Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Sustainable Recycling of Epoxy-Based Polymers: Approaches and Challenges of Epoxy Biodegradation

Epoxy resins are highly valued for their remarkable mechanical and chemical properties and are extensively used in various applications such as coatings, adhesives, and fiber-reinforced composites in lightweight construction. Composites are especially important for the development and implementation of sustainable technologies such as wind power, energy-efficient aircrafts, and electric cars. Despite their advantages, their non-biodegradability raises challenges for the recycling of polymer and composites in particular. Conventional methods employed for epoxy recycling are characterized by their high energy consumption and the utilization of toxic chemicals, rendering them rather unsustainable. Recent progress has been made in the field of plastic biodegradation, which is considered more sustainable than energy-intensive mechanical or thermal recycling methods. However, the current successful approaches in plastic biodegradation are predominantly focused on polyester-based polymers, leaving more recalcitrant plastics underrepresented in this area of research. Epoxy polymers, characterized by their strong cross-linking and predominantly ether-based backbone, exhibit a highly rigid and durable structure, placing them within this category. Therefore, the objective of this review paper is to examine the various approaches that have been employed for the biodegradation of epoxy so far. Additionally, the paper sheds light on the analytical techniques utilized in the development of these recycling methods. Moreover, the review addresses the challenges and opportunities entailed in epoxy recycling through bio-based approaches.
Stimulating Visit Intention Using Social Media Influencer: Mediating Role of Enjoyment

: Social media influencer (SMI) has emerged as one of a powerful approach in building customer’s intention to visit a tourism destination. This research therefore tries to uncover the mechanism on how the dimensions of SMI, namely attractiveness, trustworthiness, and expertise, influence visit intention by incorporating enjoyment as mediator by applying Stimuli-Organism-Response (SOR) Theory. Using purposing sampling, 115 usable samples of those who experienced an SMI’s posts regarding Aceh tourism, are gathered online. Data were analyzed by using Partial Least Square-Structural Equation Modelling (PLS-SEM). In testing the hypotheses, we conduct a Bootstrapping procedure using 5000 sub-samplings. The finding highlights that SMIs’ attractiveness and SMIs’ expertise, are found to be a strong predictor of enjoyment, of which has a significant relationship with visit intention. However, SMI trustworthiness failed to predict enjoyment. Except for SMI trustworthiness-visit intention relationship, this study also found the role of enjoyment as mediator for both SMI attractiveness and SMI expertise in predicting visit intention. Consequently, this research pinpoints two routes to obtaining visitors’ intention to visit a tourism site. They are attractiveness-enjoyment-intention and expertise-enjoyment-intention with the second pathway have bigger contribution to visit intention. The findings can be used as a guide to assist tourism destination marketers in developing effective advertising that use SMI to communicate with their visitor and differentiate themselves from the tourism destination intense competition.
Topics in the theory coherent states

Coherent states were initially introduced by Erwin Schrodinger to de- scribe quantal states whose behavior was as classical as possible. Some forty-years later, they acquired a prominent role in quantum optics, for the study of the so-called coherent light, with notable applications to lasers (and a Nobel prize assigned to Roy Glauber in 2005). Several branches of mathematics include topics of coherent states theory, from Lie group representation theory to complex analysis of holomorphic Hilbert space, from symplectic geometry and microlocal analysis to the so-called theory of geometric quantization, and much more. The theoretical results ob- tained have been applied of course to physics, but also to data analysis, leading to the foundation of multiresolution or wavelet analysis, and more recently to the modelling of the visual cortex. These notes aim to give a panoramic view of the subject without en- tering too much into the details, starting from the original motivations in quantum theory and trying to reach a unied overall picture of some of the main recent developments.
Estimation non paramétrique de la variance d'une diffusion par méthodes d'ondelettes

We introduce a non parametric estimator of the diffusion coefficient of a diffusion process using a projection method on a wavelets orthonormal basis of L 2 (R). The sample path is observed at discrete times. We study the weak convergence and the mean integrated squared error of this estimator as the sampling interval goes to 0 Notre but est de definir un estimateur fonctionnel de la variance d'une diffusion par une methode de projection sur une base d'ondelettes de L 2 (R) a partir d'une observation discretisee de la trajectoire. Nous etudions la convergence au sens faible et le risque quadratique integre de l'estimateur quand le pas de discretisation tend vers 0
Decomposing measured noise cubes: SiTF, 3D noise, uniformity, and drift

