MC Method Research Articles

Fourier-space Monte Carlo simulations of two-dimensional nematic liquid crystals

Thermal fluctuations are ubiquitous in mesoscopic and microscopic systems. Take nematic liquid crystals (LCs) as an example; their director fluctuations can strongly scatter light and give rise to random motions and rotations of topological defects and solid inclusions. These stochastic processes contain important information about the material properties of the LC and dictate the transport of the immersed colloidal particles. However, modeling thermal fluctuations of the nematic field remains challenging. Here, we introduce a new Monte Carlo simulation method, namely the Fourier-space Monte Carlo (FSMC) method, which is based on the Oseen–Frank elastic distortion energy model. This method accurately models the thermal fluctuations of a nematic LC’s director field. In contrast to the traditional real-space MC method, which perturbs the director locally, the FSMC method samples different eigenmodes of the director distortions in the Fourier space, aligning with the equipartition theorem. We apply FSMC to study defect fluctuations and trajectories in a two-dimensional nematic LC confined to various geometries. Our results show that FSMC can effectively sample degenerate defect configurations and reproduce long-range elastic interactions between defects. In addition, we conduct three-dimensional molecular dynamics simulations using a coarse-grained Gay–Berne potential, which corroborates the findings from FSMC. Taken together, we have developed a new Monte Carlo method to accurately model thermal fluctuations in nematic LCs, which can be useful for searching global free-energy minimum states in nematic, smectic, and other LC mesophases and can also be helpful in modeling the thermal motions of defects and inclusions in LCs.

Three spectrophotometric quantitative analysis of bisoprolol fumarate and telmisartan in fixed-dose combination utilizing ratio spectra manipulation methods

Hypertension is a chronic condition with multiple drug regimens. Limiting these medicines is critical to patient compliance. Therefore, bisoprolol and telmisartan were recently developed in a fixed-dose combination to control blood pressure. The UV absorption spectra of bisoprolol and telmisartan overlapped significantly. Thus, three spectrophotometric methods have been developed for simultaneous determination of bisoprolol and telmisartan without prior separation. Method A is ratio difference of ratio spectra (RD), which measures the amplitude difference between (210–224) nm for bisoprolol and between (255–365) nm for telmisartan. Method B, the first derivative of ratio spectra (1DD), measures amplitude signals at 232 and 243 nm for bisoprolol and telmisartan, respectively. Method C is the mean centering of ratio spectra (MC), which measures the mean-centered ratio spectra's values at 223 nm for bisoprolol and 245 nm for telmisartan. The applied methods showed good linearity 2–20 µg/mL for bisoprolol, 4–32 µg/mL for telmisartan, with sufficient accuracy and precision. The methods were sensitive, with LOD values of 0.243 µg/mL and 0.596 µg/mL in RD method, 0.313 µg/mL and 0.914 µg/mL in 1DD method, and 0.406 and 0.707 µg/mL in MC method for bisoprolol and telmisartan, respectively, the methods were validated per ICH criteria. The novel methods are precise and accurate and can be used for routine analysis and quality control of bisoprolol and telmisartan in pure and dosage form. Furthermore, the greenness of the approaches was evaluated using Analytical Greenness assessment (AGREE), and the suggested method received a high greenness score.

Temperature history reconstruction in steel box girders using limited data and proper orthogonal decomposition-based dimension reduction representation

