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Tax work is costly for society: Administrative tax labour is typically to a high degree shuffled off the government and onto every taxpayer by law. The higher the burden of any tax system, the costlier for society, as taxpayers are unable to engage in proper wealth creation when being kept busy with administrative tax work. This research finds evidence for a relationship between hours spent to comply with taxes and amount of tax payment. These findings help better understand tax administrative costs and ultimately may help reduce them. PwC and World Bank's final "Paying taxes"-publication (2019) contains tax data for most of the world's jurisdictions, in particular annual hours spent to comply with tax obligations (X) and annual amount of tax payments (Y), both for the year 2019. X and Y were plotted in 6 tests. A positive slope, satisfying p and r values, high mutual information and finally a conclusive scatter plot picture were the 5 requirements that all needed to be met to confirm a positive relationship between X and Y. The first 2 tests did not make any adjustments to the data, the next 2 tests removed cities --thereby avoiding the double counting of jurisdictions-- and the f

The management of Spent Nuclear Fuel (SNF) is one of the main challenges in the decommissioning of nuclear power plants. Thermal reactors, such as Light Water Reactors (LWRs), produce significant amounts of minor actinides (MAs) such as Americium, Curium, and Neptunium, which are key contributors to the long-term radiotoxicity and decay heat in SNF. Currently, the long term widely accepted solution is the geological disposal. At the same time, advanced technologies like Partitioning and Transmutation (P\&T) offer promising solutions to reduce SNF long-term radiotoxicity. While most transmutation strategies rely on neutron fluxes, in this study the adoption of photon beam to induce photonuclear reactions in SNF is investigated, without depending on neutron based systems. In particular, the study focuses on the probability of inducing transmutations and fissions on MAs, by leveraging the Giant Dipole Resonance (GDR) region of photonuclear interactions. In the investigated case study, the effect of a photon driven transmutation of minor actinides present in a spent fuel from SMR technology was evaluated. This approach offers a novel solution to the challenges of nuclear waste mana

This study addresses critical challenges in managing the transportation of spent nuclear fuel, including inadequate data transparency, stringent confidentiality requirements, and a lack of trust among collaborating parties, issues prevalent in traditional centralized management systems. Given the high risks involved, balancing data confidentiality with regulatory transparency is imperative. To overcome these limitations, a prototype system integrating blockchain technology and the Internet of Things (IoT) is proposed, featuring a multi-tiered consortium chain architecture. This system utilizes IoT sensors for real-time data collection, which is immutably recorded on the blockchain, while a hierarchical data structure (operational, supervisory, and public layers) manages access for diverse stakeholders. The results demonstrate that this approach significantly enhances data immutability, enables real-time multi-sensor data integration, improves decentralized transparency, and increases resilience compared to traditional systems. Ultimately, this blockchain-IoT framework improves the safety, transparency, and efficiency of spent fuel transportation, effectively resolving the conflict

Neutrino emission from nuclear reactors provides real-time insights into reactor power and fuel evolution, with potential applications in monitoring and nuclear safeguards. Following reactor shutdown, a low-intensity flux of ``residual neutrinos'' persists due to the decay of long-lived fission isotopes in the partially burnt fuel remaining within the reactor cores and in spent nuclear fuel stored in nearby cooling pools. The Double Chooz experiment at the Chooz B nuclear power plant in France achieved the first quantitative measurement of this residual flux based on 17.2 days of reactor-off data. In the energy range where the residual signal is most pronounced, the neutrino detector located 400$\,$m from the cores recorded $106 \pm 18$ neutrino candidate events (5.9$σ$ significance). This measurement is in excellent agreement with the predicted value of $88 \pm 7$ events derived from detailed reactor simulations modeling the decay activities of fission products and incorporating the best-available models of neutrino spectra.

The radiobiological effect on the human health of CANDU spent fuel is assessed using Monte Carlo shielding estimates. The examination of spent fuel occurs after it has been discharged from the reactor. A specific cooling interval is considered, with the radiation dose rates that characterize the used fuel being of interest. Two kinds of fuel were studied in a CANDU standard fuel bundle with 37 fuel components: natural uranium (NU) fuel and slightly enriched uranium (SEU) fuel. The fuel burnup was simulated using the ORIGEN-S algorithm, and the photon sources describing the wasted fuel were retrieved. A generic stainless steel shipping cask type B was used for spent fuel transfer, and radiation doses at the cask wall and in the air up to 8 m away from the shipping cask were computed using the Monte Carlo MORSE-SGC algorithm. To ensure nuclear safety and radiation protection, spent fuel must be maintained in temporary wet cooling storage for six months. The projected dosage rates were modest, allowing for the safe handling of the used fuel shipping cask. The corresponding dosages on human body organs for the two considered spent fuels were estimated without and with shielding. Due to

