Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Role of Cancer Diagnosis in the Associations between Personality and Life Satisfaction

Life satisfaction refers to the degree a person enjoys their life. An integrated account of life satisfaction is discussed in the literature, which proposes that life satisfaction is made up of personality traits and areas of life satisfaction (e.g., satisfaction with health, job, and social life). In addition, disruptions in one domain (e.g., health) may disrupt the association between personality traits and life satisfaction. The current research was interested in if clinically diagnosed cancer could influence the association between the Big Five personality traits and life satisfaction. The current study analyzed data from 1214 people with a diagnosis of cancer (38.55% males) with an average age of 59.70 (S.D. = 15.53) years and 13,319 people without a cancer diagnosis (38.13% males) with an average age of 59.97 (S.D. = 11.10) years who participated in Understanding Society: the UK Household Longitudinal Study (UKHLS). For the first time, our study revealed that cancer markedly influences the relationship between Agreeableness and life satisfaction, after accounting for demographic variables. Neuroticism was negatively associated with life satisfaction in people with and without clinically diagnosed cancer, whereas Agreeableness and Extraversion were positively associated with life satisfaction in people with and without clinically diagnosed cancer. Openness and Conscientiousness were positively related to life satisfaction in people without cancer but were not significant predictors in people with cancer. Health professionals should develop strategies and interventions by fostering personality traits, including Agreeableness, Openness, Conscientiousness, and Extraversion, while reducing Neuroticism.
International Clones of High Risk of Acinetobacter Baumannii—Definitions, History, Properties and Perspectives

Acinetobacter baumannii is a Gram-negative coccobacillus with exceptional survival skills in an unfavorable environment and the ability to rapidly acquire antibiotic resistance, making it one of the most successful hospital pathogens worldwide, representing a serious threat to public health. The global dissemination of A. baumannii is driven by several lineages named ‘international clones of high risk’ (ICs), two of which were first revealed in the 1970s. Epidemiological surveillance is a crucial tool for controlling the spread of this pathogen, which currently increasingly involves whole genome sequencing. However, the assignment of a particular A. baumannii isolate to some IC based on its genomic sequence is not always straightforward and requires some computational skills from researchers, while the definitions found in the literature are sometimes controversial. In this review, we will focus on A. baumannii typing tools suitable for IC determination, provide data to easily determine IC assignment based on MLST sequence type (ST) and intrinsic blaOXA-51-like gene variants, discuss the history and current spread data of nine known ICs, IC1-IC9, and investigate the representation of ICs in public databases. MLST and cgMLST profiles, as well as OXA-51-like presence data are provided for all isolates available in GenBank. The possible emergence of a novel A. baumannii international clone, IC10, will be discussed.
Genetic dysregulation in recurrent respiratory papillomatosis

Recurrent respiratory papillomatosis (RRP) is a devastating disease, caused by infection of the upper aerodigestive tract with human papillomavirus types 6 and 11. There is no cure for RRP, and surgical removal is the mainstay of treatment. The purpose of this project was to compare genes of cell cycle, apoptosis, and inflammatory cytokines in laryngeal papilloma versus normal tissue for a better understanding of the molecular mechanisms of the disease to discover novel therapies.
Factors Related to the Satisfaction of BPJS Participants on Outpatient Services in the Regional General Hospital Dr. H. Moch Ansari Saleh Banjarmasin

The hospital is a part of the Social Security Administering Agency (BPJS), this program can help to create the highest level of health for the community. The quality of a health service is found from patient satisfaction measurement. The purpose of this study was to determine the factors related to the satisfaction of BPJS participants on outpatient services. This study used an observational analytic method with a cross sectional design conducted at The Regional General Hospital Dr. H. Moch Ansari Saleh Banjarmasin. The population of this study is all patients who are registered as BPJS participants and utilize the outpatient services of The Regional General Hospital Dr. H. Moch Ansari Saleh. Sampling is based on purposive sampling that has been determined based on inclusion and exclusion criteria with a sample of 100 respondents. Data were analyzed based on chi-square and logistic regression test. Chi-Square test showed no relationship (p> 0.05) between age, education, occupation, membership status with patient satisfaction, while there was a relationship (p <0.05) between service procedures, human resource services, drug services, facility services with satisfaction of BPJS participants. Logistic regression test shows that the biggest variable related to patient satisfaction is service procedure (registration) (p <0.05).
Deep Learning Model Performance and Optimal Model Study for Hourly Fine Power Consumption Prediction

