Search

We investigate the degradation mechanisms of In0.2Ga0.8N/GaN light emitting diodes through combined experimental analysis and simulations. The devices were submitted to constant current stress at 100 mA. Depending on the measuring current level, two degradation trends were observed: at high test currents (e.g. 200 mA), a monotonic decrease in optical power was observed; at low test currents (e.g. 5 mA), an initial degradation was observed, followed by an improvement in device efficiency (positive ageing). For the first time, such recovery effect was analyzed and modeled, as due to the generation of charged defects at the InGaN/GaN interface, resulting in the increase in the injection efficiency at low bias levels. The role of interface defects was validated by means of numerical simulations, with good agreement with the experimental data. [ABSTRACT FROM AUTHOR]

With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition‐state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non‐linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint‐, descriptor‐, and graph neural network‐based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists. [ABSTRACT FROM AUTHOR]

With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition‐state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non‐linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint‐, descriptor‐, and graph neural network‐based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists. [ABSTRACT FROM AUTHOR]

Summary Policymakers require health economic modeling to guide their decision‐making over the choice of interventions for obesity. This scoping review was undertaken to report on the health economic models in use for estimating the value of behavioral interventions (individual or population level) for obesity reduction. Electronic databases (MEDLINE, Embase, PsycINFO, EconLit, and Web of Science) were searched for publications meeting inclusion criteria from January 2015 to May 2023. Seventy‐three studies were included, using 44 health economic models between them. When considered against the expert recommendations for modeling of this type, only four models (9%) met all five key elements. The element most commonly unfulfilled was the use of a microsimulation modeling approach (41%, n = 18), followed by model validation (46%, n = 20). A majority of models met each of the other elements: use of a lifetime horizon (59%, n = 26), inclusion of key health events (66%, n = 29), and a risk equation approach to event simulation (71%, n = 31). In addition, under half of the studies considered health inequalities in their reporting. Continued proliferation of models with inadequate time horizons, breadth of obesity‐related health conditions, and perspectives on costs and outcomes risks underestimation of the benefits of longer term interventions and impacts on health inequalities. [ABSTRACT FROM AUTHOR]

In this study, nanocomposites of g‐C3N4/MN4 (where M is Mn, Fe and Co) have been designed using advanced density functional theory (DFT) calculations. A comprehensive analysis was conducted on the geometry, electronic, optical properties, work function, charge transfer interaction and adhesion energy of the g‐C3N4/MN4 heterostructures and concluded that g‐C3N4/FeN4 and g‐C3N4/CoN4 heterojunctions exhibit higher photocatalytic performance than individual units. The better photocatalytic activity can be attributed mainly by two facts; (i) the visible light absorption of both g‐C3N4/FeN4 and g‐C3N4/CoN4 interfaces are higher compared to its isolated analogs and (ii) a significant enhancement of band gap energy in g‐C3N4/FeN4 and g‐C3N4/CoN4 heterostructures limited the electron–hole recombination significantly. The potential of the g‐C3N4/MN4 heterojunctions as a photocatalyst for the water splitting reaction was assessed by examining its band alignment for water splitting reaction. Importantly, while the electronic and magnetic properties of MN4 systems were studied, this is the first example of inclusion of MN4 on graphene‐based material (g‐C3N4) for studying the photocatalytic activity. The state of the art DFT calculations emphasis that g‐C3N4/FeN4 and g‐C3N4/CoN4 heterojunctions are half metallic photocatalysts, which is limited till date. [ABSTRACT FROM AUTHOR]

