Miracle-dz/UniFAR-FTUs huggingface dataset:
Preconditioned techniques for large eigenvalue problems

This research focuses on finding a large number of eigenvalues and eigen-vectors of a sparse symmetric or Hermitian matrix, for example, finding 1000 eigenpairs of a 100,000 $\times$ 100,000 matrix. These eigenvalue problems are challenging because the matrix size is too large for traditional QR based algorithms and the number of desired eigenpairs is too large for most common sparse eigenvalue algorithms. In this thesis, we approach this problem in two steps. First, we identify a sound preconditioned eigenvalue procedure for computing multiple eigenpairs. Second, we improve the basic algorithm through new preconditioning schemes and spectrum transformations. Through careful analysis, we see that both the Arnoldi and Davidson methods have an appropriate structure for computing a large number of eigenpairs with preconditioning. We also study three variations of these two basic algorithms. Without preconditioning, these methods are mathematically equivalent but they differ in numerical stability and complexity. However, the Davidson method is much more successful when preconditioned. Despite its success, the preconditioning scheme in the Davidson method is seen as flawed because the preconditioner becomes ill-conditioned near convergence. After comparison with other methods, we find that the effectiveness of the Davidson method is due to its preconditioning step being an inexact Newton method. We proceed to explore other Newton methods for eigenvalue problems to develop preconditioning schemes without the same flaws. We found that the simplest and most effective preconditioner is to use the Conjugate Gradient method to approximately solve equations generated by the Newton methods. Also, a different strategy of enhancing the performance of the Davidson method is to alternate between the regular Davidson iteration and a polynomial method for eigenvalue problems. To use these polynomials, the user must decide which intervals of the spectrum the polynomial should suppress. We studied different schemes of selecting these intervals, and found that these hybrid methods with polynomials can be effective as well. Overall, the Davidson method with the CG preconditioner was the most successful method of the eigenvalue problems we tested.
Differential Expression of Tissular miRNA-155 in Pediatric Gastritis

Background: MicroRNA molecules, among them the intensely studied miRNA-155 (miR-155), are regarded as potential biomarkers of chronic gastric inflammation and premalignant lesion progression. However, literature data are scarce in terms of pediatric studies and in the evaluation of the predictive role of miRNA in early gastric inflammation. This study aims to assess the differential expression of miR-155 in relation to pediatric gastritis. Methods: The present research was conducted on 192 patients with chronic dyspeptic symptoms who underwent upper digestive endoscopy. Bioptic samples were harvested for histopathological analysis and tissue miR-155 depiction. MiR-155 expression analysis was carried out through quantitative real-time polymerase chain reaction (qRT-PCR). The study population was divided into two groups: controls (93 patients) and study group (99 patients) with inflammatory modifications. Results: MiR-155 expression was augmented in patients with gastritis but did not differ significantly from controls (p = 0.16). An increase in miR-155 expression was noted in relation to chronic gastritis, H. pylori infection, or increase in gastritis severity, but these variations were not important (p = 0.30, p = 0.44, and p = 0.45, respectively). Conclusions: According to our study, pediatric gastritis increases, but does not greatly influence, miR-155 expression. Dynamic evaluation of miR-155 might enlighten its prognostic role in pediatric gastritis.
Posture and musculoskeletal implications for students using mobile phones because of learning at home policy

There is an increasing trend of awkward postures due to smartphone use among students learning from home. Previous research in several countries has shown that the use of smartphones during a pandemic impacts musculoskeletal complaints. This study aimed to determine the postures performed by the students when using mobile phones and their implications for musculoskeletal complaints. A total of 709 students who learned from home voluntarily completed an online questionnaire regarding identity, work posture, and duration, while muscle and bone complaints experienced when using mobile phones were obtained through the Nordic Musculoskeletal Questionnaire. Furthermore, a bivariate analysis of body postures was carried out to identify perceived complaints using the chi-square method. The results showed a significant relationship between standing and leaning on a table posture (p-value: 0.026) with elbow, knee, and ankle complaints.
Disparities in Health Care Spending and Utilization Among Black and White Medicaid Enrollees

Key Points Question Are there racial differences in health care spending and utilization for low-income individuals in the US who are covered by the Medicaid program? Findings In this cross-sectional study of 1 966 689 Black and White Medicaid enrollees in 3 states, Black enrollees used fewer services, including primary care, and generated lower spending than White enrollees, but were more likely to utilize the emergency department for avoidable reasons. Differences persisted among enrollees residing in the same zip codes who were treated by the same health care professionals. Meaning The results of this study suggest that stark differences in spending and primary care use exist between Black and White Medicaid enrollees, and additional steps to ensure equity are needed.
Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances

Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better understanding various medical conditions, designing more effective drug delivery systems, and developing novel diagnostic methods and treatments. However, despite significant advances in computational technology and resources, the expensive computational cost of these simulations still hinders their transformation from a research interest to a clinical tool. This bottleneck is even more severe for image-based, patient-specific CFD simulations with realistic boundary conditions and complex computational domains, which make such simulations excessively expensive. To address this issue, deep learning approaches have been recently explored to accelerate computational hemodynamics simulations. In this study, we review recent efforts to integrate deep learning with CFD and discuss the applications of this approach in solving hemodynamics problems, such as blood flow behavior in aorta and cerebral arteries. We also discuss potential future directions in the field. In this review, we suggest that incorporating physiologic understandings and underlying fluid mechanics laws in deep learning models will soon lead to a paradigm shift in the development novel non-invasive computational medical decisions.
Larval Instars and Adult Flight Period of Monochamus saltuarius (Coleoptera: Cerambycidae)

Monochamus saltuarius is a vector of Bursaphelenchus xylophilus in Japan, South Korea, and the middle temperate zone of China. However, there are only a few reports on this species in China, and its biological characteristics are still unclear. In this study, we aimed to elucidate the larval development and adult flight period of M. saltuarius to provide a theoretical basis for the effective control of pine wilt disease in the middle temperate zone of China. Seven morphological variables of larvae were measured to determine the number of larval instars, and the adult specimens of M. saltuarius were collected from traps in forests to study the flight period of adults in Fushun, Liaoning, the epidemic center of pine wilt disease in the middle temperate zone of China. The results revealed that the full larval period of M. saltuarius was 279.6 d, and the larvae had five instar stages, with an average duration of 7.4, 14.3, 49.8, 83.6, and 124.5 d, respectively. Additionally, 78.4% of the overwintering larvae were fourth instar, and 21.6% were fifth instar larvae. We also found that the adults began to emerge from early May to late June, and the period was from early May to mid-August in the forest. During the investigations period from 2018 to 2020, the total number of adults captured was minimal at 744, and precipitation was highest at 291.54 mm in 2019. We characterized the larval instars and adult flight period of M. saltuarius, which is a prerequisite for developing appropriate management strategies. The results of this study will provide an important reference for the formulation of strategies to control M. saltuarius and pine wilt disease.
Machine Learning with <i>Kay</i>

Abstract. Computational power is very important when training Deep Learning (DL) models with large amounts of data (Wooldridge, 2021). Hence, High-Performance Computing (HPC) can be leveraged to reduce computational cost, and the Irish Centre for High-End Computing (ICHEC) provides significant infrastructure and services for research and development to both academia and industry. A portion of ICHEC's HPC system has been allocated for institutional access, and this paper presents a case study of how to use Kay (Ireland's national supercomputer) in the remote sensing domain. Specifically, this study uses clusters of Kay Graphics Processing Units (GPUs) for training DL models to extract buildings from satellite imagery using a large number of input data samples.
Trust Me, I'm an Influencer! - A Comparison of Perceived Trust in Human and Virtual Influencers

Influencers in social media are often perceived as a trusted source for many people which is why companies increasingly promote their products through them. However, influencers can also cause reputational damage for a brand. Virtual (computer-generated) influencers can be used to minimize these risks and to better tailor content to a target group of a company. As trust is one success factor of online marketing, we examine differences in the perception of trust in human and virtual influencers. In a first online survey study, we presented N = 112 participants the content of human and virtual influencers, published on Instagram. Preliminary findings reveal that although participants were often unsure whether the presented influencer was human or computer-generated, perceived trust, social presence, and humanness was consistently rated higher for human influencers. To gain deeper insights into potential, unconscious decision conflicts which can determine trust evaluations, a follow-up neuroimaging study is discussed.
Spatial-Temporal Graphs Plus Transformers for Geometry-Guided Facial Expression Recognition