The monitoring temperature history of steel box girders inevitably contains gaps or anomalies due to the malfunctions of the structural health monitoring system. Traditional methods for reconstructing temperature histories face the challenge in accounting for the randomness of temperature variations caused by the uncertainty of complex environmental factors. This research proposes a framework for reconstructing the long-term temperature of steel box girders by combining the limited measurements with a proper orthogonal decomposition (POD) based dimension reduction representation approach, to account for the stochastic nature of daily temperature variations. Unlike the conventional Monte Carlo-based POD method, the developed POD-based dimension reduction representation approach effectively reduces the number of elementary random variables from thousands to two by introducing random functions serving as constraints, overcoming the challenge of high-dimensional random variables inherent in the Monte Carlo methods. The proposed approach divides the measured temperature histories into random high-frequency (HF) and deterministic low-frequency (LF) components and establishes the theoretical models of power spectral density and coherence functions of HF temperature components to accommodate the generation of HF temperature component samples, and finally reconstructs temperature samples by superimposing the LF components and generated HF component samples. The results from a practical example demonstrate that the statistical characteristics of representative HF temperature component samples generated by the POD-based dimension reduction representation align well with the corresponding targeted values, and the proposed method outperforms the traditional POD method, yielding a 60% efficiency enhancement without compromising computational accuracy. The developed framework owns apparent superiority in accuracy compared to the traditional POD and the long short-term memory methods, particularly in continuous and extensive missing data. Moreover, the reconstructed temperature samples with assigned probabilities present complete probability information from the level of total probability. These results advance the probabilistic methods in tackling long-term temperature history reconstruction.

Experimental methods in chemical engineering: Monte Carlo

AbstractMonte Carlo (MC) methods employ a statistical approach to evaluate complex mathematical models that lack analytical solutions and assess their uncertainties. To this end, techniques such as Markov chain Monte Carlo (MCMC), bootstrap, and sequential MC methods repeat the same operations over a specified range of conditions. Consequently, both the frequentist and Bayesian statistical approaches are computationally intensive, depending on the problem formulation. Improving sampling techniques and identifying sources of error reduce the computational demand but do not guarantee that the solution reaches the global optimum. Moreover, efficient algorithms and advances in hardware continue to decrease computation time. MC methods are applicable to a plethora of problems ranging from medicine to computational chemistry, economics, and industrial safety, making them integral to the ongoing industrial digitalization by evaluating the quality of applied models. In chemical engineering, MC simulations are used in four clusters of research: design, systems, and optimization; molecular simulation, including CO2 and carbon capture; adsorption and molecular dynamics; and thermodynamics. There is limited cross‐referencing between the design cluster and the other three, which presents an interesting area for future research. This mini‐review presents two applications within chemical engineering: emissions and energy forecasting.

Optimal estimation of MC parameter in SAE J2601 hydrogen refuelling protocol based on modified formula and artificial neural networks

The MC method was officially released in SAE J2601 protocol in 2016 to refuel light-duty hydrogen fuel cell vehicles quickly and safely. For the non-communication MC method, the pressure target for controlling the end of filling is calculated based on the formula of the final hydrogen temperature, which contains a key parameter, MC. SAE has proposed two formulas to estimate the MC parameter. This article proposes a modified formula and artificial neural networks (ANN) model for MC parameter. To generate simulated data for fitting the modified formula and training the ANN model, a hydrogen filling model (0D1D) with a zero-dimensional gas and a one-dimensional tank wall is established and thoroughly verified. In establishing the ANN model, Latin hypercube sampling (LHS) is used to sample independent variables randomly, and genetic algorithms (GA) are used to optimize the initial weights and biases of the ANN. The Sobol sensitivity analysis is used to determine the importance of various initial conditions on the MC parameter. Research shows that the final fueling time and initial pressure have a greater impact on the MC parameter, while the ambient and precooling temperatures have little impact. The modified formula of the MC parameter improves R2 from the original 0.76073 to 0.98132 when fitting the MC simulated data. In situations of short filling time, the modified formula and ANN models of the MC parameter reduce the relative error between the 0D1D model by 13.8 % − 58.8 % and 16.6 % − 82.3 %, respectively, thus improving the calculation accuracy of the final hydrogen temperature and ending pressure target. Moreover, the ANN model can predict the MC parameter during the whole fueling process, while the MC formula utilizes the fixed value when the fueling time is less than 30 s. This work has positive significance for improving refuelling protocol.

