Nonlinear ordinary differential equations (ODEs) are powerful tools for modeling real-world dynamical systems. However, propagating initial state uncertainty through nonlinear dynamics, especially when the ODE is unknown and learned from data, remains a major challenge. This paper introduces a novel continuum dynamics perspective for model learning that enables formal uncertainty propagation by constructing Taylor series approximations of probabilistic events. We establish sufficient conditions for the soundness of the approach and prove its asymptotic convergence. Empirical results demonstrate the framework's effectiveness, particularly when predicting rare events.
Our research explores the development and application of musical agents, human-in-the-loop generative AI systems designed to support music performance and improvisation within co-creative spaces. We introduce MACAT and MACataRT, two distinct musical agent systems crafted to enhance interactive music-making between human musicians and AI. MACAT is optimized for agent-led performance, employing real-time synthesis and self-listening to shape its output autonomously, while MACataRT provides a flexible environment for collaborative improvisation through audio mosaicing and sequence-based learning. Both systems emphasize training on personalized, small datasets, fostering ethical and transparent AI engagement that respects artistic integrity. This research highlights how interactive, artist-centred generative AI can expand creative possibilities, empowering musicians to explore new forms of artistic expression in real-time, performance-driven and music improvisation contexts.
Prateek Gupta, Qiankun Zhong, Hiromu Yakura
et al.
A growing body of multi-agent studies with LLMs explores how norms and cooperation emerge in mixed-motive scenarios, where pursuing individual gain can undermine the collective good. While prior work has explored these dynamics in both richly contextualized simulations and simplified game-theoretic environments, most LLM systems featuring common-pool resource (CPR) games provide agents with explicit reward functions directly tied to their actions. In contrast, human cooperation often emerges without explicit knowledge of the payoff structure or how individual actions translate into long-run outcomes, relying instead on heuristics, communication, and enforcement. We introduce a CPR simulation framework that removes explicit reward signals and embeds cultural-evolutionary mechanisms: social learning (adopting strategies and beliefs from successful peers) and norm-based punishment, grounded in Ostrom's principles of resource governance. Agents also individually learn from the consequences of harvesting, monitoring, and punishing via environmental feedback, enabling norms to emerge endogenously. We establish the validity of our simulation by reproducing key findings from existing studies on human behavior. Building on this, we examine norm evolution across a $2\times2$ grid of environmental and social initialisations (resource-rich vs. resource-scarce; altruistic vs. selfish) and benchmark how agentic societies comprised of different LLMs perform under these conditions. Our results reveal systematic model differences in sustaining cooperation and norm formation, positioning the framework as a rigorous testbed for studying emergent norms in mixed-motive LLM societies. Such analysis can inform the design of AI systems deployed in social and organizational contexts, where alignment with cooperative norms is critical for stability, fairness, and effective governance of AI-mediated environments.
Cláudio Jorge Cançado, F. M. P. Ferreira, Plínio de Campos Souza
et al.
Abstract: This study analyses the availability and quality of information in the National Basic Sanitation Information System (SINISA), using the state of Minas Gerais as a case study. The analysis is based on the Basic Sanitation Deficit Index (IDESB), developed by the João Pinheiro Foundation (FJP), using data from 2017 to 2021. The IDESB serves as a management tool for monitoring deficiencies in sanitation services, focusing on water supply, sewage collection and treatment, solid waste disposal, and stormwater management. The study reveals that 58.3% of municipalities in Minas Gerais present either insufficient or inconsistent data, compromising public policy planning. The results indicate significant information gaps particularly in smaller municipalities, and highlight the need for improved data collection, verification, and capacity building at the local level. Recommendations include restructuring the State Sanitation Information System (SEIS) with updated technologies, respondent training, and cross-validation mechanisms. Reliable data is critical to achieve the universalization goals outlined in the National Basic Sanitation Plan. Key words: sanitation deficit index, data quality, National Basic Sanitation Information System (SINISA), basic sanitation services, Minas Gerais
Carmelia Mariana Bălănică Dragomir, Dimitrie Stoica, Eduard Coropceanu
et al.
