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Introduction: The development and optimization of novel diagnostic imaging prototypes heavily rely on experimental work. In radiology, this experimental work involves the use of phantoms. When testing novel techniques to demonstrate their advantages, anthropomorphic phantoms are utilized. The aim of this study was to investigate seven materials for 3D printing to replicate the radiological properties of breast lesions. Methods: To achieve this objective, we utilized three fused filament fabrication materials, namely, polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PET-G), along with resins such as White v4 Resin, Flexible 80A v1 Resin, Model v2 Resin, and Wax40 v1 Resin, to 3D print seven irregularly shaped lesions. These lesions were used to prepare a set of seven physical phantoms, each filled with either water or liquid paraffin, and one of the printed lesions. The phantoms were then scanned using a mammography unit at 28 kVp. Additionally, six computational breast phantoms, replicating the shape of the physical phantoms, were generated. These computational models were assigned the attenuating properties of various breast tissues, including glandular tissue, adipose tissue, skin, and lesions. Mammography images were generated under the same experimental conditions as the physical scans. Both the simulated and experimental images were evaluated for their contrast-to-noise ratio (CNR) and contrast (C). Discussion: The results indicated that the studied resins and filament-based materials are all suitable for replicating breast lesions. Among these, PLA and White v4 Resin exhibited the densest formations and can effectively approximate breast lesions that are slightly less attenuating than glandular tissue, while ABS and Flexible 80A v1 Resin were the least dense and can represent fat-containing breast lesions. The remaining materials provided good approximations for malignant lesions. These materials can be utilized for constructing phantoms for experimental work, rendering the model a valuable tool for optimizing mammography protocols, ensuring quality control of mammography X-ray equipment, and aiding in the diagnosis and assessment of breast cancer. [ABSTRACT FROM AUTHOR]

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP. [ABSTRACT FROM AUTHOR]

In this work, for the first time, the catalytic metal gate (CMG) based nanosheet Field Effect Transistor (NSFET) and nanosheet Tunnel Field Effect Transistor (NSTFET) are proposed for nano‐scale device dimensions, and the different design aspects of catalytic metal gate (CMG)‐based transduction for Hydrogen (H2) sensing are extensively investigated using numerical device simulation. The influence of applied biasing conditions and structural parameter specifications on the sensing performance of CMG‐NSFET and CMG‐NSTFET are methodically analyzed from device electrostatics and carrier transport mechanisms. Furthermore, the relative maximum sensitivity variations with H2‐partial pressure, ambient temperature, and the presence of ambient Oxygen are comprehensively studied. The study reveals that compared to CMG‐NSFET, the CMG‐NSTFET demonstrates a high immunity against bias and doping variations, with a notably higher (> 50%) sensitivity within low H2 partial pressures (10−15 – 10−10 Torr). Next, the sensing performance of CMG‐NSTFET is systematically optimized through a band‐gap and gate‐stack engineering approach, leading to a 180% to 650% sensitivity improvement from lower (10−15 Torr) to higher (10−5 Torr) range of H2 partial pressure. Finally, the performance of optimized CMG‐NSFET and CMG‐NSTFET are extensively benchmarked against other reported nanostructured CMG‐FET and TFET‐based H2 gas sensors, exhibiting a notably higher sensitivity in the proposed sensors. [ABSTRACT FROM AUTHOR]

IntroductionThe development and optimization of novel diagnostic imaging prototypes heavily rely on experimental work. In radiology, this experimental work involves the use of phantoms. When testing novel techniques to demonstrate their advantages, anthropomorphic phantoms are utilized. The aim of this study was to investigate seven materials for 3D printing to replicate the radiological properties of breast lesions.MethodsTo achieve this objective, we utilized three fused filament fabrication materials, namely, polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PET-G), along with resins such as White v4 Resin, Flexible 80A v1 Resin, Model v2 Resin, and Wax40 v1 Resin, to 3D print seven irregularly shaped lesions. These lesions were used to prepare a set of seven physical phantoms, each filled with either water or liquid paraffin, and one of the printed lesions. The phantoms were then scanned using a mammography unit at 28 kVp. Additionally, six computational breast phantoms, replicating the shape of the physical phantoms, were generated. These computational models were assigned the attenuating properties of various breast tissues, including glandular tissue, adipose tissue, skin, and lesions. Mammography images were generated under the same experimental conditions as the physical scans. Both the simulated and experimental images were evaluated for their contrast-to-noise ratio (CNR) and contrast (C).DiscussionThe results indicated that the studied resins and filament-based materials are all suitable for replicating breast lesions. Among these, PLA and White v4 Resin exhibited the densest formations and can effectively approximate breast lesions that are slightly less attenuating than glandular tissue, while ABS and Flexible 80A v1 Resin were the least dense and can represent fat-containing breast lesions. The remaining materials provided good approximations for malignant lesions. These materials can be utilized for constructing phantoms for experimental work, rendering the model a valuable tool for optimizing mammography protocols, ensuring quality control of mammography X-ray equipment, and aiding in the diagnosis and assessment of breast cancer.