Cutting-Edge Monte Carlo Framework: Novel “Walk on Equations” Algorithm for Linear Algebraic Systems

In this paper, we introduce the “Walk on Equations” (WE) Monte Carlo algorithm, a novel approach for solving linear algebraic systems. This algorithm shares similarities with the recently developed WE MC method by Ivan Dimov, Sylvain Maire, and Jean Michel Sellier. This method is particularly effective for large matrices, both real- and complex-valued, and shows significant improvements over traditional methods. Our comprehensive comparison with the Gauss–Seidel method highlights the WE algorithm’s superior performance, especially in reducing relative errors within fewer iterations. We also introduce a unique dominancy number, which plays a crucial role in the algorithm’s efficiency. A pivotal outcome of our research is the convergence theorem we established for the WE algorithm, demonstrating its optimized performance through a balanced iteration matrix. Furthermore, we incorporated a sequential Monte Carlo method, enhancing the algorithm’s efficacy. The most-notable application of our algorithm is in solving a large system derived from a finite-element approximation in constructive mechanics, specifically for a beam structure problem. Our findings reveal that the proposed WE Monte Carlo algorithm, especially when combined with sequential MC, converges significantly faster than well-known deterministic iterative methods such as the Jacobi method. This enhanced convergence is more pronounced in larger matrices. Additionally, our comparative analysis with the preconditioned conjugate gradient (PCG) method shows that the WE MC method can outperform traditional methods for certain matrices. The introduction of a new random variable as an unbiased estimator of the solution vector and the analysis of the relative stochastic error structure further illustrate the potential of our novel algorithm in computational mathematics.

Improvement of MC method in SAE J2601 hydrogen refuelling protocol using dual-zone dual-temperature model

The MC method has been published in the SAE J2601 refuelling protocol to safely and quickly refuel hydrogen fuel cell vehicles. This article adopts a dual-zone dual-temperature model that distinguishes the hydrogen temperature from the tank wall temperature to replace the dual-zone single-temperature model of the original MC method. A model (0D1D) of lumped parameter gas (0D) and one-dimensional tank wall (1D) is established simultaneously to verify the modified MC method and generate simulation data. A modified formula to the original MC method for calculating final hydrogen temperature is derived, which contains a correction factor K. The formula and coefficients of the correction factor K are determined by fitting the simulated data from the 0D1D model. The correction factor K is expressed in a piecewise function. The formulas of K are relatively complicated, so another simpler correction factor k is derived, which is a linear function. Research shows that the modified MC method extends its applicability to situations of refuelling times <30 s, while the original MC method is unsuitable. In the conditions set in this article of ambient temperature 0 to 40 °C, precooling temperature −40 to −20 °C and initial pressure 2 to 60 MPa, the modified MC method reduces the errors of final hydrogen temperature between the 0D1D model by 2 to 7 °C compared to the original one, thus improving the calculation accuracy of the pressure target and reaching a more accurate state of charge. In a word, the modified MC method improves the accuracy of refuelling control and has positive significance for the operational safety of hydrogen refuelling stations and the driver's refuelling experience.

A rapid determination of wheat flours components based on near infrared spectroscopy and chemometrics

In this work, a rapid and simple analytical method for the quantitative determination of moisture, protein, wet gluten, starch and sedimentation index in the wheat flour was established by the combination of near infrared spectroscopy and chemometrics. The spectra of the 229 wheat flour samples were collected by a portable near infrared fast analyzer. The contents of these components were determined according to the relevant Chinese National Standards, and were taken as the corresponding reference database. Seven spectral pretreatment methods were employed to eliminate the optical interference from background and other noise information. The best result was obtained with FD+SG(15, 3)+MC method for moisture, protein, wet gluten and sedimentation index, FD+SG(15, 2)+MC method was more suitable for starch. The principal component numbers (PCs) were also optimized to obtain a superior model effect. Furthermore, partial least squares (PLS) and multiple linear regression (MLR) modeling methods were used to quantify the content of the components. When using FD+SG(15, 3)+MC pretreatment, all the PLS model parameters were significantly better than the MLR model. Both the predicted values and the reference values showed superior linear relationship within the calibration range. Moreover, the absolute error of the predicted values and their corresponding reference values in the PLS model were within their confidence intervals, respectively. The relative errors for moisture, protein, wet gluten and starch fluctuated little, only sedimentation index fluctuated greatly. The actual prediction correct rate of moisture, protein, wet gluten, starch and sedimentation index were 96.8%, 96.8%, 90.3%, 100.0% and 80.6%, respectively, which indicated the prediction was excellent.

