Miracle-dz/UniFAR-FTUs huggingface dataset:
A Network Analysis Approach toward Adaptive Overt Narcissism Network

The relationship between narcissistic personality and art and beauty appreciation has recently become the focus of research investigations. Adaptive narcissists raise their sense of worth in order to shield themselves from harm caused by others. Because they aspire to be more attractive, healthier, and successful versions of themselves, they frequently have greater success in life than the majority of people. Grandiose and overtly narcissistic behavior are the main recognized characteristics of an overt narcissist, which is currently regarded as a personality disorder that puts mental health and wellbeing at peril. On a random sample collection of data from 1101 respondents to an online questionnaire, we conducted a network analysis of the Adaptive Overt Narcissism Scale (AONS) items. In this study, we used a network analysis approach to examine the network structure of adaptive overt narcissism, as well as its relationships with psychological functioning. The present study utilized network analysis to investigate the centrality measures of items in the Adaptive Overt Narcissism Scale (AONS) and their interrelationships. Results indicated that item Q6.8 (“I appreciate art and beauty”) had low betweenness, closeness, and strength centrality measures, indicating that it was less influential in the network. However, it also had negative expected influence, suggesting that its absence would have a destabilizing effect on the network. These results highlight the importance of the appreciation of art and beauty in deactivating the adaptive overt narcissist network. Further research is needed to explore the mechanisms underlying this relationship and its implications for narcissism prevention and intervention.
On Interpretable Reranking-Based Dependency Parsing Systems

Reranking the 𝑘 best hypothesis parse trees from an existing parser allows to take into account more information that the model has gathered during training than simply decoding the most likely dependency tree. In this paper, we first investigate whether state-of-the-art dependency parsers can still benefit from reranking in low-resource languages. As part of this analysis, we deliver new insights concerning rerankability. Second, we propose a reranker reject option, which paves the way for designing interpretable reranking-based parsing systems in the future.
Effectiveness of Linear Extrapolation in Model-Free Implied Moment Estimation

This study proposes a new methodology with which the effectiveness of linear extrapolation (LE) of Jiang and Tian (2005) for the implied moment estimators of Bakshi et al. (2003) can be evaluated, even when the true moments are unknown. Using S&P 500 index options data and truncation sensitivity functions, this study suggests that although LE is effective for all three estimators, the implied skewness and kurtosis estimators can remain sensitive to truncation, i.e., the complete unavailability of option prices for a part of deep-out-of-the-money or deep-in-the-money region of strike price domain, even when LE is employed to mitigate truncation.
Geometric Signatures as Important Factors to Control the Photo-Stabilities of the Phosphorescent Pd(II)/Pt(II) Complexes: A Case Study

Operation lifetime, as an important parameter, determines the performance of phosphorescent organic light-emitting diodes (OLEDs). Unveiling the intrinsic degradation mechanism of emission material is crucial for improving the operation’s lifetime. In this article, the photo-stabilities of tetradentate transition metal complexes, the popular phosphorescent materials, are explored by means of density functional theory (DFT) and time-dependent (TD)-DFT, aiming to illustrate the geometric signatures as important factors to control the photo-stabilities. Results indicate that for the tetradentate Ni(II), Pd(II), and Pt(II) complexes, the coordinate bonds of the Pt(II) complex exhibit stronger strength. It seems that the strengths of coordinate bonds are closely related to the atomic number of the metal center in the same group, which could be attributed to the various electron configurations. The effect of intramolecular and intermolecular interactions on ligand dissociation is also explored here. The large intramolecular steric hindrance and strong π-π interaction between the Pd(II) complexes caused by aggregation could effectively raise the energy barriers of the dissociation reaction, leading to an unfeasible reaction pathway. Moreover, the aggregation of Pd(II) complex can change the photo-deactivation mechanism as compared to that of monomeric Pd(II) complex, which is favored for avoiding the TTA (triplet-triplet annihilation) process.
Camera Topology Graph Guided Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims to retrieve vehicles across non-overlapping cameras. Most studies consider representation learning from single appearance information of the vehicle images. Some works adopt the spatio-temporal information to remove unreasonable vehicles to refine the results in the testing phase. However, they ignore the potential topological relations among cameras under the Closed Circuit Television (CCTV) camera systems in the training phase, which usually leads to suboptimal results due to the high intra-identity variations. To handle this problem, we propose a novel vehicle re-identification framework, which explicitly models the camera topological relations of all input images to aggregate neighbor images and thus acquires camera-independent representations. Specifically, we first construct a Camera Topology Graph (CTG) to elucidate the topological relations among cameras. It takes different cameras as nodes and constructs edges from four levels of the camera system, position, orientation, and individual. Then, we introduce a Camera Topology-based Graph Convolutional Network (CT-GCN), which suppresses irrelevant neighbor images and learns different camera representation functions. Finally, we propose a topological cross-entropy loss to obtain the more discriminative vehicle representations. The whole network is trained in an end-to-end manner. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed method against state-of-the-art vehicle Re-ID methods.
Changes in the level of consolidation of the fatty acid profile of Hermetia illucens larvae grown on a substrate contaminated with heavy metals