Hox transcription factors play crucial roles in organizing developmental patterning across metazoa, but how these factors trigger regional morphogenesis has largely remained a mystery. In the developing gut, Hox genes help demarcate identities of intestinal subregions early in embryogenesis, which ultimately leads to their specialization in both form and function. Although the midgut forms villi, the hindgut develops sulci that resolve into heterogeneous outgrowths. Combining mechanical measurements of the embryonic chick intestine and mathematical modeling, we demonstrate that the posterior Hox gene HOXD13 regulates biophysical phenomena that shape the hindgut lumen. We further show that HOXD13 acts through the transforming growth factor β (TGF-β) pathway to thicken, stiffen, and promote isotropic growth of the subepithelial mesenchyme—together, these features lead to hindgut-specific surface buckling. TGF-β, in turn, promotes collagen deposition to affect mesenchymal geometry and growth. We thus identify a cascade of events downstream of positional identity that direct posterior intestinal morphogenesis. [Display omitted] • Mechanical properties define epithelial buckling in the developing chick intestine • HOXD13 promotes hindgut mechanics through increased mesenchymal TGF-β signaling • Hindgut and ectopic midgut HOXD13- expressing smooth muscle upregulates INHBA • Mesenchymal TGF-β activation tunes mechanics via extracellular matrix remodeling Gill et al. mechanistically link HOXD13 expression to morphogenesis of the chick hindgut epithelium. HOXD13 activity within smooth muscle induces INHBA expression, which causes adjacent mesenchymal cells to remodel extracellular matrix (ECM) in response to TGF-β upregulation, thus promoting the biomechanical parameters that define the hindgut morphological trajectory. [ABSTRACT FROM AUTHOR]

We discuss the evolution of a computational model of delusions, beginning with a background consideration of how computational psychiatry, with its roots firmly based in cognitive neuropsychiatry, seeks to develop descriptive and mechanistic models that reach across different levels of explanation in order to provide more comprehensive understanding of how neurobiological, cognitive, subjective and sociocultural factors may all contribute to complex psychopathology. This quest for bridging explanations – or "consilience" – across the levels is a shared goal of computational psychiatry and cognitive neuropsychiatry and is, we argue, crucial to explaining delusional beliefs. We outline how early computational models appealed to prediction error disturbances as a basis for understanding the early emergence of delusions and show that, despite empirical support, there have been certain explanatory limitations that make a simple prediction error account partially limited. Embedding the account within the increasingly influential hierarchical predictive processing framework subsequently offered a more powerful and comprehensive account, particularly by encouraging the consideration hierarchically-organized inference and its evolution over time. However, further limitations remain in its explanatory scope, most notably the fact that delusions can emerge rapidly and suddenly in a way that seems revelatory and convincing. This phenomenon is not easily encompassed by the standard predictive processing account which emphasizes an iterative process of optimizing inference. However, more recent development in the form of "Hybrid Predictive Coding" posits a complementary rapid inference mechanism. We discuss how this hybrid approach may be key to a more comprehensive computational understanding of delusions. [ABSTRACT FROM AUTHOR]

Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the C lustered R egularly I nterspaced S hort P alindromic R epeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

This article presents the results of the modeling of the behavior of the titanium alloy Ti-3Al‑2.5V at the initial stage of extrusion of a tube billet. The influence of the gaps between the surfaces of the tool and the billet on the nature of its shape change and the strain state is discussed. In particular, the most deformed tube parts of the maximum (by modulus) strain tensor values are identified. The dependence of the Ti-3Al‑2.5V alloy tube extrusion force on the stroke of the pressure pad is calculated. The results of the calculation of the maximum forces and the forces at the steady-state stage are in good agreement with the data obtained under an industrial experiment. Horizontal extrusion leads to an asymmetry in the location of the deformation fields relative to the extrusion axis. Accordingly, an asymmetric distribution of properties arises in the extruded tube, which is experimentally confirmed using hardness measurements. Correspondence is established between the simulation data on the heterogeneity of the strain distribution over the wall thickness of the hot-pressed tube made of titanium alloy Ti-3Al‑2.5V, considering the pressing-out, with the results of micro- and X‑ray structural studies of the tube semifinished product obtained. [ABSTRACT FROM AUTHOR]

