Global Variance Research Articles

Portfolio Selection with Hierarchical Isomorphic Risk Aversion

Researchers usually specify risk aversion coefficients from 1 (lowest) to M (highest) for a portfolio to indicate active or passive approaches. How effective is this practice? Recent studies suggest that the global minimum variance portfolio (GMVP) is statistically equivalent to portfolios with extensive risk aversion coefficients (the GMVP-equivalent). Expressing the risk aversion coefficient as a Taylor series of the target return and efficient set constants, we generalize the previous result to the non-GMVP-equivalents and segment mean-variance portfolios according to a hierarchy of risk aversion coefficients. In this paper, we show that hierarchical risk aversion coefficients are superior to isometric attributes.

Coinfection of Toxoplasma gondii and Other Microorganisms: A Systematic Review and Meta-Analysis.

Toxoplasmosis is a disease caused by the intracellular protozoan Toxoplasma gondii, which has infected a third of the global population. Immunocompromised individuals and children with congenital disorders are most likely to be impacted by toxoplasmosis, and accurate diagnosis is essential. Toxoplasmosis is associated with HIV, schizophrenia, and diabetes. However, few studies have analyzed the association with other microorganisms. The purpose of this study was to determine the prevalence of coinfection of Toxoplasma gondii with other pathogens. From November 1997 to June 2024, PubMed, Science Direct, LAT index, Web of Science, Google Scholar, and Research Gate were searched. The keywords used were "Toxoplasma and microorganism coinfection", "Toxoplasma coinfection and parasites", "Toxoplasma coinfection and Protozoans or Bacteria or Helminths or Nematodes or Trematodes or Mycobacterium", "Toxoplasma gondii in coinfection with virus", and "Human Toxoplasmosis and coinfection". Next, OpenMeta Analyst Software version 12.11 was used for meta-analysis, creating forest plots, and determining heterogeneity I2. A total of 17,535 patients in 48 articles, of whom 5848 were seropositive to T. gondii, were included in this review. Population studies showed that the prevalence of virus infection was most frequent (32%), followed by parasites (18.4%), bacteria (29.7%), and fungi (5.8%). The pooled prevalence of coinfection was found to be 29.1%, with a lower bound of 0.232, an upper bound of 0.350, a standard error of 0.030, and p < 0.001. Heterogeneity (I2) was 99.12%, p < 0.001, with a global variance tau2 = 0.042. Toxoplasma gondii is an opportunist that mainly affects immunocompromised populations. The main coinfections were found to be viral infections, with HIV ranking first, followed by cytomegalovirus, hepatitis B and C, rubella, herpes simplex 1 and 2, SARS-CoV-2, and coxsackie virus.

Predicting Athlete Workload in Women's Rugby Sevens Using GNSS Sensor Data, Contact Count and Mass.

The use of session rating of perceived exertion (sRPE) as a measure of workload is a popular athlete load monitoring tool. However, the nature of sRPE means the contribution of salient, sport-specific factors to athlete load in field sports is challenging to isolate and quantify. In rugby sevens, drivers of load include high-speed running and physical contact. In soccer and men's rugby, union acceleration/deceleration also influences load. These metrics are evaluated using data from global navigation satellite system (GNSS) sensors worn by athletes. Research suggests that sensor data methods for identifying load in men's rugby do not accurately quantify female athlete loads. This investigation examined how mass, contact, and accelerations and decelerations at different speeds contribute to load in women's rugby sevens. The study evaluated 99 international matches, using data from 19 full-time athletes. GNSS measures, RPE, athlete mass, and contact count were evaluated using a linear mixed-model regression. The model demonstrated significant effects for low decelerations at low and high speeds, mass, distance, and contact count explaining 48.7% of the global variance of sRPE. The use of acceleration/deceleration and speed from GNSS sensors alongside mass, as well as contact count, presents a novel approach to quantifying load.

