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Background: Energy systems, as critical infrastructures (CI), constitute Cyber-Physical-Social Systems (CPSS). Due to their inherent complexity and the importance of service continuity of CIs, digitization in this context encounters significant practical challenges. Digital Twins (DT) have emerged over the recent years as a promising solution for managing CPSSs by facilitating real-time interaction, synchronization, and control of physical assets. The selection of an appropriate architectural framework is crucial in constructing a DT, to ensure integration of enabling technologies and data from diverse sources. Objectives: This study proposes a Systematic Literature Review (SLR) to examine technological enablers, design choices, management strategies and Computational Challenges of DTs in Smart Energy Systems (SES) by also analyzing existing architectures and identifying key components. Methods: The SLR follows a rigorous workflow exploiting a multi-database search with predefined eligibility criteria, accompanied by advanced searching techniques, such as manual screening of results and a documented search strategy, in order to ensure its comprehensiveness and reliability, More specifically, research questions are first defined and then submitted as queries to scientific digital libraries (i.e., IEEE Xplore, Scopus, and WoS) selected due to their coverage and reliability (Google Scholar was excluded for the presence of grey literature and non-peer-reviewed material). Then, inclusion and exclusion criteria are established to filter the results and shortlist the significant publications. Subsequently, relevant data are extracted, summarized, and categorized in order to identify common themes, existing gaps, and future research directions, with the aim of providing a comprehensive overview of the current state of DTs for SESs. Results: From the proposed DT-based solutions described in the selected publications, the adopted architectures are examined and categorized depending on their logical building blocks, microservices, enabling technologies, human–machine interfaces (HMI), artificial intelligence and machine learning (AI/ML) implementations, data flow and data persistence choices, and Internet-of-Things (IoT) components involved. Additionally, the integration of edge-cloud computing and IoT technologies in literature are studied and discussed. Finally, gaps, opportunities, future study lines, and challenges of implementing DTs are thoroughly addressed. The results achieved also pave the way for a forthcoming design pattern catalog for DTs in CPSSs capable of supporting the engineering and research communities, by offering practical insights on implementation and integration aspects. Conclusion: The proposed SLR provides a valuable resource for designing and implementing DTs of CPSSs in general and of SESs in particular. Furthermore, it highlights the potential benefits of adopting DTs to manage complex energy systems and it identifies areas for future research. [ABSTRACT FROM AUTHOR]

Assessing adaptation is one of the critical roles of the Intergovernmental Panel on Climate Change (IPCC). In this Perspective, we focus on the what and the how that can guide this assessment for AR7: what knowledge is and should be assessed and how it is assessed. We then propose several opportunities for improvement, including accelerating adaptation assessment method innovation, increasing linkages between IPCC efforts and UNFCCC dialogues, and how author and data diversity can support a more decision- and policy-relevant assessment. [ABSTRACT FROM AUTHOR]

Humans are infected by Toxoplasma gondii worldwide and its consequences may seriously affect an immune deprived population such as HIV and transplanted patients or pregnant women and foetuses. A deep knowledge of toxoplasmosis seroprevalence in Spain is needed in order to better shape health policies and educational programs. We present the results of the first systematic review and meta-analysis on the human prevalence for this disease in Spain. Databases (PubMed, Web of Science, SCOPUS and Teseo) were searched for relevant studies that were published between January 1993 and December 2023 and all population-based cross-sectional and longitudinal studies reporting the human seroprevalence in Spain were revised. Within the population analysed, our targeted groups were immunocompetent population, pregnant women and immunocompromised patients. Among 572 studies and 35 doctoral theses retrieved, 15 studies and three doctoral theses were included in the meta-analysis. A random effects model was used for the meta-analyses due to the high heterogeneity found between studies (I2: 99.97), since it is a statistically conservative model, in addition to allowing better external validity. The global pooled seroprevalence was 32.3% (95% CI 28.7–36.2%). Most of the studies carried out were in pregnant women and the meta-analysis reported that the pooled seroprevalence of toxoplasmosis in pregnant women in Spain was 24.4% (24,737/85,703, 95% CI 21.2–28.0%), based on the random effects model. It is recommended to continue monitoring the seroprevalence status of T. gondii in order to obtain essential guidelines for the prevention and control of the infection in the population. [ABSTRACT FROM AUTHOR]

In the paper we present a model of discrete-time mean-field game with several populations of players. Mean-field games with multiple populations of the players have only been studied in the literature in the continuous-time setting. The main results of this article are the first stationary and Markov mean-field equilibrium existence theorems for discrete-time mean-field games of this type. We consider two payoff criteria: β -discounted payoff and total payoff. The results are provided under some rather general assumptions on one-step reward functions and individual transition kernels of the players. In addition, the results for total payoff case, when applied to a single population, extend the theory of mean-field games also by relaxing some strong assumptions used in the existing literature. [ABSTRACT FROM AUTHOR]

Collaborative robotics possesses the potential to revolutionise industrial automation by offering affordable and accessible solutions with reasonable skill requirements. However, identifying the most valuable and appropriate applications for this technology remains a challenge. This study conducted a comprehensive literature review to analyse the existing collaborative robotics applications, and the results showed that only a limited number of applications can be considered true collaboration, with even fewer classified as intelligent collaboration. The study comprised a survey designed to offer valuable insights to not only enhance the state-of-the-art analysis in the identification of existing challenges in the field of collaborative robotics but also to provide motivation to guide future advancements. By leveraging the survey results, researchers and practitioners will be better equipped to navigate the complex landscape of collaborative robotics and develop innovative solutions to tackle the identified challenges. This study also informs on the latest research and development in the field. [ABSTRACT FROM AUTHOR]