The School Science Activity recognises the need for science education that provides citizens with a worldview about which they can think, act and communicate. However, science teachers, especially in physics, continue to use transmissive teaching approaches and show evidence of difficulties in implementing didactic approaches such as modeling in the classroom. Some reasons include the complexity of designing sequences under this approach and the lack of comprehension of the role of modeling competences that are developed in this kind of implementation. This research analyses how the ideas of physics teachers in training in Chile change concerning modeling and its competences, after participating in a course oriented towards this approach. At the end of the course, progress is identified in comprehending the aims of the modeling competences of expression, evaluation and application of a model. The results also highlight the importance of training future teachers through classes focused on modeling. To this purpose, some design and implementation guidelines are proposed. [ABSTRACT FROM AUTHOR]

The buffer leakage phenomena have a large effect on high‐power and fast‐switching GaN high electron mobility transistor devices. Herein, the buffer leakage mechanism in a GaN‐on‐Si substrate with AlGaN buffer layers is investigated by using samples with various carbon concentrations. Comparing the leakage current with crystallinity and impurity concentration, it is found that the vertical leakage current is dominated by both threading dislocations and bulk impurities. Increasing the carbon concentration of buffer layers suppresses leakage current in the bulk conducting regime in high electric fields but increases it in the dislocation conducting regime in low electric fields. These results indicate that the optimal carbon concentration of buffer layers depends on the target electric field. [ABSTRACT FROM AUTHOR]

Innovative methods for substrate patterning provide intriguing possibilities for the development of devices based on ordered arrays of semiconductor nanowires. Control over the nanostructures' morphology in situ can be obtained via extensive theoretical studies of their formation. In this paper, we carry out an investigation of the ordered nanowires' formation kinetics depending on the growth mask geometry. Diffusion equations for the growth species on both substrate and nanowire sidewalls depending on the spacing arrangement of the nanostructures and deposition rate are considered. The value of the pitch corresponding to the maximum diffusion flux from the substrate is obtained. The latter is assumed to be the optimum in terms of the nanowire elongation rate. Further study of the adatom kinetics demonstrates that the temporal dependence of a nanowire's length is strongly affected by the ratio of the adatom's diffusion length on the substrate and sidewalls, providing insights into the proper choice of a growth wafer. The developed model allows for customization of the growth protocols and estimation of the important diffusion parameters of the growth species. [ABSTRACT FROM AUTHOR]

Few studies have examined the N kinetics of individual feeds with stable isotope tracing. We hypothesized that N partitioning to milk and excreta pools as well as the rates of the processes that drive this partitioning would differ for alfalfa silage, corn silage, corn grain, and soybean meal. Feed ingredients were endogenously labeled with 15N and included in 4 diets to create treatments with the same dietary composition and different labeled feed. Diets were fed to 12 late-lactation dairy cows for 4 d (96 h) and feces, urine, and milk collection proceeded during the 4 d of 15N enrichment and for 3 d (80 h) after cessation of label feeding. Nonlinear models of 15N enrichment and decay were fit to milk (MN), urine (UN), and fecal N (FN) in R with the nlme package, and feed-specific parameter estimates were compared. The estimated proportions of feed N that were excreted in feces supported our understanding that N from soybean meal and corn grain is more digestible than N from alfalfa and corn silage. Estimates for the N partitioning between milk and urine from the 2 concentrate feeds (soybean meal and corn grain) indicated that UN/MN ratios were less than or equal to 1:1, indicating either more or equal nitrogen partitioning to milk compared with urine. It is important to maintain factual accuracy in representing the results rather than implying a desired outcome unsupported by the data. In contrast, UN/MN ratios for forage feeds (corn and alfalfa silage) were >1:1, indicating more N partitioning to urine than milk. The modeled proportion of total FN that originated from feed N was 82.2%, which is in line with previous research using a similar 15N measurement timeframe. However, the proportion of urinary and MN originating from feed N was much lower (60.5% for urine, 57.9% for milk), suggesting that approximately 40% of urinary and MN directly originate from body N sources related to protein turnover. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [Display omitted] [ABSTRACT FROM AUTHOR]