Electricity consumption has been increasing steadily owing to technological developments since the Industrial Revolution. Technologies that can predict power usage and management for improved efficiency are thus emerging. Detailed energy management requires precise power consumption forecasting. Deep learning technologies have been widely used recently to achieve high performance. Many deep learning technologies are focused on accuracy, but they do not involve detailed time-based usage prediction research. In addition, detailed power prediction models should consider computing power, such as that of end Internet of Things devices and end home AMIs. In this work, we conducted experiments to predict hourly demands for the temporal neural network (TCN) and transformer models, as well as artificial neural network, long short-term memory (LSTM), and gated recurrent unit models. The study covered detailed time intervals from 1 to 24 h with 1 h increments. The experimental results were analyzed, and the optimal models for different time intervals and datasets were derived. The LSTM model showed superior performance for datasets with characteristics similar to those of schools, while the TCN model performed better for average or industrial power consumption datasets.
Understanding the effect of smart tourism technologies on behavioral intention with the stimulus-organism-response model: a study in Guilin, China

ABSTRACT This study develops a theoretical model to investigate the impact of smart tourism technologies (STTs) on visitors' revisit intention using the stimulus-organism-response theory. Moreover, the moderating effects of technology readiness on the relationships among STTs, visitor engagement and memorable tourism experiences (MTEs) are further examined. The results revealed that visitor engagement and MTEs significantly mediate the relationship between STTs and visitors' revisit intention. Moreover, negative technology readiness negatively moderates the relationship between STTs and visitor engagement. The findings suggest that to enhance tourists' MTEs and revisit rate, destination marketers should reduce travelers' discomfort with and insecurity about STTs.
Message Passing Neural Network Versus Message Passing Algorithm for Cooperative Positioning

Cooperative Positioning (CP) relies on a network of connected agents equipped with sensing and communication technologies to improve the positioning performance of standalone solutions. In this paper, we develop a completely data-driven model combining Long Short-Term Memory (LSTM) and Message Passing Neural Network (MPNN) for CP, where agents estimate their states from inter-agent and ego-agent measurements. The proposed LSTM-MPNN model is derived by exploiting the analogy with the probability-based Message Passing Algorithm (MPA), from which the graph-based structure of the problem and message passing scheme are inherited. In our solution, the LSTM block predicts the motion of the agents, while the MPNN elaborates the node and edge embeddings for an effective inference of the agents’ state. We present numerical evidence that our approach can enhance position estimation, while being at the same time an order of magnitude less complex than typical particle-based implementations of MPA for non-linear problems. In particular, the presented LSTM-MPNN model can reduce the error on agents’ positioning to one third compared to MPA-based CP, it holds a higher convergence speed and better exploits cooperation among agents.
CRSF: An Intrusion Detection Framework for Industrial Internet of Things Based on Pretrained CNN2D-RNN and SVM

The traditional support vector machine (SVM) requires manual feature extraction to improve classification performance and relies on the expressive power of manually extracted features. However, this characteristic poses limitations in complex Industrial Internet of Things (IIoT) environments. Traditional manual feature extraction may fail to capture all relevant information, thereby restricting the application effectiveness of SVM in IIoT settings. CNN-RNN, as a deep learning network capable of simultaneously extracting spatial and temporal features, can alleviate researchers’ burden. In this paper, we propose a novel intrusion detection system (IDS) framework based on anomalies, called CRSF. The framework’s pre-training part employs a dimension transformation function to process input data into two-dimensional images. Two-dimensional convolutional kernels are then employed to extract spatial features, and the feature sequences are passed to an RNN to capture richer temporal features. After sufficient pre-training, SVM is used as a classifier to map the pre-training data from the feature space to a high-dimensional space and learn nonlinear decision boundaries, enabling the framework to accurately differentiate feature representations of different classes. Simulation experiments on the TON_IoT-Datasets demonstrate the effectiveness of the CRSF framework in intrusion detection. When using the “linear” kernel function in SVM, the framework achieves an accuracy, F1-score, and AUC of 0.9959, 0.9959, and 0.9977, respectively, indicating its capability and superiority in intrusion detection.
Mixing and CP Violation in Decays of Charm Mesons

The phenomenon of mixing in neutral meson systems has now been observed in all flavours, but only in the past year in the D 0 system. The standard model anticipated that, for the charm sector, the mixing rate would be small, and also that CP violation, either in mixing or in direct decay, would be below the present levels of observability. It is hoped that further study of these phenomena might reveal signs of new physics. A review of recently available, experimental results is given.
Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning

Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model analytically. Machine learning (ML) is a promising approach to find suitable combinations of process parameters for manufacturing a part with desired properties without having to analytically model the process in its entirety. However, ML-based approaches are typically black box models. Therefore, it is difficult to verify their output and to derive process knowledge from such approaches. This study uses interpretable machine learning methods to derive process knowledge from interpreted data sets by analyzing the model’s feature importance. Using fused layer modeling (FLM) as an exemplary manufacturing technology, it is shown that the process can be characterized entirely. Therefore, sweet spots for process parameters can be determined objectively. Additionally, interactions between parameters are discovered, and the basis for further investigations is established.
"Melq'ilwiye" coming together in an intersectional research team - using narratives and cultural safety to transform Aboriginal social work and human service field education.