The article discusses the concept of Phase 0 microdosing in drug development, which involves testing compounds at subtherapeutic doses in humans to make early decisions. The article highlights the potential benefits of Phase 0 microdosing, such as obtaining early human data, reducing animal experiments, and assessing safety or efficacy biomarkers. It also introduces the concept of intratarget microdosing (ITM), which expands the scope and impact of Phase 0 studies. The article includes descriptions of a technology platform for intratumoral microdosing of oncology drugs and a physiologically based pharmacokinetic model to assess the use of ITM. The authors emphasize the need for further exploration and development of Phase 0 microdosing and its potential to reduce the need for animal testing and improve the drug development process. [Extracted from the article]

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

In the wild, gliding birds dodge obstacles or predators by folding and twisting their wings swiftly to perform a rapid roll. Accordingly, the authors strive to explore the feasibility of improving the roll rate of drones through this bird‐inspired morphing method, by using the asymmetric sweepback of wings to simulate the contraction of birds' wings and the deflection of the aileron to imitate wing torsion. Moreover, the effects of wing morphing on the centroid, inertia matrix, and aerodynamic characteristics of the drone are explored herein, and a nonlinear dynamic model is established. Furthermore, a novel cooperative strategy that combines wing morphing with aileron deflection for roll control is introduced, and a flight controller based on this cooperative strategy is developed. Finally, the superiority of the cooperative strategy and the accuracy of the dynamic modeling have been validated in the outdoor flights of the morphing wing drone. [ABSTRACT FROM AUTHOR]

Metachronal motions are ubiquitous in terrestrial and aquatic organisms and have attracted substantial attention in engineering for their potential applications. Hard‐magnetic soft materials are shown to provide new opportunities for metachronal wave‐modulated robotic locomotion by multi‐agent active morphing in response to external magnetic fields. However, the design and optimization of such magnetic soft robots can be complex, and the fabrication and magnetization processes are often delicate and time‐consuming. Herein, a computational model is developed that integrates granular models into a magnetic–lattice model, both of which are implemented in the highly efficient parallel computing platform large‐scale atomic/molecular massively parallel simulator (LAMMPS). The simulations accurately reproduce the deformation of single cilium, the metachronal wave motion of multiple cilia, and the crawling and rolling locomotion of magnetic cilia soft robots. Furthermore, the simulations provide insight into the spatial and temporal variation of friction forces and trajectories of cilia tips. The results contribute to the understanding of metachronal wave‐modulated locomotion and potential applications in the field of soft robotics and biomimetic engineering. The developed model also provides a versatile computational framework for simulating the movement of magnetic soft robots in realistic environments and has the potential to guide the design, optimization, and customization of these systems. [ABSTRACT FROM AUTHOR]

In the wild, gliding birds dodge obstacles or predators by folding and twisting their wings swiftly to perform a rapid roll. Accordingly, the authors strive to explore the feasibility of improving the roll rate of drones through this bird‐inspired morphing method, by using the asymmetric sweepback of wings to simulate the contraction of birds’ wings and the deflection of the aileron to imitate wing torsion. Moreover, the effects of wing morphing on the centroid, inertia matrix, and aerodynamic characteristics of the drone are explored herein, and a nonlinear dynamic model is established. Furthermore, a novel cooperative strategy that combines wing morphing with aileron deflection for roll control is introduced, and a flight controller based on this cooperative strategy is developed. Finally, the superiority of the cooperative strategy and the accuracy of the dynamic modeling have been validated in the outdoor flights of the morphing wing drone.

Metachronal motions are ubiquitous in terrestrial and aquatic organisms and have attracted substantial attention in engineering for their potential applications. Hard‐magnetic soft materials are shown to provide new opportunities for metachronal wave‐modulated robotic locomotion by multi‐agent active morphing in response to external magnetic fields. However, the design and optimization of such magnetic soft robots can be complex, and the fabrication and magnetization processes are often delicate and time‐consuming. Herein, a computational model is developed that integrates granular models into a magnetic–lattice model, both of which are implemented in the highly efficient parallel computing platform large‐scale atomic/molecular massively parallel simulator (LAMMPS). The simulations accurately reproduce the deformation of single cilium, the metachronal wave motion of multiple cilia, and the crawling and rolling locomotion of magnetic cilia soft robots. Furthermore, the simulations provide insight into the spatial and temporal variation of friction forces and trajectories of cilia tips. The results contribute to the understanding of metachronal wave‐modulated locomotion and potential applications in the field of soft robotics and biomimetic engineering. The developed model also provides a versatile computational framework for simulating the movement of magnetic soft robots in realistic environments and has the potential to guide the design, optimization, and customization of these systems.

The performance of a methanol-fed protonic ceramic fuel cell (PCFC)/gas turbine (GT) hybrid system is investigated in this work. To build the system, Thermolib software is employed with input parameters obtained from references. Effects of air stoichiometry on system performance are analyzed. Results show that, as air stoichiometry is increased, the reformer temperature and CO concentration decrease, while H 2 concentration increases. High air stoichiometry decreases PCFC temperature and performance. GT output power increases with increasing air flow. But, the power consumption by compressor also increases. Overall, to achieve higher system efficiency for this hybrid system, the optimum values of air stoichiometry are from 2.7 to 2.9. An additional heat recovery steam generator can also improve the overall system efficiency from 66.5% to 71.7%. This work helps in understanding the modeling and optimum functioning parameters of high power generation systems. • A PCFC/GT hybrid system fed by methanol is proposed. • A parametric study of air stoichiometry is investigated to obtain optimal values. • Maximum hybrid system efficiency of 71.7% can be obtained. • Higher amount of air increases gas turbine power output. • The optimal air stoichiometry is 2.7–2.9. [ABSTRACT FROM AUTHOR]

