Search

In recent years, several laboratory studies of CNO cycle-related nuclear reactions have been carried out. Nevertheless, extant models of stellar nucleosynthesis still adopt CNO reaction rates reported in old compilations, such as NACRE or CF88. In order to update these rates, we performed new calculations based on a Monte Carlo R-Matrix analysis. In more detail, a method was developed that is based on the collection of all the available data, including recent low-energy measurements obtained by the LUNA collaboration in the reduced background environment of the INFN-LNGS underground laboratory, on R-Matrix cross-section calculations with the AZURE2 code and on uncertainty evaluations with a Monte Carlo analysis. As a first scientific benchmark case, the reactions 17O (p , γ) 18F and 17O (p , α) 14N were investigated. Among the different stellar scenarios they can influence, the 16O/17O abundance ratio in RGB and AGB stars is the one that can be directly confirmed from spectroscopic measurements. The aim is to reduce the nuclear physics uncertainties, thus providing a useful tool to constrain deep mixing processes eventually taking place in these stars. In this work, we present the procedure we followed to calculate the 17O (p , γ) 18F and the 17O (p , α) 14N reaction stellar rates and preliminary comparisons with similar rates reported in widely used nuclear physics libraries are discussed. [ABSTRACT FROM AUTHOR]

Performance optimization is a crucial aspect of enhancing the efficiency of electronic systems, and scaling is a primary method for achieving optimal performance while maintaining the integrity of system architecture. This paper introduces a novel algorithm for optimizing transistor sizing in static random-access memory (SRAM) to enhance speed, improve Static Noise Margin (SNM), and reduce leakage power consumption. The SRAM is designed using 45 nm technology and operates at a supply voltage of 1.2 V. To validate the algorithm’s effectiveness, Monte Carlo simulations were conducted under varying process, voltage, and temperature conditions. The results demonstrate read access times of 11.17 ps (HIGH) and 9.97 ps (LOW), and write access times of 12.00 ps (HIGH) and 17.00 ps (LOW). The measured SNM values for the read, write, and hold states were 328.2 mV, 453.7 mV, and 452.3 mV, respectively. The inclusion of precharge and write driver circuits allows for a compact SRAM layout, occupying $9.79~\mu $ m2, with the SRAM cell itself occupying $4.1~\mu $ m2. Furthermore, the proposed SRAM design exhibits low leakage power consumption of 1.64 pW, demonstrating the efficiency and performance benefits of the optimized transistor sizing approach.

Human examiners, known as panelists, are exposed to an unknown occupational exposure risk while determining odor concentration (Cod) using dynamic olfactometry. In the literature, a few papers, based on a deterministic approach, have been proposed to establish this occupational risk. As a result, the purpose of this study is to develop and apply a probabilistic approach, based on the randomization of exposure parameters, for assessing and evaluating the occupational exposure risk among olfactometric examiners. In this methodology, the risk is assessed by computing the hazard index (HI) and inhalation risk (IR) to determine the non-carcinogenic and carcinogenic risks. To randomize the exposure parameters, a Monte Carlo simulation was described and then applied in real exposure scenario to establish the exposure risk in terms of probability. Therefore, a one-year survey of the working activity of olfactometric examiners of Laboratorio Olfattometrico of Politecnico di Milano university was conducted. Based on this data collection (exposure parameters and chemical data, divided according to sample categories), a randomized exposure scenario was constructed to estimate the probability and cumulative distribution function of risk parameters. Different distributions were obtained for different industrial samples categories and were compared with respect to acceptability criteria (the value of HI and IR at 95th percentile of distribution). The elaboration provided evidence that negligible non-carcinogenic and carcinogenic risks are associated with the panelists' activity, according to an entire annual dataset. The application of probabilistic risk assessment provides a more comprehensive and effective characterization of the general exposure scenario for olfactometric examiners, surpassing the limitations of a deterministic approach. This method can be extended to future exposure scenarios and enables the selection of the most effective risk management strategies to protect the health of olfactometric examiners. [ABSTRACT FROM AUTHOR]

Robotic disassembly of hard disk drives during their recycling process is a promising technology with significant ecological importance and high economic profitability. However, the efficiency of the robotic disassembly of screws in the cost-efficient process using the robotic system in the selective compliance assembly robot arm configuration is highly dependent on the accuracy of positioning. In such a case, the robot works based on known screw positions without a visual control loop. The paper presents the generalised method of analysis of key factors influencing the process, starting from visual geometry analysis to mechanical setup accuracy. The formalised metrological analysis was performed on the base of the Monte-Carlo method to identify the key factors influencing the screw positioning accuracy. It was stated that the robot control uncertainty plays a crucial role in the total uncertainty of the system. [ABSTRACT FROM AUTHOR]

Rotor dynamics plays a crucial role in the performance and safety of rotating machinery, with disk position and unbalance significantly impacting system behavior. This study investigates the dynamic characteristics of two rotor configurations: a centrally mounted unbalanced disk (Rotor05un) and an off-center unbalanced disk (Rotor025un). Using numerical simulations and Monte Carlo analysis, we examined critical speeds and orbital patterns for both configurations. Probability distributions of shaft orbital positions revealed distinct patterns for each configuration. Quantile analysis revealed approximate linear trends for Rotor025un, suggesting higher system stiffness and more predictable behavior near critical speeds. Cross-sectional analyses of the orbits provided insights into the complex interactions between disk position, gyroscopic effects, and system natural frequencies. These findings provide valuable insights for rotor system design, particularly for applications with non-ideal mass distributions. The study goes beyond traditional critical speed analysis to examine orbital patterns and point on orbit occurrence from a probabilistic perspective. Based on the simulation of the orbits, an orbital is determined that allows the probability of the shaft occurring at the analyzed distance from the origin to be determined. The paper also offers insights into the complex interaction behavior of chosen rotor configurations and highlights the importance of considering disk position in predicting and optimizing rotor dynamic behavior, contributing to the development of more robust and efficient rotating machinery. [ABSTRACT FROM AUTHOR]

Abstract Parking difficulties have become a social issue that people have to solve. Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents. The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method (RF-LNMPC) in order to improve the overall path tracking accuracy in parking conditions. Firstly, A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively. Secondly, the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization, of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution. Thirdly, the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented. The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically. Finally, the proposed RF-LNMPC is implemented on a self-driving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions. The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