We conducted a comparative investigation to examine the alterations in the composition and content of the fatty acid complex in the larvae of the Black Lion fly (Hermetia illucens) as they were reared under different concentrations and combinations of heavy metals. The use of the method of mass spectrometric analysis of the obtained biomass showed that linoleic, lauric and oleic fatty acids predominated in the composition of the larvae. The use of the mathematical method of fractal analysis based on the data on the profile distribution of fatty acid components in the insect body according to the experimental variants showed that samples with metal concentrations of 20 mg of cadmium, 800 mg of cobalt and Mix (200 mg of copper, 20 mg cadmium, 200 mg cobalt, 20 mg aluminium and 50 mg lead) per kilogram of dry food. The variation in the values of the indices of the biosystemic consolidation of acids, based on the conversion to their molar masses, ranged from 0.41 to 0.82.
A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models

Modeling hydrological processes for managing the available water resources effectively is often complex due to the existence of high nonlinearity, and the associated prediction uncertainty mainly arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance. This paper presents a novel modeling framework for minimizing the prediction uncertainty in the streamflow simulation of the conceptual hydrological model (HBV) by integrating with the Bayesian‐based Particle Filter technique (PF) and machine learning algorithm (Random Forest algorithm, RF). Initially, the streamflow prediction interval (PI) is derived from the stochastically estimated parameters of the HBV model through the PF technique (HBV‐PF model). As the HBV‐PF model quantifies only parametric uncertainty, the RF algorithm was employed (HBV‐PF‐RF model) for further minimizing the prediction uncertainty by inherently taking care of different sources of uncertainty. The RF algorithm inherently combines the physics of the hydrological system (i.e., process‐based variables) with machine learning‐based approach to minimize the overall prediction uncertainty. The proposed framework was analyzed on Nepal and India's Sunkoshi and Beas River basins, through several statistical performance indices for assessing the accuracy and uncertainty of the model prediction. The framework was observed to be consistently improving the model performance minimizing the uncertainty in both watersheds. Therefore, the proposed framework can be considered to be more reliable in improving the prediction capability of hydrological models.
A Scoping Review Using the Theory of Unpleasant Symptoms

Assistant Professor, University of Delaware School of Nursing, Newark. N. Jennifer Klinedinst, PhD, MPH, RN Associate Professor, Department of Organizational Systems and Adult Health, University of Maryland School of Nursing, Baltimore. Karen Larimer, PhD, ACNP-BC Director of Clinical Development, physIQ, Inc., Chicago, Illinois. Cynthia Renn, PhD, RN Professor, Pain & Translational Symptom Science, University of Maryland School of Nursing, Baltimore. Gary Fi Profess Baltimo Stephe Profess Baltimo The aut
Health systems interventions for hypertension management and associated outcomes in Sub-Saharan Africa: A systematic review