OpenMC is an open-source Monte Carlo code with increasing relevance in criticality safety and reactor physics applications. While its validation has covered a broad range of systems, its performance in spent nuclear fuel storage scenarios remains limited in the literature. This work benchmarks OpenMC against MCNP for eleven configurations based on the KBS-3 disposal concept, involving variations in geometry, fuel composition (fresh vs spent), and environmental conditions (e.g., air, argon, flooding scenarios). Effective multiplication factors (k-eff) and leakage fractions were evaluated for both codes. Results show strong agreement, with code-to-code k-eff differences below 0.8% in dry storage conditions, and consistent trends across all cases. Notably, OpenMC successfully captures inter-canister neutron interaction effects under periodic boundary conditions, demonstrating its applicability to dry storage configurations. This benchmark supports the extension of the validation domain of OpenMC toward SNF transport and disposal applications.

The recently developed method Lasso Monte Carlo (LMC) for uncertainty quantification is applied to the characterisation of spent nuclear fuel. The propagation of nuclear data uncertainties to the output of calculations is an often required procedure in nuclear computations. Commonly used methods such as Monte Carlo, linear error propagation, or surrogate modelling suffer from being computationally intensive, biased, or ill-suited for high-dimensional settings such as in the case of nuclear data. The LMC method combines multilevel Monte Carlo and machine learning to compute unbiased estimates of the uncertainty, at a lower computational cost than Monte Carlo, even in high-dimensional cases. Here LMC is applied to the calculations of decay heat, nuclide concentrations, and criticality of spent nuclear fuel placed in disposal canisters. The uncertainty quantification in this case is crucial to reduce the risks and costs of disposal of spent nuclear fuel. The results show that LMC is unbiased and has a higher accuracy than simple Monte Carlo.

This study presents a cross-disciplinary reactor-to-repository framework to compare different advanced reactors with respect to their spent nuclear fuel (SNF). The framework consists of (1) OpenMC for simulating neutronics, fuel depletion, and radioactive decays; (2) NWPY for computing the repository footprint for SNF disposal given the thermal constraints; and (3) PFLOTRAN for simulating radionuclide transport in the geosphere to compute the peak dose rate, which is used to quantify the repository performance and environmental impact. We first perform the meta-analysis of past comparative analyses to identify the factors led previously to inconsistent conclusions. We then demonstrate the new framework by comparing five reactor types. Significant findings are that (1) the repository footprint is neither linearly related to SNF volume nor to decay heat, due to the repository's thermal constraint, (2) fast reactors have significantly higher I-129 inventory, which is often the primarily dose contributor from repositories, and (3) the repository performance primarily depends on the waste forms. The TRISO-based reactors, in particular, have significantly higher SNF volumes, but result i

To prevent the unauthorised spread of radioactive materials, it is essential to detect and monitor spent nuclear fuel. This paper investigates the feasibility of using a detector based on transition edge superconductors to monitor spent CANDU fuel in two distinct scenarios. The first of these considered the monitoring of a CANSTOR container at the location of the Gentilly-2 nuclear power plant. The fuel in a CANSTOR container is contained within baskets, which are then stored in tubes in the container. An individual detector with a mass of 1 kg is not sensitive to the removal of a single basket from the container without an unfeasibly long monitoring time. When an entire tube in the container is emptied (equivalent to approximately 5% of the fuel in the container), the results are improved. The second scenario examined the feasibility of monitoring a single dry storage container (DSC) at the Pickering site. The DSCs are much smaller than a CANSTOR container and contain approximately 7.4 tonnes of spent fuel. The background due to the neutrinos from the nearby reactors at the Pickering site was also evaluated. It was found that monitoring DSC was unfeasible due to the high reactor n

During the past few decades, there has been a renewed interest in advanced reactor concepts, especially the high-temperature gas-cooled and the molten salt-cooled technologies for energy production as well as hydrogen or process heat generation. Tri-structural isotropic fuel is intended to be used in some of these emerging reactor technologies. The fuel bearing TRISO particles are encapsulated in graphite, which acts in the capacity of a moderator. TRISO based fuels can be fabricated in the form of spherical pebbles or cylindrical compacts, the latter of which are then loaded into prismatic graphite blocks.

