Analogous Model Research Articles

A Study of Deployable Structures Based on Nature Inspired Curved-Crease Folding.

Fascinating 3D shapes arise when a thin planar sheet is folded without stretching, tearing or cutting. The elegance amplifies when the fold/crease is changed from a straight line to a curve, due to the association of plastic deformation via folding and elastic deformation via bending. This results in the curved crease working as a hinge support providing deployability to the surface which is of significant interest in industrial engineering and architectural design. Consequently, finding a stable form of curved crease becomes pivotal in the development of deployable structures. This work proposes a novel way to evaluate such curves by taking inspiration from biomimicry. For this purpose, growth mechanism in plants was observed and an analogous model was developed to create a discrete curve of fold. A parametric model was developed for digital construction of the folded models. Test cases were formulated to compare the behavior of different folded models under various loading conditions. A simplified way to visualize the obtained results is proposed using visual programming tools. The models were further translated into physical prototypes with the aid of 3D printing, hybrid and cured-composite systems, where different mechanisms were adopted to achieve the folds. The prototypes were further tested under constrained boundary and compressive loading conditions, with results validating the analytical model.

Hybrid RANS/LES modeling of hypersonic turbulent boundary layers with cold walls

Various strategies for hybrid RANS/LES modeling of hypersonic turbulent boundary layers subjected to strong wall-cooling are developed and analyzed. By defining a turbulent thermal length scale, the eddy viscosity and diffusivity of the hybrid model are calculated by the same formula, ℓ2ω. This allows a similar blending of the length scales and the development of analogous dynamic models for turbulent stress and heat flux. The dynamic procedure is applied to heat transfer by equating norms, rather than invoking least-squares with a lower bound, which improves robustness. The shielding function, as used in detached eddy simulations models, is revised to prevent a spurious early switch from the RANS to the LES mode. This revision significantly improves the accuracy of hybrid models on coarse grids. Finally, the proposed models are tested and validated for Mach numbers between 2.5 and 14, and wall-to-recovery temperature ratios between 0.18 and 1.

Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission.

Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature. After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857-0.887). External validations show AUROCs of 0.805 (95% CI, 0.787-0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837-0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836-0.877) than PCT alone (0.790; 95% CI, 0.759-0.821; P < 0.001). Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety.

Cavity expansion-based interpretation of piezocone penetration test (CPTu) data in clays

A cavity expansion-based method is proposed in this paper to correlate the relevant findings with the piezocone penetration test (CPTu) data in clays. The rigorous and explicit solution for both spherical and cylindrical undrained cavity expansion is adopted based on a unified critical state constitutive model for clays and sands. The analogous model developed is comprehensively validated against two series of advanced numerical simulations and calibration chamber tests of the CPTu in fine-grained soils, along with bias analyses. The effects of the overconsolidation ratio and initial state parameter are extensively investigated for various clay types. Both theoretical and empirical correlations are then proposed for back-calculations of cone factor, undrained shear strength, overconsolidation ratio and initial state parameter of clays. Two case studies of CPTu tests are conducted in addition to examine correlations and contours of the in situ state parameter, demonstrating the applicability of the proposed theoretical approach for interpretation of the CPTu data.

Load model of high power magnet power supply for EAST based on GMPSO-BP neural network

To provide an accurate dynamic load model for the stability analysis of Tokamak, a method of back propagation (BP) neural network employing particle swarm optimization(PSO) algorithm based on mutation of Gaussian white noise disturbance (GMPSO-BP) is recommended. This method performs high-precision fitting using the GMPSO-BP neural network on the measured data of the experimental advanced superconducting Tokamak (EAST) poloidal field magnet power supply, extracts network parameters to build a dynamic load model, and then compares the simulation results with the measured data to calculate the error coefficient. The model is a component of the EAST distribution network's digital simulation model. An analogous load model is employed instead of developing the poloidal field magnet power supply from the mechanism. The revised algorithm has quick convergence times, good initial value adaptability, and error function accuracy of 0.3 to 3 %. The simulation outcomes show that the approach has a greater training effect.