Facial expression recognition (FER) is of great interest to the current studies of human-computer interaction. In this paper, we propose a novel geometry-guided facial expression recognition framework, based on graph convolutional networks and transformers, to perform effective emotion recognition from videos. Specifically, we detect and utilize facial landmarks to construct a spatial-temporal graph, based on both the landmark coordinates and local appearance, for representing a facial expression sequence. The graph convolutional blocks and transformer modules are employed to produce high-semantic emotion-related representations from the structured facial graphs, which facilitate the framework to establish both the local and non-local dependency between the vertices. Moreover, spatial and temporal attention mechanisms are introduced into graph-based learning to promote FER reasoning, via the emphasis on the most informative facial components and frames. Extensive experiments demonstrate that the proposed framework achieves promising performance for geometry-based FER and shows great generalization and robustness in real-world applications.
Long-Term Assessment of Contemporary Ion-Releasing Restorative Dental Materials

The objective was to evaluate new commercially available ion-releasing restorative materials and compare them to established anti-cariogenic materials. Four materials were tested: alkasite Cention (Ivoclar Vivadent) in self-cure or light-cure mode, giomer Beautifil II (Shofu), conventional glass-ionomer Fuji IX (GC), and resin composite Tetric EvoCeram (Ivoclar Vivadent) as a control. Flexural strength, flexural modulus, and Weibull modulus were measured one day, three months, and after three months with accelerated aging in ethanol. Water sorption and solubility were evaluated for up to one year. Degree of conversion was measured during 120 min for self-cured and light-cured Cention. In this study, Beautifil II was the ion-releasing material with the highest flexural strength and modulus and with the best resistance to aging. Alkasite Cention showed superior mechanical properties to Fuji IX. Weibull analysis showed that the glass-ionomer had the least reliable distribution of mechanical properties with the highest water sorption. The solubility of self-cured alkasite exceeded the permissible values according to ISO 4049. Degree of conversion of light-cured Cention was higher than in self-cure mode. The use of alkasite Cention is recommended only in the light-cure mode.
Defining requirements for an Augmented Reality system to overcome the challenges of creating and using design representations in co-design sessions

ABSTRACT Within co-design sessions involving designers and non-designers, the type and characteristics of the design representations employed is known to impact the performance of such sessions in terms of idea generation, idea evaluation and communication. This study captures the challenges practitioners face in creating and using design representations for co-design sessions and goes on to investigate the potential of Spatial Augmented Reality (SAR) to overcome those challenges. The advantages of SAR in this application are that, multiple concepts can be represented using one physical model, concepts can be modified live during the session, and additional equipment (such as head mounted displays or handheld devices) is not required, thus eliminating any possible interference with the natural interactions between participants. Interviews with design practitioners and trials with a prototype SAR system are used to identify the key challenges faced by practitioners in their current use of design representations, and to capture the technology requirements for a SAR system for use in co-design sessions. These findings can inform the work of technology developers and researchers working on systems to support co-design sessions.
A satellite perspective on the movement decisions of African elephants in relation to nomadic pastoralists

The African savannah ecosystem is populated by nomadic pastoralists who herd livestock in the day and corral them at night in temporary enclosures, called bomas, to protect them. The number and distribution of bomas on the savannah is important from an ecological perspective and may have a significant impact on wildlife movement. However, no study has yet examined this relationship. Here, using very high‐resolution satellite imagery from two time periods, we quanitified changes in boma distribution and density across an area of 3377 km2 in the Laikipia‐Samburu ecosystem of northern Kenya between 2011 and 2019. To assess wildlife movement in relation to bomas, we used a GPS data set on African bush elephant Loxodonta africana movement from 27 collared matriarchs representing herds of 9–15, covering 112 467 hourly GPS fixes over 31 months between 2018 and 2020. Our results showed a more than 46% increase in the total number of human‐built structures between 2011 and 2019, the majority of which were bomas, representing a 21.9% increase in human‐modified land area. Elephants readily adjusted their foraging habits and itineraries in this habitat shared with humans, who were also nomadic in space and time. Assessing the night–day activity ratio, we found elephants move more nocturnally when in closer proximity to bomas, particularly during the dry season. This temporal separation means elephants avoid the times humans are active in and around bomas while still accessing required resources—water and forage. The temporal shift was stronger during the dry season when shared resources are scarce. Using daily travel distance as a metric, we show elephants moved further in closer proximity to bomas which was likely linked to the need to travel between forage patches. Given the rise in human settlements, understanding the consequences of animals' behavioral adjustments is critical to understand the long‐term population viability of elephant populations.
Efficacy of Stem Cell Therapy for Tendon Graft Ligamentization After Anterior Cruciate Ligament Reconstruction: A Systematic Review