Parking difficulties have become a social issue that people have to solve. Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents. The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method (RF-LNMPC) in order to improve the overall path tracking accuracy in parking conditions. Firstly, A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively. Secondly, the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization, of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution. Thirdly, the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented. The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically. Finally, the proposed RF-LNMPC is implemented on a self-driving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions. The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future. [ABSTRACT FROM AUTHOR]

One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion in its dynamic evolution. The study is illustrated by the dynamical system described by the logistic map in cryptographic applications based on images. The evaluation of the statistical behavior was performed on an original scheme proposed. The Monte Carlo analysis of the applied Kolmogorov–Smirnov statistical test revealed that the dynamical system in the processing scheme with message inclusion does not modify its proper statistical behavior (revealed by definition relation). This was possible due to the proposed scheme designed. Namely, this scheme contains a decision switch which, supported by an appropriate choice of the magnitude of the scaling factor, ensures that the values of the dynamical system are maintained in the definition domain. The proposed framework for analyzing the statistical properties and for preserving the dynamical system behavior is one main contribution of this research. The message inclusion scheme also provides an enhancement with cryptographic mixing functions applied internally; the statistical behavior of the dynamical system is also analyzed in this case. Thus, the paper contributes to the theoretical complex characterization of the dynamical system considering also the message inclusion case. [ABSTRACT FROM AUTHOR]

Transistor random threshold voltage variations due to process fluctuations seriously affects the stability of Static Random Access Memory (SRAM). In this paper, a SRAM bit transistors threshold voltage $({Vth})$ deviation monitoring scheme and system is proposed. This scheme ingeniously achieves on-chip measurement of all transistors threshold voltages without altering compact SRAM bit array layout. Control signal strategies and Transistor ${Vth}$ Determination Circuit (TVDC) for different types of Devices Under Test (DUTs) have been proposed. The system is implemented using a 65 nm CMOS process with a core area of 0.01875mm2. Through Monte Carlo analysis, the Weighted Average (WA) difference of the proposed scheme and the direct measurement method is not more than 10mV, and the Root Mean Square Error (RMSE) difference is not more than 3mV. This system can also effectively detect the cell position of the transistor threshold voltage mismatch simulated by modifying the substrate voltage. For SRAM arrays of different scales, the method proposed in this paper has area efficiency and flexible reconfigurability. [ABSTRACT FROM AUTHOR]

In recent years, several laboratory studies of CNO cycle-related nuclear reactions have been carried out. Nevertheless, extant models of stellar nucleosynthesis still adopt CNO reaction rates reported in old compilations, such as NACRE or CF88. In order to update these rates, we performed new calculations based on a Monte Carlo R-Matrix analysis. In more detail, a method was developed that is based on the collection of all the available data, including recent low-energy measurements obtained by the LUNA collaboration in the reduced background environment of the INFN-LNGS underground laboratory, on R-Matrix cross-section calculations with the AZURE2 code and on uncertainty evaluations with a Monte Carlo analysis. As a first scientific benchmark case, the reactions 17O(p,γ)18F and 17O(p,α)14N were investigated. Among the different stellar scenarios they can influence, the 16O/17O abundance ratio in RGB and AGB stars is the one that can be directly confirmed from spectroscopic measurements. The aim is to reduce the nuclear physics uncertainties, thus providing a useful tool to constrain deep mixing processes eventually taking place in these stars. In this work, we present the procedure we followed to calculate the 17O(p,γ)18F and the 17O(p,α)14N reaction stellar rates and preliminary comparisons with similar rates reported in widely used nuclear physics libraries are discussed.

Human examiners, known as panelists, are exposed to an unknown occupational exposure risk while determining odor concentration (Cod) using dynamic olfactometry. In the literature, a few papers, based on a deterministic approach, have been proposed to establish this occupational risk. As a result, the purpose of this study is to develop and apply a probabilistic approach, based on the randomization of exposure parameters, for assessing and evaluating the occupational exposure risk among olfactometric examiners. In this methodology, the risk is assessed by computing the hazard index (HI) and inhalation risk (IR) to determine the non-carcinogenic and carcinogenic risks. To randomize the exposure parameters, a Monte Carlo simulation was described and then applied in real exposure scenario to establish the exposure risk in terms of probability. Therefore, a one-year survey of the working activity of olfactometric examiners of Laboratorio Olfattometrico of Politecnico di Milano university was conducted. Based on this data collection (exposure parameters and chemical data, divided according to sample categories), a randomized exposure scenario was constructed to estimate the probability and cumulative distribution function of risk parameters. Different distributions were obtained for different industrial samples categories and were compared with respect to acceptability criteria (the value of HI and IR at 95th percentile of distribution). The elaboration provided evidence that negligible non-carcinogenic and carcinogenic risks are associated with the panelists’ activity, according to an entire annual dataset. The application of probabilistic risk assessment provides a more comprehensive and effective characterization of the general exposure scenario for olfactometric examiners, surpassing the limitations of a deterministic approach. This method can be extended to future exposure scenarios and enables the selection of the most effective risk management strategies to protect the health of olfactometric examiners.

Rotor dynamics plays a crucial role in the performance and safety of rotating machinery, with disk position and unbalance significantly impacting system behavior. This study investigates the dynamic characteristics of two rotor configurations: a centrally mounted unbalanced disk (Rotor05un) and an off-center unbalanced disk (Rotor025un). Using numerical simulations and Monte Carlo analysis, we examined critical speeds and orbital patterns for both configurations. Probability distributions of shaft orbital positions revealed distinct patterns for each configuration. Quantile analysis revealed approximate linear trends for Rotor025un, suggesting higher system stiffness and more predictable behavior near critical speeds. Cross-sectional analyses of the orbits provided insights into the complex interactions between disk position, gyroscopic effects, and system natural frequencies. These findings provide valuable insights for rotor system design, particularly for applications with non-ideal mass distributions. The study goes beyond traditional critical speed analysis to examine orbital patterns and point on orbit occurrence from a probabilistic perspective. Based on the simulation of the orbits, an orbital is determined that allows the probability of the shaft occurring at the analyzed distance from the origin to be determined. The paper also offers insights into the complex interaction behavior of chosen rotor configurations and highlights the importance of considering disk position in predicting and optimizing rotor dynamic behavior, contributing to the development of more robust and efficient rotating machinery.