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Transformer performance and efficiency can be enhanced by effectively address the properties of its insulation system. The power transformer insulation system weakens as a result of operational thermal stresses brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial metric that must be maintained below the prescribed limit while power transformers are operating so as to maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the conventional mathematical models used to determine these temperatures, and complexity of thermal phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique. The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oil-filled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132 kV power transformers under dynamic operating regimes. The ANFIS model results were validated against field measured values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation. The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and 0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct determination of HST can help asset managers in thermal analysis trending of the in-service transformers, helping them to make proper loading recommendations for safeguarding the asset.

Usage and acceptability of data normalization in baseball pitching

ABSTRACT In baseball pitching biomechanics, kinetic values are commonly ratio ‘normalised’ by dividing by mass or mass*height to allow for comparison between athletes of different sizes. However, creating a normalised ratio variable should meet certain statistical assumptions. Our purpose was to determine if elbow valgus torque predicted by pitching velocity is influenced by normalisation using regression model comparison with and without normalised torque values. Motion capture data for youth to professional pitchers (n = 1988) were retrospectively analysed. Normalisation assumptions were tested by comparing linear regression models to analogous models with an intercept fixed at zero and by examining remaining correlations between the confounding variable and new, normalised variable. Both mass (p < 0.001) and mass*height (p < 0.001) normalisation did not remove their respective relationship with torque. After accounting for mass or mass and height, velocity predicted 10% of variance in elbow valgus torque, whereas velocity predicted 59% of mass normalised torque and 45% of mass*height normalised torque. Ratio normalisation does not fully account for anthropometric variables that differ across pitchers and leads to different conclusions in the magnitude of velocity’s predictive effect on elbow valgus torque. Therefore, we recommend using regression model comparison to account for anthropometric variables in baseball pitching kinetic data.

3D SPACE KINEMATICS OF A ROBOT FOR PROCESS AUTOMATION IN METALLURGY

3D SPACE KINEMATICS OF A ROBOT FOR PROCESS AUTOMATION IN METALLURGY

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

AbstractThe dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis.

A nested square combination of ε-negative high EMR symmetric metamaterial structure for triple band wireless applications

In this article, a novel metamaterial is presented that is constructed with split ring resonators (SRR) arranged in a nested square configuration. The proposed metamaterial unit cell is constructed over a 1.5 mm thick FR-4 substrate with electrical dimensions of 0.11 λ × 0.11 λ, where λ is the wavelength calculated at 2.8 GHz. The resonating patch contains five interconnected nested squares, including two square split ring resonators (SSRRs). The unit cell exhibits three distinct frequencies at 2.8 GHz, 5.8 GHz, and 11.2 GHz, with corresponding magnitudes of −45 dB, −50 dB, and −30 dB, respectively. Negative permittivity (ε-Negative) is observed in frequency ranges of 2.3–3.20 GHz, 4.4–6.9 GHz, and 10.8–11.30 GHz, with near-zero refractive index. The simulated outcome acquired in CST microwave studio is subsequently confirmed using analogous circuit modeling in Advanced System software (ADS). Moreover, the prototype of the proposed metamaterial is developed that shows similar resonances obtained in the simulation. The effective medium ratio (EMR) of the unit cell is calculated and a high EMR of 8.3 expresses the compactness of the metamaterial. Due to its structural compactness, this new metamaterial with ε-negative, near zero refractive index, and triple band resonance properties holds significant potential for wireless applications such as radar systems, satellite communications, and wireless sensor networks.