Several rural regions located in Africa are experiencing recurrent and even permanent problems in terms of energy production, supply, and distribution to citizens. This study was conducted to investigate the relevance of the use of a new solar technology that is gradually responding in Europe and in industrialized countries. It is about the use of hybrid photovoltaic thermal (PV/T) solar panels that co-produce electricity and hot water for local use. Furthermore, in Africa, local use of solar energy can provide a share in the energy mix. This work is motivated by the lack of studies on these hybrid solar panels in tropical climates. Hence, the paper examines the potential for integration of these systems in small households. A complete PV/T system consisting of solar panels, pump, storage tank, batteries, and controllers was tested and calibrated by using the TRNSYS simulation tool. A comparative study could thus be carried out for the performance of PV/T in a tropical climate (case of the city of Koua in the Comoros) to its performance in Mediterranean and continental climates (Marseille in the south and Longwy in the northeast of France). The results quantify the performance of the PV/T in the three climates and show that the performance in the town of Koua is 44% to 54% higher than in European climates. It can be concluded from this study that the Comorian market and more generally the sub-Saharan market for PVT systems has a good potential for development. [ABSTRACT FROM AUTHOR]

Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.

Protonic ceramic fuel cells (PCFCs) offer direct and efficient conversion of hydrocarbon fuels into electricity. In this study, three internal-reforming (IR)-PCFC/gas turbine (GT) hybrid systems are proposed and analyzed to achieve higher system efficiency. High-quality heat from GT in system 1 and system 2 is supplied to anode and cathode preheaters, respectively, whereas in system 3, the heat is simultaneously split into both preheaters. Effects of air flow rate, fuel utilization factor (Uf), and steam to carbon ratio (S/C) are also investigated. It is found that the best system design can be achieved by effectively utilizing GT exhaust heat for both electrode preheaters, as indicated in system 3. The maximum energy system efficiency obtained among the hybrid systems analyzed in this study is 71% with total exergy destruction of 686.7 kW. When fueled by methane, the hybrid system can achieve energy and exergy efficiencies of 71% and 77%, respectively, with 0.85 Uf. On the other hand, propane-fueled systems can achieve energy and exergy efficiencies of 68% and 75%, respectively. As S/C increases from 2 to 7, system efficiency decreases from 71% to 50%. When system 3 is fueled with butane or propane, system efficiency is only 3% lower than that fueled by methane. [ABSTRACT FROM AUTHOR]

In the last 20 years, modeling and simulations (M&S) have gained increased attention in pharmaceutical sciences. International industry and world-reference agencies have used M&S to make cost-efficient decisions through the model-informed drug development (MIDD) framework. Modeling tools include biopredictive dissolution models, physiologically based pharmacokinetic models (PBPK), biopharmaceutic models (PBBM), and virtual bioequivalence, among many others. Regulatorily, health agencies are becoming more and more open to accept the use of M&S to support regulatory applications, including setting dissolution specifications, quality-by-design (QbD), postapproval changes (SUPAC), etc. Nonetheless, the potential of M&S has been only barely explored in Latin America (Latam) across different actors: industry, regulatory agencies, and even academia. In this manuscript, we discuss the challenges and opportunities for implementing M&S approaches in Latam. Perspectives of regional experts were shared in a workshop. Attendance (professionals from industry, regulator, academia, and clinicians) also shared their views via survey. The rational development of bioequivalent generics was considered the main opportunity for M&S in regional market, particularly the use of PBPK and PBBM. Nonetheless, a critical mass of modeling scientists is needed before Latin American industry and regulators can actually benefit from M&S. Collaborations (e.g., Academia-Industry and Academia-Regulatory) may be a path to develop applied research projects and train the future modelers. Reaching that critical mass, scientists from industry may apply modeling across generic drug development process and life cycle, while regulatory scientists may issue guidelines in local language to support regional industry. Only at that stage could the full potential of MIDD be reached in Latin American generic markets.

In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed for rain rates ranging from light to moderate rainfall, they result in misleading estimations of intense or strong rainfall rates. In this paper, based on extensive data gathered during the AMMA and Megha-Tropiques data campaigns, we provided a way to improve the estimation of intense rainfall rates from radar measurements. To this end, we designed a formulation of the QPE algorithm that accounts for the co-dependency between radar observables and rainfall rate using copula simulation synthetic datasets and using the quantile regression features for a more complete picture of covariate effects. The results show a clear improvement in heavy rainfall retrieval from radar data using copula-based R(KDP) algorithms derived from a realistic simulated dataset. For a better performance, Gaussian copula-derived algorithms require a 0.8 percentile distribution to be considered. Conversely, lower percentiles are better for Student's, Gumbel and HRT copula estimators when retrieving heavy rainfall rates (R > 30). This highlights the need to investigate the entire conditional distribution to determine the performance of radar rainfall estimators. [ABSTRACT FROM AUTHOR]