Dense linear layers are the dominant computational bottleneck in foundation models. Identifying more efficient alternatives to dense matrices has enormous potential for building more compute-efficient models, as exemplified by the success of convolutional networks in the image domain. In this work, we systematically explore structured matrices as replacements for dense matrices. We show that different structures often require drastically different initialization scales and learning rates, which are crucial to performance, especially as models scale. Using insights from the Maximal Update Parameterization, we determine the optimal scaling for initialization and learning rates of these unconventional layers. Finally, we measure the scaling laws of different structures to compare how quickly their performance improves with compute. We propose a novel matrix family containing Monarch matrices, the Block Tensor-Train (BTT), which we show performs better than dense matrices for the same compute on multiple tasks. On CIFAR-10/100 with augmentation, BTT achieves exponentially lower training loss than dense when training MLPs and ViTs. BTT matches dense ViT-S/32 performance on ImageNet-1k w

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking int

The US Department of Energys Office of Nuclear Energy is planning for an integrated waste management approach to transport, store, and eventually dispose of spent nuclear fuel and other high-level radioactive waste as part of the Integrated Waste Management program. In support of this effort, the Stakeholder Tool for Assessing Radioactive Transportation is being developed within the IWM program. This is a web-based decision support tool that can be used to analyze geospatial data related to the transportation of SNF and HLW.

The United States Department of Energy has long term goals to develop solutions for managing the nations spent nuclear fuel and high-level waste inventory. The Integrated Waste Management program is employing system-level engineering and analysis principles to inform potential future waste management system architectures. Managing the SNF requires the use of system-level analysis software that considers waste generation, on-site (centralized storage), transportation infrastructure, and long-term disposal. The Next Generation System Analysis Model is an agent-based model that was developed to simulate the transportation and storage of SNF and HLW. NGSAM has the capability to detail the interaction and movement of individual components and groups, such as rail cars and casks. The SNF inventory from commercial nuclear reactors is currently in temporary storage at multiple locations spread across the US. Shipping of SNF from these locations relies on one of three transportation modes: rail, heavy-haul truck, or barge. Rail is the most preferred due to the size of the canisters and casks the SNF would be shipped in. Under some scenarios, a rail route might not be available to a reactor

START, the Stakeholder Tool for Assessing Radioactive Transportation is a web-based, decision-support tool developed by the U.S. Department of Energy (DOE) to support the Office of Integrated Waste Management (IWM). Its purpose is to provide visualization and analysis of geospatial data relevant to planning and operating large-scale spent nuclear fuel (SNF) and high-level radioactive waste transport to storage and/or disposal facilities. At present, the primary transport method for these shipments is expected to be via rail, operating predominantly on mainline track. For many shipment sites, however, access to this network will typically require initial use of a local/regional (short line) railroad or involve intermodal transport where the access leg is a movement performed by heavy-haul truck and/or barge. START has the ability to represent and analyze all of these transport options, with each transportation network segment containing site-specific physical and operational attributes. Of particular note are segment-specific accident rates and travel speeds, derived from recent data provided by the U.S. Department of Transportation, Bureau of Transportation Statistics, and other pu

This paper addresses the particularities of the uranium extraction-scrubbing operation in a spent nuclear fuel treatment process (PUREX-Plutonium Uranium Refining by Extraction) through the use of set-point tracking MPC (Model Predictive Control). The presented controller uses the feed solution flow rate as the manipulated variable to control the saturation of the solvent at the extraction step. In addition, it guarantees not to loose uranium in the raffinates, and ensures equipment limitations during operation time. Simulation results show that the tracking NMPC effectively ensures accurate set point tracking and constraints guarantee. As a result, the system can be driven to its optimal working condition, avoid and recover from constraint violations. The control performance was compared with PID and openloop controllers.

Researchers use information about the amount of time people spend on digital media for numerous purposes. While social media platforms commonly do not allow external access to measure the use time directly, a usual alternative method is to use participants' self-estimation. However, doubts were raised about the self-estimation's accuracy, posing questions regarding the cognitive factors that underline people's perceptions of the time they spend on social media. In this work, we build on prior studies and explore a novel social media platform in the context of use time: TikTok. We conduct platform-independent measurements of people's self-reported and server-logged TikTok usage (n=255) to understand how users' demographics and platform engagement influence their perceptions of the time they spend on the platform and their estimation accuracy. Our work adds to the body of work seeking to understand time estimations in different digital contexts and identifies new influential engagement factors.

High-temperature gas reactors rely on TRIstructural-ISOtropic (TRISO) fuel for enhanced fission product retention. Accurate fuel characterization would improve monitoring of efficient fuel usage and accountability. We developed a new neutron multiplicity counter (NMC) based on boron coated straw (BCS) detectors and used it in coincidence mode for 235U assay in TRISO fuel. In this work, we demonstrate that a high-efficiency version of the NMC encompassing 396 straws is able to estimate the 235U in used TRISO-fueled pebbles or compacts with a relative uncertainty below 2.5% in 100 s. We performed neutronics and fuel depletion calculation of the HTR-10 pebble bed reactor to estimate the neutron and gamma-ray source strengths of used TRISO-fueled pebbles with burnup between 9 and 90 GWd/t. Then, we measured a gamma-ray intrinsic efficiency of 10^-12 at an exposure rate of 340.87 R/h. The low gamma-ray sensitivity and high neutron detection efficiency enable the inspection of used fuel.