The addition of metastasis-directed radiotherapy (MDRT) to immunotherapy in patients with advanced urothelial carcinoma (aUC) has shown promising results. We report the real-world data from the ARON-2 study (NCT05290038) on the impact of conventional (CRT) or stereotactic body radiotherapy (SBRT) on the outcome of aUC patients receiving pembrolizumab after platinum-based-chemotherapy. Medical records of 837 patients were reviewed from 60 institutions in 20 countries. Two hundred and sixty-two patients (31%) received radiotherapy (cohort A), of whom 193 (23%) received CRT and 69 (8%) received SBRT. Patients were assessed for overall survival (OS), progression-free survival (PFS), and overall response rate (ORR). Univariate and multivariate analyses were used to explore the association of variables of interest with OS and PFS. With a median follow-up of 22.7 months, the median OS was 10.2 months, 6.8 months and 16.0 months in no RT, CRT and SBRT subgroups (p = 0.005), with an 1y-OS rates of 47%, 34% and 61%, respectively (p < 0.001). The 1y-OS rate in the SBRT subgroup were significantly higher for both lower (63%) and upper tract UC (68%), for pure urothelial histology (63%) and variant histologies (58%), and for patients with bone (40%) and lymph-node metastases (61%). Median PFS was 4.8 months, 9.6 months and 5.8 months in the CRT, SBRT and no RT subgroups, respectively (p = 0.060). The 1y-PFS rate was significantly higher (48%) in the SBRT population and was confirmed in all patient subsets. The difference in terms of ORR was in favour of SBRT. Our real-world analysis showed that the use of SBRT/pembrolizumab combination may play a role in a subset of aUC patients to increase disease control and possibly overall survival. [ABSTRACT FROM AUTHOR]

We derive methods for risk-neutral pricing of multi-asset options, when log-returns jointly follow a multivariate tempered stable distribution. These lead to processes that are more realistic than the better known Brownian motion and stable processes. Further, we introduce the diagonal tempered stable model, which is parsimonious but allows for rich dependence between assets. Here, the number of parameters only grows linearly as the dimension increases, which makes it tractable in higher dimensions and avoids the so-called "curse of dimensionality." As an illustration, we apply the model to price multi-asset options in two, three, and four dimensions. Detailed goodness-of-fit methods show that our model fits the data very well. [ABSTRACT FROM AUTHOR]

The emerging green hydrogen value chain provides an opportunity to develop joint sustainable strategies between different countries and implement measures to mitigate potential adverse effects. We analyze whether the green hydrogen value chain can aid in meeting the objectives outlined in the 2030 Agenda. The Value Chain Assessment methodology that we developed enables the identification of direct and indirect influences, and the categorization of positive and negative effects, of each segment (production, transportation, and end-use) of the green hydrogen value chain on the 17 SDGs and their 169 targets. We also analyze the associated temporal dimensions and reciprocal interdependences, to clarify the dynamics of the value chain over time and across different geographical scales. We present a strategic framework that can help in fostering the green hydrogen value chain for the implementation of sustainable development. We find that the use of green hydrogen can clearly accelerate progress on the SDGs in the short-term, but adverse effects could arise in the medium- to long-term. The realization of the green hydrogen value chain requires synchronized investments and policies across space and time. [ABSTRACT FROM AUTHOR]

Motivated by optimal allocation models with relative performance criteria, we introduce a mean field game in which the terminal expected utility of the representative agent depends on her own state as well as the average of her peers. We derive the master equation, which, in view of the presence of controls in the volatility, needs to be coupled with a compatibility condition for the mean field optimal feedback control. We concentrate on the class of separable payoffs under both general utilities and couplings. We derive a solution to the master equation and find the associated optimal feedback control expressed via the value function in the absence of competition and a dynamic coupling function solving a non-local quasilinear equation. In turn, we construct the related optimal state and control processes, and give representative examples. Projecting the mean field solutions on finite dimensions, we recover the solution of the N-game for linear couplings and arbitrary utilities, and we study the proximity of these approximations to their N-player game counterparts. [ABSTRACT FROM AUTHOR]

We consider a class of optimal advertising problems under uncertainty for the introduction of a new product into the market, on the line of the seminal papers of Vidale and Wolfe (Oper Res 5:370–381, 1957) and Nerlove and Arrow (Economica 29:129–142, 1962). The main features of our model are that, on one side, we assume a carryover effect (i.e. the advertisement spending affects the goodwill with some delay); on the other side we introduce, in the state equation and in the objective, some mean field terms that take into account the presence of other agents. We take the point of view of a planner who optimizes the average profit of all agents, hence we fall into the family of the so-called "Mean Field Control" problems. The simultaneous presence of the carryover effect makes the problem infinite dimensional hence belonging to a family of problems which are very difficult in general and whose study started only very recently, see Cosso et al. [Ann Appl Probab 33(4):2863–2918, 2023]. Here we consider, as a first step, a simple version of the problem providing the solutions in a simple case through a suitable auxiliary problem. [ABSTRACT FROM AUTHOR]