The coupled tank system is the most widely used sub-component in chemical process industries. Fluid mixing is a major step in chemical processes that alters the material properties and cost. Fluid flow and its level regulation between several tanks are important control problems. As the first step, this paper addresses the level regulation problem using classical integer order proportional, derivative, integral (PID), fractional order PID controllers. As a second step, model-based robust fractional order controllers are derived using sliding mode approach in order to achieve the desired response, parameters of the proposed controllers are tuned using genetic algorithm. Finally, system performance under all variants of control schemes has been tested using numerical simulations. [ABSTRACT FROM AUTHOR]

TV‐46000 is a long‐acting subcutaneous antipsychotic that uses a novel copolymer drug delivery technology in combination with a well‐characterized molecule, risperidone, that is in clinical development as a treatment for schizophrenia. A population pharmacokinetic (PPK) modeling and simulation approach was implemented to identify TV‐46000 doses and dosing schedules for clinical development that would provide the best balance between clinical efficacy and safety. The PPK model was created by applying pharmacokinetic data from a phase 1 study of 97 patients with a diagnosis of schizophrenia or schizoaffective disorder who received either single or repeated doses of TV‐46000. The PPK model was used to characterize the complex release profile of the total active moiety (TAM; the sum of the risperidone and 9‐OH risperidone concentrations) concentration following subcutaneous injections of TV‐46000. The PK profile was best described by a double Weibull function of the in vivo release rate and by a 2‐compartment disposition and elimination model. Simulations were performed to determine TV‐46000 doses and dosing schedules that maintained a median profile of TAM concentrations similar to published TAM exposure following oral risperidone doses that have been correlated to a 40% to 80% dopamine‐D2 receptor occupancy therapeutic window. The simulations showed that therapeutic dose ranges for TV‐46000 are 50 to 125 mg for once‐monthly and 100 to 250 mg for the once every 2 months regimens. This PPK model provided a basis for prediction of patient‐specific exposure and dopamine‐D2 receptor occupancy estimates to support further clinical development and dose selection for the phase 3 studies. [ABSTRACT FROM AUTHOR]

This paper simulates Ca2+ transmembrane transport through voltage-gated Ca2+ channels in response to terahertz electromagnetic irradiation. The active transport of Ca2+ ions is taken into considerations in the Ca2+ transport. Temperature variations due to terahertz electromagnetic loss in physiological medium are simulated. The electromagnetic interaction between terahertz fields and physiological mobile ions at the cellular level is deduced from relativistic electrodynamics. It shows that effects of 0.1 ~ 3 THz electromagnetic fields on cell mobile ions are primarily due to effects of electric fields, and effects of magnetic fields at the cellular level are insignificant. In addition, numerical simulation reveals that terahertz irradiation causes vibration of membrane potential, which is able to activate voltage-gated Ca2+ channels. Besides, bioeffects of terahertz frequency, irradiation duration and electric intensity on the increment of intracellular Ca2+ concentration due to activation of voltage-gated Ca2+ channels are revealed. Meanwhile, numerical results show that temperature rises are inconsequential in the case of different irradiation parameters, indicating the non-thermal bioeffects of voltage-gated Ca2+ transmembrane transport due to terahertz irradiation. Furthermore, the results also reveal that thermal bioeffect can be significant if the irradiation duration is raised long enough for high-dose terahertz irradiation. The numerical simulations lay the basis for understanding the bioeffects of terahertz irradiation on Ca2+ transmembrane transport and pave the way for further exploration in modulation of intracellular Ca2+ concentration with terahertz electromagnetic wave.

The voltage-gated calcium influxes modulated with terahertz Gaussian pulse is presented in this paper, and temperature rise is evaluated to monitor the terahertz thermal effects involved in the modulation. From the numerical results, inhibition effect on voltage-gated calcium influx because of the loss of the influx from electric drift is transparently revealed in the modulation with terahertz Gaussian pulse. And the inhibition effect becomes significant reflected by considerable reduction in voltage-gated calcium influx as the irradiated amplitude is raised intense enough. Compared to the terahertz sinusoidal wave, Gaussian pulse reduces the inhibition effect dramatically. The decrease in the increment of intracellular calcium concentration with the irradiated amplitude due to the inhibition effect is substantially relieved by the modulation of the calcium influx with terahertz Gaussian pulse. The thermal analyses reveal that the modulation is non-thermal effect. Meanwhile, to raise the intracellular calcium concentration to a same amount by means of the voltage-gated calcium influx, application of terahertz Gaussian pulse induces much less concurrent temperature rise than application of terahertz sinusoidal wave of the same amplitude that is intense enough to cause the inhibition effect. Additionally, terahertz Gaussian pulse irradiation remains the dependence of the increment of the calcium concentration on the irradiated time duration and frequency in terahertz sinusoidal wave irradiation. These numerical results lay the theoretical basis for the modulation of voltage-gated calcium fluxes with terahertz Gaussian pulse and for further potential clinical applications of terahertz Gaussian pulse.

