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We construct text-based measures of the primary concerns listed firms associated with the spread of COVID-19 and other epidemic diseases. We identify which firms perceive to lose or gain from a given epidemic and textually decompose the epidemic's effect on the firm's demand and supply. We find that the effects of COVID-19 manifest as a simultaneous shock to demand and supply, with both shocks affecting firms' market valuations in equal measure on average. By contrast, demand-related impacts appear more important in accounting for the observed collapse in firm-level investment during the COVID-19 crisis. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

We analyze the partisanship of Commissioners at the SEC and Governors at the Federal Reserve Board. Using recent advances in machine learning, we identify partisan phrases in Congress, such as "red tape" and "climate change," and observe their usage among regulators. Although the Fed has remained relatively nonpartisan throughout our sample period (1930–2019), we find that partisanship among SEC Commissioners rose to an all-time high during the 2010-2019 period, driven by more-partisan Commissioners replacing less-partisan ones. Partisanship at the SEC appears in both the language of new SEC rules and the voting behavior of SEC Commissioners. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and is limited in its ability to incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics of a system, enabling the denoising of the data while simultaneously learning the latent dynamics. Simulations from the fitted neural network are then used to infer models using sparse regression. We show, via model selection, that model discovery using hybrid dynamical systems outperforms alternative approaches. We find it possible to infer models correctly up to high levels of biological noise of different types. We demonstrate the potential to learn models from sparse, noisy data in application to a canonical cell state transition using data derived from single-cell transcriptomics. Overall, this approach provides a practical framework for model discovery in biology in cases where data are noisy and sparse, of particular utility when the underlying biological mechanisms are partially but incompletely known. [ABSTRACT FROM AUTHOR]

The study of the multifaceted interactions between neuroscience and cancer is an emerging field with significant implications for understanding tumor biology and the innovation in therapeutic approaches. Increasing evidence suggests that neurological functions are connected with tumorigenesis. In particular, the peripheral and central nervous systems, synapse, neurotransmitters, and neurotrophins affect tumor progression and metastasis through various regulatory approaches and the tumor immune microenvironment. In this review, we summarized the neurological functions that affect tumorigenesis and metastasis, which are controlled by the central and peripheral nervous systems. We also explored the roles of neurotransmitters and neurotrophins in cancer progression. Moreover, we examined the interplay between the nervous system and the tumor immune microenvironment. We have also identified drugs that target the nervous system for cancer treatment. In this review we present the work supporting that therapeutic agent targeting the nervous system could have significant potential to improve cancer therapy. [ABSTRACT FROM AUTHOR]

This paper uses multivariate Hawkes processes to model the transactions behavior of the US stock market as measured by the 30 Dow Jones Industrial Average individual stocks before, during and after the 36-min May 6, 2010, Flash Crash. The basis for our analysis is the excitation matrix, which describes a complex network of interactions among the stocks. Using high-frequency transactions data, we find strong evidence of self- and asymmetrically cross-induced contagion and the presence of fragmented trading venues. Our findings have implications for stock trading and corresponding risk management strategies as well as stock market microstructure design. [ABSTRACT FROM AUTHOR]

We present a class of high-order Eulerian–Lagrangian Runge–Kutta finite volume methods that can numerically solve Burgers’ equation with shock formations, which could be extended to general scalar conservation laws. Eulerian–Lagrangian (EL) and semi-Lagrangian (SL) methods have recently seen increased development and have become a staple for allowing large time-stepping sizes. Yet, maintaining relatively large time-stepping sizes post shock formation remains quite challenging. Our proposed scheme integrates the partial differential equation on a space-time region partitioned by linear approximations to the characteristics determined by the Rankine–Hugoniot jump condition. We trace the characteristics forward in time and present a merging procedure for the mesh cells to handle intersecting characteristics due to shocks. Following this partitioning, we write the equation in a time-differential form and evolve with Runge–Kutta methods in a method-of-lines fashion. High-resolution methods such as ENO and WENO-AO schemes are used for spatial reconstruction. Extension to higher dimensions is done via dimensional splitting. Numerical experiments demonstrate our scheme’s high-order accuracy and ability to sharply capture post-shock solutions with large time-stepping sizes. [ABSTRACT FROM AUTHOR]

Future financing has the potential to evolve into a forward-looking and ingenious system that can enrich diverse technologies. This investigation focuses on five attractive emerging technologies tied to the finance, namely artificial intelligence (AI), machine learning, blockchain, augmented reality (AR)/virtual reality (VR), and quantum mechanics. Within the financial milieu, there is an ongoing pursuit for more precise, optimized, secure, and agile solutions capable of managing multifarious financial undertakings. Emerging technologies present remarkable modalities for achieving these objectives. This scholarly exposition expounds upon the creation of emerging technologies within the financial world, while concurrently deliberating upon the prospective applications of these technologies within financial systems. Given the nascent stage of development that characterizes these emerging technologies, the advancement of financial performance emerges as a substantial challenge owing to both technological and operational advantages. Technology-oriented financing has emerged as an essential trajectory for shaping the future of finance. [ABSTRACT FROM AUTHOR]

