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Industrial polymeric materials often rely on antioxidants to achieve long-term reliability. Previous studies have frequently discussed the stabilization effect in the presence of macroscopic additive migration. However, the micro- to meso-scale coupling between polymer dynamics and antioxidant molecular dynamics remains insufficiently understood. In this study, we extend a polymer dynamics simulation framework that can account for oxidative aging. We also update the model so that it can explicitly incorporate antioxidant molecules into the simulation. As a result, the framework enables us to quantify how molecular architecture of antioxidants affects oxidation kinetics, which has previously been inferred only indirectly from apparent changes in reaction rates. It also allows us to evaluate the effects of antioxidant concentration and molecular architecture on the spatial heterogeneity of oxidative aging.
Antioxidants operate in biological environments where solvent effects dramatically alter their redox properties. Using ascorbic acid (vitamin C) as a paradigmatic example, we present a comprehensive quantum-chemical investigation of its global chemical reactivity indices -- ionization potential, electron affinity, HOMO-LUMO gap, hardness, softness, electronegativity, electrophilicity, and electrodonating/accepting powers -- computed at the compound chemistry CBS-QB3 and various DFT levels in vacuo and across six solvents. The results demonstrate that solvation stabilizes charged species so strongly that reactivity indices shift by several electronvolts, following a roughly Born-like dependence on dielectric constant. Most importantly, we show unequivocally that Koopmans' theorem, often used to estimate these indices from orbital energies, fails catastrophically in solution: it predicts solvent-independent values that are qualitatively and quantitatively wrong, missing the essential physics of dielectric screening and geometric relaxation. We therefore conclude that Koopmans' theorem must be abandoned for antioxidant studies in condensed phases; adiabatic calculations with solvent a
Banks must optimize risky investments, dividend payouts, and capital structure under tight Basel III solvency and liquidity constraints, while costly equity issuance serves as a distress-recovery tool. We formulate this as a stochastic control problem that reduces the high-dimensional balance-sheet dynamics to a tractable one-dimensional process in the leverage ratio, with state-dependent investment limits. The resulting policy is simple and interpretable: pay dividends at an upper reflection barrier and, when needed, recapitalize only at the distress boundary, jumping to a unique target level. We characterize these thresholds analytically and show their sensitivity to regulatory parameters. From a regulatory viewpoint, we solve an outer optimization problem that maps the efficient frontier between shareholder value and survival probability (via Monte Carlo), with and without leverage caps. Results highlight that tightening solvency requirements often yields the best safety-profitability trade-off.
Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.
This research work showcases a non-toxic approach to synthesize carbon nanoparticles (CNPs) from various medicinal plants namely Syzygium cumini, Holy basil, Azadirachta indica A, Psidium guajava, Mangifera indica, and Bergera koenigii using microwave approach. The optical, morphological, structural, and functional properties of obtained CNPs from all mentioned sources were investigated using UV-Vis, Scanning electron microscopy (SEM), Fourier transform infrared spectrophotometry (FTIR), dynamic light scattering (DLS), zeta potential tests and X-ray diffraction (XRD). With great water dispersibility, and photostability all the medicinal sources chosen yielded in bright red fluorescent nanoparticles under exposure to UV light, thereby giving a significant peak around 650 nm recorded in absorption spectrum. Antoxidant assay was performed on all these six different plant-derived nanoparticles with two different concentrations and all have exhibited excellent free radical (DPPH) scavenging activity, proving their role as antioxidants. This further opens up doors for various other plant and biomedical applications to be targeted using these CNPs.
The traditional methods of the biology, based on illustrative descriptions and linear logic explanations, are discussed. This work aims to improve this approach by introducing alternative tools to describe and represent complex biological systems. Two models were developed, one mathematical and another computational, both were made in order to study the biological process between free radicals and antioxidants. Each model was used to study the same process but in different scenarios. The mathematical model was used to study the biological process in an epithelial cells culture; this model was validated with the experimental data of Anne Hanneken's research group from the Department of Molecular and Experimental Medicine, published by the journal Investigative Ophthalmology and Visual Science in July 2006. The computational model was used to study the same process in an individual. The model was made using C++ programming language, supported by the network theory of aging.
We present an astonishingly simple and elegant proof of the celebrated Basel problem.