Several rural regions located in Africa are experiencing recurrent and even permanent problems in terms of energy production, supply, and distribution to citizens. This study was conducted to investigate the relevance of the use of a new solar technology that is gradually responding in Europe and in industrialized countries. It is about the use of hybrid photovoltaic thermal (PV/T) solar panels that co-produce electricity and hot water for local use. Furthermore, in Africa, local use of solar energy can provide a share in the energy mix. This work is motivated by the lack of studies on these hybrid solar panels in tropical climates. Hence, the paper examines the potential for integration of these systems in small households. A complete PV/T system consisting of solar panels, pump, storage tank, batteries, and controllers was tested and calibrated by using the TRNSYS simulation tool. A comparative study could thus be carried out for the performance of PV/T in a tropical climate (case of the city of Koua in the Comoros) to its performance in Mediterranean and continental climates (Marseille in the south and Longwy in the northeast of France). The results quantify the performance of the PV/T in the three climates and show that the performance in the town of Koua is 44% to 54% higher than in European climates. It can be concluded from this study that the Comorian market and more generally the sub-Saharan market for PVT systems has a good potential for development.

In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed for rain rates ranging from light to moderate rainfall, they result in misleading estimations of intense or strong rainfall rates. In this paper, based on extensive data gathered during the AMMA and Megha-Tropiques data campaigns, we provided a way to improve the estimation of intense rainfall rates from radar measurements. To this end, we designed a formulation of the QPE algorithm that accounts for the co-dependency between radar observables and rainfall rate using copula simulation synthetic datasets and using the quantile regression features for a more complete picture of covariate effects. The results show a clear improvement in heavy rainfall retrieval from radar data using copula-based R(KDP) algorithms derived from a realistic simulated dataset. For a better performance, Gaussian copula-derived algorithms require a 0.8 percentile distribution to be considered. Conversely, lower percentiles are better for Student’s, Gumbel and HRT copula estimators when retrieving heavy rainfall rates (R > 30). This highlights the need to investigate the entire conditional distribution to determine the performance of radar rainfall estimators.

The overall number of railcars recorded in the North American railcar fleet from 2010 to 2015 increased about 5%; the number of all 130 tonne (286,000 lb) gross weight railcars (heavy axle load (HAL) railcars) increased 19%. The increase in shipments in short railcars increases the loading on railway bridges, especially the 12.8-m railcars, commonly used to ship sand and cement, which is approximately a 25% increase in load per unit length compared to 16.2-m coal cars. Significant differences between maximum effects of shorter railcars and common 16.2-m railcars were predicted in analysis for bridge spans longer than 18.3 m. The differences were more prominent on spans 24.4 m and longer. This study presents analytical and measured effects of freight railcars on a two-span truss bridge, with spans of 61 m and 33.5 m, and a 35-m riveted steel deck plate girder (DPG) bridge. The investigation confirmed that short railcars cause higher load effects on main bridge components: the 35-m riveted steel DPG has 28% higher stresses at mid-span, while in the truss, the difference in stresses depends on the location of the member and ranges from 15 to 35%. [ABSTRACT FROM AUTHOR]

Validation of the Cabrera-Mott low-temperature oxidation model is proposed to better establish a procedure for the passivation of aluminum nanoparticles after production. The powder, generated in an inductively coupled plasma reactor and characterized by a specific surface of around 40 m2/g, is sampled prior to passivation. Controlled oxidation of the powder is then performed in a TGA/DSC apparatus using different oxygen concentrations. The mass increase due to the oxidation of aluminum to amorphous alumina is mathematically converted into an oxide thickness evolution. The experimental results are then compared with the Cabrera-Mott model and the values of parameters such as the distance between energy barriers, the number of ions per unit area, and the Mott potential are set. In addition, the latter two parameters are varied with the oxygen concentration, following an evolution similar to that of a Langmuir isotherm. This paper then proposes the first validation of a low-temperature oxidation model for aluminum nanoparticles. [ABSTRACT FROM AUTHOR]

OBJECTIVES Aprepitant is effective for the prevention of chemotherapy-induced or postoperative nausea and vomiting (CINV/PONV). The aim of this study was to develop a population pharmacokinetic (PK) model of aprepitant in pediatric patients and to support dosing recommendations for oral aprepitant in pediatric patients at risk of CINV. METHODS A population PK model was constructed based on data from 3 clinical studies in which children (6 months to 12 years) and adolescents (12-19 years) were treated with a 3-day regimen of oral aprepitant (capsules or suspension), with or without intravenous fosaprepitant on day 1 (CINV), or a single dose of oral aprepitant (capsules or suspension; PONV). Nonlinear mixed-effects modeling was used for model development, and a stepwise covariate search determined factors influencing PK parameters. Simulations were performed to guide final dosing strategies of aprepitant in pediatric patients. RESULTS The analysis included 1326 aprepitant plasma concentrations from 147 patients. Aprepitant PK was described by a 2-compartment model with linear elimination and first-order absorption, with allometric scaling for central and peripheral clearance and volume using body weight, and a cytochrome P450 3A4 maturation component for the effect of ontogeny on systemic clearance. Simulations established that application of a weight-based (for those <12 years) and fixed-dose (for those 12-17 years) dosing regimen results in comparable exposures to those observed in adults. CONCLUSIONS The developed population PK model adequately described aprepitant PK across a broad pediatric population, justifying fixed (adult) dosing for adolescents and weight-based dosing of oral aprepitant for children. [ABSTRACT FROM AUTHOR]