Background: Prolonged postoperative ileus (PPOI) reportedly leads to compromised postoperative recovery and increased healthcare costs. However, the evidence for this claim was obtained from studies that included patients with both primary and secondary PPOI. How primary PPOI affects the hospital length of stay (LOS) and healthcare costs is not well documented. A multicenter cohort analysis was performed to investigate the potentially detrimental effect of primary PPOI on hospital LOS and healthcare costs. Methods: In total, 2083 patients who underwent open abdominal surgery from 22 tertiary hospitals in China were prospectively registered in a PPOI cohort. Of these, 1863 patients without secondary PPOI were analyzed. Poisson regression for hospital LOS and log-transformed linear regression for healthcare costs were performed to identify whether primary PPOI was an independent risk factor. Results: The incidence of primary PPOI was 13.2% (246/1863). The median LOS was significantly longer in the PPOI than non-PPOI group (12 vs. 11 days, p < 0.001). The median healthcare cost was significantly higher in the PPOI than non-PPOI group (70,672 vs. 67,597 CNY, p = 0.016). Multivariate Poisson regression and log-transformed linear regression showed that 12% of prolonged LOS and 4.6% of healthcare costs were due to primary PPOI. Conclusions: Primary PPOI is a potential source of prolonged hospital LOS and extra healthcare costs for patients undergoing open abdominal surgery. Cost-effective approaches are needed to manage and prevent primary PPOI. [ABSTRACT FROM AUTHOR]

Introduction: Colorectal cancer is the third most common malignancy, with around half of patients developing liver metastases. Hepatectomy is the preferred treatment, but its success depends on several factors, including surgical margins. Various surgical margins have been suggested to achieve optimal results. This systematic review and meta-analysis aim to explore the impact of negative surgical margins ranging from 1 to 10 mm, and >10 mm on survival, with the objective of identifying optimal surgical margins. Methods: A systematic literature search was conducted on the MEDLINE, Scopus, and Cochrane databases. The six included studies that examined the effect of surgical margins at the aforementioned distances on patient survival. Studies were assessed for risk of bias using the Quality in Prognosis Studies tool. Statistical analysis was performed using SPSS software. Results: The results of the meta-analysis revealed the superiority of wider surgical margins (>10) on overall survival compared to smaller margins (1–10 mm), as the HR was calculated to be 1.38 [1.10; 1.73]. Specifically, negative margins between 1 and 10 mm are linked to a 38% increased risk of mortality compared to margins larger than 10 mm. The low heterogeneity indicates consistent findings across studies, and the statistically significant hazard ratio underscores the importance of aiming for larger surgical margins to enhance patient outcomes. In the subgroup that included only studies in which patients received neoadjuvant therapy, the HR was 1.48 [1.06; 2.07], further emphasizing the importance of ensuring negative surgical margins in today's era. Conclusions: In summary, this systematic review and meta-analysis highlights the impact of surgical margin width on the survival of patients with colorectal liver metastases, as well as the importance of margin optimization in surgical management strategies. [ABSTRACT FROM AUTHOR]

High-frequency data is widely used and studied in many fields. In this paper, the asymptotic normality of kernel density estimator under ρ-mixing high-frequency data is studied. We first derive some moment inequalities for mixing high-frequency data, and then use them to study the asymptotic normality of the kernel density estimator, and give Berry-Esseen upper bounds. The numerical simulations report that the kernel density estimation of high-frequency data has asymptotic normality, and the result is consistent with the theoretical conclusions. The actual data analysis shows that the kernel density estimation can well capture the characteristics of the distribution, and can use these features and the least square deviation principle to fit the parameter model, which is more convenient for further theoretical analysis and application analysis. [ABSTRACT FROM AUTHOR]

The continuous combination of digital network technology and traditional financial services has given birth to digital financial networks, which explore massive economic data under the AI-driven models to achieve intelligent connections among financial institutions, markets, transactions, and instruments. Empirical asset pricing is a challenging task in financial analysis, which has attracted research attention. However, existing studies only focus on tackling the challenges of equity risk premium in the single stock market. Considering multiple economic linkages between the two countries, the transaction history of the US stock market as empirical knowledge is a powerful supplement to improve the prediction of equity risk premium in the China market. In this paper, we aim to fully leverage the prior information in two stock markets for empirical asset pricing models. Due to the rich financial domain knowledge, there may be various characteristic signals that partially overlap in different periods. To address these issues, we propose a framework based on long-short dual-mode knowledge distillation, termed as LSDM-KD, which incorporates US and China stock market models, and a shared characteristic signals model. The method effectively understands the relationships between assets and market behaviour, reducing reliance on expensive correlation databases and professional knowledge. Extensive experiments conducted on US and China stock market datasets demonstrate that our LSDM-KD can significantly improve the performance of empirical asset pricing. [ABSTRACT FROM AUTHOR]

Radiative heat transfer is a fundamental process in high energy density physics and inertial fusion. Accurately predicting the behavior of Marshak waves across a wide range of material properties and drive conditions is crucial for design and analysis of these systems. Conventional numerical solvers and analytical approximations often face challenges in terms of accuracy and computational efficiency. In this work, we propose a novel approach to model Marshak waves using Fourier Neural Operators (FNO). We develop two FNO-based models: (1) a base model that learns the mapping between the drive condition and material properties to a solution approximation based on the widely used analytic model by Hammer & Rosen (2003), and (2) a model that corrects the inaccuracies of the analytic approximation by learning the mapping to a more accurate numerical solution. Our results demonstrate the strong generalization capabilities of the FNOs and show significant improvements in prediction accuracy compared to the base analytic model. [ABSTRACT FROM AUTHOR]