ABSTRACT This study aimed to analyze the economic viability of the implementation of a mechanized harvesting of broilers where the activity is carried out entirely manually. The viability indicators used were net present value (NPV), net future value (NFV), net uniform value (NUV), discounted payback (DP), and internal rate of return (IRR). Scenario analysis, sensitivity, and Monte Carlo simulation were performed in the present study. The results showed that the initial investment was US$ 1,868,302.76. The average price paid by the slaughterhouse to third-party manual harvesting companies was US$ 18.17 per thousand broilers, which was converted into revenue in the cash flow of the project. The cash flow result was positive at US$ 22,256.14 over the entire study period considering a daily catch of 144 thousand broilers. The results of the economic viability analysis were NPV of US$ 64,786.23, NFV of US$ 333,382.11, NUV of US$ 735.19, DP of 13.82 years, IRR of 0.965 monthly, and modified IRR of 0.933 monthly. These values prove the economic viability of implementing the project considering the market conditions at the time of the study. The analysis of scenarios showed great sensitivity to the exchange rate and the price of fuels. The Monte Carlo simulation highlighted a moderate risk of negative NPV, emphasizing the importance of considering this variable when making decisions. Despite these challenges, the potential benefits of mechanized harvesting, such as increased efficiency and reduced labor costs, make it a promising alternative to manual harvesting, even for small to medium-sized poultry industries.

Twenty-seven scientists met for the annual Auxological conference held at Aschau, Germany, to particularly discuss the interaction between social factors and human growth, and to highlight several topics of general interest to the regulation of human growth. Humans are social mammals. Humans show and share personal interests and needs, and are able to strategically adjust size according to social position, with love and hope being prime factors in the regulation of growth. In contrast to Western societies, where body size has been shown to be an important predictor of socioeconomic status, egalitarian societies without formalized hierarchy and material wealth-dependent social status do not appear to similarly integrate body size and social network. Social network structures can be modeled by Monte Carlo simulation. Modeling dominance hierarchies suggests that winner-loser effects play a pivotal role in robust self-organization that transcends the specifics of the individual. Further improvements of the St. Nicolas House analysis using re-sampling/bootstrap techniques yielded encouraging results for exploring dense networks of interacting variables. Customized pediatric growth references, and approaches towards a Digital Rare Disease Growth Chart Library were presented. First attempts with a mobile phone application were presented to investigate the associations between maternal pre-pregnancy overweight, gestational weight gain, and the child’s future motor development. Clinical contributions included growth patterns of individuals with Silver-Russell syndrome, and treatment burden in children with growth hormone deficiency. Contributions on sports highlighted the fallacy inherent in disregarding the biological maturation status when interpreting physical performance outcomes. The meeting explored the complex influence of nutrition and lifestyle on menarcheal age of Lithuanian girls and emphasized regional trends in height of Austrian recruits. Examples of the psychosocial stress caused by the forced migration of modern Kyrgyz children and Polish children after World War II were presented, as well as the effects of nutritional stress during and after World War I. The session concluded with a discussion of recent trends in gun violence affecting children and adolescents in the United States, and aspects of life history theory using the example of "Borderline Personality Disorder." The features of this disorder are consistent with the notion that it reflects a "fast" life history strategy, with higher levels of allostatic load, higher levels of aggression, and greater exposure to both childhood adversity and chronic stress. The results were discussed in light of evolutionary guided research. In all contributions presented here, written informed consent was obtained from all participants in accordance with institutional Human investigation committee guidelines in accordance with the Declaration of Helsinki amended October 2013, after information about the procedures used.

This work presents an estimate of the slip activation potential of existing fractures in a remote northern community located on Canadian Shield rocks for geothermal purposes. To accomplish this objective, we analyzed outcrop analogues and recorded geometrical properties of fractures, namely the strike and dip. Then, we estimated the stress regime in the study area through an empirical approach and performed a probabilistic slip tendency analysis. This allowed us to determine the slip probability of the pre-existing fractures at the current state of stress, the orientation of fractures that are most likely to be activated and the fluid pressures needed for the slip activation of pre-existing fractures, which are key aspects for developing Enhanced Geothermal Systems. The results of this simple, yet effective, analysis suggest that at the current state of stress, the pre-existing natural fractures are relatively stable, and an injection pressure of about 12.5 MPa/km could be required to activate the most optimally oriented fractures to slip. An injection of water at this pressure gradient could open the optimally oriented pre-existing fractures and enhance the permeability of the reservoir for geothermal fluid extraction. The information described in this paper provides a significant contribution to the geothermal research underway in remote northern communities. [ABSTRACT FROM AUTHOR]

The Carbon Border Adjustment Mechanism was developed to ensure that industrial sectors operating outside the EU follow the same environmental standards and targets while competing with the EU's carbon market. This mechanism aims to calculate the carbon footprint of goods and services imported into the EU and make carbon adjustments accordingly. The transition phase, starting in 2023, represents the period when the Carbon Border Adjustment Mechanism will be implemented. The completion of the transition phase is targeted for 2025. By this date, the effective implementation of this mechanism is aimed at demonstrating that countries outside of the EU comply with emissions regulations using Carbon at Border certificates. The textile industry's products have a significant environmental impact throughout their life cycle, from the production of raw materials to the disposal of the finished product. Textile production, especially synthetic yarns, requires large amounts of energy, contributing to greenhouse gas emissions and climate change. In this study, a "cradle-to-customer plus waste" life cycle assessment (LCA) is conducted to evaluate the environmental impacts of two products in the textile sector. The Monte Carlo analysis method can be used to handle uncertainties in LCA calculations. It is a method for modeling uncertainties and statistically evaluating results. In this study, this method is preferred at the stage of determining uncertainties. The processes from chips to yarns are investigated for two synthetic yarns: polyester (PES) and polypropylene (PP). The carbon emissions of PP and PES used in textiles are calculated for the first time in this study using detailed modeling with LCAs and a real application. The main production operations are considered: (i) transport of raw materials and packaging material, (ii) energy consumption during the production process, (iii) transport of products, and (iv) end-of-life steps. When the actual data obtained from a company are analyzed, the carbon footprints (CFs) of the PES and PP are calculated to be 13.40 t CO2-eq (t PES)-1 and 6.42 t CO2-eq (t PP)-1, respectively. These data can be used as reference points for future studies and comparisons. According to the results obtained, when the energy consumption and raw material stages in the production of the PES and PP products are compared, it is seen that the CF of PP yarn is lower, and it is more environmentally friendly. These findings can be utilized to enhance government policies aimed at reducing greenhouse gas emissions and managing synthetic yarn production in Türkiye. Since PP and PES raw materials are predominantly used in synthetic yarns, this study's objective is to quantify the carbon emissions associated with the utilization of these raw materials and provide guidance to companies engaged in their production. [ABSTRACT FROM AUTHOR]