Hypertension is a significant global health problem, particularly in Sub-Saharan Africa (SSA). Despite the effectiveness of medications and lifestyle interventions in reducing blood pressure, shortfalls across health systems continue to impede progress in achieving optimal hypertension control rates. The current review explores the health system interventions on hypertension management and associated outcomes in SSA. The World Health Organization health systems framework guided the literature search and discussion of findings. We searched PubMed, CINAHL, and Embase databases for studies published between January 2010 and October 2022 and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed studies for the risk of bias using the tools from the Joanna Briggs Institute. Twelve studies clustered in 8 SSA countries met the inclusion criteria. Two thirds (8/12) of the included studies had low risk of bias. Most interventions focused on health workforce factors such as providers’ knowledge and task shifting of hypertension care to unconventional health professionals (n = 10). Other health systems interventions addressed the supply and availability of medical products and technology (n = 5) and health information systems (n = 5); while fewer interventions sought to improve financing (n = 3), service delivery (n = 1), and leadership and governance (n = 1) aspects of the health systems. Health systems interventions showed varied effects on blood pressure outcomes but interventions targeting multiple aspects of health systems were likely associated with improved blood pressure outcomes. The general limitations of the overall body of literature was that studies were likely small, with short duration, and underpowered. In conclusion, the literature on health systems internventions addressing hypertension care are limited in quantity and quality. Future studies that are adequately powered should test the effect of multi-faceted health system interventions on hypertension outcomes with a special focus on financing, leadership and governance, and service delivery interventions since these aspects were least explored.
Prevalence and Risk Factors of Pelvic Floor Disorders After Delivery in Japanese Women Using the Pelvic Floor Distress Inventory: A Retrospective Cohort Study

Introduction Female pelvic floor disorders (PFDs) include clinical conditions such as urinary and fecal incontinence and pelvic organ prolapse. Disease-specific questionnaires like the Pelvic Floor Distress Inventory-20 (PFDI-20) have facilitated pelvic floor disorder assessment. We aimed to investigate the prevalence of pelvic floor disorders in Japanese women after different modes of delivery and the association of pelvic floor disorders with epidural anesthesia. Material and methods We included 212 women who gave birth at our institution. The PFDI-20 questionnaire (validated in Japanese) was used to evaluate the symptoms of pelvic floor disorders 6-15 months postpartum. Results Out of the 212 postpartum women who participated in this study, 156 (73.6%) had symptoms of pelvic floor disorder; the most prevalent symptom was urinary distress inventory in 114 (53.8%) women [79 (37.3%) of them experienced urine leakage related to increased abdominal pressure]. A comparison of the epidural and non-epidural groups to determine the association between pelvic floor disorder and delivery mode revealed a higher disease burden score of 8.67 points in the epidural group. Conclusion The prevalence of pelvic floor disorder symptoms is relatively high, affecting 156 (73.6%) of 212 women. Accurate diagnosis of women and appropriate and regular follow-up until improvement of their symptoms are crucial. Furthermore, healthcare workers should advise pregnant women on whether to choose vaginal delivery with or without anesthesia. To the best of our knowledge, our study is the first to investigate postpartum pelvic floor disorder in Japan.
Compliance with the World Health Organization’s 2016 prenatal care contact recommendation reduces the incidence rate of adverse birth outcomes among pregnant women in northern Ghana

