Investigation of the large Magnetocaloric effect through DFT and Monte Carlo simulations in Cu- substituted MnCoGe

We aimed to facilitate the comparison and communication of magnitudes of health inequality impact across interventions for different diseases, and to indicate the potential range of such impacts. We propose rescaling the slope index of inequality to measure the health inequality impact as the change in the gap in total predicted quality-adjusted life-years between the least and most socially disadvantaged groups, with linear regression predictions used to account for effects on intermediate groups. We suggest reporting the inequality impact relative to the total health opportunity cost to facilitate comparison across interventions varying in scale and unit costs. We illustrated the approach with aggregate distributional cost-effectiveness analyses of hypothetical treatments for 1336 diseases in England. We approximated benefit shares for neighbourhood deprivation quintile groups using disease-specific hospital admissions. We tested between-group equality using generalised linear regression and constructed uncertainty intervals using Monte Carlo simulation. We assumed an equal total health opportunity cost and benefit-cost ratio of one, with alternative scenarios in a sensitivity analysis. Health inequality impacts of hypothetical treatments ranged from - 33.1% of the total health opportunity cost (inequality increasing) to + 45.3% (inequality decreasing), and were ≤ - 5% for 1.6% of diseases, ≥ + 5% for 41.8% and ≥ + 20% for 1.6%. The impact was positively associated with the benefit-cost ratio and decreased when more deprived groups were assumed to incur proportionately more total health opportunity costs. Health inequality impacts can be compared using the change in the total predicted quality-adjusted life-year gap between the least and most socially disadvantaged groups as a proportion of the total health opportunity cost.

The use of Fisher's exact test in contingency table analysis in palaeopathology.

Robust multi-innovation full parameter identification for separable fractional-order systems based on online measurements.

Abstract The rapid expansion of Radio Frequency Identification (RFID) technology has intensified the need for compact, efficient, and reliable power management. Passive RFID tags, which operate without batteries by harvesting electromagnetic energy, specifically require voltage regulators with extremely low power consumption and a small physical footprint. Traditional Low Dropout (LDO) regulators often struggle to meet these needs, as their reliance on resistive voltage dividers typically increases both the circuit area and power overhead. This article presents the design and analysis of an LDO linear voltage regulator specifically optimized for RFID applications. The proposed LDO integrates an error signal generator, a control transistor, and a closed loop circuit consisting solely of MOSFETs, thereby achieving low power consumption, high gain, and a high Power Supply Rejection Ratio (PSRR). Designing all sub-circuits exclusively with MOSFETs and operating a significant number of MOSFETs in the sub-threshold region effectively minimizes chip area and power consumption. Designed in 90 nm CMOS, simulation results validate the regulator’s ability to consistently maintain a steady 1V output despite variations in external conditions. The regulator demonstrates an impressive quiescent current of 3 μA and a small power consumption of 0.67 mW at room temperature, while occupying a minimal die area of 0.000671 mm 2 . Additionally, the model undergoes rigorous evaluation, including transient analysis, process corner variations, temperature sensitivity analysis, and Monte Carlo simulation, to ensure its compatibility and performance under diverse environmental conditions. The proposed LDO demonstrates its significant potential in advancing energy-efficient RFID systems and other low power applications.

Theoretical investigation of alternative 177Lu production methods using proton accelerator: A Monte Carlo study.

A new statistical estimator with theoretical foundation and Monte Carlo simulation: Its applications in safety management and radiation sciences

Accurate quantification of the energy distribution of backscattered electrons (BSEs) contributing to electron backscatter diffraction (EBSD) patterns remains as an active challenge. This study introduces an energy-resolved EBSD methodology based on a monolithic active pixel sensor direct electron detector and an electron-counting algorithm to enable the energy quantification of individual BSEs, providing direct measurements of electron energy spectra within diffraction patterns. Following detector calibration of the detector signal as a function of primary beam energy, measurements using a 12 keV primary beam on Si(100) reveal a broad BSE energy distribution across the diffraction pattern, extending down to 3 keV. Furthermore, an angular dependence in the weighted average BSE energy is observed, closely matching predictions from Monte Carlo simulations. Pixel-resolved energy maps reveal subtle modulations at Kikuchi band edges, offering insights into the backscattering process. By applying energy filtering within spectral windows as narrow as 2 keV centered on the primary beam energy, significant enhancement in pattern clarity and high-frequency detail is observed. Notably, BSEs in the 9-10 keV range dominate Kikuchi pattern formation, while BSEs in the 2-8 keV range, despite having undergone substantial energy loss, still produce Kikuchi patterns. By enabling energy determination at the single-electron level, this approach introduces a versatile tool-set for expanding the quantitative capabilities of EBSD, thereby offering the potential to deepen the understanding of diffraction contrast mechanisms and to advance the precision of crystallographic measurements.