PRAGMATIC DEVIATIONS IN TRANSLATION OF LEXICAL BLENDS WITHIN POLITICAL DISCOURSE

The research touches upon the innovations in the English word formation processes, i.e., the role of analogy and intralingual borrowings as significant sources and ways of replenishing English vocabulary, interfering entirely with all the languages in the world and greatly influencing their development. Nominative units consisting of two or more words with a contraction of at least one of them at the place of a junction, i.e., blends, are an integral feature of the English language in general and modern English socio-political discourse in particular. Blending has been growing recently among the most productive means of word formation. Blends are needed to denote new concepts and phenomena and are often used to manifest the author’s word-formation skills; they become popular due to their expressiveness and novelty of form and content. The goal of the article is to study the functional features of blends as a means of strengthening the pragmatic component of the socio-political discourse, as well as the strategy and techniques of their translation. The general and special methods were used to achieve the goal and objectives of the study: information retrieval method – to select research material and process basic theoretical knowledge; generalization method – to highlight the most critical academic positions; deduction and induction – to clarify the theoretical foundations, generalize data and formulate conclusions; discourse analysis – to identify specific communicative and pragmatic features of socio-political communication; contextual and functional methods – to actualize the linguopragmatic meaning of the lexical units under the study, i.e., blends; the vocabulary definitions analysis – to examine their linguopragmatic peculiarities; structuralsemantic and component analysis – to determine the ways of blend formation and their main structural elements – all this is necessary for the implementation of translation analysis. The use of pragmalinguistic elements (blends, in our case) involves investigating relationships between language units and the conditions of the communicative-pragmatic space, tracing the relationship between the addressee’s intentional component and the choice of language means when translating the studied units within the socio-political discourse into another language. Conclusions. Regarding the focus of socio-political discourse on speech influence, the conveying of blend stylistics and the transfer of speech realia that form the basis of the blend implication and strengthen the socio-pragmatics of the given text cause difficulties in comprehending the English original and translation variants. The study of the translation aspect of blends within socio-political discourse revealed a lot of challenges, such as the need for a unique means for conveying blend semantics in the translation language, the complex nature of blend explication, and the problem of their interpretation. Among the translation techniques that are most effective in overcoming the outlined translation difficulties of socio-political blends, such ones should be mentioned first: transcription, transliteration, tracing, and creating an analogical model. The descriptive translation method is considered inappropriate when translating political blends (under research) because such a technique does not allow conveying pragmatics of blends and implement them in the translation language as expressive lexical and stylistic units that are components of speech influence on the addressee, and the society as a whole. The principles of the techniques studied in the article can be used for further research as being universal for translating English blends in inflectional languages (Ukrainian including), where such a phenomenon is non-typical. The process of blending hypothetically proves the activation of the redistribution of components within the different language structures and systems and their ability to self-reorganize.

Synthesis of new thiazolyl-thienyl and thiazolyl-thiadiazolyl ketones: Molecular modelling and docking studies as antimicrobial agents

Eight thiazolyl-thienyl and thiazolyl-thiadiazolyl ketones 5a-d and 6a-d were synthesized based on the reaction of 5-(2-bromoacetyl)-4-methyl-2-(methylamino)thiazole (2) and/or N-(4-chlorophenyl)-2-(4-methyl-2-(methylamino)thiazol-5-yl)-2-oxoacetohydrazonoyl bromide (3) with four various thiocarbamoyl compounds 4a-d. The synthesized thiazolyl-thienyl and thiazolyl-thiadiazolyl ketones were optimized using DFT approach offered analogous models in which the thiazolyl, thienyl and thiazolyl nuclei have a planar structure. The HOMO and LUMO of studied hybrids have been consisted mainly of the π- and π*-orbitals of the whole molecule, respectively. Hence, the thiazolyl-thiadiazolyl ketones revealed smaller energy gap (ΔEH-L) than the thiazolyl-thienyl derivatives and may be sorted as 5c < 6c < 6a ≈ 6d < 6b < 5a < 5b < 5d. The antimicrobial activity of synthesized ketones 5 and 6 was examined against representative Gram’s positive and negative bacterial strains along with fungal strains. The amide-thiazolyl thienyl 5d and the nitrile-thiazolyl thiadiazolyl 6a ketones displayed the highest activity against S. aureus and S. pneumoniae, respectively. Conversely, the nitrile-thiazolyl thienyl ketone 5a demonstrated good efficiency against A. fumigatus. Moreover, in-silico molecular docking stimulation was applied to discover the metabolic effectiveness associated to methicillin resistance through the methicillin-resistant strain of Staphylococcus over (PDB:3VSL).

