BackgroundClinical researchers have often preferred to use a fixed effects model for the primary interpretation of a meta-analysis. Heterogeneity is usually assessed via the well known Q and I2 statistics, along with the random effects estimate they imply. In recent years, alternative methods for quantifying heterogeneity have been proposed, that are based on a 'generalised' Q statistic.MethodsWe review 18 IPD meta-analyses of RCTs into treatments for cancer, in order to quantify the amount of heterogeneity present and also to discuss practical methods for explaining heterogeneity.ResultsDiffering results were obtained when the standard Q and I2 statistics were used to test for the presence of heterogeneity. The two meta-analyses with the largest amount of heterogeneity were investigated further, and on inspection the straightforward application of a random effects model was not deemed appropriate. Compared to the standard Q statistic, the generalised Q statistic provided a more accurate platform for estimating the amount of heterogeneity in the 18 meta-analyses.ConclusionsExplaining heterogeneity via the pre-specification of trial subgroups, graphical diagnostic tools and sensitivity analyses produced a more desirable outcome than an automatic application of the random effects model. Generalised Q statistic methods for quantifying and adjusting for heterogeneity should be incorporated as standard into statistical software. Software is provided to help achieve this aim.
This study presents a detailed numerical analysis of a three-dimensional micro pin fin heat sink incorporating 55 fins arranged in a single channel in four distinct cross-sectional geometries: square, circular, triangular, and pentagonal. A conventional microchannel heat sink (MCHS) without pin fins serves as a baseline for comparison. Water is employed as the working fluid, and simulations are conducted over a laminar flow regime with Reynolds numbers ranging from 500 to 1500. To efficiently capture the thermo-hydrodynamic behavior and reduce computational cost, a representative single flow channel is simulated under symmetrical boundary conditions, and key pin fin parameters such as height and spacing are systematically varied. The cross-sectional hydraulic diameter and spacing are held constant for all cases, with step sizes and non-dimensional spacing ratios sp/hp adjusted to assess their effect on heat sink performance. Results indicate that among all geometries, circular fins exhibit the highest heat transfer enhancement, with the Nusselt number increasing by 60% at Re = 500 and by 90% at Re = 1500 compared to the baseline. However, this improved thermal performance is accompanied by a greater pressure drop relative to the other tested pin fin shapes. Following the circular fins, triangular and square configurations offer progressively lower heat transfer rates, while pentagonal pin fins demonstrate the minimum enhancement. Furthermore, for all fin geometries, increasing the Reynolds number leads to a consistent improvement in heat transfer. Overall, the study provides quantitative insights into the impact of pin fin geometry and arrangement on the thermal and fluid dynamic performance of micro pin fin heat sinks, offering valuable guidelines for the design of advanced cooling solutions in microelectronics.
We consider the pricing and hedging of counterparty credit risk and funding when there is no possibility to hedge the jump to default of either the bank or the counterparty. This represents the situation which is most often encountered in practice, due to the absence of quoted corporate bonds or CDS contracts written on the counterparty and the difficulty for the bank to buy/sell protection on her own default. We apply local risk-minimization to find the optimal strategy and compute it via a BSDE.
Dr. Ghaouti ZIANI, Pr. Malika DJABER, Pr. Yacine MAHDJAR
et al.
This study investigates the relationship between students’ appropriation of generative artificial intelligence technologies and the contribution of these tools to the development of their critical thinking skills. To examine this relationship, a questionnaire was administered to 200 students at the University of Ouargla. The analysis of the collected data revealed extensive use of these technologies, including the preparation of academic reports, idea generation, summarization of instructional materials, documentary research, and linguistic translation. Despite this diversity of uses, notable differences emerged regarding students’ levels of proficiency with these tools. Most respondents reported being aware of the risks associated with potential errors and biases, which led them to verify the information produced and to adopt critical stances toward the generated content. Overall, the study suggests that generative AI can serve as an effective tool for enhancing critical thinking, provided it is used in a guided manner. Such use strengthens analytical capacity and reflective thinking, contributing to the development of self-directed learning skills and to the formation of a generation of students equipped with strong critical and analytical abilities. In conclusion, the findings highlight the importance of providing students with methodological and ethical training, increasing awareness of the limitations of these technologies, and establishing clear institutional policies defining their conditions of use.
Rohini Kumar, Frederick "Forrest" Miller, Hussein Nasralah
et al.
This paper studies the pricing of contingent claims of American style, using indifference pricing by fully dynamic convex risk measures. We provide a general definition of risk-indifference prices for buyers and sellers in continuous time, in a setting where buyer and seller have potentially different information, and show that these definitions are consistent with no-arbitrage principles. Specifying to stochastic volatility models, we characterize indifference prices via solutions of Backward Stochastic Differential Equations reflected at Backward Stochastic Differential Equations and show that this characterization provides a basis for the implementation of numerical methods using deep learning.