Handling geotechnical peculiarities in specialized ground models – an experience report Dr. Spang GmbH has developed numerous specialized ground models in recent years and has gained corresponding experience in ground modeling. Within the company, a standard procedure for creating these specialized models has been established. From a software perspective, automatic meshing (triangulated 3D surface mesh of the layer boundaries) already delivers useful results for many standard cases. However, in geotechnics, there are not only standard cases and features to assess. Geotechnical peculiarities (e. g. inhomogeneous layering) result in an increased manual modeling effort. This article aims to shed light on how to handle these peculiarities in modeling, present the additional modeling steps involved, and highlight the software limitations that arise in automatic meshing (interpolation or triangulation) in such special cases. [ABSTRACT FROM AUTHOR]

Deadwood represents a significant carbon pool and unique biodiversity reservoir in forests and savannas but has been largely overlooked until recently. Storage and release of carbon from deadwood is controlled by interacting decomposition drivers including biotic consumers (animals and microbes) and abiotic factors (water, fire, sunlight, and freeze–thaw). Although previous research has focused mainly on forests, we synthesize deadwood studies across diverse ecosystems with woody vegetation. As changing climates and land-use practices alter the landscape, we expect accelerating but variable rates of inputs and outputs from deadwood pools. Currently, Earth system models implicitly represent only microbial consumers as drivers of wood decomposition; we show that many other factors influence deadwood pools. Forest management practices increasingly recognize deadwood as an important contributor to forest dynamics, biodiversity, and carbon budgets. Together, emerging knowledge from modeling and management suggests a growing need for additional research on deadwood contributions to carbon storage and greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Aim of the study was to analyze the metabolic profile of badminton matches and training drills. Therefore, 11 male (23.2 ± 3.8 years, 182 ± 7 cm, 74.4 ± 8.4 kg) and five female (19.3 ± 1.5 years, 170 ± 6 cm, 62.6 ± 9.2 kg) elite badminton players participated in either a training match (TM; n = 7) and/or three protocols of multifeeding drills (T10, T30,T50;n = 13), that varied in interval and rest durations (10 s/10 s, 30 s/30 s, 50 s/50 s). Absolute and relative energetic costs (Wtot and Etot) and contribution to oxidative (WOxid), phosphagen (WPCr), and anaerobic glycolytic (WLa) metabolism were calculated by the three‐component PCr‐La‐O2‐method based on an indirect calorimetric approach from oxygen consumption during exercise, post exercise, and net blood lactate concentration. A novel intermittent approach was used to consider replenishment of phosphocreatine during each resting phase. Results show that during TM, Etot was 676 ± 98J·kg−1 min−1, while metabolic pathways contributed by 56.9 ± 8.6% (WOxid), 42.7 ± 8.7% (WPCr), and 0.4 ± 0.6% (WLa). In the multifeeding drills Etot was comparable between T10 (1020 ± 160J·kg−1 min−1) and T30 (985 ± 173 J·kg−1 min−1) but higher in T50 (1266 ± 194J·kg−1 min−1) (p < 0.001). Relative contribution of WOxid was lower in T10 (47.3 ± 7.7%) but similar in T30 (56.5 ± 6.2%) and T50 (57.3 ± 6.0%) (p < 0.001). WPCr was highest in T10 (51.1 ± 8.3%) followed by T30 (42.2 ± 6.9%) and lowest in T50 (31.2 ± 7.7%) (p < 0.001). WLa was similar between T10 (1.6 ± 1.0%) and T30 (2.1 ± 1.0%) but higher in T50 (11.6 ± 4.8%) (p < 0.001). Concludingly, metabolic costs in badminton are predominantly covered by oxidative and phosphagen energetic pathways. Metabolic profiles of the multifeeding drills differ depending on rally/interval duration, with increasing contribution of anaerobic glycolysis and decreasing phosphagen contribution in case of longer intervals. Plain Language Summary: Badminton stands out as a high‐paced and exceptionally demanding sport, necessitating a very complex interaction between the aerobic and anaerobic metabolic systems. Using an innovative approach of indirect caloric measurement, the main aim of the investigation was to determine the metabolic profiles of elite badminton match play and badminton‐specific training drills. Therefore, 16 elite players conducted either a training match and/or three different protocols of a multifeeding drill (with varying interval/rest durations). The study underscores a pivotal role of anaerobic alactic energy supply in badminton, highlighting the critical need for rapid phosphocreatine (PCr) replenishment to prevent performance decline attributed to increasing acidosis. While aerobic capacity remains vital for PCr replenishment, occasional reliance on lactic metabolism may be important in certain probable match deciding situations. The multifeeding drills exhibit variations in the metabolic profile depending on rally and rest durations. As the interval length increases, the dominance of aerobic and lactic metabolism rises, while the proportion of anaerobic alactic contribution decreases. Emphasizing that in on‐court drills different interval durations address different metabolic pathways, training content should be chosen wisely to accurately address individual training goals and to align with a long‐term training periodization perspective. Highlights: This is the first study using an intermittent approach of the three‐component PCr‐La‐O2‐method, to determine the metabolic profile of elite badminton match play and the impact of drill prescription on metabolic stress in badminton‐specific training drills.The present findings reveal that energy in badminton generally is predominantly derived from oxidative and phosphagen energetic pathways, with the anaerobic glycolytic system contributing only minimally.Multifeeding drills essentially mirror the metabolic demands of matches but exhibit variations depending on rally and rest periods. These findings underscore the need to select training protocols thoughtfully, tailor training according to individualized goals and to structure those goals within a long‐term training periodization. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields. [ABSTRACT FROM AUTHOR]