The current deployment of 5G NR in the frequency band 4800–4990 MHz in multiple countries near the coastlines indicated that there might be a potential risk of harmful interference impacting aeronautical and maritime systems located in international airspace and international waters. This raised numerous concerns about whether 5G NR system rollouts in that band need to be restricted by the power flux density limits created at the border of the territorial waters, which equals 12 nautical miles from the coastline. This work contains a case study based on the example of the Pacific region, where a simulation of aggregate interference from 5G NR base stations and user equipment deployed in the cities near the coastline using Monte Carlo analysis was conducted. The results of the study show that no harmful interference to the aeronautical and maritime services operating in international airspace and waters is expected even when the aircraft or vessels are located at the border of the territorial airspace and waters from the coastline. Therefore, no restriction to the 5G NR deployment in the coastline regions in the frequency band 4800–4990 MHz is required.

The present paper provides an environmental assessment and ranking of different treatment options for the organic fraction of municipal solid waste (OFMSW), which is a biowaste that typically contains food- and green-waste. The main biochemical treatment systems for OFMSW are composting (by-product: fertilizer) and anaerobic digestion (by-products: biogas and digestate), which are often combined in practice. Total impact scores as evaluated by means of Life Cycle Assessment (LCA) with the Ecological Scarcity Method range from 8 to 31 eco-points for Germany (EPG) per kg treated OFMSW with “Global Warming” and “Main Air Pollutants” (such as NH3) as main environmental impacts. In spite of significant uncertainties in the available emission data, the subsequent application of a Monte Carlo Analysis provides a robust ranking of the treatment systems. The analysis reveals that the transportation distance is a crucial factor to increase the ecological benefit of an OFMSW treatment system. Consequently, decentral structures with short transportation distances (e.g. 17 km) as well as an optimal plant operation mode are more important to reduce emissions than the choice of the treatment system itself. Further research should focus on monitoring campaigns to generate better representative operational datasets for the different types of OFMSW treatment plants such as plug-flow or batch digestion systems as well as for the composting technologies.

Nowadays, due to advancements in power electronic devices as well as the rise in consumer awareness of the need to protect the environment on a global scale, many people are turning to the use of solar photovoltaic (PV) technology in the distributed power generation side. In the field of power electronics, manufacturers need to develop products that have high lifespans. Power electronic device reliability is important for the maintenance of the device and may be scheduled under that information. Rather than preventing failures, reliability can be improved by predicting them. Even though some research has been conducted over the past few years to investigate the reliability of power electronic devices, the reliability is many common circuits has not been investigated and this leads to a big challenge for researchers. In this review paper, an overview of the grid-connected multilevel inverters for PV systems with motivational factors, features, assessment parameters, topologies, modulation schemes of the multilevel inverter, and the selection process for specific applications are presented. In this paper, the findings of a comprehensive reliability analysis of fundamental multilevel inverters are studied. To evaluate the reliability of three basic multilevel inverters, a calculation is made using each component's mean time before its failure. Two techniques of computation approximate and exact were used to arrive at the final result. To calculate power losses in temperature-sensitive components such as diodes and switches, MATLAB Simulink is employed. In addition, the concept of oversizing photovoltaic (PV) arrays is presented in this study. This concept proposes that energy output may be increased by increasing the size of the PV array under conditions of poor solar irradiation. Finally, the mission-profile-based and Monte Carlo simulation-based methods process flows are discussed for the accurate lifetime prediction and reliability assessments of PV inverters in a real-time scenario, followed by a conclusion with future work. [ABSTRACT FROM AUTHOR]

Due to the continuous scaling of MOSFET (Metal Oxide Semiconductor Field-Effect Transistor) devices over the past few decades, power consumption has increased tremendously. To reduce power dissipation at lower technology nodes, digital logic circuits are designed with modern (FinFET) devices. In this paper, FinFET INDEP (input dependent) technique-based short gate (SG) FinFET Adder circuits are proposed at 10 nm technology node. The performance comparison of INDEP technique-based adder circuits is done with the SG FinFET adder circuits. The analysis of adder circuits is performed first in terms of functional verification (transient characteristics) and finally for different performance parameters such as propagation delay, power dissipation and power delay product (PDP). The proposed FinFET INDEP technique proves as one of the best leakage reduction techniques for FinFET adder circuits at lower technology nodes. To test the reliability of the circuits, Monte Carlo analysis is also performed. The PDP is improved by 16.8% and 13.73% in INDEP SG FinFET half adder(HA) and INDEP SG FinFET full adder(FA) at 10 nm technology, respectively, in comparison with the ones without INDEP technique. The Monte Carlo simulation results with 3σ Gaussian distribution at ±10% process, voltage and temperature variations show the improvement in PDP in case of SG INDEP FinFET FA and SG INDEP FinFET HA circuit in comparison to SG FinFET FA and FinFET HA circuit, respectively. Simulation is performed using HSPICE tool at 10 nm process technology node. [ABSTRACT FROM AUTHOR]

The current deployment of 5G NR in the frequency band 4800-4990 MHz in multiple countries near the coastlines indicated that there might be a potential risk of harmful interference impacting aeronautical and maritime systems located in international airspace and international waters. This raised numerous concerns about whether 5G NR system rollouts in that band need to be restricted by the power flux density limits created at the border of the territorial waters, which equals 12 nautical miles from the coastline. This work contains a case study based on the example of the Pacific region, where a simulation of aggregate interference from 5G NR base stations and user equipment deployed in the cities near the coastline using Monte Carlo analysis was conducted. The results of the study show that no harmful interference to the aeronautical and maritime services operating in international airspace and waters is expected even when the aircraft or vessels are located at the border of the territorial airspace and waters from the coastline. Therefore, no restriction to the 5G NR deployment in the coastline regions in the frequency band 4800-4990 MHz is required. [ABSTRACT FROM AUTHOR]

Abstract Component damage and failure in sensor or actuator will cause system faults, resulting in reduced or lost performance. This topic has become an interesting and challenging issue that if we know the effect of faults on variables, control schemes can be designed to reduce the effect of faults on the system. Therefore, the quantification of the effect of the faults is very valuable in improving the performance of wind turbines. In this paper, the effect of some important faults is investigated in the three parts: rotor, drive, and power on the output power and the measured variables of wind turbines in the partial and full load regions using the Monte Carlo method. Then, the severity of these faults on the output power is ranked in the two regions by importance factor (IF). Also, due to the fact that some faults cause structural stress due to severe changes in the angle of the blades or their asymmetry, so the stresses caused by faults are discussed. The results show that these faults severity on the output power and the measured variables of wind turbines is different in the two regions. Our conclusion provides an opportunity for wind power plant operators and investors to improve plant performance and scheduled repairs by knowing the effect of faults on wind turbine variables. Knowing effect of the faults or the severity of each of them can increase the operating time of the wind turbine and reduce costs.