ABSTRACT Interior decoration is a major source of embodied carbon emissions, highlighting the need for reliable low-carbon decision-making tools. However, the influence of preference uncertainty on ranking stability is seldom addressed in existing low-carbon Multi-Criteria Decision-Making (MCDM) studies. To overcome this limitation, this study proposes an integrated probabilistic decision-making framework that combines cradle-to-site Life Cycle Assessment (LCA), TOPSIS-based multi-criteria evaluation, and Dirichlet-driven Monte Carlo simulation to explicitly quantify weight uncertainty and ranking robustness. The study evaluated a real university Academic Exchange Center interior decoration project (GFA ≈ 4,200 m²) in Chongqing with three alternative schemes: high-end, medium-end, and basic. Results show that material production accounts for over 90% of cradle-to-site embodied carbon emissions across all schemes, indicating material substitution as the most effective emission reduction pathway. Although deterministic TOPSIS initially identified the medium-end scheme as optimal, 1600 Monte Carlo iterations revealed that the high-end scheme achieves the highest median closeness degree (0.5372) and the greatest probability of ranking first (54.94%), demonstrating it as the most robust optimal solution under preference uncertainty. The proposed framework advances low-carbon design methodology by shifting from deterministic ranking to probability- and robustness-based decision analysis, providing more compelling guidance for multi-stakeholder, preference-sensitive interior design practice.

The Impact of Two Data-Generating Processes for Competing Risk Data on the Discrimination and Calibration of Two Types of Competing Risk Regression Models.

Source, bioaccessibility and oral exposure risk assessment of rare earth elements in soils with typical land-use types.

The feasibility of diol-assisted fractionation for lignin-first biorefinery: An integrated process design and sustainability study.

Problem definition: Much of the focus of queueing theory (QT) is on performance evaluation that supports comparative analytics—that is, comparing performance measures under different interventions. However, closed-form queueing models are very sensitive to assumptions. We develop a data-driven Structural Causal Queueing Model (SCQM)—a form of structural causal models that automatically adapts to the data-generating process of queueing systems, finds causal relations, and supports comparative analytics. Numerical experiments show that the accuracy of SCQM is competitive with QT, even for examples where analytical queueing solutions are available. Methodology: We employ structural causal modeling methodology that uses queueing-relevant features to develop a simulator that replicates the system’s data-generating process without requiring prior knowledge of its dynamics. We apply Machine Learning models for identifying the parent sets and causal relations. We then provide intervention analysis using Monte Carlo simulation. Managerial implications: We use queueing knowledge to develop an accurate self-adapting data-driven performance evaluator for congested systems that requires no prior knowledge of the system dynamics. Using this method, companies can perform comparative analytics of interventions for queueing systems that may not be analytically solvable. History: This paper was selected as part of the 1RR initiative between M&SOM and the MSOM Society. This paper was part of the 2024 MSOM Service Operations Service Management Special Interest Group Conference. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1515 .