Background: Sufficient intra-articular graft ligamentization enhances the biomechanical and biological properties of the femur-graft-tibia complex to ensure knee stability after anterior cruciate ligament (ACL) reconstruction using a tendon graft. It remains unclear whether stem cell therapy promotes tendon graft ligamentization. Purpose/Hypothesis: The purpose of this study was to compare tendon graft ligamentization after primary ACL reconstruction with versus without stem cell therapy. It was hypothesized was that stem cell therapy would promote tendon graft ligamentization by enhancing the biomechanical and histological properties of the tendon graft after ACL reconstruction. Study Design: Systematic review. Methods: A systematic review was performed according to the guidelines outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement to identify controlled animal studies that compared tendon graft ligamentization outcomes after primary ACL reconstruction in groups with and without stem cell therapy. Biomechanical and histological outcomes were assessed. Results: A total of 4 studies met the eligibility criteria and were included in this review. Bone marrow–derived mesenchymal stem cells were used in 3 studies, while tendon-derived stem cells were used in 1 study. An intra-articular injection was used to deliver conditioned medium and stem cells in 2 studies, while around-graft application was used to deliver bone marrow–derived mesenchymal stem cells in 2 studies. Stem cell therapy enhanced the biomechanical and histological properties of the tendon graft after ACL reconstruction. Conclusion: This review revealed that stem cell therapy is a promising technique that promotes graft ligamentization by enhancing the biomechanical and histological properties of the tendon graft after ACL reconstruction in animal models. There is a need for future preclinical studies aimed at evaluating the effect of stem cells on graft ligamentization and identifying the optimal method of intra-articular stem cell delivery.
Escherichia coli Clonal Variability Based on Genetic Diversity Pattern with Enterobacterial Repetitive Intergenic Consensus-Polymerase Chain Reaction Methods for Traveler’s Diarrhea Cases in Bali

BACKGROUND: Bali is a favorite tourism destination in the world. As a major tourist destination, the incidence of illness that afflicts tourists greatly affects the image of tourism. Diarrhea is a health problem that is most often experienced and is a major obstacle for foreign tourists when traveling, especially to Bali. Escherichia coli (E. coli) bacteria cause diarrhea more often than viruses in some developing countries. Genetic differences can affect the characteristics of E. coli, especially in relation to the medical field. AIM: We would like to assess the genetic diversity of the different pathogenic E. coli from various clinical isolates including those from traveler’s diarrhea in Bali, Indonesia. MATERIALS AND METHODS: One of the molecular techniques used in this study is to use enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR). The sample in this study was the feces of foreign tourists with traveler’s diarrhea in Bali. This study carried out research procedures in the form of Isolation of E. coli genome DNA from culture, amplification of E. coli 16S rRNA encoding genes, sequencing of E. coli 16S rRNA encoding genes, phylogenetic tree construction, and then analysis of E. coli genetic diversity with ERIC-PCR sequences. RESULTS: The results showed that the ERIC-PCR method was more discriminatory than other methods to analyze the genetic diversity of E. coli from fecal samples of patients with traveler’s diarrhea. It was found that clonal variability based on the genetic similarity of all sample E. coli isolates varied from 0% to 100%. CONCLUSIONS: This shows that the source of transmission and the strains of E. coli that cause it comes from diverse populations.
Optimizing Electric Vehicle Charging Schedules Based on Probabilistic Forecast of Individual Mobility

Abstract. The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if the charging of many EVs is not coordinated. Among the many strategies to cope with this challenge, next-day EV energy demand forecasting plays a key role. Existing studies have focused on predicting the next-day energy demand of EVs on the aggregated and individual levels. However, these studies have not yet extensively considered individual user mobility behaviors, which exhibit a high level of predictability. In this study, we consider several mobility features of individual users when forecasting the next-day energy demand of individual EVs. Three types of quantile regression models are used to generate probabilistic forecasts of energy demand, particularly the next-day energy consumption and parking duration. Based on the prediction results, two time-shifting smart charging strategies are designed: unidirectional and bidirectional smart charging. These two strategies are compared with an uncontrolled charging baseline to evaluate their financial benefits and peak-shaving effects. Our results show that human mobility features can partially improve the prediction of next-day individual EV energy demand. Additionally, users and distribution grids can benefit from smart charging strategies both financially and technically.