We develop a dynamic model economy where self-employed entrepreneurs allocate their net worth to their firm capital and risk-less government bonds, facing borrowing constraints, uninsurable labour endowment and capital depreciation risk. We derive a numerical approximation of the model's equilibrium and compare it with a benchmark economy with no capital risk. Unlike labour endowment risk, capital risk reduces aggregate capital accumulation and wages and generates a positive risk premium. Low- (high-) net-worth entrepreneurs, whose consumption depends primarily on labour (financial) income, hold higher (lower) capital risk exposure. These patterns exacerbate inequality by increasing the share of financially constrained individuals and fattening the tails of the net worth distribution. Fiscal policy affects these outcomes by redistributing resources and affecting the risk premium. Capital tax cuts benefit more low- or high-net-worth entrepreneurs, depending on whether taxes on bonds or labour income finance them. [ABSTRACT FROM AUTHOR]

We study binary opinion formation in a large population where individuals are influenced by the opinions of other individuals. The population is characterised by the existence of (i) communities where individuals share some similar features, (ii) opinion leaders that may trigger unpredictable opinion shifts in the short term (iii) some degree of incomplete information in the observation of the individual or public opinion processes. In this setting, we study three different approximate mechanisms: common sampling approximation, independent sampling approximation, and, what will be our main focus in this paper, McKean–Vlasov (or mean-field) approximation. We show that all three approximations perform well in terms of different metrics that we introduce for measuring population level and individual level errors. In the presence of a common noise represented by the major influencers opinions processes, and despite the absence of idiosyncratic noises, we derive a propagation of chaos type result. For the particular case of a linear model and particular specifications of the major influencers opinion dynamics, we provide additional analysis, including long term behavior and fluctuations of the public opinion. The theoretical results are complemented by some concrete examples and numerical analysis, illustrating the formation of echo-chambers, the propagation of chaos, and phenomena such as snowball effect and social inertia. [ABSTRACT FROM AUTHOR]

We consider a stochastic differential game, where each player continuously controls the diffusion intensity of her own state process. The players must all choose from the same diffusion rate interval [ σ 1 , σ 2 ] , and have individual random time horizons that are independently drawn from the same distribution. The players whose states at their respective time horizons are among the best p ∈ (0 , 1) of all terminal states receive a fixed prize. We show that in the mean field version of the game there exists an equilibrium, where the representative player chooses the maximal diffusion rate when the state is below a given threshold, and the minimal rate else. The symmetric n-fold tuple of this threshold strategy is an approximate Nash equilibrium of the n-player game. Finally, we show that the more time a player has at her disposal, the higher her chances of winning. [ABSTRACT FROM AUTHOR]

In this paper we further extend the optimal bubble riding model proposed in Tangpi and Wang (Optimal bubble riding: a mean field game with varying entry times, 2022) by allowing for price-dependent entry times. Agents are characterized by their individual entry threshold that represents their belief in the strength of the bubble. Conversely, the growth dynamics of the bubble is fueled by the influx of players. Price-dependent entry naturally leads to a mean field game of controls with common noise and random entry time, for which we provide an existence result. The equilibrium is obtained by first solving discretized versions of the game in the weak formulation and then examining the measurability property in the limit. In this paper, the common noise comes from two sources: the price of the asset which all agents trade, and also the the exogenous bubble burst time, which we also discretize and incorporate into the model via progressive enlargement of filtration. [ABSTRACT FROM AUTHOR]

We study the impact of transition scenario uncertainty, namely that of future carbon price and electricity demand, on the pace of decarbonization of the electricity industry. To this end, we develop a theory of optimal stopping mean-field games with non-Markovian common noise and partial observation. For mathematical tractability, the theory is formulated in discrete time and with common noise restricted to a finite probability space. We prove the existence of Nash equilibria for this game using the linear programming approach. We then apply the general theory to build a discrete time model for the long-term dynamics of the electricity market subject to common random shocks affecting the carbon price and the electricity demand. We consider two classes of agents: conventional producers and renewable producers. The former choose an optimal moment to exit the market and the latter choose an optimal moment to enter the market by investing into renewable generation. The agents interact through the market price determined by a merit order mechanism with an exogenous stochastic demand. We illustrate our model by an example inspired by the UK electricity market, and show that scenario uncertainty leads to significant changes in the speed of replacement of conventional generators by renewable production. [ABSTRACT FROM AUTHOR]

In this study, we address the challenge of accurately solving physical problems involving complex partial differential equations using deep learning methods. Traditional approaches typically employ uniformly sampled points from the entire domain as training data. In practical PDE problems, achieving uniform precision across the entire domain is often unnecessary. Instead, it is more advantageous to employ random sampling during neural network training, provided that areas demanding precision can be identified and appropriately refined, akin to the concept of mesh refinement in classical numerical methods. To overcome this limitation, we propose a novel method based on the partition of unity approach. Our method introduces an approximation of the integral in the loss function, enabling localized calculation of the loss function within each subdomain. This innovative approach allows us to focus on areas of the domain that require higher precision while maintaining a fully automated process for selecting random samples. To validate the effectiveness of our proposed method, we conduct extensive testing on various physical problems, including Poisson, heat, and wave equations representing elliptic, parabolic, and hyperbolic problems, respectively. Additionally, we consider Burgers' equation as a nonlinear example. Through benchmarking against the standard Deep Ritz Method (DRM) and Deep Galerkin Method (DGM), we demonstrate that our method consistently outperforms these existing approaches, producing superior results. This approach offers researchers and practitioners the opportunity to significantly improve the accuracy of deep learning models when tackling complex physical problems. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the global approximation of solutions of stochastic differential equations (SDEs) driven by countably dimensional Wiener process. Under certain regularity conditions imposed on the coefficients, we show lower bounds for exact asymptotic error behaviour. For that reason, we analyse separately two classes of admissible algorithms: based on equidistant, and possibly not equidistant meshes. Our results indicate that in both cases, decrease of any method error requires significant increase of the cost term, which is illustrated by the product of cost and error diverging to infinity. This is, however, not visible in the finite-dimensional case. In addition, we propose an implementable, path-independent Euler algorithm with adaptive step-size control, which is asymptotically optimal among algorithms using specified truncation levels of the underlying Wiener process. Our theoretical findings are supported by numerical simulation in Python language. [ABSTRACT FROM AUTHOR]