Sensitivity of a camera is most often measured by recording video segments while viewing a constant (space and time) scene. This video, commonly referred to as a noise cube, provides information about how much the signals are varying away from the average. In this work, we describe the systematic decomposition of noise cubes into components. First, the average of a noise cube (when combined with other cube measurements) is used to determine the cameras Signal Transfer Function (SiTF). Removing the average results in a cube that exhibits variations in both spatial and temporal directions. These variations also occur at different scales (spatial/temporal frequencies), therefore we propose applying a 3-dimensional filter to separate fast and slow variation. Slowly varying temporal variation can indicate an artifact in measurement, the camera signal, or the camera’s response to measurement. Slowly varying spatial variation can be considered as non-uniformity, and conventional metrics applied. Fast varying spatial/temporal noise is combined and evaluated through the conventional 3D noise model (providing 7 independent noise measurements. In support of the reproducible research effort, the functions associated with this work can be found on the Mathworks file exchange.
Semi-supervised classification based on affine subspace sparse representation

Graph-based semi-supervised classification is one of the hottest research areas in machine learning and data mining. These methods usually model an entire dataset as a graph, then utilize the structure information extracted by the graph to help with the classification of unlabeled data. Generally speaking, the performance of graph-based semi-supervised classification methods highly depends on the constructed graphs. In this paper, we propose a new kind of graph construction method based on affine subspace sparse representation. The proposed sparse coding method minimizes the construction error of the input signal, considering three constraints: (1) the input signal being approximately reconstructed by the affine combination of the dictionary; (2) the nonnegativity constraint of the reconstruction coefficients; (3) the sparsity constraint of the reconstruction coefficients. Based on the constraints, we present the ι 0 - norm constrained optimization problem for sparse coding; then, we propose the algorithm to solve the problem and further construct the ι 0 - graph of data. Finally, under the manifold regularization framework, we propose a new kind of semi-supervised classification method by introducing the regularization term that measures the structure preserving error of the ι 0 - graph. The proposed semi-supervised classification method has an explicit multiclass classification function and inherits the strong discriminative information from sparse representation. As a result, it has efficient and effective classification ability. Experimental results on artificial and real-world datasets are provided to show the effectiveness of the proposed method.
COVID-19-Associated Pulmonary Aspergillosis in Intensive Care Unit Patients from Poland

Coronavirus disease 2019 (COVID-19) has been shown to be a favoring factor for aspergillosis, especially in a severe course requiring admission to the intensive care unit (ICU). The aim of the study was to assess the morbidity of CAPA among ICU patients in Poland and to analyze applied diagnostic and therapeutic procedures. Medical documentation of patients hospitalized at the temporary COVID-19 dedicated ICU of the University Hospital in Krakow, Poland, from May 2021 to January 2022 was analyzed. In the analyzed period, 17 cases of CAPA were reported with an incidence density rate of 9 per 10 000 patient days and an incidence rate of 1%. Aspergillus fumigatus and Aspergillus niger were isolated from lower respiratory samples. Antifungal therapy was administered to 9 patients (52.9%). Seven patients (77.8%) received voriconazole. The CAPA fatality case rate was 76.5%. The results of the study indicate the need to increase the awareness of medical staff about the possibility of fungal co-infections in ICU patients with COVID-19 and to use the available diagnostic and therapeutic tools more effectively.
Plant and Salamander Inspired Network Attack Detection and Data Recovery Model

The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC’s 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra.
Influence of bearing waviness on the lubrication performances of coupled journal-thrust water-lubricated bearings

The purpose of this study is to discuss the effect of the bearing waviness on the lubrication performances of coupled journal-thrust water-lubricated bearings using an elastohydrodynamic lubrication model. The results of the numerical model are compared to the published experimental data to verify the validity of the numerical model. The load capacity and maximum fluid pressure under different parameters of waviness of coupled journal-thrust bearings are calculated by numerical model, and then the influence of the bearing waviness on the performance of coupled journal-thrust water-lubricated bearings is analyzed. The simulation results show that the waviness amplitude has great influence on the bearing performances and the effect becomes increasingly pronounced with the growth of waviness amplitude. For the waviness of journal bearings, the performances are more sensitive to the waviness arranged in bearing circumferential direction than radial direction. For the waviness of thrust bearings, the presence of circumferential waviness can improve the load capacity, while radial waviness can reduce the load capacity. With the increase of wavelength, the load capacity and maximum fluid pressure have similar periodic changes, but the change is irregular. More investigations are required regarding the influence of geometrical shape of the waviness and its wavelength on the performance of the bearing.
An ab initio Study of Structural, Electronic and Optical Properties of Janus AlInS2 homo-bilayer