MC 기법을 이용한 수소 탱크 충전 성능 향상에 관한 연구

MC 기법을 이용한 수소 탱크 충전 성능 향상에 관한 연구

Establishment and effect analysis of traffic load for long-span bridge via fusion of parameter correlation

Traffic flow is the main driver behind the dynamic response in operation of long-span bridges. To further improve the effectiveness of safety analysis for long span bridges, the bridge dynamic response should be analyzed under traffic flow. This paper investigated the simulation method of traffic flow and the bridge dynamic response under it. Firstly, the potential parameter correlation in the traffic flow was analyzed based on WIM data. Secondly, a refined simulation method of stochastic traffic flow that considers the correlation in vehicle arriving sequence and axle weight (AW) was proposed and validated. The Markov Chain Monte Carlo (MCMC) method and the correlation random sampling Monte Carlo simulation (CMCS) method were used to simulate the correlation of vehicle arriving sequence and axle weight, respectively. Finally, the constructed stochastic traffic flow is used to evaluate the influence of parameter correlation of traffic flow on dynamic response for long span suspension bridges. Results highlight the correlation of traffic flow. For the vehicle arriving sequence, those vehicles with a small quality rarely follow those with a large quality. The vehicles of different types follow certain transfer relationship. The correlation of vehicle arriving sequence can be effectively simulated that the vehicle type distribution and transition probability of the samples are both close to the WIM data. Meanwhile, the correlation of vehicle AW is mainly observed among multi-axle vehicles, and that in two- and three-axle vehicles is not significant. In the bridge application, the proposed method outperforms the MC method in the simulation of multi-axle vehicles. The parameter correlation of traffic flow has obvious influence on the response of the girder of long-span suspension bridge. The distribution of vertical displacement and bending moment of SC is closer to WIM data, and due to the small ratio of live load to dead load, some indexes such as the suspender force are less affected. The consideration of parameter correlation brings the load effect closer to the WIM data, the parameter correlation should be considered in the evaluation of the fatigue life of components, which can positively contribute to the long-term evaluation of bridge response under traffic flow.

Dose calculation in nuclear medicine for internal organs using Gamos NMGUI software

Accurately calculating the dose for patients in cancer treatment is a crucial task. Currently, there are many methods as well as simulation software for dose calculation, most of which have certain limitations in terms of flexibility, ease of use, and error in organ’s borders when calculating the dose at voxel level using the traditional Monte Carlo method. However, GAMOS developers have implemented an application specifically for nuclear medicine dosimetry - Gamos NMGUI. This software simplifies the dose calculation process with just a few buttons. The addition of the “parallel geometry” utility eliminates error at the boundaries, resulting in more optimal dosesfor patients. The results of the S values for the ICRP phantoms with Gamos NMGUI show a good agreement with the results calculated by Stephanie Lamart et al. The doses for the boundaries of some organs (Gallbladder Wall, Stomach Wall, Heart Wall, Urinary Bladder Wall) calculated by the “parallel geometry” method has deviations of up to 26% (heart wall) when compared with the traditional MC method. This shows the need to improve the dose calculation by using the parallel geometry technique. In summary, Gamos NMGUI is a convenient, simple but highly effective dose software.

Event-driven sorting algorithm-based Monte Carlo method with neighbour merging method for solving aerosol dynamics