This document provides a summary of a collection of research articles focused on the intersection of education and technology. The articles cover a range of topics, including the integration of IoT in higher education, a tool to improve programming instruction, the impact of COVID-19 on education, and the use of virtual reality and augmented reality in online learning. The research presented in these articles aims to enhance educational experiences, address the needs of diverse learners, and provide recommendations for future research and implementation. [Extracted from the article]

This paper proposes a multidimensional social open model to evaluate the teaching strategies adopted during the COVID-19 pandemic by assessing the decisions made by teachers by a group of teachers acting as evaluators. Based on the analysis of previous studies on teaching, this study aims to propose a formal model for the evaluation of teaching strategies in four dimensions: sustainability, usability, accessibility, and creativity. The use of information technologies to measure teaching strategies can bring decisive advantages. This work has been inspired by social rating systems of social networks to propose a measurement system in which a potential large number of evaluators with different levels assess the strategies. In addition, the proposal also includes the evaluation of the evaluators' own work, assigning confidence levels that are based on their experience but also on their evaluations. In this way, we have a social measurement system, in the sense that participation is open to a large number of evaluators. A large community of teacher evaluators will increase the objectivity of the measurement. The outcome of the system will be a characterization of the teaching strategies that will allow to decide in the future which ones should be adopted according to the needs of each one. [ABSTRACT FROM AUTHOR]

This study aimed to identify the neuroanatomical predictors of oropharyngeal dysphagia and tube dependency in patients with supratentorial or infratentorial ischemic strokes. Patients with acute ischemic stroke were enrolled and were classified into 3 groups: right supratentorial (n = 61), left supratentorial (n = 89), and infratentorial stroke (n = 50). Dysphagia was evaluated by a modified water swallowing test and the Food Intake LEVEL Scale to evaluate oropharyngeal dysphagia and tube dependency, respectively. As two dysphagia parameters, we evaluated the durations from onset of stroke to (1) success in the modified water swallowing test and to (2) rating 7 points or above on the Food Intake LEVEL Scale: patients regained sufficient oral intake and were not tube-dependent. Voxel-based lesion-symptom mapping analysis was performed for a spatially normalized lesion map of magnetic resonance imaging to explore the anatomies that are associated with the two dysphagia parameters for each stroke group. The right precentral gyrus and parts of the internal capsule are associated with oropharyngeal dysphagia. The four supratentorial areas are associated with tube dependency. The dorsal upper medulla is associated with both oropharyngeal dysphagia and tube dependency. These results suggest that supratentorial stroke patients can be tube-dependent due to an impaired anticipatory phase of ingestion. The simultaneous damage in the four supratentorial areas: the inferior part of the precentral gyrus, lenticular nucleus, caudate head, and anterior insular cortex, predicts tube dependency. In contrast, infratentorial stroke patients can be tube-dependent due to oropharyngeal dysphagia caused by lesions in the dorsal upper medulla, damaging the swallowing-related nucleus. [ABSTRACT FROM AUTHOR]

This paper is concerned with mean field social control for dynamic production adjustment. We first introduce a social control problem with many firms in a market, where the price is sticky and affected by random shocks. By tackling a centralized social control problem, we obtain a system of coupled forward–backward stochastic differential equations (FBSDEs). Decoupling the FBSDEs and applying mean field approximations, we design a set of decentralized strategies in terms of two Riccati equations. Such a set of decentralized strategies is shown to have asymptotic social optimality. The infinite-horizon problem is further considered and a neat condition is given to ensure asymptotic optimality of decentralized strategies. [ABSTRACT FROM AUTHOR]

We consider both finite- and infinite-horizon discounted mean-field games where there is a large population of homogeneous players sequentially making strategic decisions, and each player is affected by other players through an aggregate population state. Each player has a private type that only she observes and all players commonly observe a mean-field population state which represents the empirical distribution of other players' types. Mean-field equilibrium (MFE) in such games is defined as solution of coupled Bellman dynamic programming backward equation and Fokker–Planck forward equation, where a player's strategy in an MFE depends on both, her private type and current population state. In this paper, we present a novel backward recursive algorithm to compute all MFEs of the game. Each step in this algorithm consists of solving a fixed-point equation. We provide sufficient conditions that guarantee the existence of this fixed-point equation for each time t. Using this algorithm, we study versions of security problem in cyber-physical system where infected nodes put negative externality on the system, and each node makes a decision to get vaccinated. We numerically compute MFE of the game. [ABSTRACT FROM AUTHOR]

The world is becoming increasingly dependant in computing intensive applications. The appearance of new paradigms, such as Internet of Things (IoT), and advances in technologies such as Computer Vision (CV) and Artificial Intelligence (AI) are creating a demand for high-performance applications. In this regard, Graphics Processing Units (GPUs) have the ability to provide better performance by allowing a high degree of data parallelism. These devices are also beneficial in specialized fields of manufacturing industry such as CAD/CAM. For all these applications, there is a recent tendency to offload these computations to the Cloud, using a computing offloading Cloud architecture. However, the use of GPUs in the Cloud presents some inefficiencies, where GPU virtualization is still not fully resolved, as our research on what main Cloud providers currently offer in terms of GPU Cloud instances shows. To address these problems, this paper first makes a review of current GPU technologies and programming techniques that increase concurrency, to then propose a Cloud computing outsourcing architecture to make more efficient use of these devices in the Cloud. [ABSTRACT FROM AUTHOR]