Spatial mobility is a distinctive feature of human history and has important repercussions in many aspects of societies. Spatial mobility has always been a subject of interest in many disciplines, even if only mobility observable from traditional sources, namely migration (internal and international) and more recently commuting, is generally studied. However, it is the other forms of mobility, that is, the temporary forms of mobility, that most interest today's societies and, thanks to new data sources, can now be observed and measured. This contribution provides an empirical and data-driven reflection on human mobility during the COVID pandemic crisis. The paper has two main aims: (a) to develop a new index for measuring the attrition in mobility due to the restrictions adopted by governments in order to contain the spread of COVID-19. The robustness of the proposed index is checked by comparing it with the Oxford Stringency Index. The second goal is (b) to test if and how digital footprints (Google data in our case) can be used to measure human mobility. The study considers Italy and all the other European countries. The results show, on the one hand, that the Mobility Restriction Index (MRI) works quite well and, on the other, the sensitivity, in the short term, of human mobility to exogenous shocks and intervention policies; however, the results also show an inner tendency, in the middle term, to return to previous behaviours. [ABSTRACT FROM AUTHOR]

This article introduces a new class of volatility models known as Periodic log-Generalized Autoregressive Conditional Heteroscedastic (P-logGARCH) models, which incorporate periodic variations in the parameters. These parameter variations are particularly relevant when incorporating seasonality into economic decision-making theory. The P-logGARCH formulation demonstrates several desirable properties, including unconstrained positivity of parameters and effective management of extreme values, along with a volatility trend. Specifically, in this paper we investigate the probabilistic structure of this model and establish necessary and sufficient conditions for the existence of stationary solutions in a periodic sense. Additionally, we examine the strong consistency and asymptotic normality of the Generalized Quasi-Maximum Likelihood Estimator (GQMLE) under mild assumptions. To assess the performance of our model, we conduct a Monte Carlo study to examine the finite-sample properties of the GQMLE. Finally, we present empirical evidence by applying the P-log GARCH model to analyze the exchange rates of the Algerian Dinar against the U.S. dollar and the Euro, thereby demonstrating its practical utility. [ABSTRACT FROM AUTHOR]

Stochastic models with global parameters and latent variables are common, and for which variational inference (VI) is popular. However, existing methods are often either slow or inaccurate in high dimensions. We suggest a fast and accurate VI method for this case that employs a well-defined natural gradient variational optimization that targets the joint posterior of the global parameters and latent variables. It is a hybrid method, where at each step the global parameters are updated using the natural gradient and the latent variables are generated from their conditional posterior. A fast to compute expression for the Tikhonov damped Fisher information matrix is used, along with the re-parameterization trick, to provide a stable natural gradient. We apply the approach to deep mixed models, which are an emerging class of Bayesian neural networks with random output layer coefficients to allow for heterogeneity. A range of simulations show that using the natural gradient is substantially more efficient than using the ordinary gradient, and that the approach is faster and more accurate than two cutting-edge natural gradient VI methods. In a financial application we show that accounting for industry level heterogeneity using the deep mixed model improves the accuracy of asset pricing models. MATLAB code to implement the method and replicate the results can be found at [ABSTRACT FROM AUTHOR]

In this paper, a linear second-order finite difference scheme is proposed for the Allen–Cahn equation with a general positive mobility. The Crank–Nicolson scheme and the Taylor's formula are used for temporal discretization, and the central finite difference method is used for spatial approximation. The discrete maximum bound principle (MBP), the discrete energy stability and -norm error estimate are discussed, respectively. Finally, some numerical examples are presented to verify our theoretical results. [ABSTRACT FROM AUTHOR]

Background: Loneliness can occur at any age, but it is more prevalent among older adults due to the associated risk factors. Various interventions exist to improve this situation, but little is known about their long-term effects. Our aims were to determine if these interventions have long-lasting effects and for how long they can be sustained. Additionally, we aimed to analyze if the interventions carried out by volunteers affected the outcomes regarding loneliness and psychological impact. Methods: A systematic review was performed by searching the literature in the MEDLINE PubMed, SCOPUS, Web of Science, PsycINFO, and Web of Science databases for interventions focused on the lonely population. The inclusion criteria for this review were the assessment of loneliness using a validated tool, and loneliness being the primary or secondary outcome. The CASPe checklist was used to assess the risk of bias in the selected studies, and the PRISMA-ScR recommendations were followed to present and synthesize the results. Results: Thirty articles were included. The interventions identified were classified into five categories: psychosocial, technological, health promotion, physical exercise, and multicomponent interventions. Loneliness improved in 24 studies during the post-intervention analysis. Social connectivity and depressive symptoms also improved in most interventions. Long-term follow-ups were conducted with positive results in a total of 16 interventions. Depressive symptoms and social connectivity were also improved. Eight of the interventions were carried out by volunteers and showed good results regarding loneliness. Conclusions: The results obtained in this work suggested that multidisciplinary interventions can reduce loneliness, but more controlled clinical studies are needed. [ABSTRACT FROM AUTHOR]

Surrogate modeling is of great practical significance for parametric differential equation systems. In contrast to classical numerical methods, using physics-informed deep learning-based methods to construct simulators for such systems is a promising direction due to its potential to handle high dimensionality, which requires minimizing a loss over a training set of random samples. However, the random samples introduce statistical errors, which may become the dominant errors for the approximation of low-regularity and high-dimensional problems. In this work, we present a deep adaptive sampling method for surrogate modeling of low-regularity parametric differential equations and illustrate the necessity of adaptive sampling for constructing surrogate models. In the parametric setting, the residual loss function can be regarded as an unnormalized probability density function (PDF) of the spatial and parametric variables. In contrast to the non-parametric setting, factorized joint density models can be employed to alleviate the difficulties induced by the parametric space. The PDF is approximated by a deep generative model, from which new samples are generated and added to the training set. Since the new samples match the residual-induced distribution, the refined training set can further reduce the statistical error in the current approximate solution through variance reduction. We demonstrate the effectiveness of the proposed method with a series of numerical experiments, including the physics-informed operator learning problem, the parametric optimal control problem with geometrical parametrization, and the parametric lid-driven 2D cavity flow problem with a continuous range of Reynolds numbers from 100 to 3200. [ABSTRACT FROM AUTHOR]