A new event-driven sorting algorithm-based merging Monte Carlo (SAMMC) method is proposed and developed for solving the general dynamic equation in aerosol dynamics. A neighbour merging method is proposed to maintain a constant-volume and constant-number scheme with minimal interference to the numerical particle population, where absolute volume difference (AVD) and relative volume difference (RVD) are used as the crucial merging criteria. The SAMMC method can be used for simulating all aerosol dynamic processes with very high computational accuracy, especially effective in those aerosol dynamic processes generating additional numerical particles. In the present study, comprehensive computational conditions are used to study their impacts on computational accuracy and efficiency by comparing the SAMMC method to previous MC methods and analytical solutions. Numerical results show that the SAMMC method has excellent agreement with analytical solutions for all specified cases of different aerosol dynamic processes and shows higher computational accuracy than equal-weight-based MC methods. In addition, the computational accuracy of the SAMMC method in the total particle number concentration is much higher than those of the weighted fraction Monte Carlo (WFMC) method and sorting algorithm-based merging weighted fraction Monte Carlo (SAMWFMC) method in non-homogeneous coagulation. The SAMMC method can also achieve the same computational precision as the multi-Monte Carlo (MMC) method at only slightly higher computational cost in homogeneous coagulation. More importantly, the SAMMC method can deal with breakage-related processes and simultaneous coagulation and nucleation with very high computational accuracy and efficiency, while the numerical results of the MMC method may significantly deviate from analytical solutions due to the introduction of systematic errors. Furthermore, the RVD can achieve higher computational accuracy in multi-breakage modelling than AVD, but AVD and RVD have almost the same computational efficiency and accuracy in other aerosol dynamic processes.

Learning a restricted Boltzmann machine using biased Monte Carlo sampling

Restricted Boltzmann Machines are simple and powerful generative models that can encode any complex dataset. Despite all their advantages, in practice the trainings are often unstable and it is difficult to assess their quality because the dynamics are affected by extremely slow time dependencies. This situation becomes critical when dealing with low-dimensional clustered datasets, where the time required to sample ergodically the trained models becomes computationally prohibitive. In this work, we show that this divergence of Monte Carlo mixing times is related to a phenomenon of phase coexistence, similar to that which occurs in physics near a first-order phase transition. We show that sampling the equilibrium distribution using the Markov chain Monte Carlo method can be dramatically accelerated when using biased sampling techniques, in particular the Tethered Monte Carlo (TMC) method. This sampling technique efficiently solves the problem of evaluating the quality of a given trained model and generating new samples in a reasonable amount of time. Moreover, we show that this sampling technique can also be used to improve the computation of the log-likelihood gradient during training, leading to dramatic improvements in training RBMs with artificial clustered datasets. On real low-dimensional datasets, this new training method fits RBM models with significantly faster relaxation dynamics than those obtained with standard PCD recipes. We also show that TMC sampling can be used to recover the free-energy profile of the RBM. This proves to be extremely useful to compute the probability distribution of a given model and to improve the generation of new decorrelated samples in slow PCD-trained models. The main limitations of this method are, first, the restriction to effective low-dimensional datasets and, second, the fact that the Tethered MC method breaks the possibility of performing parallel alternative Monte Carlo updates, which limits the size of the systems we can consider in practice.

Prediction of dose deposition matrix using voxel features driven machine learning approach.

A dose deposition matrix (DDM) prediction method using several voxel features and a machine learning (ML) approach is proposed for plan optimization in radiation therapy. Head and lung cases with the inhomogeneous medium are used as training and testing data. The prediction model is a cascade forward backprop neural network where the input is the features of the voxel, including 1) voxel to body surface distance along the beamlet axis, 2) voxel to beamlet axis distance, 3) voxel density, 4) heterogeneity corrected voxel to body surface distance, 5) heterogeneity corrected voxel to beamlet axis, and (6) the dose of voxel obtained from the pencil beam (PB) algorithm. The output is the predicted voxel dose corresponding to a beamlet. The predicted DDM was used for plan optimization (ML method) and compared with the dose of MC-based plan optimization (MC method) and the dose of pencil beam-based plan optimization (PB method). The mean absolute error (MAE) value was calculated for full volume relative to the dose of the MC method to evaluate the overall dose performance of the final plan. For patient with head tumor, the ML method achieves MAE value 0.49 × 10-4 and PB has MAE 1.86 × 10-4. For patient with lung tumor, the ML method has MAE 1.42 × 10-4 and PB has MAE 3.72 × 10-4. The maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 1.2% for patient with head tumor, while the difference is larger than 10% using the PB method. For patient with lung tumor, the maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 2.1%, while the difference is larger than 16% using the PB method. In this work, a reliable DDM prediction method is established for plan optimization by applying several voxel features and the ML approach. The results show that the ML method based on voxel features can obtain plans comparable to the MC method and is better than the PB method in achieving accurate dose to the patient, which is helpful for rapid plan optimization and accurate dose calculation. Establishment of a new machine learning method based on the relationship between the voxel and beamlet features for dose deposition matrix prediction in radiation therapy.