Duplex structural stainless steels (DP) are highly corrosion-resistant with austenite (γ)-ferrite (δ) compositions and processing routes. These DP steels are used in chemical, petrochemical, marine, power generation, and nuclear industries due to their unique properties. Experimental data of 2D slices obtained from optical micrography were used in a multilevel finite element analysis to investigate the interface stresses between the two phases of DP material. Level #1 (macro-level) finite element analysis involves homogenizing the 2D slices based on their representative constituent properties to identify high-stress regions. Level #2 (micro-level) finite element analysis was conducted with representative austenite/ferrite microstructure identified from level #1 analysis. The results from finite element simulations from both macro- and micro-levels revealed that interface stresses are affected by the phase's microstructure distributions. Micro-level analysis revealed maximum interface stress is much higher than macro-level analysis. It was also found that stress localization occurred at the ferrite/austenite interfaces based on their distributions. [ABSTRACT FROM AUTHOR]

Models for the automated analysis and simulation of the complex phenomena observable in built environment crowded by pedestrians have been studied for over thirty years. Nonetheless, one of the commonly agreed upon rules guiding regulation of distance among pedestrian, i.e. proxemics, was defined and discussed in static settings, whereas scenarios of interest generally deal with individual and collective movements in crowds. The present paper presents a systemic perspective on the research aimed at defining a dynamic form of proxemics. The paper firstly reports the results of an experiment focused on proxemics and pedestrians personal space, as the hidden dimension of human spatial behavior in crowded environments. We propose a representation of personal space through discrete potentials and an innovative crowding estimation method (i.e. Cumulative Mean Crowding), going beyond simple perceived density evaluation. The experimental setting is introduced and applied to appraise the potential impact of this novel pedestrian perception mechanism on innovative simulation models. [ABSTRACT FROM AUTHOR]

This paper presents a novel minimum seeking algorithm referred to as the Hunger Games Search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses.

Single-Photon Avalanche Diodes (SPAD) in Complementary Metal-Oxide Semiconductor (CMOS) technology are potential candidates for future “Light Detection and Ranging” (Lidar) space systems. Among the SPAD performance parameters, the Photon Detection Probability (PDP) is one of the principal parameters. Indeed, this parameter is used to evaluate the SPAD sensitivity, which directly affects the laser power or the telescope diameter of space-borne Lidars. In this work, we developed a model and a simulation method to predict accurately the PDP of CMOS SPAD, based on a combination of measurements to acquire the CMOS process doping profile, Technology Computer-Aided Design (TCAD) simulations, and a Matlab routine. We compare our simulation results with a SPAD designed and processed in CMOS 180 nm technology. Our results show good agreement between PDP predictions and measurements, with a mean error around 18.5%, for wavelength between 450 and 950 nm and for a typical range of excess voltages between 15 and 30% of the breakdown voltage. Due to our SPAD architecture, the high field region is not entirely insulated from the substrate, a comparison between simulations performed with and without the substrate contribution indicates that PDP can be simulated without this latter with a moderate loss of precision, around 4.5 percentage points.

The overall number of railcars recorded in the North American railcar fleet from 2010 to 2015 increased about 5%; the number of all 130 tonne (286,000 lb) gross weight railcars (heavy axle load (HAL) railcars) increased 19%. The increase in shipments in short railcars increases the loading on railway bridges, especially the 12.8-m railcars, commonly used to ship sand and cement, which is approximately a 25% increase in load per unit length compared to 16.2-m coal cars. Significant differences between maximum effects of shorter railcars and common 16.2-m railcars were predicted in analysis for bridge spans longer than 18.3 m. The differences were more prominent on spans 24.4 m and longer. This study presents analytical and measured effects of freight railcars on a two-span truss bridge, with spans of 61 m and 33.5 m, and a 35-m riveted steel deck plate girder (DPG) bridge. The investigation confirmed that short railcars cause higher load effects on main bridge components: the 35-m riveted steel DPG has 28% higher stresses at mid-span, while in the truss, the difference in stresses depends on the location of the member and ranges from 15 to 35%.

Over the last few decades there has been a paradigm shift in orphan drug research and development. The development of the regulatory framework, establishment of rare disease global networks that support drug developments, and advances in technology, has resulted in tremendous growth in orphan drug development. Nevertheless, several challenges during orphan drug development such as economic constraints; insufficient clinical information; fewer patients and thus inadequate power; etc. still exist. While the standard regulatory requirements for drug approval stays the same, applications of scientific judgment and regulatory flexibility is significantly important to help meeting some of the immense unmet medical need in rare diseases. Clinical pharmacology presents a vital role in accelerating orphan drug development and overcoming some of these challenges. This review highlights the critical contributions of clinical pharmacology in orphan drug development; for example, dose finding, optimizing clinical trial design, indication expansion, and population extrapolation. Examples of such applications are reviewed in this article. [ABSTRACT FROM AUTHOR]