Background Children born with adverse birth outcomes (ABOs) have a greater risk of mortality, stunting, and poor cognitive development. In 2016, the World Health Organization (WHO) recommended at least eight antenatal care (ANC) contacts before delivery for a healthy mother and baby. We examined the association between compliance with this recommendation and the risk of ABOs, such as low birthweight (LBW) and preterm birth (PTB), in the Tamale Metropolitan Area of the northern region of Ghana. Methods We conducted a cross-sectional study in the Tamale Metropolis of the northern region of Ghana. We analysed a systematic random sample of 402 postnatal women aged 15–49 drawn from five public health facilities. We gathered information electronically on their birth outcomes, specifically their birthweight and gestation at delivery, using a structured questionnaire. Information on women’s background characteristics, including the number of ANC contacts made before delivery, was also collected. The association between the number of ANC contacts and ABOs was investigated using regression models. Results We found that 37.6% (95% CI: 32.9, 42.4) of our sample had at least eight ANC contacts before delivery. We estimated that 18.9% of babies were born prematurely and 9.0% were born LBW. ABOs were found in 22.9% (95% CI: 19.0, 27.3) of babies. A minimum of eight ANC contacts before delivery reduced the risk of ABOs (adjusted IRR = 0.43; 95% CI: 0.25, 0.73), PTB (AOR = 0.28; 95% CI: 0.14, 0.58), and LBW (AOR = 0.36; 95% CI: 0.14, 0.91). Conclusion In the current study’s setting, about a quarter of newborns have ABOs, jeopardising their survival, health, and development. Compliance with at least eight ANC contacts prior to birth was associated with a reduced incidence rate ratio of ABOs. However, less than four out of every ten pregnant women make at least eight ANC contacts before delivery. Efforts are needed to increase coverage of eight contacts among pregnant women before delivery to reduce the risk of ABOs in the study setting.
Improving the isolated microspore culture in eggplant (Solanum melongena L.) with amino acid nutrition

It has been proposed that the composition of the culture medium, especially its amino acids, is an important part of getting microspore androgenesis to occur in some plants. However, there have been far fewer studies done on the Solanaceae family. In this study, we studied what happened to eggplant microspore culture when we mixed casein hydrolysate (0 and 100 mg L-1) with four amino acids: proline (0, 100, 500, and 900 mg L-1), glutamine (0 and 800 mg L-1), serine (0 and 100 mg L-1), and alanine (0 and 100 mg L-1). The results showed that a combination of 800 mg L-1 of glutamine, 100 mg L-1 of serine, 100 mg L-1 of casein hydrolysate, and 500 mg L-1 of proline produced the maximum number of calli per Petri dish (938). Calli had a globular shape and a compact appearance when formed in media containing 500 mg L-1 of proline (alone or combined with serine, alanine, and/or casein hydrolysate). Most of these structures were observed in a medium with 500 mg L-1 of proline, 100 mg L-1 of casein hydrolysate, and 100 mg L-1 of serine. We also investigated what happened when gum arabic (2400, 2600, 3600, 4600, and 5600 mg L-1) was combined with proline (0 and 500 mg L-1), casein hydrolysate (0 and 100 mg L-1), and glutamine (0, 400, and 800 mg L-1). The findings demonstrated the involvement of proline in the increase of calli. Overall, the results give us new information about how amino acids work in eggplant microspore culture and suggest that proline can move this plant’s microspore androgenesis pathway forward.
Control Point Distribution in Trend Surface Modelling Revisited: An Application of the Concept of Leverage

Using the concept of the leverage of a data point, the effects of control point distributions on fitted polynomial trend surface models is re-examined. Points close to the edge of the study region are shown to exert a substantial, often excessive, influence on the fitted surfaces, as do the few solitary points where the distribution of control is clustered. An example is given of a study of the spatial distribution of precipitation over Leicestershire, UK
Correlating continuously captured home-based digital biomarkers of daily function with postmortem neurodegenerative neuropathology