The Conditional Autoregressive F-Riesz Model for Realized Covariance Matrices

Abstract We introduce a new model for the dynamics of fat-tailed (realized) covariance-matrix-valued time-series using the F-Riesz distribution. The model allows for heterogeneous tail behavior across the coordinates of the covariance matrix via two vector-valued degrees of freedom parameters, thus generalizing the familiar Wishart and matrix-F distributions. We show that the filter implied by the new model is invertible and that a two-step targeted maximum likelihood estimator is consistent. Applying the new F-Riesz model to U.S. stocks, both tail heterogeneity and tail fatness turn out to be empirically relevant: they produce significant in-sample and out-of-sample likelihood increases, ex-post portfolio standard deviations that are in the global minimum variance model confidence set, and economic differences that are either in favor of the new model or competitive with a range of benchmark models.

Drill Hole Spacing Analysis for Evaluation of Quartz Sand Resources as Residual Material from On-Land Alluvial Tin Processing based on Global Estimation Variance and Kriging Variance, with Case Study in the Bangka and Belitung Islands, Indonesia

On-land alluvial tin mining activities produce residual materials from mineral processing or mineral washing; the major one of these is quartz sand, which can be utilized in the manufacturing industry. As time goes by, the reserves of alluvial tin are decreasing, which is in line with the increasing residual material from abundant mineral processing. Mining procedures are carried out following the Indonesian government regulations, covering mining as well as reprocessing. This motivated us to evaluate the potential of quartz sand at two mining sites in Bangka and Belitung Islands. This study evaluated alluvial tin processing residual quartz sand through drill hole spacing analysis (DHSA) by comparing two geostatistics parameters, namely global estimation variance (GEV) and kriging variance (KV). Drill hole samples were taken with varying spacing, ranging from 50 to 200 m, after which geostatistical analysis was carried out. With several simulations, the incorporation of GEV and KV was able to produce the optimal drill hole spacing with measured resource categories in the range of 40 to 55 m, indicated resources in the range of 55 to 85 m, and inferred resources &gt;85 m. Accordingly, the total estimated quartz sand resources of both sites were obtained.

Improved estimation of the correlation matrix using reinforcement learning and text-based networks

We propose a data-driven methodology to shrink the correlation matrix and, hence, the covariance matrix using reinforcement learning (RL). Our approach does not impose any assumptions on the stock returns and can be applied to any covariance matrix target. It focuses on the special case of the global minimum variance portfolio and investigates the economic value of our methodology by utilizing text-based networks (Hoberg and Phillips, 2016). The portfolio selection problem, hence, boils down to determining the optimal shrinkage policy using RL. The empirical analysis utilizes a large universe of stocks covering more than 400 assets and 20 years as a testing period. Overall, the proposed portfolio rule outperforms state-of-the-art shrinkage techniques in terms of out-of-sample volatility, Sharpe ratio, and downside risk net of transaction costs. Our research highlights the effectiveness of our RL-driven approach and underscores the value of alternative data sources in ex-ante forming robust portfolio rules.

Isolation's Echo Current Perspectives in Loneliness Research

Despite increasing incidence across the world, not much evidence is available for understanding and effective management of loneliness. More recently, the COVID-19 pandemic, hard economic times, parental stress, marriage issues and other factors pressed the global population with severe loneliness in high-income, low and middle-income countries (LMICs). This literature review investigates the current landscape of loneliness in high and LMICs to identify new developments, knowledge gaps and insights. This literature search in electronic databases, PubMed/Medline and Google Scholar, to gather qualitative and quantitative evidence and evaluate reported symptoms, prevalence and variance of loneliness across populations. The review focused on unaddressed or contentious issues that are vital for evidence-based interventions. The incidence of loneliness among adolescents, married, children, and separated partners in high and LMIC resonated around vulnerability due to socio-economic, cultural, political and ethnic features. A global incidence of loneliness manifested through parental stress, family conflict, abuse and victimization, racial discrimination, loss of jobs, the COVID-19 pandemic and weak social connections. In children aged 4 to 10 years, loss of pro-social activities like hobbies, sports, school, family conflicts, child-parent disagreements, sedentary behaviors like increased screen time, health disparities and racism have been associated with loneliness. Loneliness has been deemed a trigger of poor social development, quality of life and family fragmentation. The global variance of loneliness can be attributed to unique risk factors. Loneliness implicates social isolation, stress, anxiety, and sleep disturbances among children, adolescents and elderly people. Apart from parental stress, the recent pandemic amplified its prevalence due to hard economic times, loss of jobs and curfews. Males, adolescents and children are the major victims of ensuing loneliness. nonetheless, our study established that physical exercise is an effective intervention against loneliness. Keywords: Loneliness Research, Social Isolation, Mental Health, Psychological Well-being, Current Perspectives