Anionic Redox in Alkali-Rich Chalcogenides for Energy-Dense, Lithium-Ion Batteries

Lithium-rich transition-metal oxides and chalcogenides exhibit reversible charge-storage reactions centered at anionic sites (e.g., O2–, S2–, or Se2–), augmenting well-established metal cation-sited redox reactions to increase the energy density of lithium-ion or sodium-ion batteries by up to 100%.1 Despite these promising characteristics, the structure–property relationships that govern cation- and anion-coupled redox reactions in chalcogenides remain poorly understood. We deconvolve the cation/anion redox mechanisms of Li+-insertion in alkali-rich chalcogenides by modulating the transition metal–sulfide electronic band structure via systematic metal substitution for iron in Li2Fe1–xMxS2. Key electrochemical characteristics (cation/anion redox potential, Li+-insertion capacity, reversibility) will be correlated with electronic and structural properties as a function of chalcogenide composition using such techniques as X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and Raman scattering. Experimental results will be compared with density functional theory (DFT) calculations of analogous model systems. Understanding the relationship between band structure and reversible charge-storage will guide the design of alkali-rich chalcogenides that are candidates to transition into commercial Li-ion batteries. Due to the absence of oxygen in the chalcogenide crystal structure, batteries that use these as cathode materials will be inherently safer to operate at elevated temperatures and challenging charge–discharge duty cycles. C.J. Hansen, J.J. Zak, A.J. Martinolich, N.H. Bashian, F. Kaboudvand, A. Van der Ven, B.C. Melot, J.N. Welker, and K.A. See. Multielectron, Cation and Anion Redox in Lithium-Rich Iron Sulfide Cathodes. J. Am. Chem. Soc. 142 (2020) 6737–6749.

Solvent Dependent Folding of an Amphiphilic Polyaramid.

A series of synthetic alternating and amphiphilic aromatic amide polymers were synthesized by a step growth polymerization. Alternating meta- and para-linkages were introduced to force the polymer chain into a helical shape in the highly polar solvent water. The polymers were analyzed by 1H NMR spectroscopy and SEC in polar aprotic solvents such as DMSO and DMF. However, the polymers also showed good solubility in water. 1H NMR spectroscopy, small-angle X-ray scattering, and dynamic light scattering provided clear evidence of polymer folding in water but not DMF. We employed parallel tempering metadynamics in the well-tempered ensemble (PTMetaD-WTE) to simulate the free energy surfaces of an analogous model polymer in DMF and water. The simulations gave a molecular model of an unfolded structure in DMF and a helically folded tubular structure in water.

Identification of surface water - groundwater nitrate governing factors in Jianghuai hilly area based on coupled SWAT-MODFLOW-RT3D modeling approach

A comprehensive understanding of the key controlling factors on NO3-N spatiotemporal distribution in surface and groundwater is of great significance to nitrogen pollution control and water resources management in watershed. Hence, the coupled SWAT-MODFLOW-RT3D model was employed to simulate nitrate (NO3−) fate and transport in Huashan watershed system. The model was calibrated using a combination of stream discharge, groundwater levels, NO3-N in-stream loading and groundwater NO3-N concentrations. The simulation revealed the significant spatiotemporal variations in surface water-groundwater nitrate interactions. The annual average percolation of NO3− from rivers to groundwater was 171.5 kg/km2 and the annual average discharge NO3− content from groundwater into rivers was 451.9 kg/km2 over the simulation period. The highest percolation of NO3− from rivers to groundwater occurred in April and the highest discharge NO3− content from groundwater into rivers occurred in July. Grassland and agriculture land contributed more nitrate contents in river water and groundwater compared to bare land and forest in the study area and the water exchange was the primary driving force for nitrate interactions in the surface water-groundwater system. Sensitivity analysis indicated that river runoff and groundwater levels were most influenced by the SCS runoff curve number f (CN2) and aquifer hydraulic conductivity (K), which, in turn, significantly affected nitrate transport. Regarding water quality parameters, the denitrification exponential rate coefficient (CDN) had the most pronounced impact on NO3-N in-stream loading and groundwater NO3-N concentrations. This study underscores the central role of surface-groundwater (SW-GW) interactions in watershed-scale nitrate research and suggests that parameters with higher sensitivity should be prioritized in analogous watershed modeling.