Background Outcome measures available for use in Alzheimer’s disease (AD) clinical trials are limited in ability to detect gradual changes. Measures of everyday function and cognition assessed unobtrusively at home using embedded sensing and computing generated “digital biomarkers” (DBs) have been shown to be ecologically valid and to improve efficiency of clinical trials. However, DBs have not been assessed for their relationship to AD neuropathology. Objectives The goal of the current study is to perform an exploratory examination of possible associations between DBs and AD neuropathology in an initially cognitively intact community-based cohort. Methods Participants included in this study were ≥65 years of age, living independently, of average health for age, and followed until death. Algorithms, run on the continuously-collected passive sensor data, generated daily metrics for each DB: cognitive function, mobility, socialization, and sleep. Fixed postmortem brains were evaluated for neurofibrillary tangles (NFTs) and neuritic plaque (NP) pathology and staged by Braak and CERAD systems in the context of the “ABC” assessment of AD-associated changes. Results The analysis included a total of 41 participants (M±SD age at death = 92.2±5.1 years). The four DBs showed consistent patterns relative to both Braak stage and NP score severity. Greater NP severity was correlated with the DB composite and reduced walking speed. Braak stage was associated with reduced computer use time and increased total time in bed. Discussion This study provides the first data showing correlations between DBs and neuropathological markers in an aging cohort. The findings suggest continuous, home-based DBs may hold potential to serve as behavioral proxies that index neurodegenerative processes.
Improved oscillation criteria for second order nonlinear delay difference equations with non-positive neutral term

Abstract In this paper, we present some oscillation criteria for second order nonlinear delay difference equation with non-positive neutral term of the form Δ(an(Δzn)α)+qnf(xn−σ)=0, n≥n0>0, $$\Delta (a_n (\Delta z_n )^\alpha ) + q_n f(x_{n - \sigma } ) = 0,\;\;\;n \ge n_0 > 0,$$ where zn = xn − pnxn−τ, and α is a ratio of odd positive integers. Examples are provided to illustrate the results. The results obtained in this paper improve and complement to some of the existing results.
Combination determining weights method for multiple attribute decision making based on maximizing deviations

There are many methods to determine weights for multiple attribute decision making, every method has its own characteristics. Based on maximizing deviations principle, an optimal combined weights model is proposed in order to make advantage of these methods. The optimal programming model determines the combined weights. Its solutions are studied, the formula of calculating weights is also given. The test of combined weights is discussed to ensure optimal combination weights can have objective and subjective information, and the calculating steps of the method are summarized. Finally, an example is illustrated to show effectiveness of this optimal combination weights model.
Association of 25(OH)-Vitamin D and metabolic factors with colorectal polyps

Background Studies have revealed the association of vitamin D with specific types of cancer development, however, its correlation with colorectal polyps (CRPs) remains unverified. Our study aimed to investigate the relationship between vitamin D levels, metabolic factors, and CRPs. Methods A cross-sectional study from 2017 to 2019 involving 1306 participants was conducted to investigate the association among vitamin D levels, metabolic factors, uric acid and CRPs in Taiwan. CRPs diagnoses were determined via colonoscopies conducted by experienced gastrointestinal physicians, and biopsied polyps were inspected under a microscope by experienced pathologists. We employed both simple and multiple logistic regression analyses to identify significant factors associated with CRPs and adenomatous polyps, respectively. Results Our result showed that the prevalence of 25(OH)-vitamin D deficiency (≦ 20 ng/mL) and CRPs was 21.21% and 40.89%, respectively. Multiple logistic regression revealed that the risk of CRPs increased with old age, male sex, hyperglycemia, high triglyceride levels, and low 25(OH)D levels after adjustment for other factors. Besides, low 25(OH)D levels were significantly associated with CRPs risk in women, whereas elevated blood pressure was associated with CRPs risk in men. 25(OH)D Deficiency was revealed to be significantly associated with risk of CRPs in adults over 50 years old. Compared to nonadenomatous polyps, older age, higher 25(OH) vitamin D and higher uric acid levels were at increased risk for adenomatous polyps. Conclusions Our study revealed that vitamin D deficiency was significantly associated with the risk of CRPs, especially in adults over 50 years old and women. We should therefore be concerned about the CRP risk of vitamin D deficiency and metabolic syndrome (especially hyperglycemia, elevated blood pressure in men, and high triglyceride levels) in this population.