Novel Secure Data Transmission Methods for IoT Based on STP-CS With Multilevel Critical Information Concealment Function

The Internet of Things (IoT) is a large-scale network of various sensing devices connected via the Internet to achieve intelligent functions. Efficient secure data transmission is a significant guarantee for specific functions of IoT. In recent years, compressive sensing (CS) has been applied to the field of the IoT and a substantial number of CS-based IoT data transmission schemes have been proposed. While existing IoT data transmission schemes based on CS could mostly accomplish signal sampling, data compression, and encrypted transmission, the goal of privacy protection has rarely been achieved. Based on chaos theory and semi-tensor product CS (STP-CS), this article proposes two novel secure data transmission methods for different scenarios of IoT: 1) multiple concealing method (MC method) and 2) precise concealing method (PC method). Both methods can provide different levels of reconstruction results for different receivers with various authorization levels. The feasibility, storage requirements, robustness, and security of the proposed methods are theoretically analyzed and experimentally simulated. The results show that the proposed methods ensure both the stability and security of data transmission, save storage space of sensors, and flexibly protect the privacy of the content of data transmitted.

Phase Purity Regulated by Mechano-Chemical Synthesis of Metal–Organic Frameworks for the Electrocatalytic Oxygen Evolution Reaction

Three new metal organic frameworks (ZnTIA-1mc, CuTIA-1mc, and CoTIA-1mc) were synthesized by the mechanochemical grinding (mc) method in the unadulterated form. They compared with their solvothermally synthesized (st) counterparts, where the mixtures of isomeric forms have been isolated. Kinetics study with the function of grinding time during the mechanosynthesis process revealed the formation of new metastable phases. Less crystallinity and short of mechanical defects in the structure of synthesized mc metal organic frameworks showed enhanced electrocatalytic activity toward oxygen evolution reaction (OER). Among all, CoTIA-1mc showed high OER activity with 289 mV overpotential, 10 mA cm-2 current density, and 55.4 mV dec-1 Tafel slope in 1 M KOH which is close to the commercially used RuO2.

(Quasi-)Monte Carlo Improtance Sampling with QMCPY

(Quasi-)Monte Carlo, (Q)MC, methods are a class of powerful numerical integration algorithms that have been proven to scale well to high dimensions. Various techniques exist to decrease the computational cost of (Q)MC methods. This article focuses on importance sampling, a technique that performs variable transformations to make the integral easier for (Q)MC approximation. The build up to composed importance sampling is paralleled by code from our QMCPy package that implements these concepts.

Biphasic dissolution combined with modified cylinder method—A new promising method for dissolution test in drug-loaded nanoemulsions

Dissolution testing is important in assessing the in vitro drug release performance for oral administration dosage forms. However, currently, a simple and efficient in vitro test to investigate critical factors that may impact the drug release and bioavailability at the development stage of a drug-loaded nanoemulsion (NE) is lacking. Thus, in this study, we developed a new combined biphasic and modified cylinder (BP + MC) method to evaluate the dissolution profile of NEs. Flubendazole (FLZ), a Biopharmaceutical Classification System (BCS) Class II drug, offers a new prospective for drug repositioning for treating lung cancer and cryptococcal meningitis. We compared the drug release profiles of three different FLZ formulations (micronized as a suspension, loaded in NE, and solubilized in oil) by using three different methods (dialysis bag, modified cylinder method, and a new BP + MC method). The results showed potential higher drug release of FLZ from the suspension compared to FLZ-loaded NE at pH 1.2, and higher drug release from FLZ-loaded NE compared to other forms in octanol phase. These results correlate well with the in vivo test performed in mice carried out in our previous works. Furthermore, the partition mechanism of the drug released from the NE is discussed in-depth in this article, as well as the advantage of drug-loaded NEs over other preparations in creating supersaturable conditions. Based on the results, we provide new insights into how dissolution methods for a poorly water-solubility drug can be designed. Therefore, we present this new combined BP + MC method as a potential new discriminative dissolution test for future studies when developing drug-loaded NE and comparing with other dosage forms.