By using the first principle calculations, the structural, electronic and optical properties of Janus AlInS2 Bilayer have been calculated. The Janus AlInS2 shows a honey-comb stable structure with semiconducting behaviour having indirect bandgap. The bilayer is found to be energetically favourable. The density of states (DOS) is calculated for Janus AlInS2 Bilayer. It shows absorptions in the visible and ultraviolet regions where prominent peaks of absorption coefficient is found in ultraviolet region of order 105 cm−1. The Janus AlInS2 Bilayer has a high static refractive Index. From the results, it is concluded that Janus AlInS2 Bilayer has a potential application in optoelectronic devices such as coating material to increase reflectivity.
Associations of Cumulative Perceived Stress with Cardiovascular Risk Factors and Outcomes: Findings from The Dallas Heart Study

Background: Data remain sparse regarding the impact of chronic stress on cardiovascular disease (CVD) risk factors and outcomes. Prior work has been limited by incomplete assessments of perceived stress and focus on single stress domains. We evaluated the association between a composite measure of perceived stress and CVD risk factors and outcomes. Methods: Participants from the Dallas Heart Study phase 2 (2007-2009) without prevalent CVD who completed questionnaire assessments of perceived stress were included (n=2685). Individual perceived stress subcomponents (generalized stress, psychosocial, financial, and neighborhood stress) were standardized and integrated into a single cumulative stress score (CSS) with equal weighting for each component. Associations between CSS and demographics, psychosocial variables and cardiac risk factors were assessed in univariable and multivariable analyses. Cox proportional hazards models were used to determine associations of the CSS with atherosclerotic CVD (ASCVD) and Global CVD (ASCVD, heart failure, and atrial fibrillation) after adjustment for demographics and traditional risk factors. Results: Median age of the study population was 48 years, 55% were female, 49% Black and 15% Hispanic/Latinx. CSS was higher among participants who were younger, female, Black or Hispanic, and those with lower income and educational attainment (p<.0001 for each). Higher CSS was associated with self-report of racial/ethnic discrimination, lack of health insurance and last medical contact > one year previously (p<.0001 for each). In multivariable regression models adjusting for age, gender, race/ethnicity, income and education, higher CSS associated with hypertension, smoking, and higher body mass index, waist circumference Hemoglobin A1C, hs-CRP and sedentary time (p< 0.01 for each). Over a median follow-up of 12.4 years, higher CSS associated with ASCVD (adjusted HR 1.22 per SD, 95% CI 1.01-1.47) and Global CVD (HR 1.20, 95% CI 1.03-1.40). No interactions were seen between CSS, demographic factors, and outcomes. Conclusion: Composite multidimensional assessments of perceived stress may help to identify individuals at risk for CVD who may be targeted for stress mitigation or enhanced prevention strategies. These approaches may be best focused on vulnerable populations, given the higher burden of stress in women, Black and Hispanic individuals, and those with lower income and education.
Omicron vs. the rest: Assessing the competitive dynamics and coinfection scenarios of COVID-19 strains on a social network

The rapid spread and evolving nature of COVID-19 variants have raised concerns regarding their competitive dynamics and coinfection scenarios. In this study, we assess the competitive interactions between the Omicron variant and other prominent variants (Alpha, Beta, and Delta) on a social network, considering both single infection and coinfection states. Using the SIRS model, we simulate the progression of these variants and analyze their impact on infection rates, mortality, and overall disease burden. Our findings demonstrate that the Alpha and Beta strains exhibit comparable contagion levels, with the Alpha strain displaying higher infection and mortality rates. Moreover, the Delta strain emerges as the most prevalent and virulent strain, surpassing the other variants. When introduced alongside the less virulent Omicron strain, the Delta strain results in higher infection and mortality rates. However, the Omicron strain's dominance leads to an overall increase in disease statistics. Remarkably, our study highlights the efficacy of the Omicron variant in supplanting more virulent strains and its potential role in mitigating the spread of infectious diseases. The Omicron strain demonstrates a competitive advantage over the other variants, suggesting its potential to reduce the severity of the disease and alleviate the burden on healthcare systems. These findings underscore the importance of monitoring and understanding the dynamics of COVID-19 variants, as they can inform effective prevention and mitigation strategies, particularly with the emergence of variants that possess a relative advantage in controlling disease transmission.
Predictors and long-term outcomes of oropharyngeal dysphagia in Persons with Multiple Sclerosis: A systematic review protocol