Dynamical signatures of Chaos to integrability crossover in generalized random matrix ensembles

We introduce a two-parameter ensemble of generalized real symmetric random matrices called the β-Rosenzweig–Porter ensemble (β-RPE), parameterized by β, a fictitious inverse temperature of the analogous Coulomb gas model, and γ, controlling the relative strength of disorder. β-RPE encompasses RPE from all of the Dyson’s threefold symmetry classes: orthogonal, unitary and symplectic for . Firstly, we study the energy correlations by calculating the density and 2nd moment of the nearest neighbor spacing (NNS) and robustly quantify the crossover among various degrees of level repulsions. Secondly, the dynamical properties are determined from an exact calculation of the temporal evolution of the fidelity enabling an identification of the characteristic Thouless and the equilibration timescales. The relative depth of the correlation hole in the average fidelity serves as a dynamical signature of the crossover from chaos to integrability and enables us to construct the phase diagram of β-RPE in the γ-β plane. Our results are in qualitative agreement with numerically computed fidelity for matrix ensembles. Furthermore, we observe that for large N the 2nd moment of NNS and the relative depth of the correlation hole exhibit a second order phase transition at γ = 2.

A mixed integer programming model for vaccine pricing within a group purchasing organization

Background:Setting prices for life-saving medical or pharmaceutical products needs to consider multiple factors, e.g., affordability and health outcomes across different populations. When a group of buyers (e.g., countries) combine their purchasing power (e.g., via a group purchasing organization), the average procurement price decreases in the total volume. Decisions about what price to then charge to each member in a group are particularly challenging, considering the disparities in their respective ability and willingness to pay. Tiered pricing can be an effective way to set prices for a group of buyers, but its performance needs to be quantified and evaluated. Methods:We modeled the decision of setting prices of a medical product (for example, a vaccine) for a group of buyers using a mixed integer programming model, considering the buyers’ ability and willingness to pay. The objective is to minimize the unit price disparity adjusted by the buyers’ willingness to pay, subject to the constraint that the prices decrease in the buyers’ ability to pay. We also developed an analogous subsidy allocation model that applies if the group receives philanthropic donations to support procurement. The models were illustrated with two case studies based on the Bacillus Calmette-Guerin (BCG) vaccine procurement by Gavi, the Vaccine Alliance and Pan American Health Organization, and the performances of uniform, tiered, and differentiated pricing schemes were examined. Results:The adjusted unit price disparity is non-increasing in the number of price tiers allowed. The biggest decrease in the adjusted price disparity occurs when switching to two-tier pricing from uniform pricing. Tiered pricing performs better in the Gavi group compared to the PAHO group, in part because the ability to pay and willingness to pay have a higher degree of rank correlation within the former group of countries. Conclusions:This work provides a model for price-setting (subsidy allocation) decisions for a group of buyers and provides a quantitative comparison of different pricing schemes. The results of the case studies suggest that the performance of tiered pricing depends on various factors, including the disparities in the ability and willingness to pay across the buyers. Funding:This research has been supported in part by the Center for Health and Humanitarian Systems, the William W. George endowment, and the following benefactors at Georgia Tech: Andrea Laliberte, Richard Rick E. and Charlene Zalesky, and Claudia and Paul Raines.

Bridging the Gap: Development of frontal crash mode ATD Analogous human body models

Anthropomorphic variation is an important factor in computational studies using Human Body Models (HBMs), particularly regarding how such differences can influence observed kinematics and loading. Currently, a gap exists between Anthropomorphic Test Devices (ATDs) and human body models (HBMs). By necessity, there are differences in constitutive behaviors at a material level, however segment mass distribution and anthropometry differences can make matched simulations of ATDs and HBMs difficult to interpret, which has real-world implications for current or future regulatory applications. In response to this gap, we present Global Human Body Model Consortium (GHBMC) 50th percentile male models (M50-OS) analogous to Hybrid III and THOR 50th percentile male models to serve as intermediaries between the HBMs and ATDs in computational biomechanics studies.Statistical human shape models were sized to each ATD, individually, using measurement metrics from ANSUR I. The M50-OS model surface and body shape surface were then converted to both polygon data (STL) and nodal coordinates. Landmark registration was performed, and the morphed nodes were imported back into the existing M50-OS models to generate two morphed versions analogous to the ATDs. The qualification suites were obtained from each respective ATD manual and run against the baseline and morphed models. The models were also simulated in a frontal NCAP crash pulses (56 km/hr) to assess variation in loading in a regulatory crash environment. 33 simulations were run between all models. The LSTC Hybrid III version 151,214 (open access software, ANSYS, Canonsburg, PA), Adult Shape Parametric Model (open access model, UMTRI, Ann Arbor, MI), GHBMC M50-OS (Elemance, LLC, Winston-Salem, NC), Thor v2.7 (University of Virginia School of Engineering and Applied Science/NHTSA) were used in this study. All simulations were run using LS-Dyna R. 9.3.1 (ANSYS) on an in-house computational cluster.The Male 50th Percentile THOR Analogous Occupant Simplified (M50 THAN-OS) has 364 × 103 elements and weighs 77.1 kg, the Hybrid III Analogous Occupant Simplified (M50 HAN-OS) has 370 × 103 elements and weighs 79.2 kg. The M50 HAN-OS and M50 THAN-OS response data exhibited a closer match to their respective models overall when compared to the baseline M50-OS human. In the vehicle test environment, the airbag and seatbelt peaks between M50 HAN-OS and M50 Hybrid III at nearly the same time.The results indicate that the ATD analogous models exhibit more similar response characteristics to their ATD counterparts after correcting for shape and mass distribution discrepancies. The ATD analogous models therefore represent a novel means to compare anticipated biomechanical loading on an occupant under similar testing conditions that use ATDs. Furthermore, the models represent a potential bridge towards testing modes of interest that ATDs are not well suited or are not validated for, such as low speed maneuvering, or oblique and far-side modes of crash.