In contrast to the classical Allen–Cahn equation, the conservative Allen–Cahn equation with a nonlocal Lagrange multiplier not only satisfies the maximum bound principle (MBP) and energy dissipation law but also ensures mass conservation. Many existing schemes often fail to preserve all these properties at the discrete level or require high regularity in time on the exact solution for convergence analysis. In this paper, we construct a new class of low regularity integrators (LRIs) for time discretization of the conservative Allen–Cahn equation by repeatedly using Duhamel’s formula. The proposed first- and second-order LRI schemes are shown to conserve mass unconditionally and satisfy the MBP under some time step size constraints. Temporal error estimates for these schemes are derived under a low regularity assumption that the exact solution is only Lipschitz continuous in time, followed by a rigorous proof for energy stability of the corresponding time-discrete solutions. Various numerical experiments and comparisons in two and three dimensions are presented to verify the theoretical results and illustrate the performance of the LRI schemes, especially when the interfacial parameter approaches zero. [ABSTRACT FROM AUTHOR]

The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on analyzing the failure of transmission lines, specifically considering the effects of wind, ambient temperature, and current demands, incorporating minimal and significant pre-existing damage. We propose a multiphysics framework to analyze the transmission line failures across sensitive and affected states of the United States, integrating historical data on wind and ambient temperature. By combining numerical simulation with historical data analysis, our research assesses the impact of varying environmental conditions on the reliability of transmission lines. Our methodology begins with a deterministic approach to model temperature and damage evolution, using phase-field modeling for fatigue and damage coupled with electrical and thermal models. Later, we adopt the probability collocation method to investigate the stochastic behavior of the system, enhancing our understanding of uncertainties in model parameters, conducting sensitivity analysis to identify the most significant model parameters, and estimating the probability of failures over time. This approach allows for a comprehensive analysis of factors affecting transmission line reliability, contributing valuable insights into improving power line's resilience against environmental conditions. [ABSTRACT FROM AUTHOR]

With the benefits of reduced travel time and alleviated traffic congestion, tunnel construction significantly enhances urban mobility. Meanwhile, tunnel construction accidents result in many casualties and property losses. To minimize accidents associated with tunnel construction while keeping its benefits, it is important to enhance the effectiveness and efficiency of training programs for construction workers. However, there is a lack of training evaluation systems specifically designed for tunnel construction workers, along with limited research on the effectiveness and efficiency of training programs for this group. This paper targets personnel from the China Railway 14th Bureau Group Company, aiming to evaluate a training program designed for tunnel construction workers. Three popular training approaches are proposed to evaluate the effectiveness and efficiency of training outcomes, including WeChat push safety construction knowledge (WP), the emergency drill (ED), and the unitive lecture (UL). Additionally, a concept of study is conducted to examine the performance of the proposed approaches in eighteen schemes that vary at different levels of training intensity by using VR (virtual reality), an electroencephalogram (EEG) system, and data enveloping analysis (DEA). The results show that the ED is the most effective training method, enabling industry professionals to respond effectively to unsafe situations by equipping them with critical skills through comprehensive training. Additionally, the ED has great potential for training tunnel construction workers via the provision of simulated experiences to enhance their safety preparedness. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed hybrid crow search algorithm (CSA) and long short-term memory (LSTM) system can be utilized for stock price forecasting by simultaneously leveraging multiple datasets, such as technical indicators, leading indicators, and various time series data related to stock prices. This system enhances prediction accuracy. This system can help financial institutions, investment firms, and individual traders make more informed trading decisions and optimize portfolio strategies by accurately forecasting future stock prices. This study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. This method utilizes a stacking ensemble of long short-term memory (LSTM) networks, with the crow search algorithm (CSA) optimizing the weights assigned to the predictions from multiple LSTM models. To improve the overall accuracy, this system leverages three distinct datasets: technical analysis indicators; price fluctuation limits; and variation mode decomposition (VMD) subsignal sequences. The predictions for the three reference-data types are more comprehensive than single-model or single-data-type approaches. The prediction accuracies of the recurrent neural network, gate recurrent unit, and the LSTM network for five stocks were compared. The proposed CSLSTM system outperforms the other standalone models. Furthermore, we conducted backtesting to demonstrate that the prediction information from our model could generate profit in the stock market, enabling users to benefit from complex stock-market dynamics. The stock prices in this study are expressed in New Taiwan Dollars (TWD), the official currency of Taiwan. [ABSTRACT FROM AUTHOR]

Featured Application: This study provides a method that can quickly and accurately predict subway tunnel settlement, which can be effectively applied to prevent and control the safety of subway projects. The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. First, the original data are optimized via the SSA, which has global optimization capability, high noise immunity, and high adaptivity. The original signal is subsequently decomposed into multiple subsignal sequences via a variational modal decomposition (VMD) algorithm combined with a rolling decomposition technique. Finally, the decomposed signals are fed into the machine learning model to construct a high-precision settlement prediction model based on rolling decomposition. The prediction accuracy of different models was analyzed via the measured settlement data during the construction period of the Beijing subway as an example. The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model. [ABSTRACT FROM AUTHOR]