Circulating proteins may provide insights into the varying biological mechanisms involved in heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). We aimed to identify specific proteomic patterns for HF, by comparing proteomic profiles across the ejection fraction spectrum. We investigated 4210 circulating proteins in 739 patients with normal (Stage A/Healthy) or elevated (Stage B) filling pressures, HFpEF, or ischemic HFrEF (iHFrEF). We found 2122 differentially expressed proteins between iHFrEF-Stage A/Healthy, 1462 between iHFrEF–HFpEF and 52 between HFpEF-Stage A/Healthy. Of these 52 proteins, 50 were also found in iHFrEF vs. Stage A/Healthy, leaving SLITRK6 and NELL2 expressed in lower levels only in HFpEF. Moreover, 108 proteins, linked to regulation of cell fate commitment, differed only between iHFrEF–HFpEF. Proteomics across the HF spectrum reveals overlap in differentially expressed proteins compared to stage A/Healthy. Multiple proteins are unique for distinguishing iHFrEF from HFpEF, supporting the capacity of proteomics to discern between these conditions. [ABSTRACT FROM AUTHOR]

Indigenous Peoples and local communities are heavily affected by climatic changes. Investigating local understandings of climate change impacts, and their patterned distribution, is essential to effectively support monitoring and adaptation strategies. In this study, we aimed to understand the consistency in climate change impact reports and factors influencing consistency at site and individual levels. We conducted cross-cultural research among iTaukei (Fiji), Dagomba (Ghana), fisherfolks (Tanzania), Tsimane' (Bolivia), Bassari (Senegal), ribeirinhos (Brazil), Mapuche (Chile), Mongolian (China), Tibetan (China) and Daasanach (Kenya) communities using semi-structured interviews, focus groups, and surveys among 1860 individuals. We found that cross-culturally more than two-thirds of individual reports of climate change impacts match site-confirmed reports. Consistency in reports is higher for changes related to pastoralism than crop production and wild plant gathering. Individual's experience with nature, Indigenous and local knowledge, and local family roots are not significantly associated with consistency across sites, but site-specific associations are prevalent. Despite high average consistency among sites, there is considerable variation caused by site-specific factors, including livelihood activities, socio-cultural settings, and environmental conditions. Site contexts and related consistency in climate change impact reports need to be taken into account for climate change monitoring and adaptation planning. [ABSTRACT FROM AUTHOR]

The responses of Ulva species to diurnal temperature difference remain poorly understood. In this present study, we cultured Ulva prolifera under different diurnal temperature treatments with 22°C for photoperiod and 22, 20, 18, 16, 14, and12°C for dark period, respectively (noted as 22-22, 22-20, 22-18, 22-16, 22-14, and 22-12°C treatments). The growth, pigment contents, photosynthesis, superoxide dismutase (SOD) activity, soluble proteins, and sugars were measured. The main results were shown as follows: (1) The growth of U. prolifera was enhanced by the moderate diurnal temperature difference, and the highest growth rate was observed at 22-18°C. (2) Compared with 22-22°C treatment, the thalli grown under 22-18°C condition showed lower chlorophyll a (Chla) content, respiration rate (Rd), the ratio of Rd, and gross photosynthetic rate (Rd/Pg) as well as the net photosynthetic rate (Pn), while the lowest Pn was observed at 22-12°C. (3) The maximum quantum yield (Fv/Fm) was enhanced by diurnal temperature difference, while the effective quantum yield (Fv′/Fm′) decreased with the decreased in temperature in the nighttime. (4) With the increase of the diurnal temperature difference gradient, the SOD activity decreased and then increased, with the lowest value observed at 22-18 °C, and the soluble protein content showed similar trend. Then we cultured this species at 22-22°C and 22-16°C both under 250 and 60 μmol m−2s−1 conditions in order to study the combined effects of diurnal temperature change and light intensities. It was found that under both two light levels, 22-16°C-grown thalli showed higher growth rate, while the SOD activity was lower than that grown under 22-22°C condition. Overall, the suitable range of diurnal temperature difference for the growth of U. prolifera was about 4–6°C and also was mediated by light intensity. [ABSTRACT FROM AUTHOR]

Computing spectra is a central problem in computational mathematics with an abundance of applications throughout the sciences. However, in many applications gaining an approximation of the spectrum is not enough. Often it is vital to determine geometric features of spectra such as Lebesgue measure, capacity or fractal dimensions, different types of spectral radii and numerical ranges, or to detect gaps in essential spectra and the corresponding failure of the finite section method. Despite new results on computing spectra and the substantial interest in these geometric problems, there remain no general methods able to compute such geometric features of spectra of infinite-dimensional operators. We provide the first algorithms for the computation of many of these long-standing problems (including the above). As demonstrated with computational examples, the new algorithms yield a library of new methods. Recent progress in computational spectral problems in infinite dimensions has led to the solvability complexity index (SCI) hierarchy, which classifies the difficulty of computational problems. These results reveal that infinite-dimensional spectral problems yield an intricate infinite classification theory determining which spectral problems can be solved and with which type of algorithm. This is very much related to S. Smale's comprehensive program on the foundations of computational mathematics initiated in the 1980s. We classify the computation of geometric features of spectra in the SCI hierarchy, allowing us to precisely determine the boundaries of what computers can achieve (in any model of computation) and prove that our algorithms are optimal. We also provide a new universal technique for establishing lower bounds in the SCI hierarchy, which both greatly simplifies previous SCI arguments and allows new, formerly unattainable, classifications. [ABSTRACT FROM AUTHOR]