Adaptive dual-domain filtering for random seismic noise removal

Random noise significantly reduces the signal-to-noise ratio of seismic data and affects the accuracy of seismic interpretation. Traditional denoising methods typically require manual parameter tuning to increase robustness and accuracy across various random noise levels. In this study, based on the statistical definition of random noise, we use the variance of random noise as the level of random noise and develop an adaptive dual-domain filter (ADDF). The ADDF method estimates the random noise variance in the seismic data and uses this estimation to effectively denoise the seismic data. First, we use a difference operator in two directions to remove the useful structures from the seismic data. The processed data are then used to estimate the global random noise variance through iterative statistical processing. In the denoising process of the ADDF, the seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in synthetic examples and field data when compared with two typical denoising methods: f- x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.

Face-brain correlates as potential sex-specific biomarkers for schizophrenia and bipolar disorder

Given the shared ectodermal origin and integrated development of the face and the brain, facial biomarkers emerge as potential candidates to assess vulnerability for disorders in which neurodevelopment is compromised, such as schizophrenia (SZ) and bipolar disorder (BD).The sample comprised 188 individuals (67 SZ patients, 46 BD patients and 75 healthy controls (HC)). Using a landmark-based approach on 3D facial reconstructions, we quantified global and local facial shape differences between SZ/BD patients and HC using geometric morphometrics. We also assessed correlations between facial and brain cortical measures. All analyses were performed separately by sex.Diagnosis explained 4.1 % - 5.9 % of global facial shape variance in males and females with SZ, and 4.5 % - 4.1 % in BD. Regarding local facial shape, we detected 43.2 % of significantly different distances in males and 47.4 % in females with SZ as compared to HC, whereas in BD the percentages decreased to 35.8 % and 26.8 %, respectively. We detected that brain area and volume significantly explained 2.2 % and 2 % of facial shape variance in the male SZ - HC sample.Our results support facial shape as a neurodevelopmental marker for SZ and BD and reveal sex-specific pathophysiological mechanisms modulating the interplay between the brain and the face.

CPD: Setting a new standard of care for driving patients

Eluned Creighton-Sims looks at the global variance in driving vision standards and that using more visual testing methods is related to improved driver safety

The R2D2 Prior for Generalized Linear Mixed Models

In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model instead. In this work, we propose a prior on the model fit, as measured by a Bayesian coefficient of determination ( R 2 ) , which then induces a prior on the individual parameters. We achieve this by placing a beta prior on R 2 and then deriving the induced prior on the global variance parameter for generalized linear mixed models. We derive closed-form expressions in many scenarios and present several approximation strategies when an analytic form is not possible and/or to allow for easier computation. In these situations, we suggest approximating the prior by using a generalized beta prime distribution and provide a simple default prior construction scheme. This approach is quite flexible and can be easily implemented in standard Bayesian software. Lastly, we demonstrate the performance of the method on simulated and real-world data, where the method particularly shines in high-dimensional settings, as well as modeling random effects.

Cracking the code to female sexual satisfaction: the serial mediation of sexual behavior and the perceived importance of healthy sexuality from sexual self-efficacy.