Both globally and in Romania, one of the major challenges facing decision-makers is the degradation of surface and groundwater quality. Human settlements and localities that do not have wastewater collection systems or appropriate sludge collection and disposal systems from wastewater treatment plants, as well as localities that have non-compliant household waste dumps, agro-zootechnical farms without appropriate manure storage systems, and the excessive use of pesticides can lead to significant pollutant emissions. The main purpose of this study is to present a clear and comprehensive picture regarding the management of water resources and access to drinking water and sanitation for the population in Romania. The data used in this study were collected by the National Institute of Statistics in 2008-2023, and the statistical survey for the collection, treatment and disposal of wastewater has, as main purpose, to obtain information necessary to substantiate Romania's national policy in the field of environment and water, thus ensuring the harmonization of environmental statistics in Romania with the standards and norms of the European Union. In 2008 the population served by the public water supply system was 11,336,676 compared to 14,705,481 in 2023. If we talk about the population connected to sewage and wastewater treatment systems with treatment, we can see an increase of 5,022,884people in 2023 compared to 2008, the first year analyzed in this study. The number of those connected to untreated sewage systems has decreased from 3022657 in 2008 to 274,586 in 2023, thus demonstrating that lately, the quality of discharged water is definitely superior.
Dragan Milićević, Marija Milićević, Rastislav Trajković
In locations with low population density or constraints in technology, resources, and personnel, the use of centralized wastewater treatment systems is not be justified. In such areas, decentralized wastewater treatment systems offer several advantages overcentralized systems. In these systems, the treatment and disposal of effluent is close to the source of waste water production, which reduces investments in a long sewage network and enables the application of other methods of wastewater transport, such as pressure sewerage and vacuum sewerage. A significant advantage of decentralized systems is their ability to be installed quickly, while also enabling local water reuse and implementation of the principles of circular economy, thereby enhancing productivity. In Serbia, according to the 2011 census, there are 449 settlements with more than 2,000 equivalent inhabitants whose wastewater should undergo at least secondary biological treatment. Given that approximately 80% of these settlements have populations ranging from 2,000 to 10,000, the implementation of decentralized wastewater treatment systems becomes imperative for sustainable water protection in Serbia. This paper provides a brief overview of decentralized wastewater treatment systems and, using the example of the municipality of Pirot, highlights the advantages and significance of implementing decentralized treatment to ensure a safe, reliable, economically justified, and ecologically sound solution for protecting water resources from pollution.
Simon Stock, Davood Babazadeh, Christian Becker
et al.
While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic behavior under measurement noise. This paper takes the next natural step and addresses the more significant challenge, exploring how BPINN perform in estimating power system dynamics under increasing uncertainty from many Inverter-based Resources (IBRs) connected to the grid. These introduce a different type of uncertainty, compared to noisy measurements. The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification. We explore the BPINN performance on a wide range of systems, starting from a single machine infinite bus (SMIB) system and 3-bus system to extract important insights, to the 14-bus CIGRE distribution grid, and the large IEEE 118-bus system. We also investigate approaches that can accelerate the BPINN training, such as pretraining and transfer learning. Throughout this paper, we show that in presence of uncertainty, the BPINN achieves orders of magnitude lower errors than the widely popular method for system identification SINDy and significantly lower errors than PINN, while transfer learning helps reduce training time by up to 80 %.
In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by propagating cyberattacks among graphical vulnerabilities. Smart technologies deployed in risk modeling include smart monitoring and job thread assignment. Smart monitoring boosts the substation availability against cyberattacks with preventive and corrective measures. The job thread assignment solution reduces the execution failures by distributing the control and monitoring tasks to multiple threads. Reliability assessment is deployed to estimate load losses convertible to monetary losses. These monetary losses would be shared through a mutual insurance plan. To ensure a fair distribution of indemnity, a new Shapley mutual insurance principle is devised. Effectiveness of the proposed Shapley mutual insurance design is validated via case studies. The Shapley premium is compared with existent premium designs. It is shown that the Shapley premium has high indemnity levels closer to those of Tail Conditional Expectation premium. Meanwhile, the Shapley premium is nearly as affordable as the coalitional premium and keeps a relatively low insolvency probability.