Heavy metal pollution is a major concern in China because of its serious effects on human health. To assess potential human health and ecological risks of heavy metal pollution, concentration data for seven heavy metals (As, Pb, Cd, Cr, Hg, Cu, Zn) from 14 sites spanning the rural-urban interface of the Wen-Rui Tang River watershed in southeast China were collected from 2000 to 2010. The heavy metal pollution index (HPI), hazard index (HI) and carcinogenic risk (CR) metrics were used to assess potential heavy metal risks. Further, we evaluated the uncertainty associated with the risk assessment indices using Monte Carlo analysis. Results indicated that all HPI values were lower than the critical level of 100 suggesting that heavy metal levels posed acceptable ecological risks; however, one site having an industrial point-source input reached levels of 80-97 on several occasions. Heavy metal concentrations fluctuated over time, and the decrease after 2007 is due to increased wastewater collection. The HI suggested low non-carcinogenic risk throughout the study period (HI  10-4). While most metrics for human health risk did not exceed critical values for heavy metals, there is still a potential human health risk from chronic exposure to low heavy metal concentrations due to long-term exposure and potential metal interactions. Results of this study inform water pollution remediation and management efforts designed to protect public health in polluted urban area waterways common in rapidly developing regions.

Rats produce ultrasonic vocalisation (USVs) that are classified into different types, based on their average frequency. In pups 40 kHz USVs are produced upon social isolation, and in adults USVs can be associated with affective states and specific behavioural patterns (i.e., appetitive 50 kHz vocalisations of frequency range 30-100 kHz, or aversive 20 kHz vocalisations of frequency range 18-30 kHz). Generally, USVs of frequency around 50 kHz are linked to activation of brain reward pathways, during anticipation or experience of rewarding stimuli. Previous studies have described several subtypes of 50 kHz USVs, according to their acoustic properties. We asked whether USV production might be relevant to feeding behaviour. We recorded USVs from 14-week old adult rats during the satisfaction of a physiological need: refeeding following mild food deprivation (17 h overnight fast). We analysed a 10 min consummatory phase, preceded by a 10 min anticipatory phase, as a control for the experimental meal. Following identification of USV subtypes, we applied frequentist and Bayesian (Monte Carlo shuffling) statistical analyses to investigate the relationship between USV emission and rat behaviour. We found that it was not total USV quantity that varied in response to food consumption, but the subtype of USV produced. Most importantly we found that rats who feed tend to produce flat USVs of a frequency around 40 kHz. Beyond the previous reports of circumstantial association feeding-flat USVs, our observation directly correlate vocalisation and ingestive behaviour. Our study highlights that, in addition to quantification of the production rate, study of USV subtypes might inform us further on rat consummatory behaviour. Since this vocalisation behaviour can have a communicative purpose, those findings also illustrate nutrition studies might benefit from considering the possible social dimension of feeding behaviour. [ABSTRACT FROM AUTHOR]

Measurement and response decoding is an ongoing challenge in the chipless radio-frequency identification (RFID) field. Measurement uncertainties, including tag/reader misalignment, S-parameter error, and clutter, can cause response distortions, such as magnitude changes and resonant frequency shifts, that can lead to the improper assignment of a binary code or sensing parameter (i.e., decoding). This work aims to use local sensitivity analysis and Monte Carlo simulation to fully characterize the effects of misalignment, response parameter measurement error (e.g., VNA S-parameter error), and clutter on chipless RFID responses that are measured in the near-field with a monostatic setup. From this type of comprehensive characterization, conclusions are drawn about the identification (ID) and sensing capabilities of the tags. While the effect of misalignment-based uncertainty was examined in Part I, here in Part II, $S_{11}$ uncertainty and clutter-based uncertainty are examined both individually and in combination with misalignment-based uncertainty. An example, demonstrating the application of the proposed tag performance assessment framework is also provided.

Measurement and response decoding is an ongoing challenge in the chipless radio-frequency identification (RFID) field. Measurement uncertainties, including tag/reader misalignment, radar cross-section or S-parameter error, and clutter, can cause response distortions, such as magnitude changes and resonant frequency shifts. These response distortions can lead to the improper assignment of a binary code or sensing parameter (i.e., improper decoding). This work aims to use local sensitivity analysis and Monte Carlo simulation to fully characterize the effects of misalignment, response parameter measurement error (e.g., vector network analyzer S-parameter error), and clutter on chipless RFID tag responses. From this type of comprehensive characterization, conclusions are drawn about the identification (ID) and sensing capabilities of the tags. In this work, the simulations are performed for two specific tags and the results are then corroborated with measurements of one of the tags. While the work is done for a near-field monostatic measurement setup, it is presented such that the same procedures can be applied to other tags and measurement setups, including far-field scenarios. Thus, a novel comprehensive tag performance assessment framework is provided. This work is divided into two parts. In Part I, the effects of tag/reader misalignment uncertainty are examined in depth through both simulations and measurements. In Part II, the effects of S-parameter error, clutter-based uncertainty, and the combination of these uncertainties with misalignment uncertainty are investigated. An example demonstrating the application of this tag performance assessment framework is also provided in Part II.