Sexual satisfaction has been shown to have a strong association with many aspects of sexual health and wellbeing. It is further considered a robust indicator of an individual's health status and general wellbeing, revealing that a person can enjoy pleasurable and healthy sexual experiences, beyond the mere absence of sexual and reproductive health issues. This study aimed to analyze the relationship between sexual satisfaction, sexual behaviors, sexual self-efficacy, and the importance personally attributed to maintaining an active and satisfying sexual life among young and middle-aged women aged 18-50. A descriptive correlational study with a cross-sectional design was conducted. Participants (N = 1,076 women) completed self-reports on sexual self-efficacy beliefs, frequency of sexual behaviors, the importance attributed to active and healthy sexuality, and multidimensional sexual satisfaction. The supported mediation model indicated that sexual self-efficacy was related to sexual satisfaction directly and indirectly through sexual behavior and a serial path through sexual behavior and the perceived importance of healthy sexuality. The total effect was significant, and the full model explained 7.3% of the global sexual satisfaction variance (F = 17.218, p = 0.000), with the mediated effect accounting for 44.3%. This study confirms a partial serial mediation model by which sexual self-efficacy significantly predicts sexual satisfaction through sexual behaviors and the importance attributed to a healthy sexuality. Due to its significant contribution, the perceived importance of sexuality should be considered when studying correlates of sexual satisfaction. These findings have interesting implications for the development of strategies aimed at sexual health promotion and sexual education among women in early and middle adulthood.

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Probabilistic safety and reliability studies toward licensing and deploying HTGR technology in the Polish chemical industry

The pivotal aspects of the Probabilistic Safety Assessment (PSA) and reliability studies for the foreseen HTGR-based nuclear-chemical (Nuc-Chem) facilities are discussed. The traditional PSA methodology has been extended to include the mutual dependence of nuclear and chemical systems of the hybrid Nuc-Chem installations, where HTGR serves as a heat source for various chemical processes. The Dynamic Bayesian Network (DBN) approach has been utilized for modeling time sequences of failures, including interactions between nuclear and non-nuclear systems, as well as external hazards. The Global Variance Method (GVM), based on the Sobol indices, has been proposed for uncertainty and sensitivity studies concerning numerous sources of variability in a consistent manner. The paper presents the outcomes and achievements from research oriented toward licensing and deploying High Temperature Gas-cooled Reactors (HTGR) within the Polish chemical industry. The general framework developed in this work for PSA of the hybrid Nuc-Chem facilities can be applied to support the licensing process and safe operation of the Polish chemical plants to be equipped with HTGRs.

Shrinkage and thresholding approaches for expected utility portfolios: An analysis in terms of predictive ability

In this paper, we estimate Expected Utility Portfolios (EUPs) in high-dimensional, low-sample settings using various covariance matrix estimation methods, including shrinkage and thresholding-based methods. We perform synthetic experiments comparing these methods, using Average Out-of-Sample Variance (AOV) for Global Minimum Variance (GMV) portfolios and Average Out-of-Sample Utility (AOU) for EUPs. Additionally, we propose a practical method for fund managers to select optimal models based on empirical data, relying on AOV and AOU performance measures. The results indicate that shrinkage-based methods outperform thresholding-based ones in high-dimensional settings, with non-linear shrinkage being particularly effective.

Construction of GVR weight windows maps from very low density transport simulations

Fusion-related facilities present relevant neutron radiation fields even after penetrating through a considerable thickness of shielding material. Neutronic analyses performed via Monte Carlo codes, then, need Global Variance Reduction (GVR) techniques so that low statistical uncertainty is reached efficiently throughout the geometry.Mesh-based Weight Windows is a flexible methodology used extensively for variance reduction purposes, both for Local and Global Variance Reduction. Purely stochastic GVR methodologies based on Weight Windows usually construct weight maps so that they are proportional to the forward particle flux, which is unknown a priori. Therefore, an iterative cycle is established. In each iteration, a weight map is obtained from the forward flux that allows the next iteration to reach further into the geometry, until all of it is populated.However, this iterative cycle may take a considerable amount of computer time, as many iterations are needed to fully populate the geometry. An alternative to achieve relevant penetration in a single iteration is to perform calculations at very low densities. However, a reconstruction method is needed to estimate the flux at the real density. This work studies a scheme to reconstruct the fluxes from low density calculations and compares it to already existing techniques.