No suid remains have been reported from the Miocene site of Can Missert (Terrassa; Vallès-Penedès Basin, NE Iberian Peninsula), variously correlated to MN7+8 (late Aragonian) or MN9 (early Vallesian) due to the uncertain presence of hipparionin equids. The recent donation of fossils collected decades ago by amateur naturalists has confirmed the presence of Hippotherium and enabled the description of a sample of 27 suid teeth from Can Missert 2. They are here attributed to the suine Propotamochoerus palaeochoerus and the tetraconodontine Parachleuastochoerus valentini, while Listriodon splendens is also recorded from Can Missert based on a previously unpublished male upper canine. The co-occurrence of Pr. palaeochoerus and hipparionins at Can Missert strengthens the view that the former is a biochronological marker of the Vallesian. In turn, the described tetraconodontine remains support the distinction of Pa. valentini from Conohyus simorrensis—considered its senior subjective synonym by some authors—but do not contribute to the ongoing debate about their potential congeneric status. The co-occurrence of Pr. palaeochoerus and Pa. valentini has also been recorded from the earliest Vallesian sites of Creu de Conill 20 and Castell de Barberà within the same basin. However, the composition of the Can Missert 2 suid assemblage more clearly differs from that of Castell de Barberà, where both Listriodon splendens and Albanohyus castellensis are also abundant. Given the restricted size of Can Missert sample, it is uncertain whether such differences are indicative of more open and/or seasonal paleoenvironmental conditions at Can Missert or just the result of sampling biases. [ABSTRACT FROM AUTHOR]

Abundant evidence has tracked the labour market and health assimilation of immigrants, including static analyses of differences in how foreign-born and native-born residents consume health care services. However, we know much less about how migrants' patterns of healthcare usage evolve with time of residence, especially in countries providing universal or quasi-universal coverage. We investigate this process in Spain by combining all the available waves of the local health survey, which allows us to separately identify period, cohort, and assimilation effects. We find robust evidence of migrant assimilation in health care use, specifically in visits to general practitioners and emergency care and among foreign-born women. The differential effects of ageing on health care use between foreign-born and native-born populations contributes to the convergence of utilisation patterns in most health services after 15 years in Spain. Substantial heterogeneity by the time of arrival and by region of origin both suggest that studies modelling future welfare state finances would benefit from a more thorough assessment of migration. [ABSTRACT FROM AUTHOR]

In this paper, we provide a generalization of the forward-backward splitting algorithm for minimizing the sum of a proper convex lower semicontinuous function and a differentiable convex function whose gradient satisfies a locally Lipschitz-type condition. We prove the convergence of our method and derive a linear convergence rate when the differentiable function is locally strongly convex. We recover classical results in the case when the gradient of the differentiable function is globally Lipschitz continuous and an already known linear convergence rate when the function is globally strongly convex. We apply the algorithm to approximate equilibria of variational mean field game systems with local couplings. Compared with some benchmark algorithms to solve these problems, our numerical tests show similar performances in terms of the number of iterations but an important gain in the required computational time. [ABSTRACT FROM AUTHOR]

We deal with McKean–Vlasov and Boltzmann-type jump equations. This means that the coefficients of the stochastic equation depend on the law of the solution, and the equation is driven by a Poisson point measure with intensity measure which depends on the law of the solution as well. Alfonsi and Bally (Construction of Boltzmann and McKean Vlasov type flows (the sewing lemma approach), 2021, arXiv:2105.12677) have proved that under some suitable conditions, the solution X t of such equation exists and is unique. One also proves that X t is the probabilistic interpretation of an analytical weak equation. Moreover, the Euler scheme X t P of this equation converges to X t in Wasserstein distance. In this paper, under more restrictive assumptions, we show that the Euler scheme X t P converges to X t in total variation distance and X t has a smooth density (which is a function solution of the analytical weak equation). On the other hand, in view of simulation, we use a truncated Euler scheme X t P , M which has a finite numbers of jumps in any compact interval. We prove that X t P , M also converges to X t in total variation distance. Finally, we give an algorithm based on a particle system associated with X t P , M in order to approximate the density of the law of X t . Complete estimates of the error are obtained. [ABSTRACT FROM AUTHOR]

We propose a probabilistic representation of the ground states of massive and massless Schrödinger operators with a potential well in which the behaviour inside the well is described in terms of the moment-generating function of the first exit time from the well and the outside behaviour in terms of the Laplace transform of the first entrance time into the well. This allows an analysis of their behaviour at short to mid-range from the origin. In a first part, we derive precise estimates on these two functionals for stable and relativistic stable processes. Next, by combining scaling properties and heat kernel estimates, we derive explicit local rates of the ground states of the given family of non-local Schrödinger operators both inside and outside the well. We also show how this approach extends to fully supported decaying potentials. By an analysis close-by to the edge of the potential well, we furthermore show that the ground state changes regularity, which depends qualitatively on the fractional power of the non-local operator. [ABSTRACT FROM AUTHOR]