Rats produce ultrasonic vocalisation (USVs) that are classified into different types, based on their average frequency. In pups 40 kHz USVs are produced upon social isolation, and in adults USVs can be associated with affective states and specific behavioural patterns (i.e., appetitive 50 kHz vocalisations of frequency range 30−100 kHz, or aversive 20 kHz vocalisations of frequency range 18−30 kHz). Generally, USVs of frequency around 50 kHz are linked to activation of brain reward pathways, during anticipation or experience of rewarding stimuli. Previous studies have described several subtypes of 50 kHz USVs, according to their acoustic properties. We asked whether USV production might be relevant to feeding behaviour. We recorded USVs from 14-week old adult rats during the satisfaction of a physiological need: refeeding following mild food deprivation (17 h overnight fast). We analysed a 10 min consummatory phase, preceded by a 10 min anticipatory phase, as a control for the experimental meal. Following identification of USV subtypes, we applied frequentist and Bayesian (Monte Carlo shuffling) statistical analyses to investigate the relationship between USV emission and rat behaviour. We found that it was not total USV quantity that varied in response to food consumption, but the subtype of USV produced. Most importantly we found that rats who feed tend to produce flat USVs of a frequency around 40 kHz. Beyond the previous reports of circumstantial association feeding-flat USVs, our observation directly correlate vocalisation and ingestive behaviour. Our study highlights that, in addition to quantification of the production rate, study of USV subtypes might inform us further on rat consummatory behaviour. Since this vocalisation behaviour can have a communicative purpose, those findings also illustrate nutrition studies might benefit from considering the possible social dimension of feeding behaviour.

Uncertainty surrounding the total cost of ownership, system costs, and life cycle environmental impacts means that stakeholders may lack the required information to evaluate the risks of transitioning to low-carbon fuels and powertrains. This paper assesses the life cycle costs and well-to-wheel environmental impacts of using electricity and electrofuels in Heavy Good Vehicles (HGVs) whilst considering input parameter uncertainty. The complex relationship between electricity cost, electrolyser capacity factor, CO2 capture cost and electricity emissions intensity is assessed within a Monte Carlo based framework to identify scenarios where use of electricity or electrofuels in heavy goods vehicles makes economic and environmental sense. For vehicles with a range of less than 450 km, battery electric vehicles achieve the lowest total cost of ownership for an electricity cost less than 100 €/MWh. For vehicles that require a range of up to 900 km, hydrogen fuel cell vehicles represent the lowest long-term cost of abatement. Power-to-methane and power-to-liquid scenarios become economically competitive when low-cost electricity is available at high-capacity factors and CO2 capture costs for fuel synthesis are below 100 €/tCO2; these fuels may be more applicable to decarbonise shipping and aviation. Battery electric HGVs reduce greenhouse gas emissions by 50% compared to the diesel baseline with electricity emissions of 350 gCO2e/kWh. Electricity emissions less than 35 gCO2e/kWh are required for the power-to-methane and power-to-liquid scenarios to meet EU emissions savings criteria. High vehicle capital costs and a lack of widespread refuelling infrastructure may hinder initial uptake of low-carbon fuels and powertrains for HGVs.

Featured Application: A methodological approach to assess human health risks from new technologies that have potentially harmful emissions in indoor environments. New technologies are facing the market to overcome high energy costs and efficiency regulations. Among these, the use of ozone in domestic appliances has been recently proposed for a cold-water sanitizing system for clothes or even a no-water cycle to refresh clothes. Ozone is a contaminant in indoor environments and its toxicity is mainly due to the strong oxidizing action on cellular components that can reduce lung function and increase respiratory symptoms. This study illustrates a risk assessment for ozone emissions released from new domestic clothes washer/dryer during normal operation and in the case of a failure. Indoor ozone concentrations were measured, and a mass-balance model was applied, considering ozone released from the appliance during the no-water cycle and the outdoor ozone contribution. Monte Carlo analysis was used to derive the probability of exceeding the air quality standards established by the main international organizations for the protection of human health. This study indicated the most suitable ozone generator and the best refresh cycle to minimize health risks. This method can be generally used to assess the potential health risk for the indoor environment, due to the release of harmful emissions from household appliances. [ABSTRACT FROM AUTHOR]

The efficiency of sampling is a critical concern in Monte Carlo analysis, which is frequently used to assess the effect of the uncertainty of the input variables on the uncertainty of the model outputs. The projection pursuit multivariate transform is proposed as an easily applicable tool for improving the efficiency and quality of a sampling design in Monte Carlo analysis. The superiority of the projection pursuit multivariate transform, as a sampling technique, is demonstrated in two synthetic case studies, where the random variables are considered to be uncorrelated and correlated in low (bivariate) and high (five-variate) dimensional sampling spaces. Five sampling techniques including Monte Carlo simulation, classic Latin hypercube sampling, maximin Latin hypercube sampling, Latin hypercube sampling with multidimensional uniformity, and projection pursuit multivariate transform are employed in the simulation studies, considering cases where the sample sizes (n) are small (i.e., 10 ≤ n ≤ 100 ), medium (i.e., 100 < n ≤ 1000 ), and large (i.e., 1000 < n≤ 10,000). The results of the case studies show that the projection pursuit multivariate transform appears to yield the fewest sampling errors and the best sampling space coverage (or multidimensional uniformity), and that a significant amount of computer effort could be saved by using this technique. [ABSTRACT FROM AUTHOR]

This paper deals with design and implementation of fractional-order (FO) oscillator based upon two topologies which employ operational transconductance amplifier (OTA). The first topology is developed on a trans-admittance mode fractional-order all-pass filter (FAPF) cascaded with an trans-impedance mode integrator whereas the other one consist of a trans-admittance mode FAPF cascaded with a trans-impedance mode differentiator. The presence of OTA makes the proposed FO oscillator electronic tunable. Both topologies are verified through SPICE simulations using 180nm CMOS technology node. The ability of proposed FO oscillator, to generate very low frequency is also explored in this work. The stability analysis is done using forlocus function of MATLAB and the mathematical equations for sensitivity of the oscillation frequency with respect to various circuit parameters are also derived. Further, to ensure the robustness of the proposed circuits process-voltage-temperature (PVT) and Monte Carlo analyses have been performed. [ABSTRACT FROM AUTHOR]

Comet Interceptor (Comet-I), to be launched in 2029 as a piggyback to ESA's ARIEL mission, is aimed to perform the first fly-by of a pristine long-period comet. The mission will be composed of a main spacecraft, SC A (ESA), and two small probes to be released prior to the fly-by, SC B1 (JAXA) and SC B2 (ESA). This work analyzes the attitude performance of JAXA's 24U-sized spacecraft through the dust environment of a yet-to-be-discovered target comet. Main challenges to the mission are associated to the high levels of uncertainty and extremity of fly-by conditions: highly-active dust environment, uncertain fly-by altitude (750 ± 250 km (1σ), as of 2021), and large and unknown relative fly-by speeds (15–70 km/s). A Monte Carlo analysis is performed to characterize the effect of dust particle impacts on the attitude of SC B1, and to evaluate the likelihood of satisfying pointing and angular velocity requirements of the science camera. Analysis initially shows that particles of mass 10−8–10−5 kg represent the most relevant source of perturbation due to their transferred angular momentum and likelihood of being encountered, and saturation of reaction wheels is shown unlikely given the large fly-by speeds and short fly-by durations (20 min–2 h). More detailed analysis ultimately suggests a probability larger than 90% of satisfying science camera requirements despite the extreme, uncertain fly-by conditions, dust environment, and component inaccuracies (star tracker, gyroscopes, and reaction wheels). Results also show that upgrading the reaction wheel that is implemented along the camera line-of-sight can improve, but only marginally, attitude performance, and proper alignment of solar arrays parallel to the incoming flow of particles is shown essential to maximize probability of success. [ABSTRACT FROM AUTHOR]

