openalexZenodo (CERN European Organization for Nuclear Research)2026-07-26Cited by 0
Reproducibility archive for "Risk-averse optimization of Internet of Things sensor placement in semiconductor wastewater networks: A mass-balanced simulation and Monte Carlo framework"
This record contains the complete anonymized reproducibility archive for the manuscript “Risk-averse optimization of Internet of Things sensor placement in semiconductor wastewater networks: A mass-balanced simulation and Monte Carlo framework.” The archive includes three reconstructed wastewater-network definitions, versioned configurations, mass-balanced simulation and event-generation code, exact sensor-placement enumeration, independent Monte Carlo evaluation, controller-aligned robustness analyses, time-step convergence checks, objective-form sensitivity analyses, machine-readable results, and a SHA-256 checksum manifest. Design selection uses 2,500 simulated training years, and assessment uses 50,000 independently generated annual Monte Carlo draws. The networks and results are computational benchmarks and do not represent proprietary factory layouts, private incident records, or external plant validation.
As artificial intelligence assumes an increasingly dominant role in global capital markets, financial regulation faces an unprecedented institutional challenge: how can legal systems govern autonomous algorithms capable of making trading decisions at speeds beyond human supervisi…
The deployment of decentralized, low-cost Internet of Things sensor networks has revolutionized emergency situation awareness and disaster management. However, during catastrophic events such as high-magnitude earthquakes, these networks are highly susceptible to nodal failures a…
Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…
Federated learning has emerged as the dominant architectural response to the privacy and communication constraints of centralised intrusion detection in Internet of Things environments, yet the field lacks a synthesis that maps the concurrent state of architecture diversity, priv…
Scenarios such as mass religious gathering events face extreme, short-lived surges in radio access network traffic demand that a fixed license or capacity allocation cannot efficiently absorb. We propose a two-stage pipeline that couples a machine-learning demand forecaster with…
This code implements a Variational Monte Carlo algorithm where the total state is taken as a superposition of fermionic gaussian States combined with a machine learning-based gauge wavefunction. This code was used to study a magnetic versus fermionic phase transition and the conf…