We consider the renormalized relativistic Nelson model in two spatial dimensions for a finite number of spinless, relativistic quantum mechanical matter particles in interaction with a massive scalar quantized radiation field. We find a Feynman–Kac formula for the corresponding semigroup and discuss some implications such as ergodicity and weighted L p to L q bounds, for external potentials that are Kato decomposable in the suitable relativistic sense. Furthermore, our analysis entails upper and lower bounds on the minimal energy for all values of the involved physical parameters when the Pauli principle for the matter particles is ignored. In the translation invariant case (no external potential), these bounds permit to compute the leading asymptotics of the minimal energy in the three regimes where the number of matter particles goes to infinity, the coupling constant for the matter–radiation interaction goes to infinity and the boson mass goes to zero. [ABSTRACT FROM AUTHOR]

This study aimed to assess two homogenization methods to recover norovirus from Minas artisanal cheese (MAC) made with raw bovine milk obtained from four microregions of the Minas Gerais state, Brazil, with different ripening times and geographical and abiotic characteristics. For this purpose, 33 fiscal samples were artificially contaminated with norovirus GI and GII, and Mengovirus (MgV), used as an internal process control (IPC). TRIzol® reagent and Proteinase K homogenization methods were evaluated for all samples were then subjected to RNA extraction using viral magnetic beads and RT-qPCR Taqman® for viral detection/quantification. Proteinase K method showed better efficiency results for both norovirus GI and GII, with means recovery efficiency of 45.7% (95% CI 34.3–57.2%) and 41.4% (95% CI 29.1–53.6%), respectively, when compared to TRIzol method (16.6% GI, 95% CI 8.4–24.9%, and 12.3% GII, 95% CI 7.0–17.6%). The limits of detection for norovirus GI and GII for this method were 101GC/g and 103GC/g, respectively, independent of cheese origin. MgV was detected and revealed in 100% success rate in all types of cheese, with mean recovery efficiency of 25.6% for Proteinase K, and 3.8% for the TRIzol method. According to cheese origin, Triangulo Mineiro MAC had the highest mean recovery rates for the three viral targets surveyed (89% GI, 87% GII, and 51% MgV), while Serro MAC showed the lowest rates (p < 0.001). Those results indicate that the proteinase K adapted method is suitable for norovirus GI and GII detection in MAC and corroborated MgV as an applicable IPC to be used during the process. [ABSTRACT FROM AUTHOR]

A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models for communicating the insights to learners. This work demonstrates a predictive modelling framework for identifying learners at risk of qualification non-completion based on a real-world dataset comprising ∼ 7000 learners with their outcomes, covering 2018 - 2022. The study further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies to generate human-readable prescriptive feedback for those who are at risk using ChatGPT. [ABSTRACT FROM AUTHOR]

Introduction: Graves' ophthalmopathy (GO) is an autoimmune inflammatory disorder observed in a substantial proportion of patients with Graves' disease (GD), with debilitating symptoms of disfiguring, periorbital pain, dry eyes, diplopia, and even visual disturbances. Previous studies involving Western populations have noted discrepancies in risk factors for GO. Therefore, this study aimed to determine the risk factors for GO development and the protective effect of statins in newly diagnosed patients with GD in Taiwan. Methods: This retrospective case–control study was based on a tertiary center cohort involving patients with GD diagnosed between 2010 and 2019 at the National Taiwan University Hospital (n = 11,035). Patients who were diagnosed or treated elsewhere, had been followed up for less than 6 months or were with a diagnosis of orbital tumor were excluded. Overall, 3578 patients with GD met the inclusion criteria. Univariate and multivariate logistic regression analyses were used to ascertain the odds ratio (OR) of developing GO, with adjustment for sociodemographic factors, interventions for managing GD and thyroid hormone levels, to determine protective and risk factors for GO. Results: In our multivariate model, the use of statins reduced the risk of GO development (OR 0.2; 95% confidence interval [CI] 0.08–0.50; p < 0.001). Thyroid dysfunction including hyperthyroidism (OR 4.2; 95% CI 2.97–5.88; p < 0.001) and hypothyroidism (OR 4.7; 95% CI 3.02–7.19; p < 0.001) was associated with an increased risk of developing GO. Smoking status and lipid profile were not risk factors in our cohort. Conclusion: In newly diagnosed patients with GD, the use of statins decreased the risk of developing GO by 80%, whereas serum lipid levels were not considered risk factors. Further nationwide population-based studies may help clarify the differences in risk factors between various ethnic groups. Trail Registration: This trial was approved by the Research Ethics Committee of National Taiwan University Hospital (202202066RINC), retrospectively registered from January 1, 2010 to December 31, 2019. [ABSTRACT FROM AUTHOR]

The planning problem for the mean field game implies that one tries to transfer the system of infinitely many identical rational agents from the given distribution to the final one using the choice of the terminal payoff. It can be formulated as the mean field game system with the boundary condition only on the distribution. In the paper, we consider the continuous-time finite state mean field game, assuming that the space of actions for each player is compact. It is shown that the planning problem in this case may not admit a solution even if the final distribution is reachable from the initial one. Further, we introduce the concept of generalized solution of the planning problem for the finite state mean field game based on the minimization of regret of a fictitious player. This minimal regret solution always exists. Additionally, the set of minimal regret solution is the closure of the set of classical solution of the planning problem, provided that the latter is nonempty. Finally, we examine the uniqueness of the solution to the planning problem using the Lasry–Lions monotonicity arguments. [ABSTRACT FROM AUTHOR]