There is currently no validated full-body lifting model publicly available on the OpenSim modelling platform to estimate spinal loads during lifting. In this study, the existing full-body-lumbar-spine model was adapted and validated for lifting motions to produce the lifting full-body model. Back muscle activations predicted by the model closely matched the measured erector spinae activation patterns. Model estimates of intradiscal pressures and in vivo measurements were strongly correlated. The same spine loading trends were observed for model estimates and reported vertebral body implant measurements. These results demonstrate the suitability of this model to evaluate changes in lumbar loading during lifting. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, chemical and petroleum engineers give incremental attention to different techniques of chemical enhanced oil recovery (CEOR) owing to continual and scared energy consumption, and enormous trapped oil reserves after primary and secondary flooding. Hydrogels are three-dimensional crosslinked macromolecules which retain excessive water amounts. In this research, we investigate the synthesis and spectroscopic characterization of Lutensol AT 25 E -Methacrylate surfmer, then its copolymerization with acrylates monomers through free radical polymerization, which considered an effective tool to generate block copolymers with hydrophilic and hydrophobic moieties. Characterization and structure determination occurs by traditional spectroscopic techniques. Since mathematical formulation and prediction of hydrogel swelling ability has incremental consideration recently, so mathematical modeling of different rheological properties including swelling performance, post-degraded gel viscosity, gel strength, viscoelastic properties as well as shear thinning effects, salinity resistance, and long-term thermal aging was investigated at simulated harsh reservoir conditions. Modeling analysis showed that the Herschel-Bulkley model fits effectively the stress performance of the hydrogel suspensions, while the power law model fits the viscosity-shearing behavior. Dynamic properties examined by oscillatory tests indicating that hydrogel suspension exhibits viscoelastic performance since elastic modulus (G′) predominate viscous modulus (G″). On lab scale, sandpack flooding tests performed on a linear assembly at imitated reservoir environment, where the results indicate that the hydrogel able to reduce water permeability, adjust profile conformance, so increase incremental oil recovery up to 85.35% and alter rock wettability which assessed through relative permeability curves at different water saturation. On a field scale, a simulated run on actual reservoir data written by Fortran 90 visual studio carried out through UTCHEM software to evaluate hydrogel compatibility as an enhanced oil recovery candidate. Highlights • Preparation of surfmer-co-poly acrylates for profile conformance • Investigation and mathematical modeling of gel properties • Evaluation of rheological criteria at simulated reservoir conditions • Assessment of water shut-off and permeability reduction • Simulation through actual field data by applying UTCHEM simulator [ABSTRACT FROM AUTHOR]

Agent-based simulations of pedestrian crowd dynamics can support the design of transportation facilities in terms of efficiency, comfort and safety. The development of realistic models requires the acquisition of empirical evidences about human behavior. The paper reports the results of an experiment of pedestrian personal space: the area surrounding human body, linked to crowding due to spatial intrusion/restriction. We propose a discrete representation of personal space through discrete potentials and an innovative crowding estimation method (i.e. Cumulative Mean Crowding), introducing also the notion of shared personal space among group members. Simulation results are focused on the parametric estimation of pedestrians' psychological stress reaction to density. [ABSTRACT FROM AUTHOR]

Bottlenose dolphins (Tursiops truncatus) are highly versatile breath-holding predators that have adapted to a wide range of foraging niches from rivers and coastal ecosystems to deep-water oceanic habitats. Considerable research has been done to understand how bottlenose dolphins manage O2 during diving, but little information exists on other gases or how pressure affects gas exchange. Here we used a dynamic multi-compartment gas exchange model to estimate blood and tissue O2, CO2, and N2 from high-resolution dive records of two different common bottlenose dolphin ecotypes inhabiting shallow (Sarasota Bay) and deep (Bermuda) habitats. The objective was to compare potential physiological strategies used by the two populations to manage shallow and deep diving life styles. We informed the model using species-specific parameters for blood hematocrit, resting metabolic rate, and lung compliance. The model suggested that the known O2 stores were sufficient for Sarasota Bay dolphins to remain within the calculated aerobic dive limit (cADL), but insufficient for Bermuda dolphins that regularly exceeded their cADL. By adjusting the model to reflect the body composition of deep diving Bermuda dolphins, with elevated muscle mass, muscle myoglobin concentration and blood volume, the cADL increased beyond the longest dive duration, thus reflecting the necessary physiological and morphological changes to maintain their deep-diving life-style. The results indicate that cardiac output had to remain elevated during surface intervals for both ecotypes, and suggests that cardiac output has to remain elevated during shallow dives in-between deep dives to allow sufficient restoration of O2 stores for Bermuda dolphins. Our integrated modeling approach contradicts predictions from simple models, emphasizing the complex nature of physiological interactions between circulation, lung compression, and gas exchange.

Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.

Abstract Laws of spatial averaging on nanoscale for local electrostatic potential differences in ionic conductors are of key importance for the understanding of ion-transport phenomena and effects in space-charge layers. Currently, these laws are not investigated. As a consequence, the leading software packages for nanoscience do not have any competence for modeling of space-charge formation/relaxation in nanostructures with ionic hopping conductivity. So the article addresses the theme. For a layered nanostructure { X k }, formed by parallel crystallographic X k planes, an average electrostatic potential difference (< V k , k +1 >) in a non-uniform field is defined in terms of normalized sums Σ of the local works W k , k +1 for X k → X k +1 transitions of mobile ions in a field of a single point charge. At a weak external electrical influence on { X k }, the < V k , k +1 > can be easily generalized for a non-uniform field created by a set of point charges. The sums Σ W k , k +1 are calculated for small average distances (~0.6 nm) between mobile ions (as in advanced superionic conductors). Computer experiments show: (i) Σ W k , k +1 sums are independent of k -indices with the accuracy ~10%, (ii) probability density functions for local potential differences V k , k +1 are close to normal distributions and (iii) < V k , k +1 > ≈ V U k , k +1 , where V U k , k +1 is a potential difference in a uniform electrostatic field. These nanoscale laws allow manipulating by an average potential difference (< V k , k + N >) between X k and X k + N planes (N > 1), so the modeling of non-stationary ion-transport processes and energy flows in parallel combinations of nanostructures become possible. Graphical abstract Unlabelled Image Highlights • Potential differences in space-charge region as key for nanoscale electrochemistry • Solid state ionic conductors as dynamical non-linear systems • Distribution of ionic hopping currents in potential landscapes of nanostructures • Laws of nanoscale averaging of local potential differences in solid state ionics • Simple relations for average potential difference in nanoionics [ABSTRACT FROM AUTHOR]