Charles Opondo, Siân Harrison, Julia Sanders
et al.
Abstract Background Perineal trauma, involving either naturally occurring tears or episiotomy, is common during childbirth but little is known about its psychological impact. This study aimed to determine the associations between childbirth related perineal trauma and psychological outcomes reported by women three months after giving birth and to explore factors that could mediate relationships between perineal trauma and maternal psychological outcomes. Methods This study was a secondary analysis of data from a cross-sectional population-based survey of maternal and infant health. A total of 4,578 women responded to the survey, of which 3,307 had a vaginal birth and were eligible for inclusion into the analysis. Symptoms of depression, anxiety, and post-traumatic stress (PTS) symptoms were assessed using validated self- report measures. Physical symptoms were derived from a checklist and combined to produce a composite physical symptoms score. Regression models were fitted to explore the associations. Results Nearly three quarters of women experienced some degree of perineal trauma. Women who experienced perineal trauma reported having more postnatal physical symptoms (adjusted proportional odds ratio 1.47, 95%CI 1.38 to 1.57, p-value < 0.001), were more likely to report PTS symptoms (adjusted OR 1.19, 95%CI 1.04 to 1.36, p-value 0.010), and there was strong evidence that each unit increase in the physical symptoms score was associated with between 38 and 90% increased adjusted odds of adverse psychological symptoms. There was no evidence of association between perineal trauma and satisfaction with postnatal care, although there was strong evidence that satisfaction with labour and birth was associated with 16% reduced adjusted odds of depression and 30% reduced adjusted odds of PTS symptoms. Conclusions Women who experienced perineal trauma were more likely to experience physical symptoms, and the more physical symptoms a woman experienced the more likely she was to report having postnatal depression, anxiety and PTS symptoms. There was some evidence of a direct association between perineal trauma and PTS symptoms but no evidence of a direct association between perineal trauma and depression or anxiety. Assessment and management of physical symptoms in the postnatal period may play an important role in reducing both physical and psychological postnatal morbidity.
Transition risk can be defined as the business-risk related to the enactment of green policies, aimed at driving the society towards a sustainable and low-carbon economy. In particular, the value of certain firms' assets can be lower because they need to transition to a less carbon-intensive business model. In this paper we derive formulas for the pricing of defaultable coupon bonds and Credit Default Swaps to empirically demonstrate that a jump-diffusion credit risk model in which the downward jumps in the firm value are due to tighter green laws can capture, at least partially, the transition risk. The empirical investigation consists in the model calibration on the CDS term-structure, performing a quantile regression to assess the relationship between implied prices and a proxy of the transition risk. Additionally, we show that a model without jumps lacks this property, confirming the jump-like nature of the transition risk.
This paper explores asset pricing implications of unemployment risk from sectoral shifts. I proxy for this risk using cross-industry dispersion (CID), defined as a mean absolute deviation of returns of 49 industry portfolios. CID peaks during periods of accelerated sectoral reallocation and heightened uncertainty. I find that expected stock returns are related cross-sectionally to the sensitivities of returns to innovations in CID. Annualized returns of the stocks with high sensitivity to CID are 5.9% lower than the returns of the stocks with low sensitivity. Abnormal returns with respect to the best factor model are 3.5%, suggesting that common factors can not explain this return spread. Stocks with high sensitivity to CID are likely to be the stocks, which benefited from sectoral shifts. CID positively predicts unemployment through its long-term component, consistent with the hypothesis that CID is a proxy for unemployment risk from sectoral shifts.
Matteo Gambara, Giulia Livieri, Andrea Pallavicini
Evaluating financial products with early-termination clauses, in particular those with path-dependent structures, is challenging. This paper focuses on Asian options, look-back options, and callable certificates. We will compare regression methods for pricing and computing sensitivities, highlighting modern machine learning techniques against traditional polynomial basis functions. Specifically, we will analyze randomized recurrent and feed-forward neural networks, along with a novel approach using signatures of the underlying price process. For option sensitivities like Delta and Gamma, we will incorporate Chebyshev interpolation. Our findings show that machine learning algorithms often match the accuracy and efficiency of traditional methods for Asian and look-back options, while randomized neural networks are best for callable certificates. Furthermore, we apply Chebyshev interpolation for Delta and Gamma calculations for the first time in Asian options and callable certificates.
In this work, the Fourier-cosine series (COS) method has been combined with the Boundary Element Method (BEM) for a fast evaluation of barrier option prices. After a description of its use in the Black and Scholes (BS) model, the focus of the paper is on the application of the proposed methodology to the barrier option evaluation in the Heston model, where its contribution is fundamental to improve computational efficiency and to make BEM appealing among Finance practitioners as a valid alternative to Monte Carlo (MC) or other more traditional approaches. An error analysis is provided on the number of terms used in the Fourier-cosine series expansion, where the error bound estimation is based on the characteristic function of the log-asset price process.