Adavosertib (AZD1775) is a potent small‐molecule inhibitor of Wee1 kinase. This analysis utilized pharmacokinetic data from 8 Phase I/II studies of adavosertib to characterize the population pharmacokinetics of adavosertib in patients (n = 538) with solid tumors and evaluate the impact of covariates on exposure. A nonlinear mixed‐effects modeling approach was employed to estimate population and individual parameters from the clinical trial data. The model for time dependency of apparent clearance (CL) was developed in a stepwise manner and the final model validated by visual predictive checks (VPCs). Using an adavosertib dose of 300 mg once daily on a 5 days on/2 days off dosing schedule given 2 weeks out of a 3‐week cycle, simulation analyses evaluated the impact of covariates on the following exposure metrics at steady state: maximum concentration during a 21‐day cycle, area under the curve (AUC) during a 21‐day cycle, AUC during the second week of a treatment cycle, and AUC on day 12 of a treatment cycle. The final model was a linear 2‐compartment model with lag time into the dosing compartment and first‐order absorption into the central compartment, time‐varying CL, and random effects on all model parameters. VPCs and steady‐state observations confirmed that the final model satisfied all the requirements for reliable simulation of randomly sampled Phase I and II populations with different covariate characteristics. Simulation‐based analyses revealed that body weight, renal impairment status, and race were key factors determining the variability of drug‐exposure metrics. [ABSTRACT FROM AUTHOR]

High‐aluminum advanced high‐strength steels have received increasing interest due to their excellent combination of strength and ductility. The control of nonmetallic inclusions in steel is among the most important issue for the production of high‐aluminum steel. This study proposes a model for the evolution of size distributions of AlN inclusions during solidification of steel based on the Kampmann–Wagner numerical model and microsegregation calculation. Both homogeneous (pure AlN) and heterogeneous precipitations (AlN+oxide or MnS) are described using homogeneous nucleation and free growth models of Greer et al. respectively. The model is applied to calculate the size distributions of AlN in high‐Al (up to 5%) Fe–Cr–Al steels and high‐ and medium‐manganese steels and validated by the experimental data in the literature. The model correctly describes the influence of cooling rates on the precipitation of AlN in Fe–Cr–Al steels and high‐manganese steels, and the sizes of AlN are reduced and the number densities of AlN inclusions are increased by increasing the cooling rate. The effects of nitrogen and aluminum concentration in medium‐manganese steel on inclusion precipitation are investigated by the model calculation. The increases in nitrogen and aluminum concentration facilitate the homogeneous precipitation of AlN inclusions. [ABSTRACT FROM AUTHOR]