This article describes a unique community-based, participatory action research partnership between Elders, an urban Aboriginal community health and social services agency, Aboriginal university faculty, non-Aboriginal faculty, and research participants working together to develop culturally safe best practices in social work and human service field education in Aboriginal community health settings. The article reflects on the process of creating the space to talk together about issues of power, trust, and relationship with respect to histories of colonization past and present within research partnerships. The authors use narratives to link their experiences working as members of this interdisciplinary and intersectional research team moving across various social locations. Experiences are discussed alongside descriptions outlining the process of engagement for respective research team members within a culturally safe and intersectional framework. Grounding the research team in its community health context is Melq’ilwiye, the Secwepemc word for coming together. Thompson Rivers University (TRU) and Nicola Valley Institute of Technology (NVIT) campuses are located on the traditional territory of the Secwepemc and Nlaka’pamux peoples, noted throughout the paper as Aboriginal.
Social media intervention for promoting breastfeeding among WIC participants

Abstract Social media have emerged as a promising communication channel for promoting breastfeeding among a new generation of mothers. Yet, there is no published study reporting the effects of a large‐scale social media intervention on key breastfeeding‐related perceptions, attitudes, and behaviors. As a component of its breastfeeding promotion campaign, the Women, Infants, and Children (WIC) program implemented a 12‐month intervention using Facebook and Instagram and subsequently evaluated the outcomes by surveying WIC‐participating women (N = 832) twice, immediately before and after the intervention. Based on their level of exposure to the intervention messages, the women were retrospectively classified into two groups, resulting in a two‐group (no–low exposure vs. medium–high exposure) quasi‐experiment. Women in the medium–high exposure group, in comparison with women in the no–low exposure group, exhibited higher campaign awareness (p < .001), visits to the campaign website (p < .001), and engagement with the website content (p < .001). They also reported more positive breastfeeding attitudes (M = 17.26 vs. M = 16.51, p < .05), self‐efficacy (M = 54.48 vs. M = 49.94, p < .01), and social support (M = 27.37 vs. M = 25.11, p < .001). But they did not differ from women in the no–low exposure group in breastfeeding initiation (p > .05) and duration (p > .05). In conclusion, a social media‐based intervention resulted in more positive breastfeeding attitudes, higher self‐efficacy, and higher perceived social support. Future studies need to investigate the optimal level of intervention message dosage that prompts significant behavioral changes.
Structural and Biochemical Analysis of the Recombination Mediator Protein RecR from Campylobacter jejuni

The recombination mediator complex RecFOR, consisting of the RecF, RecO, and RecR proteins, is needed to initiate homologous recombination in bacteria by positioning the recombinase protein RecA on damaged DNA. Bacteria from the phylum Campylobacterota, such as the pathogen Campylobacter jejuni, lack the recF gene and trigger homologous recombination using only RecR and RecO. To elucidate the functional properties of C. jejuni RecR (cjRecR) in recombination initiation that differ from or are similar to those in RecF-expressing bacteria, we determined the crystal structure of cjRecR and performed structure-based binding analyses. cjRecR forms a rectangular ring-like tetrameric structure and coordinates a zinc ion using four cysteine residues, as observed for RecR proteins from RecF-expressing bacteria. However, the loop of RecR that has been shown to recognize RecO and RecF in RecF-expressing bacteria is substantially shorter in cjRecR as a canonical feature of Campylobacterota RecR proteins, indicating that cjRecR lost a part of the loop in evolution due to the lack of RecF and has a low RecO-binding affinity. Furthermore, cjRecR features a larger positive patch and exhibits substantially higher ssDNA-binding affinity than RecR from RecF-expressing bacteria. Our study provides a framework for a deeper understanding of the RecOR-mediated recombination pathway.
An Additive 3D-Printed Hemispherical Lens With Flower-Shaped Stub Slot Ultra-Wideband Antenna for High-Gain Radiation