The aim of this study was to provide a quantitative exposure assessment to essential elements through the consumption of canned fish in Serbia. This objective was fulfilled by analyzing content of essential elements in canned fish and by using data from a food consumption survey. Consumption survey of canned fish was designed and performed to general principles and EFSA guidelines on data collection of national food consumption. The questionnaire was performed on 1,000 respondents during 2018. Determination of copper, zinc and iron levels were performed on 454 canned fish and seafood samples divided into four groups (canned tuna, canned sardines, canned other sea fish and canned seafood) during five consecutive years (2014–2018). This study showed significant association between sex, BMI and weight and consumption patterns. Obtained average weekly consumption of canned fish confirms our assumption that consumption of canned fish is significant in Serbia. Zinc and iron were found in all 454 samples (100%), and copper in 222 samples (48.9%). The average obtained concentration in all samples were 1.268 mg kg–1 for Cu, 5.661 mg kg–1 for Zn and 9.556 mg kg–1 for Fe. The highest concentration for all three minerals were found in canned sardines (Cu — 6.49 mg kg–1, Zn — 37.2 mg kg–1 and Fe — 21.8 mg kg–1). Obtained mean exposure to intake of copper, zinc and iron from canned fish was 1.2241 μg/kg bw/day, 5.4634 μg/kg bw/day and 9.2231 μg/kg bw/day, respectively. Exposure of Serbian population to zinc, copper, and iron through consumption of canned fish is less than recommended daily reference intakes and there is no risk of reaching toxic levels by consuming fish.

Infrastructure Projects (IP) are characterised by their extreme complexity in construction and management. Lack of effective risk analysis in construction of IP can cause failure in projects delivery. This paper aims to produce specific guidelines for the efficient application of quantitative risk analysis process. The literature guided contractors into efficient quantitative risk analysis (QRA) of IP to improve contractors QRA practices in the presence of uncertainty during the construction stage. The paper reviewed the literature of QRA process in construction of IP, and then performed a full quantitative risk analysis on a case study of IP in Egypt. The study includes sewage networks, water networks, irrigation networks, and district cooling networks of Cairo festival city project. The paper used an infrastructure schedule to apply the quantitative risk analysis. The schedule included activities for the construction of sewage networks, irrigation network, water network, and district cooling network. Based on the findings, Monte Carlo analysis technique has proved to be an efficient quantitative technique in supporting project managers in allocating deviations. Whereas, sensitivity analysis technique helped in ranking the most driving activities to failure in delivery of IP. The paper added value by providing knowledge and practical guidance for contractors in improving their QRA practice and decision making during the construction of IP.

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ± 10 % variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector. • Introduced an agent-based model to simulate residential consumer enrollment in DR. • Utilized economic and noneconomic factors to identify consumer decision-making to enroll. • Introduced different enrollment contract types in DR for consumers. • Analyzed different parameters influencing consumer enrollment rates. [ABSTRACT FROM AUTHOR]

In this paper, four variants of Pehlivan–Uyarŏglu chaotic system (PUCS) have been proposed. The properties of the proposed PUCSs are examined through numerical simulations and the parameter values are obtained by observing the bifurcation diagrams for state variables. Further, the convergence/divergence of nearby orbits is investigated by noticing the evolution of Lyapunov exponents with time. It is found that the values of Lyapunov exponents are negative, zero and positive for all proposed variants thus confirming the chaoticity of the proposals. The strangeness of the proposed variants is also studied. The stability of PUCS and its proposed variants is examined using Jacobi stability analysis. A current feedback operational amplifier (CFOA) based circuit is put forward that can realize the existing PUCS and its proposed variants, by simply adjusting the component values. The proposed realization is compact (23% saving in overall component count) in comparison to its operational amplifier (OpAmp) based counterpart. The behavior of the proposed variants in time domain, frequency domain and phase space have been examined through simulations in LTspice design environment. Furthermore, the feasibility of the proposed variants is also discussed through presenting the electronic circuit implementation of two of the variants and the results obtained are in good agreement with the LTspice simulations. Monte Carlo (MC) simulations are also included to show the robustness of the proposed circuit against parameter variations. [ABSTRACT FROM AUTHOR]

US energy system development consistent with the Paris Agreement will depend in part on future fuel prices and technology costs, which are highly uncertain. Energy system optimization models (ESOMs) represent a critical tool to examine clean energy futures under different assumptions. While many approaches exist to examine future sensitivity and uncertainty in such models, most assume that uncertainty is resolved prior to the model run. Policy makers, however, must take action before uncertainty is resolved. Robust optimization represents a method that explicitly considers future uncertainty within a single model run, yielding a near-term hedging strategy that is robust to uncertainty. This work focuses on extending and applying robust optimization methods to Temoa, an open source ESOM, to derive insights about low carbon pathways in the United States. A robust strategy that explicitly considers future uncertainty has expected savings in total system cost of 12% and an 8% reduction in the standard deviation of expected costs relative to a strategy that ignores uncertainty. The robust technology deployment strategy also entails more diversified technology mixes across the energy sectors modeled.

An accurate and efficient noise-based simulation technique for predicting the impact of device-parameter variability on the DC statistical behavior of integrated circuits is presented. The proposed method is validated on a source follower, a diode-load inverter and a current mirror based on organic thin-film transistors. Taking advantage of the standard noise analysis of a circuit, after translating the statistical variation of the electrical parameters of the transistors into equivalent-noise circuit components, the proposed technique yields results identical to those obtained from a Monte Carlo simulation, but in a significantly shorter amount of time.