Forest therapy is known to promote physical and mental well-being, but its specific impact on physiological and psychological recovery in university students, particularly across genders, remains underexplored. This study examined the effects of a five-hour forest therapy session involving sensory-based activities on 48 students (30 males and 18 females). The key outcomes measured included pulse rate, heart rate variability (HRV), and mood states. Significant reductions in pulse rate and anxiety were observed, with females showing greater physiological and emotional recovery. These findings suggest that forest therapy may serve as an effective intervention for stress relief, particularly for females, and highlight the potential of sensory-based therapies in promoting student well-being. [ABSTRACT FROM AUTHOR]

This study employs a variety of machine learning models and a wide range of economic and financial variables to enhance the forecasting accuracy of the Korean won–U.S. dollar (KRW/USD) exchange rate and the U.S. and Korean stock market returns. We construct international asset allocation portfolios based on these forecasts and evaluate their performance. Our analysis finds that the Elastic Net and LASSO regression models outperform traditional benchmark models in predicting exchange rate and stock market returns, as evidenced by their superior out-of-sample R-squared values. We also identify the key factors crucial for improving the accuracy of forecasting the KRW/USD exchange rate and stock market returns. Furthermore, a machine learning-driven global portfolio that accounts for exchange rate fluctuations demonstrated superior performance. Global portfolios constructed using LASSO (Sharpe ratio = 3.45) and Elastic Net (Sharpe ratio = 3.48) exhibit a notable performance advantage over traditional benchmark portfolios. This suggests that machine learning models outperform traditional global portfolio construction methods. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to conduct a thorough investigation of a stochastic eddy-current testing problem, when the geometric parameters of the system under study are characterized by uncertainty. Focusing on the case of subsurface defect detection, we devise reliable surrogates for the quantities of interest (QoI) based on the principles of the generalized polynomial chaos (PC) and using the orthogonal matching pursuit (OMP) solver to promote sparsity in the approximate models. In addition, a variance-based approach is implemented for the sequential construction of the necessary sample set, enabling more accurate estimation of the statistical metrics without imposing additional computational overhead. Apart from quantifying the inherent uncertainty, a sensitivity analysis is performed that assesses the impact of each geometric variable on the QoI, via the computation of Sobol indices. The efficiency of the OMP-PC algorithm is demonstrated in two variants of the subsurface-discontinuity problem, yielding at the same time useful conclusions regarding the properties of the stochastic outputs. [ABSTRACT FROM AUTHOR]

This paper identifies the presence of anomalies in Chinese urban construction investment bonds (UCIBs) market using variable ranking portfolio analysis and finds that liquidity anomalies, downside risk anomalies, and historical return anomalies significantly exist. By conducting Fama–MacBeth regressions on the cross-sectional returns of UCIBs and anomalies, we find that only the 6-month momentum in the historical return anomaly can generate statistically significant risk premium which cannot be explained by long-established bond pricing factors, and thus it's an anomaly for UCIBs. This paper also finds that portfolios constructed based on significant anomalies in the UCIBs market can generate more profits than other models through the out-of-sample cross-sectional return forecasting. [ABSTRACT FROM AUTHOR]

In urbanised landscapes, the scarcity of green spaces and increased exposure to anthropogenic noise have adverse effects on health and wellbeing. While reduced speed limits have historically been implemented to address traffic safety, their potential impact on residents' wellbeing, especially in relation to engagement with natural soundscapes, remains understudied. Our study investigates the influence of i) natural soundscapes, including bird song, and ii) the addition of traffic noise to natural soundscapes at two speeds (20 mi/h and 40 mi/h) on mood. We found that natural soundscapes were strongly linked with the lowest levels of anxiety and stress, with an increase in stress levels associated with mixed natural soundscapes with the addition of 20 mi/h traffic noise and the highest levels with 40 mi/h traffic noise. Higher levels of hedonic tone, indicative of positive mood, was noted with natural soundscapes, but diminished when combined with 40 mi/h traffic noise. Our results show that anthropogenic soundscapes including traffic sounds can mask the positive impact of natural soundscapes including birdsong on stress and anxiety. However, reducing traffic speeds in cities could be a positive intervention for enhancing access to nature. Technological solutions, such as the widespread adoption of hybrid and electric vehicles, and urban planning strategies like integrating green spaces into transit routes, offer potential opportunities to mitigate the impact of noise pollution and benefit humans in urban environments. [ABSTRACT FROM AUTHOR]

Background: Recent years, exosomes have been increasing used to treat diseases, but there is little research on how exosomes affect the metabolism of the body after entering. Therefore, in this study, we discussed the changes of metabolic spectrum and determined the differentially expressed metabolites in the serum of cynomolgus monkeys after injecting exosomes. Six cynomolgus monkeys were divided into control group and exosomes group. After intravenous injection of exosomes, the peripheral blood serum of cynomolgus monkeys was collected at baseline, day 1, day 7 and day 14 respectively. An ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based non-targeted metabolomics platform was used to detect the metabolites. The metabolic spectra of two groups of cynomolgus monkeys were identified and compared, and the time series changes of metabolites in exosomes were described. Results: The results showed that there was significant difference in metabolic spectrum between the two groups. 45, 114, 49, 39 differentially expressed metabolites were identified in baseline, day 1, day 7, and day 14, respectively. 6-hydroxydopamine, a metabolite related to the regulation of nerve function, was also found. Tryptophan metabolism, choline metabolism in cancer, porphyrin and chlorophyll metabolism were involved in day 1. Sphingolipid metabolism and histidine metabolism were involved in day 7. Three pathways, including choline metabolism, sphingolipid metabolism and biotin metabolism in cancer were involved in day 14. Through time series analysis, it was found that the level of propionylcarnitine and biliverdin increased on day 1 after inoculation with exosomes, while the level of hippuric acid decreased. These changes of immune-related metabolites suggested that exosomes might participate in the immunoregulation reaction after entering the body of cynomolgus monkeys. Conclusions: In our current study, we found that exosomes injected intravenously affect the changes of metabolites and metabolic pathways in cynomolgus monkeys. Intravenous injection of exosomes may affect the metabolite 6-hydroxydopamine, sphingolipid metabolic pathway, and choline metabolic in cancer pathway, which is of some significance for the treatment of Parkinson's disease. In addition, exosomes may also affect the immune-related metabolites in vivo, such as propionylcarnitine, biliverdin, hippuric acid metabolites, as well as tryptophan metabolism pathway, sphingolipid metabolism pathway involved in immune regulation, which is of great significance for the future study of immune-regulatory mechanisms of exosomes. [ABSTRACT FROM AUTHOR]