This paper presents a 3D-printed hemispherical lens integrated with a planar ultra-wideband (UWB) antenna. The flower-shaped stub slot UWB antenna is made of 0.8-mm FR-4. The operating frequency of the UWB covers 3.10 GHz – 11.6 GHz with a nominal gain at zero degrees of 1.74 dBi. To enhance the UWB antenna’s high-gain radiation, a 3D-printed additive hemispherical lens is designed and fabricated from acrylonitrile butadiene styrene (ABS). The electrical properties, i.e., relative permittivity and loss tangent, of ABS are 2.66, and 0.003, respectively. Four different lens radii (8 mm, 10 mm, 12 mm, and 14 mm) are chosen to investigate the gain of the antenna. In all four cases, the 3D-printed lens is fixed in place in front of the UWB antenna with an optimum gap of 3 mm chosen to reduce the wave reflection between the lens and source antenna. Based on the measurement results, the reflection coefficient, $S_{11}$ , of four conditions still covers the UWB frequency range. The nominal gain at zero-degree values for lens radii of 8 mm, 10 mm, 12 mm, and 14 mm are 3.43 dBi, 4.22 dBi, 4.73 dBi, and 5.18 dBi, respectively. The proposed additive 3D-printed dielectric lens antenna also offers many advantages, i.e., ease of design and assembly, low-cost fabrication, and size reduction for high-gain antennas. Furthermore, the high-gain antenna provides a narrow half power beamwidth, which can be implemented to increase the resolution of the imaging system.
Hoste’s conjecture for generalized Fibonacci polynomials

Abstract One very long-standing theme in the theory of knots and links in S3 is the description of Alexander polynomials of alternating links. Hoste, based on computer verification, made the following conjecture: If is a root of the Alexander polynomial of an alternating knot, then . For 15 years Hoste’s problem has remained open for any moderately noteworthy subclass of alternating knots. We prove that for every sequence of polynomials , for integers ai, the complex roots of satisfy . This confirms the conjecture of Hoste for 2-bridge knots. The resolution even for 2-bridge knots requires us to bring up a substantial new technical framework. Our approach will be to work numerically and fundamentally rely on the capacity of MATHEMATICA. It draws on methods and motivation from several fields (it proves numerically an algebraic statement with knot theory background.)
Advances and Applications of Optimal Polynomial Approximants

The history of optimal polynomial approximants (OPAs) dates back to the engineering literature of the 1970s. Here, these polynomials were studied in the context of the Hardy space H(X), where X denotes the open unit disk D or the bidisk D. Under certain conditions, it was thought that these polynomials had all of their zeros outside the closure of X. Hence, it was suggested that these polynomials could be used to design a stable digital filter. In recent mathematics literature, OPAs have been studied in many different function spaces; in these settings, numerous papers have been devoted to studying the properties of their zeros. In this dissertation, we begin by introducing the notion of optimal polynomial approximant in the space L(T), where T denotes the unit circle and 1 ≤ p ≤ ∞. Here, we shed light on an orthogonality condition that allows us to study OPAs in L(T) with the additional tools from the L(T) setting. We later use this orthogonality condition to compute the coefficients of some OPAs in L(T); this will give us insight into the location of their zeros. We continue the dissertation by discussing the connection of OPAs to 1D digital filter design; a majority of these discussions will be devoted to surveying the design process of Chui and Chan [13]. Toward the end of this dissertation, we extend the operator theoretic approach of Izumino [22] to the L(T) setting; in light of the orthogonality condition, this provides us additional tools to study the zeros of OPAs in L(T).
The Resistance Training Effects on Skeletal Muscle Stem Cells in Older Adult: A Systematic Review and Meta-Analysis

The objective of this systematic review and meta-analysis is to examine the effects of resistance exercise training on muscle stem cells in older adults. A database search was performed (PubMed, Scopus, Web of Science and Google Scholar) to identify controlled clinical trials in English language. The mean difference (MD) with 95% confidence intervals (CIs) and overall effect size were calculated for all comparisons. The PEDro scale was used to assess the methodological quality. Nineteen studies were included in the review. The meta-analysis found a significant effect of resistance training (RT) on muscle stem cells in the elderly (difference in means=-0.008, Z=-3.415, P=0.001). Also, muscle stem cells changes were similar in men and women (difference in means=-0.004, Z=-1.558, P=0.119) and significant changes occur in type II muscle fibers (difference in means=-0.017, Z=-7.048, P=0.000). Resistance-type exercise training significantly increased muscle stem cells content in intervention group that this result is similar in men and womenthis increase occurred more in type II muscle fibers.
UvA-DARE (Digital Academic Repository) Do not blame it on the algorithm An empirical assessment of multiple recommender systems and their impact on content diversity

In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users ( N =21,973 articles, N =500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.