This paper presents an innovative yield-aware synthesis strategy based on a hierarchical bottom-up methodology that uses a multiobjective evolutionary optimization algorithm to design a complete radiofrequency integrated circuit from the passive component level up to the system level. Within it, performances’ calculation aims for the highest possible accuracy. A surrogate model calculates the performances for the inductive devices, with accuracy comparable to full electromagnetic simulation; and, an electrical simulator calculates circuit- and system-level performances. Yield is calculated using Monte-Carlo (MC) analysis with the foundry-provided models without any model approximation. The computation of the circuit yield throughout the hierarchy is estimated employing parallelism and reducing the number of simulations by performing MC analysis only to a reduced number of candidate solutions, alleviating the computational requirements during the optimization. The yield of the elements not accurately evaluated is assigned using their degree of similitude to the simulated solutions. The result is a novel synthesis methodology that reduces the total optimization time compared to a complete MC yield-aware optimization. Ultimately, the methodology proposed in this work is compared against other methodologies that do not consider yield throughout the system’s complete hierarchy, demonstrating that it is necessary to consider it over the entire hierarchy to achieve robust optimal designs.

Component damage and failure in sensor or actuator will cause system faults, resulting in reduced or lost performance. This topic has become an interesting and challenging issue that if we know the effect of faults on variables, control schemes can be designed to reduce the effect of faults on the system. Therefore, the quantification of the effect of the faults is very valuable in improving the performance of wind turbines. In this paper, the effect of some important faults is investigated in the three parts: rotor, drive, and power on the output power and the measured variables of wind turbines in the partial and full load regions using the Monte Carlo method. Then, the severity of these faults on the output power is ranked in the two regions by importance factor (IF). Also, due to the fact that some faults cause structural stress due to severe changes in the angle of the blades or their asymmetry, so the stresses caused by faults are discussed. The results show that these faults severity on the output power and the measured variables of wind turbines is different in the two regions. Our conclusion provides an opportunity for wind power plant operators and investors to improve plant performance and scheduled repairs by knowing the effect of faults on wind turbine variables. Knowing effect of the faults or the severity of each of them can increase the operating time of the wind turbine and reduce costs. [ABSTRACT FROM AUTHOR]

This case illustrates capital budgeting in a service industry context. Three features should make this case attractive to instructors. First, the firm's rationing of capital means that students must select one investment among competing investment alternatives. Second, the project involves renovation of an existing hotel. Most cases analyze a business expansion by estimating the net present value of a single series of cash flows (i.e., either future cash flows occur or do not occur). In this case, students model cash flows if the project is accepted, comparing those cash flows to a model of cash flows if the hotel continues without renovation. Third, we introduce Monte Carlo analysis, which is an advanced technique for assessing uncertainty. The extensive data students use in this case are from an actual hotel chain's project database. The case has been used in undergraduate and graduate managerial accounting classes. Data Availability: Available as downloadable supplemental material files. [ABSTRACT FROM AUTHOR]

Due to excellent characteristics of specific stiffness and thermal stability, silicon carbide-based (SiC) material is commonly selected to construct large-scale lightweight mirror. In general, the fabrication process of SiC mirror is similar to the casting process. The blank error of SiC mirror is 0~1 mm. Due to the high hardness of SiC, only the mirror surface and some positioning surface will be milled. The mirror surface accuracy will be degraded due to the fact that the blank error can cause significant changes in weight distribution. In this paper, Monte Carlo analysis is firstly performed to examine the blank error on gravity center, stiffness and mirror accuracy of a SiC mirror. It is found that according to the designed mount location, the amount of degradation is more than 2.5 nm of which the probability is 40.3%. It is known that the error of gravity center can be compensated by optimizing the axial mount location. Then inverse modeling and testing of gravity center for the SiC mirror is carried out in order to determine the optimal axial mount location. Based on the proposed method, the mirror degradation introduced by the blank error has been eliminated to the greatest extend. [ABSTRACT FROM AUTHOR]

Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches. Plain Language Summary: Simulation models are often used to explore future development pathways and their impacts on energy, emissions, economies and the environment. This requires making assumptions about various socio‐economic conditions, such as how fast populations and economies will grow, the cost of technology options, or the amount of fossil fuels available. Different assumptions have significant impacts on model results, yet analyses typically only test a few alternatives. Here, we develop and use probability distributions to capture this uncertainty. We draw samples from these distributions, run an energy‐economic model hundreds of times, and quantify the resulting uncertainty in model outcomes, providing insight into their likelihood. We focus on results related to emissions and output from different economic sectors, as well as energy and electricity technologies. We also apply approaches to find scenarios of interest from within the database of scenarios. We find that many patterns of energy and technology development are possible under a given long‐term environmental pathway (such as a 2C scenario) or a given economic outcome (such as high or low GDP). This approach can help identify biases in perceptions of what "needs" to happen to achieve certain outcomes, and shows that there are many pathways to a successful energy transition. Key Points: Traditional uncertainty quantification approaches can be combined with scenario discovery techniques to provide new insightsMany patterns of energy and technology development are possible under a given long‐term environmental pathwayLong‐term temperature targets have a much larger impact on sectoral emissions intensities than on sectoral output through 2050 [ABSTRACT FROM AUTHOR]

Abstract Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

This study aimed to investigate the acute and chronic hazard quotients of chlorpyrifos and diazinon for tomatoes in preharvest and postharvest conditions, and to evaluate the relationship between the main variables, including temperature and contact time and pesticide dissipation at different conditions using response surface methodology for the first time. The qualification analyses were conducted by a gas chromatography-tandem mass spectrometry. The Monte Carlo simulation technique was utilized to evaluate the variability and uncertainty and achieve more accurate results in the health risk assessment process. A quadratic model and the second-order polynomial analysis were employed to investigate the mutual effect of time and temperature on removing diazinon and chlorpyrifos. Based on findings, the chronic hazard quotient values of chlorpyrifos and diazinon residues ranged from 0.43 − 1.33 to 0.13 − 2.27 for boiling, 0.65 − 1.49 to 3.05 − 7.15 for room condition, and 0.63 − 1.92 to 3.28 − 7.47 for refrigerator condition, respectively. According to the Monte Carlo simulation, the hazard quotient and estimated daily intake values were more affected by the consumption rate, pesticide concentration, and body weight. The results of response surface methodology showed that the effect of temperature variations on the dissipation of both pesticides was more than that of contact time variations. [ABSTRACT FROM AUTHOR]

Copyright of Informacije MIDEM: Journal of Microelectronics, Electronic Components & Materials is the property of MIDEM Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)