A nonlinear transport model for single-phase gas flow in tight porous media is developed. The model incorporates many important physical processes that occur in such porous systems: continuous flow, transition flow, slip flow, Knudsen diffusion, adsorption and desorption into and out of the rock material, and a correction for high flow rates. This produces a nonlinear advection-diffusion type of partial differential equation with pressure-dependent model parameters and associated compressibility coefficients, and highly nonlinear apparent convective flux (velocity) and apparent diffusivity. A key finding is that all model parameters should be kept pressure dependent for the best results. An application is to the determination of rock properties, such as porosity and permeability, by history matching of the simulation results to data from pressure-pulse decay tests in a rock core sample (Pong et al. in ASME Fluids Eng Div 197:51-56, 1994). [ABSTRACT FROM AUTHOR]

Modeling and simulations were used to support body weight-based dose selection for eslicarbazepine acetate (ESL) in pediatric subjects aged 4-17 years with partial-onset seizures. A one-compartment pediatric population pharmacokinetic model with formulation-specific first-order absorption, first-order elimination, and weight-based allometric scaling of clearance and distribution volume was developed with PK data from subjects 2-18 years of age treated with ESL 5-30 mg/kg/day. Covariate analysis was performed to quantify the effects of key demographic and clinical covariates (including body weight and concomitant use of carbamazepine, levetiracetam, and phenobarbital-like antiepileptic drugs [AEDs]) on variability in PK parameters. Model evaluation performed using a simulation-based visual predictive check and a non-parametric bootstrap procedure indicated no substantial bias in the overall model and in the accuracy of estimates. The model estimated that concomitant use of carbamazepine or phenobarbital-like AEDs with ESL would decrease the exposure of eslicarbazepine, and that concomitant use of levetiracetam with ESL would increase the exposure of eslicarbazepine, although the small effect of levetiracetam may not represent a true difference. Model-based simulations were subsequently performed to apply target exposure matching of selected ESL doses for pediatric subjects (aged 4-17 years) to attain eslicarbazepine exposures associated with effective and well-tolerated ESL doses in adults. Overall, model-based exposure matching allowed for extrapolation of efficacy to support pediatric dose selection as part of the submission to obtain FDA approval for ESL (adjunctive therapy and monotherapy) in subjects aged 4-17 years, without requiring an additional clinical study. [ABSTRACT FROM AUTHOR]

Bottlenose dolphins (Tursiops truncatus) are highly versatile breath-holding predators that have adapted to a wide range of foraging niches from rivers and coastal ecosystems to deep-water oceanic habitats. Considerable research has been done to understand how bottlenose dolphins manage O2 during diving, but little information exists on other gases or how pressure affects gas exchange. Here we used a dynamic multi-compartment gas exchange model to estimate blood and tissue O2, CO2, and N2 from high-resolution dive records of two different common bottlenose dolphin ecotypes inhabiting shallow (Sarasota Bay) and deep (Bermuda) habitats. The objective was to compare potential physiological strategies used by the two populations to manage shallow and deep diving life styles. We informed the model using species-specific parameters for blood hematocrit, resting metabolic rate, and lung compliance. The model suggested that the known O2 stores were sufficient for Sarasota Bay dolphins to remain within the calculated aerobic dive limit (cADL), but insufficient for Bermuda dolphins that regularly exceeded their cADL. By adjusting the model to reflect the body composition of deep diving Bermuda dolphins, with elevated muscle mass, muscle myoglobin concentration and blood volume, the cADL increased beyond the longest dive duration, thus reflecting the necessary physiological and morphological changes to maintain their deep-diving life-style. The results indicate that cardiac output had to remain elevated during surface intervals for both ecotypes, and suggests that cardiac output has to remain elevated during shallow dives in-between deep dives to allow sufficient restoration of O2 stores for Bermuda dolphins. Our integrated modeling approach contradicts predictions from simple models, emphasizing the complex nature of physiological interactions between circulation, lung compression, and gas exchange. [ABSTRACT FROM AUTHOR]

A non-isothermal phase-field model was employed to simulate the columnar formation during rapid solidification in binary Ni{Cu alloy. Heat ux at different boundaries was applied to investigate the temperature gradient effect on the morphology, concentra- tion and temperature distributions during directional solidifications. With the heat ux input/extraction from boundaries, coupling with latent heat release and initial temper- ature gradient, temperature distributions are significantly changed, leading to solute diffusion changes during the phase-transition. Thus, irregular columnar structures are formed during the directional solidification, and the concentration distribution in solid columnar arms could also be changed due to the different growing speeds and tempera- ture distributions at the solid{liquid interfaces. Therefore, applying specific heat condi- tions at the solidifying boundaries could be an efficient way to control the microstructure during solidifications. [ABSTRACT FROM AUTHOR]