Together, they provide appearance and location estimates of the objects of interest. This framework provides a basis for higher level video content analysis tasks.
The habitat functional response links seasonal third‐order selection to second‐order landscape characteristics

Abstract Determining how animals respond to differences in resource availabilities across spatiotemporal extents is critical to our understanding of organism distributions. Variations in resource distribution leading to changes in spatial arrangements across landscapes are indicative of a habitat functional response. Our goal was to assess how resource availabilities influenced both second‐order (i.e., home ranging behavior) and third‐order (i.e., habitat or resource selection) selection by feral pigs (Sus scrofa) in an agricultural landscape. We defined agriculturally based seasons to estimate home range characteristics using autocorrelated kernel density estimation within each season. We then modeled home range size as a function of resource availability (i.e., resource selection analyses) to determine whether individual behaviors were predicted by shifts in home ranging behavior. Both home range analyses and resource selection analyses indicated seasonal differences in selection for agricultural resources as availabilities changed, suggesting second‐ and third‐order selection is mechanistically linked through a habitat functional response.
Near-surface modeling challenges highlighted at SEG/DGS Near-Surface Modeling Workshop

In conjunction with the March GEO 2016 conference held in Manama, Bahrain, SEG hosted a joint workshop with the Dhahran Geoscience Society (DGS), focusing on near-surface modeling challenges. This is the second workshop on the topic. The goal was to follow up on 2014 discussions from the first workshop and share recent case studies and advancements in emerging technologies addressing near-surface modeling, imaging, and characterization challenges.
Customers’ Trust Transferability in Social Commerce and Its Impact on Perceived Risk and Purchase Intention

This study attempts to provide a strengthened understanding of how to mitigate perceived product risk by utilizing the effect of consumers’ trust transfer. Grounding on the theory of perceived risk, trust transfer framework, and social commerce theory, we propose an integrated model of trust-risk perspective on cross-border social commerce use. We tested the proposed model by conducting an online survey in Indonesia. In this work, we used a PLS-SEM analysis, a mediation and a suppression test, and a Multi-Group analysis. The results demonstrated that consumers’ trust can be transferred from the source of trust (platform and friend) to the target of trust (brand), which subsequently reduce perceived product as well as increase purchase intention. More interestingly, the results of the suppression effect testing show that friend trust was able to play a role as a suppressor factor in the relationship between brand trust and perceived product risk. The finding of the present study offers important implications for theory and literature by integrating trust transfer and perceived risk theory and taking into account perceived product risk as an important factor that potentially deters the effect of trust transfer on consumers’ intention to purchase. From a practical implication, we suggest that the best away to gain consumers’ trust in the global market is by conducting an intensive approach to prospective consumers through social media and promoting in popular marketplaces simultaneously.
Prediction of climatic suitability for wine grape production under the climatic change in Hokkaido

This paper investigates the shifts in climatic suitability for cultivating grapes (Vitis vinifera) in Hokkaido, Japan, throughout the 20th and 21st century. To assess area suitability over time, we used two climate model datasets and three climatic criteria (summer temperature, summer precipitation, and winter temperature). The optimum area for growth gradually enlarged from the 1990s to present (2010s), and this area will cover almost all of the Hokkaido region by the 2050s. Even when considering model uncertainties, almost all low elevation areas in Hokkaido are projected to be suitable for V. vinifera cultivation in the future.
Towards an Integrated Perspective of Teachers’ Technology Integration: A Preliminary Model and Future Research Directions

Technology integration is regarded as a crucial and complex endeavour to enhance students’ learning and prepare them to participate in a digital society. Although the research landscape on teachers’ technology integration is vivid and stimulating, an analytical model which synthesises different strands of research to model antecedents (i.e., teachers’ professional competences), processes and outcomes of technology integration in an integrated manner is missing. That said, previous research was often rather product-oriented and ignored potential effects on students’ learning processes and their achievement. To fill this gap, in this paper, we outline a preliminary model, the TPTI-model (teachers’ professional competence for technology integration), in which we deliberately link different research perspectives on teachers’ professional competences, professional vision and students’ learning (processes) to model technology integration during teaching. Based on the preliminary TPTI-model, we propose future research directions, which may allow to gain a better understanding of the teacher- and student-related conditions as well as processes of technology integration and their effects on students’ learning.
Multifrequency Graph Convolutional Network With Cross-Modality Mutual Enhancement for Multisource Remote Sensing Data Classification