Heteroepitaxial diamond has recently gained attention as a radiation detector material due to its wide bandgap, radiation hardness, and near-tissue equivalence. Despite these advantages, its use as a solid-state ionization chamber for diagnostic X-ray dosimetry has not yet been established. Demonstrating stable, high-efficiency operation at low voltage would enable compact dosimeters with a very small sensitive volume, which is difficult to achieve with conventional air ionization chambers. To perform the first characterization of a heteroepitaxial diamond ionization chamber (HED-IC) operated at low bias voltage under diagnostic X-ray conditions and to evaluate its feasibility as a compact, high-efficiency dosimeter. A heteroepitaxial diamond detector (4×4×0.5mm3) with Ti/Au electrodes was fabricated and evaluated using diagnostic X-ray beams at tube voltages from 50 to 120kV. Charge-collection characteristics, dose linearity, energy dependence, and temporal response were assessed at negative bias voltages with magnitudes between -1 and -100V. Monte Carlo simulations were performed using PHITS to compute the expected diamond-to-air sensitivity ratio under the same beam qualities for comparison with the experimental measurements. The HED-IC exhibited excellent dose linearity (R2>0.997) and weak energy dependence (<10%) across effective energies from 28.4 to 40.1keV. The detector enables dose measurements within a very small sensitive volume, only 1/1250 of that of a typical air ionization chamber. The volume-normalized sensitivity exceeded theoretical expectations, suggesting enhanced effective ionization efficiency. An increased response with higher bias voltage further indicated potential for high-sensitivity operation. The results demonstrate that the HED-IC can operate as a low-voltage, high-efficiency solid-state ionization chamber under diagnostic X-ray conditions. Owing to the scalability of heteroepitaxial diamond growth, this detector concept provides a promising basis for compact, tissue-equivalent dosimeters capable of real-time dose monitoring across a wide range of radiological applications.

Abstract Reconfigurable intelligent surfaces capable of simultaneous signal transmission and reflection (STAR-RIS) have recently attracted attention as a means to extend coverage and improve spectral efficiency in post-5G wireless networks. When integrated with non-orthogonal multiple access (NOMA), STAR-RIS enables concurrent service to multiple users located in both transmission and reflection regions. This paper investigates the ergodic capacity of a multi-user STAR-RIS–NOMA system over Nakagami-m fading channels by jointly accounting for imperfect channel state information (CSI), residual successive interference cancellation (SIC) errors, and co-channel interference. In contrast to existing studies that typically consider these effects individually or restrict the analysis to two-user scenarios, a unified analytical framework is developed for a realistic multi-user setting. Closed-form approximations of the ergodic capacity are derived using a moment-generating-function-based approach combined with Gauss–Laguerre polynomial expansion and are validated via Monte Carlo simulations. Numerical results show that STAR-RIS–NOMA achieves ergodic capacities of approximately 10–10.5 bits/s/Hz for near users and 4.5–5 bits/s/Hz for far users at 40 dB SNR, whereas orthogonal multiple access remains below 1.5 bits/s/Hz. The results further indicate that far users are particularly sensitive to imperfect CSI and residual SIC errors, underscoring the importance of accurate channel estimation and robust interference cancellation. These findings confirm the effectiveness of STAR-RIS–NOMA and provide useful design insights for beyond-5G wireless systems.

Association Between Ultraprocessed Food Intake and Inflammatory Bowel Disease Risk: A Propensity-Matched Analysis With Monte Carlo Simulation From the Prospective Urban Rural Epidemiology Study.

Impact of arc therapy in boron neutron capture therapy (BNCT) on dose uniformity and motion robustness.

Bridging scales in wastewater treatment: Moroccan clays as eco-friendly adsorbents explored by experiments, response surface methodology, and Monte Carlo simulations

Bremsstrahlung of electrons from the "Elektronika U-003" accelerator on a tungsten target.

ABSTRACT In this paper, we focus on estimating some unknown parameters of a geometric bifractional Brownian motion. A geometric bifractional Brownian motion satisfies a stochastic differential equation driven by a bifractional Brownian motion. Firstly, using the method of quadratic variation for a Gaussian process and the maximum likelihood method, we give the estimators for the unknown parameters. Then, we prove the asymptotic properties of the estimators. Secondly, the Monte Carlo method is used for simulation. Compared with the single maximum likelihood estimation method, the results show that the method in this paper is effective, reliable, and superior. Finally, we conduct an empirical study of financial markets with real financial data from Danimer Scientific Inc‐A (DNMR.N). By using path simulation, Euclidean distance and out‐of‐sample forecasting compared to other classical models, we effectively validate the superiority of the model in this paper in describing financial time series.