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.
Medicinal plants have been a key component in producing traditional and modern medicines, especially in the field of Ayurveda, an ancient Indian medical system. Producing these medicines and collecting and extracting the right plant is a crucial step due to the visually similar nature of some plants. The extraction of these plants from nonmedicinal plants requires human expert intervention. To solve the issue of accurate plant identification and reduce the need for a human expert in the collection process; employing computer vision methods will be efficient and beneficial. In this paper, we have proposed a model that solves such issues. The proposed model is a custom convolutional neural network (CNN) architecture with 6 convolution layers, max-pooling layers, and dense layers. The model was tested on three different datasets named Indian Medicinal Leaves Image Dataset,MED117 Medicinal Plant Leaf Dataset, and the self-curated dataset by the authors. The proposed model achieved respective accuracies of 99.5%, 98.4%, and 99.7% using various optimizers including Adam, RMSprop, and SGD with momentum.
In this paper we present the first system in Spanish capable of answering questions about medicines for human use, called MeQA (Medicines Question Answering), a project created by the Spanish Agency for Medicines and Health Products (AEMPS, for its acronym in Spanish). Online services that offer medical help have proliferated considerably, mainly due to the current pandemic situation due to COVID-19. For example, websites such as Doctoralia, Savia, or SaludOnNet, offer Doctor Answers type consultations, in which patients or users can send questions to doctors and specialists, and receive an answer in less than 24 hours. Many of the questions received are related to medicines for human use, and most can be answered through the leaflets. Therefore, a system such as MeQA capable of answering these types of questions automatically could alleviate the burden on these websites, and it would be of great use to such patients.
Traditional Indian (Ayurvedic) and Chinese herbal medicines have been used in the treatment of a variety of diseases for thousands of years because of their natural origin and lesser side effects. However, the safety and efficacy data (including dose and quality parameters) on most of these traditional medicines are far from sufficient to meet the criteria needed to support their world-wide therapeutic use. Also, the mechanistic understanding of most of these herbal medicines is still lacking due to their complex components which further limits their wider application and acceptance. Metabolomics -a novel approach to reveal altered metabolism (biochemical effects) produced in response to a disease or its therapeutic intervention- has huge potential to assess the pharmacology and toxicology of traditional herbal medicines (THMs). Therefore, it is gradually becoming a mutually complementary technique to genomics, transcriptomics and proteomics for therapeutic evaluation of pharmaceutical products (including THMs); the approach is so called pharmaco-metabolomics. The whole paradigm is based on its ability to provide metabolic signatures to confirm the diseased condition and then to us
Discovering new medicines is the hallmark of human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning-offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature of AI for drug discovery into three relatively stable sub-areas: (a) representation learning over molecular sequences and geometric graphs; (b) data-driven reasoning where we predict molecular properties and their binding, optimize existing compounds, generate de novo molecules, and plan the synthesis of target molecules; and (c) knowledge-based reasoning where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.
Recent advances in neural weather forecasting have shown significant potential for accurate short-term forecasts. However, adapting such gridded approaches to smaller, topographically complex regions like Switzerland introduces computational challenges, especially when aiming for high spatial (1km) and temporal (10 min) resolution. This paper presents a Graph Neural Network (GNN)-based approach for high-resolution nowcasting in Switzerland using the Anemoi framework and observational inputs. The proposed architecture combines surface observations with selected past and future numerical weather prediction (NWP) states, enabling an observation-guided interpolation strategy that enhances short-term accuracy while preserving physical consistency. We evaluate two models, one trained using local nowcasting analyses and one trained without, on multiple surface variables and compare it against operational high-resolution NWP (ICON-CH1) and nowcasting (INCA) baselines. Results over the test period show that both GNNs consistently outperform ICON-CH1 when verified against INCA analyses across most variables and lead times. Relative to the INCA forecast system, scores against INCA analyses sh
Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the a
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.
We present an astonishingly simple and elegant proof of the celebrated Basel problem.
Risk allocation, the decomposition of a portfolio-wide risk measure into component contributions, is a fundamental problem in financial risk management due to the non-additive nature of risk measures, the layered organizational structures of financial institutions, and the range of possible allocation strategies characterized by different rationales and properties. In this work, we conduct a systematic review of the major risk allocation strategies typically used in finance, comparing their theoretical properties, practical advantages, and limitations. To this scope we set up a specific testing framework, including both simplified settings, designed to highlight basic intrinsic behaviours, and realistic financial portfolios under different risk regulations, i.e. Basel 2.5 and FRTB. Furthermore, we develop and test novel practical solutions to manage the issue of negative risk allocations and of multi-level risk allocation in the layered organizational structure of financial institutions, while preserving the additivity property. Finally, we devote particular attention to the computational aspects of risk allocation. Our results show that, in this context, the Shapley allocation str
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.
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.
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).
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
The Basel problem consists in finding the sum of the reciprocals of the squares of the positive integers. It was finally solved in 1735 by Leonhard Euler. In this paper, we propose a simple proof based on the Weierstrass Sine product formula and L'Hôpital's rule.
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.
To analyze the bacteriostatic effect of Chinese traditional herbal medicines on E. coli, total 35 different preparations (decoction, volatile oil and distillate) of Chinese traditional herbal medicines were tested using plate culture method. The results showed that 18 preparations of traditional Chinese herbal medicines have different inhibition effect on E. coli in vitro. The results also revealed that different process and combination affect the bacteriostatic effect and different medicines could be used in singles or combined to treat E.coli disease
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).
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
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