Deep reinforcement learning has successfully been applied for molecular discovery as shown by the Molecule Deep Q-network (MolDQN) algorithm. This algorithm has challenges when applied to optimizing new molecules: training such a model is limited in terms of scalability to larger datasets and the trained model cannot be generalized to different molecules in the same dataset. In this paper, a distributed reinforcement learning algorithm for antioxidants, called DA-MolDQN is proposed to address these problems. State-of-the-art bond dissociation energy (BDE) and ionization potential (IP) predictors are integrated into DA-MolDQN, which are critical chemical properties while optimizing antioxidants. Training time is reduced by algorithmic improvements for molecular modifications. The algorithm is distributed, scalable for up to 512 molecules, and generalizes the model to a diverse set of molecules. The proposed models are trained with a proprietary antioxidant dataset. The results have been reproduced with both proprietary and public datasets. The proposed molecules have been validated with DFT simulations and a subset of them confirmed in public "unseen" datasets. In summary, DA-MolDQN
The adoption of the Pao Tang digital wallet in Thailand, promoted under the Khon la Krueng (50-50 Co-Payment) Scheme, illustrates Thailand's receptiveness to digital financial instruments, amassing over 40 million users in just three years during the COVID-19 social distancing era. Nevertheless, acceptance of this platform does not confirm a broad understanding of cryptocurrencies and Web 3.0 technologies in the region. Through a mix of documentary research, online surveys and a targeted interview with the Pao Tang app's founder, this study evaluates the factors behind the Pao Tang platform's success and contrasts it with digital practices in Switzerland. Preliminary outcomes reveal a pronounced knowledge gap in Thailand regarding decentralized technologies. With regulatory frameworks for Web 3.0 and digital currencies still nascent, this research underscores the need for further exploration, serving as a blueprint for shaping strategies, policies, and awareness campaigns in both countries.
Glioblastoma multiforme, the most frequent type of primary brain tumor, is a rapidly evolving and spatially heterogeneous high-grade astrocytoma that presents areas of necrosis, hypercellularity and microvascular hyperplasia. The aberrant vasculature leads to hypoxic areas and results in an increase of the oxidative stress selecting for more invasive tumor cell phenotypes. In our study we assay in silico different therapeutic approaches which combine antithrombotics, antioxidants and standard radiotherapy. To do so, we have developed a biocomputational model of glioblastoma multiforme that incorporates the spatio-temporal interplay among two glioma cell phenotypes corresponding to oxygenated and hypoxic cells, a necrotic core and the local vasculature whose response evolves with tumor progression. Our numerical simulations predict that suitable combinations of antithrombotics and antioxidants may diminish, in a synergetic way, oxidative stress and the subsequent hypoxic response. This novel therapeutical strategy, with potentially low or no toxicity, might reduce tumor invasion and further sensitize glioblastoma multiforme to conventional radiotherapy or other cytotoxic agents, hop
Suillus species, in general, are edible mushrooms, and environmentally important that are associated mostly with pine trees in the tropics regions. These fungi considered a remarkable source of phenolic compounds that play a crucial role as antioxidants which may reduce the risk of most human chronic diseases such as cancer, diabetes, asthma, atherosclerosis, Alzheimer, and others. On the other hand, carotenoids (\b{eta} carotene) are the most popular natural pigments which play an important role to protect the plants from photo-oxidative reactions. In human, these compounds prevent oxidative stress and expects to have antimicrobial activity. Here, the phenolic compounds were extracted with Ethyl acetate from fruiting bodies of Suillus sp and analyzed by HPLC, the antioxidant activity (reducing power%) of phenolic compounds was determined at the concentrations of 1, 2.5, and 5 mg/mL. Antimicrobial activity of \b{eta} carotene pigment was measured at a concentration of 100 mg/mL against some human pathogenic bacteria such as Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumonia, and Staphylococcus aureus. The specific DNA region ITS was amplified and sequenced using ITS1 an
In this study we test whether principal components of the strain rate and stress tensors align within Switzerland. We find that 1) Helvetic Nappes line (HNL) is the relevant tectonic boundary to define different domains of crustal stress/surface strain rates orientations and 2) orientations of T- axes (of moment tensor solutions) and long-term asthenosphere cumulative finite strain (from SKS shear wave splitting) are consistent at the scale of the Alpine arc in Switzerland. At a more local scale, we find that seismic activity and surface deformation are in agreement but in three regions (Basel, Swiss Jura and Ticino); possibly because of the low levels of deformation and/or seismicity. In the Basel area, deep seismicity exists while surface deformation is absent. In the Ticino and the Swiss Jura, where seismic activity is close to absent, surface deformation is detected at a level of ~2 10E-8/yr (~6.3 10E-16/s).
On 22 September 2023 the inauguration of the "Stachelschützenhaus" at Basel's Petersplatz (St. Peter's Square) as an EPS Historic Site was hosted by the University of Basel. In the following article we will focus first on Daniel Bernoulli's career path, before discussing his major scientific achievements and finally adding some aspects of the inauguration ceremony (the colloquium talks, the visit of today's laboratories in the Stachelschützenhaus, and the unveiling of the plaque).
Euler's solution in 1734 of the Basel problem, which asks for a closed form expression for the sum of the reciprocals of all perfect squares, is one of the most celebrated results of mathematical analysis. In the modern era, numerous proofs of it have been produced, each emphasizing a different style of calculation, as a way of testing the power of some demonstration method. It's often thought that solutions using calculus need to involve advanced contour integration techniques or geometric coordinate transformations. We show that this is not the case, as the result can be derived by analyzing basic properties of a particular one-dimensional integral, and as such, can be obtained with techniques typical of regular calculus tests and math competitions.