Development of multilevel mesh adaptivity for global variance reduction in Monte Carlo code cosRMC

The mesh-based weight window method is a widely used global variance reduction technique for Monte Carlo particle transport calculation. In order to meet the fine voxel division required for high-quality weight window distribution, the uniform structured mesh division will lead to large memory footprint pressure. This work focused on the research of the multilevel adaptive mesh algorithm, using a recursive algorithm to adjust the voxel resolution adaptively according to the material of geometry. Then the adaptive mesh algorithm was applied to the weight window generator based on the response matrix method by establishing the neighbor relationship of adaptive mesh. And an optimization method for memory allocation of the response matrix solver was proposed, effectively reducing the memory footprint of the response matrix solver in parallel computing. In the calculation of the CFETR model, the calculation efficiency under the weight window based on adaptive mesh was increased by 53.3 % compared with the weight window based on uniform mesh, and the proportion of voxels with a statistical error less than 10 % reached 93.33 %, which verified the effectiveness of the multilevel adaptive mesh algorithm.

An extension of NASCA for SDDR analysis of mobile activated components with global variance reduction

During the plasma operation period of fusion reactor facilities, neutrons generated by DT reaction can penetrate deeply into structures and induce strong activation. Highly activated components have to be transferred from the on-load position to the hot cell for the purpose of maintenance. This movement produces a high-dose field, originating from the mobile radiation source, which could affect the functionality of important devices or pose risks to the workers. Therefore, it is imperative to compute the shutdown dose rate (SDDR) maps while transporting activated components in the facility and develop an optimized solution for this calculation process. In this study, we established a new extension of the NASCA method combined with the OTF GVR method to compute SDDR maps. NASCA-OTF is a method developed based on the shutdown dose simulation code NASCA to ensure automatic modification of the geometry structure on the fly. In the meantime, it uses OTF method for speeding up the Calculation. Thus, this modified method produces convergent SDDR maps corresponding to different positions of activated components within a single Monte Carlo (MC) simulation. The NASCA-OTF was verified by comparison with R2S method through benchmark analysis. The method was also used for SDDR analysis of the mobile blanket sector for the China Fusion Engineering Test Reactor (CFETR), and the results indicated that the NASCA-OTF is feasible for computing the SDDR resulting from the movement of activated components.

A blending method for forward-looking sonar mosaicing handling intra- and inter-frame artifacts

Forward-looking sonar (FLS) has been gaining attractions in the realm of near-bottom, close-range underwater inspections, owing to its high-resolution and rapid framerate capabilities. Although automatic target recognition (ATR) algorithms have been tentatively employed for object detection tasks, the necessity for human oversight remains vital, particularly in sensitive areas. A comprehensive FLS mosaic encapsulating all relevant information is highly desired to aid experts in managing an extensive array of perception data. Yet, prior research has often presumed that FLS operates under an ideal system configuration, assuming optimal sonar imaging setups and the availability of precise positioning data. Without these assumptions, intra-frame and inter-frame artifacts would emerge, deteriorating the quality of the resultant mosaic by obscuring essential information. In this study, we propose an innovative blending method specifically for FLS mosaicing aiming preserve important information. We first develop a long–short time sliding window (LST-SW) to rectify the local statistics of raw sonar images. These statistics then serve to create a global variance map (GVM). This step helps to emphasize the useful information contained in images in the blending phase by classifying the informative and featureless pixels, thereby enhancing the quality of final mosaic. This approach is substantiated through data collected in real-world scenarios. The results show that our method can preserve more details in FLS mosaics for human inspection purposes in practice.

Portfolio Optimization Based on U.S. Stock

In the financial domain, portfolio optimization is of immense significance due to its potential for risk diversification and return enhancement. Particularly in today's society where inflation is on the rise, a well-constructed portfolio can effectively assist investors in maintaining or increasing real assets. This study selected 10 U.S. stocks and analyzed their data for the past decade. Utilizing Monte Carlo simulation, the study identified the efficient frontier and constructed three types of portfolios: equal-weighted (1/N) portfolio, global minimum variance portfolio (hereinafter referred to as GMVP), and maximum Sharpe ratio portfolio. The findings show that Microsoft and Nvidia have the greatest investment weights in the portfolio with the maximum Sharpe ratio. In the GMVP, JNJ and KO have the highest investment weights. By contrasting the three portfolios' combined performance with the S&amp;P 500 index, both the maximum Sharpe ratio and the 1/N portfolios outperformed the benchmark index, while the GMVP performed below the benchmark. The findings of this study may offer insights into portfolio management for investors looking to increase their real assets.