A Game-Theoretic Analysis of Baccara Chemin de Fer, II

In a previous paper, we considered several models of the parlor game baccara chemin de fer, including Model B2 (a 2×2484 matrix game) and Model B3 (a 25×2484 matrix game), both of which depend on a positive-integer parameter d, the number of decks. The key to solving the game under Model B2 was what we called Foster’s algorithm, which applies to additive 2×2n matrix games. Here “additive” means that the payoffs are additive in the n binary choices that comprise a player II pure strategy. In the present paper, we consider analogous models of the casino game baccara chemin de fer that take into account the 100α percent commission on Banker (player II) wins, where 0≤α≤1/10. Thus, the game now depends not just on the discrete parameter d but also on a continuous parameter α. Moreover, the game is no longer zero sum. To find all Nash equilibria under Model B2, we generalize Foster’s algorithm to additive 2×2n bimatrix games. We find that, with rare exceptions, the Nash equilibrium is unique. We also obtain a Nash equilibrium under Model B3, based on Model B2 results, but here we are unable to prove uniqueness.

Metformin shows anti-neoplastic properties by inhibition of oxidative phosphorylation and glycolysis in epidermolysis bullosa-associated aggressive cutaneous squamous cell carcinoma.

While most cutaneous squamous cell carcinomas (cSCCs) are treatable, certain high-risk cSCCs, such as those in recessive dystrophic epidermolysis bullosa (RDEB) patients, are particularly aggressive. Owing to repeated wounding, inflammation and unproductive healing, RDEB patients have a 68% cumulative risk of developing life-threatening cSCCs by the age of 35, and a 70% risk of death by the age of 45. Despite aggressive treatment, cSCC represents the leading cause of premature mortality in these patients, highlighting an unmet clinical need. Increasing evidence points to a role of altered metabolism in the initiation and maintenance of cSCC, making metabolism a potential therapeutic target. We sought to determine the feasibility of targeting tumour cell energetics as a strategy to selectively hinder the growth advantage of aggressive cSCC. We evaluated the cell energetics profiles of RDEB-SCC cells by analysing available gene expression data against multiple gene signatures and single-gene targets linked to metabolic reprogramming. Additionally, we employed real-time metabolic profiling to measure glycolysis and respiration in these cells. Furthermore, we investigated the anti-neoplastic properties of the metformin against human and murine high-risk cSCCs in vitro and in vivo. Gene expression analyses highlighted a divergence in cell energetics profiles between RDEB-SCC and non-malignant RDEB keratinocytes, with tumour cells demonstrating enhanced respiration and glycolysis scores. Real-time metabolic profiling supported these data and additionally highlighted a metabolic plasticity of RDEB-SCC cells. Against this background, metformin exerted an anti-neoplastic potential by hampering both respiration and glycolysis, and by inhibiting proliferation in vitro. Metformin treatment in an analogous model of fast-growing murine cSCC resulted in delayed tumour onset and slower tumour growth, translating to a 29% increase in median overall survival. Our data indicate that metformin exerts anti-neoplastic properties in aggressive cSCCs that exhibit high-risk features by interfering with respiration and glycolytic processes.