Inspired by the recent debate on the macroeconomic implications of the new bank regulatory standards known as Basel III, we tried to find out in this study that the impact of Basel III liquidity and capital requirements in Bangladesh proposed by Basel Committee on Banking Supervision (BCBS, 2010a). A small set of macro variables, using a sample of 22 private commercial banks operating in Bangladesh for the period of 2010-2014, are used to estimate long-run relationships among the variables. The macroeconomic variables are included The profitability of banks, GDP, banks' lending to private sector, Net Stable Funding Ratio, Tier 1 capital Ratio, Interest rate spread, real interest rate. The cost is quantified using Driscoll and Kraay panel data models with fixed effect. Impact of higher capital and liquidity requirement on Interest rate spread and lending to private sector of banks were considered as the cost to the economy as a whole whereas impact of higher capital and liquidity requirement on profitability of banks(ROE) was considered as the cost of banks. Here it is found that, the interest rate level is positively affected by the tighter liquidity and capital requirements which
We present SwissBERT, a masked language model created specifically for processing Switzerland-related text. SwissBERT is a pre-trained model that we adapted to news articles written in the national languages of Switzerland -- German, French, Italian, and Romansh. We evaluate SwissBERT on natural language understanding tasks related to Switzerland and find that it tends to outperform previous models on these tasks, especially when processing contemporary news and/or Romansh Grischun. Since SwissBERT uses language adapters, it may be extended to Swiss German dialects in future work. The model and our open-source code are publicly released at https://github.com/ZurichNLP/swissbert.
Switzerland experienced one of the warmest summers during 2022. Extreme heat has been linked to increased mortality. Monitoring the mortality burden attributable to extreme heat is crucial to inform policies, such as heat warnings, and prevent heat-related deaths. In this study, we evaluate excess mortality during summer 2022, identify vulnerable populations and estimate temperature thresholds for heat warnings. We use nationwide mortality and population data in Switzerland during 2011-2022 by age, sex, day and canton. We develop a Bayesian ensemble modelling approach with dynamic population to predict expected mortality in summer 2022 and calculate excess by comparing expected with observed mortality. We account for covariates associated with mortality such as ambient temperature, national holidays and spatiotemporal random effects to improve predictions. After accounting for the effect of the COVID-19 pandemic, we found a 3% (95% credible interval: 0%-6%) excess mortality during summer 2022. We observed a total of 456 (5-891) excess deaths during summer 2022 in people older than 80 years. There was weak evidence of excess mortality in the other age groups. The highest excess mort
This paper investigates the mental health penalty for women after childbirth in Switzerland. Leveraging insurance data, we employ a staggered difference-in-difference research design. The findings reveal a substantial mental health penalty for women following the birth of their first child. Approximately four years after childbirth, there is a one percentage point (p.p.) increase in antidepressant prescriptions, representing a 50% increase compared to pre-birth levels. This increase rises to 1.7 p.p. (a 75% increase) six years postpartum. The mental health penalty is likely not only a direct consequence of giving birth but also a consequence of the changed life circumstances and time constraints that accompany it, as the penalty is rising over time and is higher for women who are employed.
This paper introduces a novel data-driven approach to address challenges faced by city policymakers concerning the distribution of public funds. Providing budgeting processes for improving quality of life based on objective (data-driven) evidence has been so far a missing element in policy-making. This paper focuses on a case study of 1,204 citizens in the city of Aarau, Switzerland, and analyzes survey data containing insightful indicators that can impact the legitimacy of decision-making. Our approach is twofold. On the one hand, we aim to optimize the legitimacy of policymakers' decisions by identifying the level of investment in neighborhoods and projects that offer the greatest return in legitimacy. To do so, we introduce a new context-independent legitimacy metric for policymakers. This metric allows us to distinguish decisive vs. indecisive collective preferences for neighborhoods or projects on which to invest, enabling policymakers to prioritize impactful bottom-up consultations and participatory initiatives (e.g., participatory budgeting). The metric also allows policymakers to identify the optimal number of investments in various project sectors and neighborhoods (in ter
This review mainly focuses on the relation between antioxidants with cancer therapy. Antioxidants have been reported to play an essential role to reduce free radical species. Free radicals commonly cause oxidative damage which is a common factor in the aging process, and also the vital factor of formation, and development of major disease specially cancer. Although, since last many decades several antioxidants belong to natural and synthetic origin have been tested in clinical trials against oxidative stress, however these clinical trials end up with undesirable effects. This review also complied with the most recent findings of oxidative stress, highlighting of free racial production, and its related oxidative damage at cellular and molecular level, with the new and existing natural and synthetic classes of free radical scavenger and their related clinical trials.