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable. [ABSTRACT FROM AUTHOR]

Pancreatic neoplasm, a highly aggressive and often fatal cancer, poses challenges due to late detection and nonspecific symptoms. Therefore, both early diagnosis and appropriate therapeutic approaches are necessary to augment the condition of these patients. Cancer cells undergo metabolic deregulation, which enables their proliferation, survival, and invasion. As a result, it is crucial to focus on the metabolic pathways in prevalent cancers and explore treatment strategies that target these pathways to control tumor growth effectively. This is particularly relevant in cancers like pancreatic cancer, which undergo numerous metabolic alterations. The ketogenic regimen, characterized by low carbohydrate and protein contents and high-fat sources, does not involve caloric restriction. This allows for the induction of ketogenesis and an increase in ketone bodies, while insulin and glucose levels remain low even after meals. This unique metabolic state may influence the tumor microenvironment. Given the lack of unanimous agreement on the precise role and mechanism of the ketogenic diet, this review aims to clarify the diagnostic value and accuracy of ketone bodies in various types of pancreatic tumors and explore the potential anti-cancer effects of the ketogenic diet when used alone or in conjunction with chemotherapy, also to determine the potential of the ketogenic diet to be used as adjuvant therapy. The outcomes of this study are instrumental in enhancing our understanding of the benefits and drawbacks associated with employing this diet for the management and diagnosis of pancreatic cancer. [ABSTRACT FROM AUTHOR]

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM 1 and PM 2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam's razor principle. For the PM 1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM 2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models. [ABSTRACT FROM AUTHOR]

The subject of this study is the interaction between 5,10,15,20-tetrakis (4-sulfonatophenyl)–porphyrin (TSPP), a potential photosensitizer for photodynamic therapy (PDT) and radiotherapy, and human serum albumin (HSA), a crucial protein in the body. The main objective was to investigate the binding mechanisms, structural changes, and potential implications of these interactions for drug delivery and therapeutic applications. Spectroscopic techniques and computational methods were employed to investigate the mechanism and effects of TSPP binding by HSA. The results suggest the possibility of simultaneous binding of three TSPP ions at binding sites of different affinity within albumin. The estimated values of the binding constant Kb for these sites were in the range of 0.6 to 6.6 μM−1. Laser flash photolysis indicated the stabilization of TSPP in the HSA structure, which resulted in prolonged lifetimes of the excited states (singlet and triplet) of porphyrin. Circular dichroism analysis was used to assess the changes in the secondary and tertiary structures of HSA upon TSPP binding. An analysis of the molecular docking results allowed us to identify the preferred TSPP binding sites within HSA and provided information on the specific interactions of amino acids involved in the stabilization of TSPP–HSA complexes. The estimated free energy of the binding of porphyrin at the three most favorable docking sites found in the HSA structure that was considered native were in the range of −80 to −41 kcal/mol. Finally, thermal unfolding studies showed that TSPP increased the stability of the secondary structure of albumin. All these findings contribute to the understanding of the interactions between TSPP and HSA, offering valuable insights for the development of novel cancer therapy approaches. [ABSTRACT FROM AUTHOR]

Disturbances in carbohydrate metabolism are suggested to be the early symptoms of pancreatic ductal adenocarcinoma (PDAC). The accumulated data suggests that endocrine function-related biomarkers may represent a breakthrough in the early detection of PDAC. Factors which may predispose one to the development of PDAC are insulin resistance and hyperinsulinemia. Elevated insulin levels induce the onset of carcinogenesis by altering the differentiation and function of islet cells through stimulating growth factors, including insulin-like growth factors (IGFs). Impaired β cell function, along with the impact of PDAC-released factors (e.g., adrenomedullin (ADM), IGF-1, and macrophage inhibitory factor (MIF) on pancreatic islets, may contribute to the induction of diabetes associated with PDAC. Recently, exosomes have attracted worldwide attention due to their role in varied features of cell function, particularly in cancer progression. Exosomes comprise of small extracellular vesicles produced by almost all cells. These vesicles contain a vast array of biomolecules, including proteins and microRNAs. Exosomes participate in cancer growth and promote angiogenesis. They promote tumorigenesis and metastasis, and are associated with the acquisition of cancer cells resistant to chemotherapy. Data have been accumulating recently on the role of exosomes in the rapid recognition, prognosis and potential therapy of pancreatic cancer. [ABSTRACT FROM AUTHOR]

Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters are never exactly known, but vary according to some distribution. A key question is then to determine how the uncertainties in the parameters affect the model outcome. Knowing the latter uncertainties is crucial when a model is used for, e.g., experimental design, optimisation, or decision-making. To establish how parameter and model prediction uncertainties are related, Monte Carlo approaches could be used. Then, the model is evaluated for a huge number of parameters sets, drawn from the multivariate parameter distribution. However, when model solutions are computationally expensive this approach is intractable. To overcome this problem, so-called spectral expansion (SE) methods have been developed to quantify prediction uncertainty within a probabilistic framework. Such SE methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. The computational costs of SE schemes mainly stem from the calculation of the expansion coefficients. Furthermore, SE effectively leads to a surrogate model which captures the dependence of the model on the uncertainty parameters, but is much simpler to execute compared to the original model. In this paper, we present an innovative scheme for the calculation of the expansion coefficients. It guarantees that the model has to be evaluated only a restricted number of times. Especially for models of high complexity this may be a huge computational advantage. By applying the scheme to a variety of examples we show its power, especially in challenging situations where solutions slowly converge due to high computational costs, bifurcations, and discontinuities. [ABSTRACT FROM AUTHOR]

The non-coding RNAs (ncRNAs) comprise a large part of human genome that mainly do not code for proteins. Although ncRNAs were first believed to be non-functional, the more investigations highlighted tthe possibility of ncRNAs in controlling vital biological processes. The length of long non-coding RNAs (lncRNAs) exceeds 200 nucleotidesand can be present in nucleus and cytoplasm. LncRNAs do not translate to proteins and they have been implicated in the regulation of tumorigenesis. On the other hand, One way cells die is by a process called autophagy, which breaks down proteins and other components in the cytoplasm., while the aberrant activation of autophagy allegedly involved in the pathogenesis of diseases. The autophagy exerts anti-cancer activity in pre-cancerous lesions, while it has oncogenic function in advanced stages of cancers. The current overview focuses on the connection between lncRNAs and autophagy in urological cancers is discussed. Notably, one possible role for lncRNAs is as diagnostic and prognostic variablesin urological cancers. The proliferation, metastasis, apoptosis and therapy response in prostate, bladder and renal cancers are regulated by lncRNAs. The changes in autophagy levels can also influence the apoptosis, proliferation and therapy response in urological tumors. Since lncRNAs have modulatory functions, they can affect autophagy mechanism to determine progression of urological cancers. [ABSTRACT FROM AUTHOR]

To study the nonlinear dynamic behavior and system stability of a rubbing overhung rotor with viscoelastic and memory-effect damping and random uncertain parameters, this paper introduces a fractional-order modeling and stochastic dynamic analysis method for the nonlinear overhung rotor system with frictional impact faults. Firstly, the dynamic equations of the overhung rotor considering friction effect and fractional damping effect are established based on the transfer matrix method and fractional order derivative. Then, the time-domain response of the fractional-order dynamic equations is solved by combining the Runge–Kutta method and the continuous fractional expansion, and the steady-state response characteristics of different fractional damping are analyzed in the deterministic case. Finally, to analyze the response of the system under the effect of stochastic parameters, the sparse grid-based PCE metamodel of the system response is developed. Statistical moments, probability distributions, and sensitivity indices of the response of stochastic systems are revealed. The results of this paper provide a theoretical basis for efficient and accurate prediction of the stochastic response of nonlinear rubbing overhung rotor systems. [ABSTRACT FROM AUTHOR]

In this study, we propose an adversarial learning approach to the asset pricing model estimation problem which aims to find estimates of factors and loadings that capture time-series covariations while minimizing the worst-case cross-sectional pricing errors. The proposed estimator is defined by a novel min-max optimization problem in which finding a solution is known to be difficult. This contrasts with other related estimators that admit a well-defined analytic solution but do not effectively account for correlations among the pricing errors. To this end, we propose an approximate algorithm based on the alternating optimization procedure and empirically demonstrate that our proposed adversarial estimation framework outperforms other existing factor models, especially when the explanatory power of the pricing model is limited. [ABSTRACT FROM AUTHOR]

The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). Specifically, we utilize a CNN to capture individual stock information and a GCN to capture relationships among stocks. Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. We empirically validate the proposed two-stage approach using real-world data through numerical studies, achieving a roughly 35% increase in Cumulative Returns compared to portfolio optimization models without stock pre-selection, demonstrating its robust performance in the Average Return, Sharp Ratio, Turnover-adjusted Sharp Ratio, and Sortino Ratio. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors. [ABSTRACT FROM AUTHOR]

BACKGROUND: Advanced pancreatic ductal adenocarcinoma (PDAC) remains a disease with a dismal prognosis, significantly limited therapeutic options, and few innovative drugs. Inflammation plays a significant role in the development and progression of PDAC. Systemic inflammatory indexes reflect the anti-tumor inflammatory capacity of and are of prognostic and predictive value in the treatment of patients with PDAC. METHODS: In our retrospective study, we investigated the prognostic and predictive significance of inflammatory markers in chemonaive patients with locally advanced unresectable pancreatic cancer (LAPC) and metastatic pancreatic cancer (mPDAC), in relation to progression-free survival (PFS) and overall survival (OS). Survival analysis was conducted using the Kaplan-Meier method with log-rank tests in univariate analysis. We used multivariate Cox regression analysis to determine the impact of inflammatory markers on survival time. RESULTS: The present clinical study included 46 patients with LAPC and mPDAC treated with FOLFIRINOX (folinic acid, fluorouracil, irinotecan, oxaliplatin) or GEM/Nab-P (gemcitabine/nab-paclitaxel) as first-line chemotherapy regimens. Performance status (PS) ECOG 0-1, neutrophil-to-lymphocyte ratio (NLR)=2.09 and the prognostic nutritional index (PNI)=49.09 were associated with significantly longer OS in the analyzed patient cohort, Multivariate analysis confirmed PS, NLR and PNI as independent prognostic factors for OS. CONCLUSION: In our cohort of patients with advanced PDAC, PS, NLR and PNI were confirmed as independent prognostic factors for OS. [ABSTRACT FROM AUTHOR]