In this paper, we study the n-player game and the mean field game under the constant relative risk aversion relative performance on terminal wealth, in which the interaction occurs by peer competition. In the model with n agents, the price dynamics of underlying risky assets depend on a common noise and contagious jump risk modeled by a multi-dimensional nonlinear Hawkes process. With a continuum of agents, we formulate the mean field game problem and characterize a deterministic mean field equilibrium in an analytical form under some conditions, allowing us to investigate some impacts of model parameters in the limiting model and discuss some financial implications. Moreover, based on the mean field equilibrium, we construct an approximate Nash equilibrium for the n-player game when n is sufficiently large. The explicit order of the approximation error is also derived. [ABSTRACT FROM AUTHOR]

We consider the problem faced by a central bank which bails out distressed financial institutions that pose systemic risk to the banking sector. In a structural default model with mutual obligations, the central agent seeks to inject a minimum amount of cash in order to limit defaults to a given proportion of entities. We prove that the value of the central agent's control problem converges as the number of defaultable institutions goes to infinity, and that it satisfies a drift controlled version of the supercooled Stefan problem. We compute optimal strategies in feedback form by solving numerically a regularized version of the corresponding mean field control problem using a policy gradient method. Our simulations show that the central agent's optimal strategy is to subsidise banks whose equity values lie in a non-trivial time-dependent region. [ABSTRACT FROM AUTHOR]

In this study, we propose a new formula for spread option pricing with the dependence of two assets described by a copula function. The proposed method's advantage lies in its requirement of solely computing one-dimensional integrals. Any univariate stock price process, admitting an affine characteristic function, can be used in our formula to get an efficient numerical pricing procedure for a spread option. In the numerical analysis we present a comparison with the Monte Carlo simulation method to assess the performance of our approach, assuming that the univariate stock price follows three widely applied models: variance gamma, Heston's stochastic volatility and affine Heston–Nandi GARCH(1,1) models. [ABSTRACT FROM AUTHOR]

We consider an energy system with n consumers who are linked by a Demand Side Management (DSM) contract, i.e. they agreed to diminish, at random times, their aggregated power consumption by a predefined volume during a predefined duration. Their failure to deliver the service is penalised via the difference between the sum of the n power consumptions and the contracted target. We are led to analyse a non-zero sum stochastic game with n players, where the interaction takes place through a cost which involves a delay induced by the duration included in the DSM contract. When n → ∞ , we obtain a Mean-Field Game (MFG) with random jump time penalty and interaction on the control. We prove a stochastic maximum principle in this context, which allows to compare the MFG solution to the optimal strategy of a central planner. In a linear quadratic setting we obtain a semi-explicit solution through a system of decoupled forward-backward stochastic differential equations with jumps, involving a Riccati Backward SDE with jumps. We show that it provides an approximate Nash equilibrium for the original n-player game for n large. Finally, we propose a numerical algorithm to compute the MFG equilibrium and present several numerical experiments. [ABSTRACT FROM AUTHOR]

We consider a large random network, in which the performance of a node depends upon that of its neighbours and some external random influence factors. This results in random vector valued fixed-point (FP) equations in large dimensional spaces, and our aim is to study their almost-sure solutions. An underlying directed random graph defines the connections between various components of the FP equations. Existence of an edge between nodes i, j implies the i-th FP equation depends on the j-th component. We consider a special case where any component of the FP equation depends upon an appropriate aggregate of that of the random 'neighbour' components. We obtain finite dimensional limit FP equations in a much smaller dimensional space, whose solutions aid to approximate the solution of FP equations for almost all realizations, as the number of nodes increases. We use Maximum theorem for non-compact sets to prove this convergence.We apply the results to study systemic risk in an example financial network with large number of heterogeneous entities. We utilized the simplified limit system to analyse trends of default probability (probability that an entity fails to clear its liabilities) and expected surplus (expected-revenue after clearing liabilities) with varying degrees of interconnections between two diverse groups. We illustrated the accuracy of the approximation using exhaustive Monte-Carlo simulations.Our approach can be utilized for a variety of financial networks (and others); the developed methodology provides approximate small-dimensional solutions to large-dimensional FP equations that represent the clearing vectors in case of financial networks. [ABSTRACT FROM AUTHOR]

Order execution is an important operational level of activity encountered in portfolio investment and risk management. We study a sequential Stackelberg order execution game which arises naturally from the practice of algorithm trading in financial markets. The game consists of two risk-neutral traders, one leader and one follower, who compete to maximize their expected payoffs respectively by trading a single risky asset whose price dynamics follows a linear-price market impact model over a finite horizon. This new Stackelberg game departs from the Nash games which have been the main focus in the algorithm trading literature. We derive a closed-form solution for the unique open-loop Stackelberg equilibrium by exploiting the special structures of the model. This analytic solution enables us to develop new and complementary managerial insights by looking at both players' equilibrium behavior in terms of trading speeds and positions, expected price dynamics, price of anarchy, first mover's advantage, and trading horizon effect. [ABSTRACT FROM AUTHOR]

Co-moments of asset returns play a major role in financial contagion during crises. We study the properties of a particular specification of the generalized bivariate normal distribution which allows for co-volatility and co-skewness. With this probability distribution, formulae for single-name and exchange options can be evaluated quickly since they are based on one-dimensional integrals. We provide a very precise approximation formula for spread option prices and derive the corresponding greeks. We perform a day-to-day re-estimation of the probability distribution on a dataset of WTI vs Brent spread options, showing the ability of this specification to capture the salient empirical features observed in the market. Finally, we show the impact of co-movements on portfolio risk management. [ABSTRACT FROM AUTHOR]