Carbon Radiotheraphy For Head and Neck Cancer: Dosimetric Comparison with Photon Plans

Radiation therapy is one of the most widely used treatment methods for tumors. The therapeutic use of carbon ions is more advantageous than other radiotherapy techniquies especially photon-based irradiation due to its physical properties and radiobiological effects, and therefore it has received more attention. One of the most important reasons for that carbon ion beams are more effective than photon beams while minimizing the dose in the normal tissues around the target, it offers an improved dose distribution that leads to sufficient dose concentration in tumors. In addition, the carbon beam reaches its maximum at the end of its range, which increases with depth, and due to this feature, it provides a higher biological efficiency. In radiotherapy studies, Monte Carlo simulation is widely used to determine the dose distributions and to obtain the correct properties of the beams. With MC simulation, it helps to understand the relative biological efficiency as well as the spatial model of energy storages. In this study, a geometry with critical organs (skull, brain, nasopharynx and thyroid) based on a MIRD phantom was modeled with the Monte Carlo simulation tool GATE (vGATE 9.0). In this experiment, the tumor was irradiated with different carbon beam energies and photon beams. The aim is to calculate the energy accumulations in the region and surrounding organs with the MC method, and as a result, to show the dosimetric advantages of carbon radiotherapy over photon radiotherapy.

Building “First Expire, First Out” models to predict food losses at retail due to cold chain disruption in the last mile

Current less-than-truckload (LTL) shipping practices allow for temperature abuse (TA) in the last segment (last mile) of the food supply chain. When this TA is combined with “First In, First Out” product rotation methods, it could lead to food spoilage and food waste; therefore, data-based decision models are needed to aid retail managers. An experiment was designed using pallets (4 layers/pallet × 5 boxes/layer) of commercially produced boneless chicken breast filet trays. The pallets were exposed to 24 h of simulated LTL TA (cyclic 2 h at 4°C, then 2 h at 23 ± 2°C). Filet temperatures were recorded for all 20 boxes using dataloggers with thermocouple wires. Additionally, microbiological sampling of filets [aerobic plate counts (APC) and psychrotrophic plate counts (PSY)] was conducted before (0 h of LTL TA) and after (24 h of LTL TA) the TA experiment for select boxes of the pallet and compared to control filets (maintained at 4°C). After TA, a shelf-life experiment was conducted by storing filets from predetermined boxes at 4°C until spoilage (7 log CFU/ml). Temperature and microbiological data were augmented using Monte Carlo simulations (MC) to build decision making models using two methods; (1) the risk of each box on the pallet reaching the bacterial “danger zone” (&amp;gt;4°C) was determined; and (2) the risk-of-loss (shelf-life &amp;lt; 4 days; minimum shelf-life required to prevent food waste) was determined. Temperature results indicated that boxes on the top and bottom layers reached 4°C faster than boxes comprising the middle layers while the perimeter boxes of each layer reached 4°C faster than centrally located boxes. Shelf-life results indicate simulated LTL TA reduced shelf-life by 2.25 and 1.5 days for APC and PSY, respectively. The first MC method showed the average risk of boxes reaching 4°C after 24 h of simulated LTL TA were 94.96%, 43.20%, 27.20%, and 75.12% for layers 1–4, respectively. The second MC method indicated that exposure at &amp;gt;4°C for 8 h results in a risk-of-loss of 43.8%. The findings indicate that LTL TA decreases shelf-life of chicken breast filets in a heterogenous manner according to location of boxes on the pallet. Therefore, predictive models are needed to make objective decisions so that a “First Expire, First Out” method can be implemented to reduce food wastes due to TA during the last mile.