Water treatment is necessary to ensure the availability of clean and safe water for various uses. Integrating Internet of Things (IoT) technology with water purification systems has shown enormous potential in recent years for enhancing the efficiency and efficacy of the treatment process. Monitoring the disposal of sewage in treatment facilities is the primary obstacle. As a result, a Supervisory Control And Data Acquisition (SCADA) system, including the IoT, has been proposed to ensure the proper operation of these sewer systems and limit the risk of overflow and malfunction. In this paper, we suggest a novel approach that blends Deep Belief Networks (DBNs) with an IoT-based water treatment system equipped with a SCADA system for increased monitoring and control. An IoT–SCADA system can be implemented at various wastewater collection and treatment phases. Secondly, incorporating DBNs enhances the system's predictive capabilities, enabling proactive maintenance and decision-making to prevent potential failures and optimize resource allocation. The proposed technique computes the efficacy of the effluent treatment facility and ensures that chemical emissions do not exceed permissible limits. Furthermore, Complex Event Processing (CEP) can be utilized to evaluate and analyze the massive influx of real-time data sets provided by IoT sensors.
Soulaimane Berkane, Dionysis Theodosis, Tarek Hamel
et al.
This letter deals with the problem of state estimation for a class of systems involving linear dynamics with multiple quadratic output measurements. We propose a systematic approach to immerse the original system into a linear time-varying (LTV) system of a higher dimension. The methodology extends the original system by incorporating a minimum number of auxiliary states, ensuring that the resulting extended system exhibits both linear dynamics and linear output. Consequently, any Kalman-type observer can showcase global state estimation, provided the system is uniformly observable.
Gioele Zardini, Nicolas Lanzetti, Giuseppe Belgioioso
et al.
The evolution of existing transportation systems,mainly driven by urbanization and increased availability of mobility options, such as private, profit-maximizing ride-hailing companies, calls for tools to reason about their design and regulation. To study this complex socio-technical problem, one needs to account for the strategic interactions of the heterogeneous stakeholders involved in the mobility ecosystem and analyze how they influence the system. In this paper, we focus on the interactions between citizens who compete for the limited resources of a mobility system to complete their desired trip. Specifically, we present a game-theoretic framework for multi-modal mobility systems, where citizens, characterized by heterogeneous preferences, have access to various mobility options and seek individually-optimal decisions. We study the arising game and prove the existence of an equilibrium, which can be efficiently computed via a convex optimization problem. Through both an analytical and a numerical case study for the classic scenario of Sioux Falls, USA, we illustrate the capabilities of our model and perform sensitivity analyses. Importantly, we show how to embed our framework into a "larger" game among stakeholders of the mobility ecosystem (e.g., municipality, Mobility Service Providers, and citizens), effectively giving rise to tools to inform strategic interventions and policy-making in the mobility ecosystem.
Md Mahmudul Hasan, Tasnim Ibn Faiz, Alicia Sasser Modestino
et al.
Opioid Use Disorder (OUD) has reached an epidemic level in the US. Diversion of unused prescription opioids to secondary users and black market significantly contributes to the abuse and misuse of these highly addictive drugs, leading to the increased risk of OUD and accidental opioid overdose within communities. Hence, it is critical to design effective strategies to reduce the non-medical use of opioids that can occur via diversion at the patient level. In this paper, we aim to address this critical public health problem by designing strategies for the return and safe disposal of unused prescription opioids. We propose a data-driven optimization framework to determine the optimal incentive disbursement plans and locations of easily accessible opioid disposal kiosks to motivate prescription opioid users of diverse profiles in returning their unused opioids. We develop a Mixed-Integer Non-Linear Programming (MINLP) model to solve the decision problem, followed by a reformulation scheme using Benders Decomposition that results in a computationally efficient solution. We present a case study to show the benefits and usability of the model using a dataset created from Massachusetts All Payer Claims Data (MA APCD). Our proposed model allows the policymakers to estimate and include a penalty cost considering the economic and healthcare burden associated with prescription opioid diversion. Our numerical experiments demonstrate the ability of model and usefulness in determining optimal locations of opioid disposal kiosks and incentive disbursement plans for maximizing the disposal of unused opioids. The proposed optimization framework offers various trade-off strategies that can help government agencies design pragmatic policies for reducing the diversion of unused prescription opioids.