Stock price prediction has traditionally been known as a challenging task. However, recent advancements in machine learning and deep learning models have spurred extensive research in predicting stock returns. This study applies these predictive models to U.S. stock data to forecast stock returns and develop investment strategies based on these forecasts. Additionally, the performance of the model-based investment strategy was compared with that of a widely recognized method, market capitalization-weighted investing. The results indicate that, overall, market capitalization-weighted investing outperformed model-based investing. However, the highest returns were observed in the model-based strategy. It was also found that model-based investing exhibits higher volatility in returns, with significant disparities between years of high and low returns. While investing through machine learning methodologies may be attractive to investors seeking high risk and high return, market capitalization-weighted investing is likely more suitable for those desiring stable returns. [ABSTRACT FROM AUTHOR]

Aeroelastic simulations are employed to assess wind turbines in accordance with IEC standards in the time domain. These analyses enable the evaluation of fatigue and extreme loads experienced by wind turbine components. Such simulations are essential for several reasons, including but not limited to reducing safety margins in wind turbine component design by accounting for a wide range of uncertainties in wind and wave conditions and fulfilling the requirements of the digital twin, which necessitates a comprehensive set of simulations for calibration. Thus, it is essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. To address this challenge, we propose a data-driven approach to construct surrogate models for the damage equivalent load (DEL) based on aeroelastic simulation outputs. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need for time-consuming aeroelastic simulations. Our study focuses on utilizing a sequential machine learning (ML) method to map wind speed time series to DEL. Additionally, we demonstrate the versatility of the developed and trained surrogate models by testing them on a wind turbine in the wake and applying transfer learning to enhance their predictive accuracy. [ABSTRACT FROM AUTHOR]

Understanding the probabilistic nature of brittle materials due to inherent dispersions in their mechanical properties is important to assess their reliability and safety for sensitive engineering applications. This is all the more important when elements composed of brittle materials are exposed to dynamic environments, resulting in catastrophic fatigue failures. The authors propose the application of a non-intrusive polynomial chaos expansion method for probabilistic studies on brittle materials undergoing fatigue fracture when geometrical parameters and material properties are random independent variables. Understanding the probabilistic nature of fatigue fracture in brittle materials is crucial for ensuring the reliability and safety of engineering structures subjected to cyclic loading. Crack growth is modelled using a phase-field approach within a finite element framework. For modelling fatigue, fracture resistance is progressively degraded by modifying the regularised free energy functional using a fatigue degradation function. Number of cycles to failure is treated as the dependent variable of interest and is estimated within acceptable limits due to the randomness in independent properties. Multiple 2D benchmark problems are solved to demonstrate the ability of this approach to predict the dependent variable responses with significantly fewer simulations than the Monte Carlo method. This proposed approach can accurately predict results typically obtained through 10 5 or more runs in Monte Carlo simulations with a reduction of up to three orders of magnitude in required runs. The independent random variables' sensitivity to the system response is determined using Sobol' indices. The proposed approach has low computational overhead and can be useful for computationally intensive problems requiring rapid decision-making in sensitive applications like aerospace, nuclear and biomedical engineering. The technique does not require reformulating existing finite element code and can perform the stochastic study by direct pre/post-processing. [ABSTRACT FROM AUTHOR]

Simple Summary: The quality of life is influenced by individual's characteristics and values, personal's beliefs, physical and mental health and the quality of relationship to others. The researchers want to investigate how the quality of life of primary caregivers changes during care for patient with palliative care needs. It is a prospective longitudinal study that assesses the different aspects of the quality of life of caregivers in care process of palliative care. The caregiver burden which increased during the care of patient have a great and negative impact on quality of life as decrease significantly physical, psycho-emotional and social aspects of this. The primary caregiver is feeling the perception of deteriorating general health. Background/Objectives: The quality of life is a complex concept that is insufficiently assessed in clinical practice. It is influenced by different factors, as follows: the individual's characteristics, personal values and beliefs, physical and mental state, and relationship to other members of their community. The quality of life of the primary caregiver influences their health and the quality of their care interventions. This study aims to investigate how the quality of life of caregivers changes during palliative patients' care. Methods: This is a prospective longitudinal study that assesses the different aspects of the quality of life of primary caregivers who care for patients with palliative needs. The tool used in this study was the Medical Outcomes Scale-Short Form 36 (MOS-SF36). Results: This study included 140 caregivers, of which 63 were involved in the care of patients with cancer and 77 were involved in the care of patients with non-oncological diseases. Almost 9 out of 10 caregivers were a family member of the patient and over two-thirds of these were women. The caregivers of patients with non-malignant diseases had a decreased quality of life in the following aspects: limitations in their usual role due to emotional problems, social functioning, energy, and their perception of their general health. In the group of oncological patients, the caregivers displayed limitations in their daily role due to physical health, emotional problems, and social functioning. Conclusions: The large number of responsibilities, the long time spent caring, and the uncertainty about the evolution of the disease as well as the marginalization and lack of time for oneself are some of the elements that increase caregiver burden. Along with this, the quality of life of caregivers decreases significantly in different aspects, such as physical, psycho-emotional, and social, with the perception of deteriorating general health. [ABSTRACT FROM AUTHOR]