Aabstract Background: Oropharyngeal dysphagia (OPD) can be functionally debilitating in persons with multiple sclerosis (pwMS). OPD induces alterations in safety and efficacy of food and/or liquid ingestion and may incur negative sequalae such as aspiration pneumonia or malnutrition/dehydration. Early detection and timely management of OPD in pwMS could prevent such complications and reduce mortality rates. Identifying predictors of OPD relative to its onset or repeat manifestation will enable the development of care pathways that target early assessment and sustained management. The aims of this systematic review are to compile, evaluate, and summarise the existing literature reporting potential predictors and associated long-term outcomes (e.g., aspiration pneumonia, malnutrition, dehydration, and/or death) of OPD in pwMS. Methods: We will undertake a systematic review to identify studies that describe patterns and complications of OPD in pwMS. Variables of interest include predictors of OPD along with long-term outcomes. We will search MEDLINE, EMBASE, CINAHL, AMED, the Cochrane Library, Web of Science, and Scopus. We will consider studies for inclusion if they involve at least 30 adult participants with MS and report risk factors of OPD and/or its long-term outcomes. Studies will be excluded if they refer to esophageal or oropharyngeal dysphagia induced by causes other than multiple sclerosis. Study selection and data extraction will be performed by two independent assessors for abstract and full article review. We will present study characteristics in tables and document research findings for dysphagia-related risk factors or its complications via a narrative format or meta-analysis if warranted (e.g., mean difference and/or risk ratios measurements). All included studies will undergo risk of bias assessment conducted independently by two authors with consensus on quality ratings. Conclusion: There is a lacune with respect to systematic reviews involving predictors and long-term outcomes of dysphagia in in pwMS to date. Our systematic review will provide the means to develop accurate and efficient management protocols for careful monitoring and evaluation by dysphagia experts. The results of this systematic review will be published in a peer-reviewed journal. Systematic review registration: PROSPERO CRD42022340625 Key words: Predictors, oropharyngeal dysphagia, Multiple Sclerosis, Systematic review, Protocol
Gene-environment interactions for Parkinson disease

Objective: Parkinson s disease (PD) is a neurodegenerative disorder with complex aetiology. Multiple genetic and environmental factors have been associated with PD, but most PD risk remains unexplained. The aim of this study was to test for statistical interactions between PD-related genetic and environmental exposures/phenotypic traits in the 23andMe, Inc. research dataset. Methods: Using a validated PD polygenic risk score (PRS) and common PD-associated variants in the GBA gene, we explored interactions between genetic susceptibility factors and phenotypic traits: body mass index (BMI), type 2 diabetes (T2D), tobacco use, caffeine consumption, pesticide exposure, head injury, and physical activity (PA). Results: In models without gene-by-environment interactions, we observed that higher BMI, T2D, caffeine consumption, and tobacco use were associated with lower odds of PD, while head injury, pesticide exposure, and GBA carrier status were associated with increased odds. We observed no significant association between PA and PD. PRS was associated with increased odds of PD and there was statistical evidence for an interaction between PRS and BMI, PRS and T2D, PRS and PA, and PRS and tobacco use (p=4.314E-4; p=6.502E-8; p=8.745E-5, p=2.236E-3, respectively). Whilst BMI and tobacco use were associated with lower odds of PD regardless of the extent of individual genetic liability, the direction of the relationship between odds of PD and T2D as well as PA, varied depending on PRS. Interpretation: We provide preliminary evidence that associations between phenotypic traits and PD may be modified by genotype.
Personalized Image Enhancement Featuring Masked Style Modeling

We address personalized image enhancement in this study, where we enhance input images for each user based on the user’s preferred images. Previous methods apply the same preferred style to all input images (i.e., only one style for each user); in contrast to these methods, we aim to achieve content-aware personalization by applying different styles to each image considering the contents. For content-aware personalization, we make two contributions. First, we propose a method named masked style modeling, which can predict a style for an input image considering the contents by using the framework of masked language modeling. Second, to allow this model to consider the contents of images, we propose a novel training scheme where we download images from Flickr and create pseudo input and retouched image pairs using a degrading model. We conduct quantitative evaluations and a user study, and our method trained using our training scheme successfully achieves content-aware personalization; moreover, our method outperforms other previous methods in this field. Our source code is available at https://github.com/satoshi-kosugi/masked-style-modeling.