Abstract Study region Puakō, Hawai‘i Island. Study focus Locations of sewage pollution in the Puakō watershed were identified through measurements of sewage indicators at groundwater wells and within Puakō’s and adjacent resorts’ shoreline waters. Dye tracer tests, water quality, δ15N macroalgal, and δ15N- and δ18O-NO3- measurements, along with stable isotope mixing models, were combined to assess water quality impairment caused by different Onsite Sewage Disposal System (OSDS) types, and used to predict water quality improvements from future management actions. New hydrological insights for the region Sewage indicators were highest within Puakō’s shoreline waters, including: Enterococcus spp., Clostridium perfringens, human-associated Bacteroides, and δ15N-NO3-. Mixing model results using δ15N- and δ18O-NO3- suggest that sewage was a dominant NO3- source, comprising > 40% at 10 of the 16 shoreline stations. δ15N macroalgae measurements confirmed presence of sewage at most stations. In groundwater wells and at adjacent resorts’ shoreline waters, sewage indicators were low, and δ15NO3-was indicative of soils and fertilizers. Puakō dye tracer tests revealed that sewage reached the shoreline within 5 h to 10 d, and that OSDS type did not affect travel time. Water quality was similar in front of homes with different OSDS. In conclusion, sewage is entering the groundwater at Puakō, and the underlying geology, rather than OSDS type, primarily controls the speed at which sewage reaches the shoreline. Our findings highlight the need for improved sewage treatment and collection at Puakō.
The mechanical-biological waste treatment plants (MBTP), which include the municipal waste biogas plants, have an important role in sustainable urban development. Some plants are equipped with a sewage pre-treatment plant, which is then directed to the sewerage system and the treatment plant. Others, on the other hand, have only a non-drainage tank. The parameters of technological sewage (TS) or processing technology could reduce sewage contamination rates. In addition to the quality of sewage from waste treatment plants, the emission of odours is also an important problem, as evidenced by the results obtained over the sewage pumping station tank. The conducted statistical analysis shows a significant positive correlation between odour concentration (cod) and volatile organic compounds (VOCs). Analysing the individual compounds, a high positive correlation was also found—the strongest being between H2S, NH3 and VOCs. In the case of sewage compounds, the insignificant correlation between P total and other parameters was found. For the rest of the compounds, the highest positive correlation was found between COD and BOD and N-NO2 and N-NH3 as well as COD and N-NO2. The dilution of sewage is only an ad hoc solution to the problem. Further work should be aimed at reducing sewage pollution rates. The obtained results indicate large pollution of technological sewage and a high level of odour and odorants concentration. The novelty and scientific contribution presented in the paper are related to analyses of various factors on technological sewage parameters and odour and odorant emission from TS tank at biogas plant processing municipal waste, which may be an important source of knowledge on the management of TS, its disposal and minimisation of emitted compound emissions.
In this paper, we propose a probabilistic physics-guided framework, termed Physics-guided Deep Markov Model (PgDMM). The framework targets the inference of the characteristics and latent structure of nonlinear dynamical systems from measurement data, where exact inference of latent variables is typically intractable. A recently surfaced option pertains to leveraging variational inference to perform approximate inference. In such a scheme, transition and emission functions of the system are parameterized via feed-forward neural networks (deep generative models). However, due to the generalized and highly versatile formulation of neural network functions, the learned latent space often lacks physical interpretation and structured representation. To address this, we bridge physics-based state space models with Deep Markov Models, thus delivering a hybrid modeling framework for unsupervised learning and identification of nonlinear dynamical systems. The proposed framework takes advantage of the expressive power of deep learning, while retaining the driving physics of the dynamical system by imposing physics-driven restrictions on the side of the latent space. We demonstrate the benefits of such a fusion in terms of achieving improved performance on illustrative simulation examples and experimental case studies of nonlinear systems. Our results indicate that the physics-based models involved in the employed transition and emission functions essentially enforce a more structured and physically interpretable latent space, which is essential for enhancing and generalizing the predictive capabilities of deep learning-based models.
Abdul Saleem Mir, Abhinav Kumar Singh, Nilanjan Senroy
An observer based adaptive detection methodology (ADM) is proposed for estimating frequency and its rate of change (RoCoF) of the voltage and/or current measurements acquired from an instrument transformer. With guaranteed convergence and stability, the proposed methodology effectively neutralizes the effect of the measurement distortions like harmonics, decaying DC components and outliers by adding its counter negative. It is robust to noise statistics, performs well while encountering step changes in amplitude/phase and is demonstrably superior to its precursors as established by test results. A benchmark IEEE NETS/NYPS 16 machine 68 bus power system has been used for performance evaluation of robust ADM against its precursors and scaled laboratory setup based on OP5600 multiprocessors was used for establishing its real-time applicability.