The mining of meaningful features and effective fusion of multisource remote sensing (RS) data have always been the challenging research problems in the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. In this article, we propose a multifrequency graph convolutional network with cross-modality mutual enhancement (MFGCN-CME) for multisource RS data classification. Specifically, we design an adaptive multifrequency graph feature learning module to capture the low- and high-frequency multiscale features of HSI and LiDAR in parallel and further adaptively aggregate them. Then, we propose a bipartite graph (BG) enhancement learning module to obtain the spatial-enhanced HSI features and spectral-enhanced LiDAR features by propagating intermodality information. To the best of our knowledge, the BG is first used to multisource RS data classification task. Furthermore, compared with traditional fusion methods, a gated fusion module is used to fully explore the complementarity of two data sources. Finally, a joint loss function combing a classification loss and a semisupervised contrastive loss is developed to improve the model robustness. Comprehensive experiments on different HSI and LiDAR datasets demonstrate that our proposed method can yield better performance compared with several state-of-the-art multisource RS data classification methods.
Numerical methods in fluid mechanics – an overview

The article presents in a review way the most important numerical methods used in modern fluid mechanics. The individual chapters discuss Finite Difference Method, Finite Volume Method, Lattice Boltzmann Method, Discrete Element Method and Smoothed Particle Hydrodynamics. The aim of the article is to familiarize the reader with the most important concepts, features and mathematical equations used in particular methods. The article is intended mainly for people who want to get acquainted with the current possibilities of numerical modelling in the field of broadly understood fluid mechanics. The material is intended to facilitate the decision on how to implement the planned play research.
Exploring Approaches to Optimize the Performance of Predictive Coding on Multilanguage Data Sets

Predictive modeling - known in the legal industry as ‘predictive coding’ or ‘Technology Assisted Review (TAR)’ - is a popular tool used by legal professionals to augment a historically manual document review and classification process in responding to data requests for various legal proceedings. It has become more commonplace over the past decade due to its proven ability to minimize manual document classification, thus reducing the time and cost associated with this aspect of legal proceedings. There is significant research supporting the effectiveness of this technology that includes topics, such as identifying the most performant machine learning algorithms (e.g., logistic regression) or establishing the best methodologies for selecting representative training and testing data. Primarily, this research has been performed without a focus on multilanguage data sets because many legal proceedings typically involve one primary, dominant language. As acceptance of predictive modeling technology grows, legal practitioners have developed independent preferences for handling multiple languages in data sets. These preferences have been primarily based on anecdotal experience rather than empirical assessments and have resulted in two modeling approaches. The first approach uses a single model, which is less complex, and more cost effective than the next approach. The second develops multiple language-specific models, which can create workflow complexity that may impact the overall cost savings that predictive coding seeks to achieve. Proponents of the second approach believe that creating models per language results in better performance over a single model approach. In this study, we empirically explore the performance differences between the two approaches - single model and multiple language-specific models. We hypothesize that a single model approach performs similarly to a language-specific modeling approach when classifying documents for relevance. We evaluate both approaches by comparing their resulting precision-recall curves across real-world test data from legal document review projects. Excitingly, our results demonstrate that in most scenarios, a single model (trained with multiple languages) approach will perform as well, or sometimes better than the approach that uses a group of language-specific models. The results of our research provide a roadmap for legal practitioners and other parties to thoughtfully engage in dialogue around the most effective way to deploy predictive coding within a multilanguage data set. This research will change how predictive models are deployed for multilanguage data sets across the legal industry and enable significant cost savings for clients and legal practitioners by utilizing the most efficient modeling method.
An Integrated ML-Ops Framework for Automating AI-based Photovoltaic Forecasting

Energy digitization holds significant importance for various energy applications, encompassing aspects like production, consumption, and distribution within power grids. The digital transformation of energy plays a pivotal role in enhancing the integration of Artificial Intelligence (AI) into energy management systems, leveraging extensive datasets. The development of AI systems and the utilization of Machine Learning (ML) techniques empower users with precise predictions related to renewable energy production, thereby expediting the shift towards clean energy. Nevertheless, the effective use of data demands a high level of expertise, thereby excluding energy stakeholders from the benefits modern technologies offer. In this paper, we introduce an AI forecasting system designed to bridge the knowledge gap in data processing methods and ML models for energy stakeholders. This system focuses on delivering a user-friendly interface for photovoltaic (PV) production forecasting by automating the entire ML operations pipeline. Consequently, users can obtain data-driven model results without the need to manually code all the requisite steps for model training and fine-tuning. To demonstrate the system’s capabilities, we provide an experimental application using real PV data from a Portuguese aggregator.