One-step (F100) and three-step (F30-60-40) hot forging of Al0.35CoCrFeNi alloy was investigated to achieve a uniform equiaxed grain structure. In the as-cast and forged state, only a single-phase face-centered cubic structure was observed. The formation of twins, recrystallized and partially recrystallized grains in the volume of the samples was observed depending on used forging process. To predict uniform grain-size formation numerical simulation of the hot-forging process was used. The numerical model was calibrated and validated by means of measured compression experimental data of as-cast Al0.35CoCrFeNi alloy before forging. Thermal analysis using finite element analysis was used to simulate cooling of sample during the relocation from the furnace on the lower die. Simulations were run under different thermo-mechanical conditions and the regions for the formation of dynamically recrystallized grains were predicted. Room temperature mechanical properties were evaluated after F100 and F30-60-40 hot-forging process. The F30-60-40 hot forging optimized the grain size, which was evident in the very small dispersion of the room temperature mechanical properties in tension. Elongation after F30-60-40 hot forging increased by 17%. The correlation between temperature, equivalent stress, equivalent plastic strain, microstructure, tensile properties, and strain-hardening behavior is discussed. [ABSTRACT FROM AUTHOR]

A longstanding project of the chemical kinetics community is to predict reaction rates and the behavior of reacting systems, even for systems where there are no experimental data. Many important reacting systems (atmosphere, combustion, pyrolysis, partial oxidations) involve a large number of reactions occurring simultaneously, and reaction intermediates that have never been observed, making this goal even more challenging. Improvements in our ability to compute rate coefficients and other important parameters accurately from first principles, and improvements in automated kinetic modeling software, have partially overcome many challenges. Indeed, in some cases quite complicated kinetic models have been constructed which accurately predicted the results of independent experiments. However, the process of constructing the models, and deciding which reactions to measure or compute ab initio, relies on accurate estimates (and indeed most of the numerical rate parameters in most large kinetic models are estimates.) Machine‐learned models trained on large datasets can improve the accuracy of these estimates, and allow a better integration of quantum chemistry and experimental data. The need for continued development of shared (perhaps open‐source) software and databases, and some directions for improvement, are highlighted. As we model more complicated systems, many of the weaknesses of the traditional ways of doing chemical kinetic modeling, and of testing kinetic models, have been exposed, identifying several challenges for future research by the community. [ABSTRACT FROM AUTHOR]

This study primarily focuses on design, mathematically model and simulate a novel two wheel-legged hybrid robot called W-Leg Jumping robot, which has a unique ability to overcome step-like obstacles efficiently. In general, in transformable wheel-leg robot studies, the leg and wheel structure perform their movements in an interdependent manner. However, in this study, it is aimed to design a robot in which the leg and wheel structure can move independently of each other and to develop a robot that can easily overcome obstacles on flat surfaces with the wheel mode and with the leg mode. The robot can fold its legs hidden within the wheels and deploy its two degree of freedom (DoF) legs when it detects step-like obstacles. This mechanism allows the robot to overcome an obstacle with a height of twice the radius of the robot's open/close mechanism of the legs, along with the two-dimensional kinematic and dynamic analyzes of the legs, are presented in detail within the scope of this study proportional-integral-derivative (PID) controller is designed to control the joint angles of the legs. The reference angle values to be followed according to the height of the obstacle are determined using artificial neural network (ANN). Additionally, motion simulations of the robot are conducted for four different obstacle heights (20, 30, 40, and 50 cm). As a result of the PID controller, when exceeding the highest obstacle of 50 cm, the average absolute joint angular tracking error is max. 1.8829°, average tracking error max. 0.265 s and max. [ABSTRACT FROM AUTHOR]

Realistic estimates of evaporation from unsaturated soils, which are important for many geotechnical and geoenvironmental applications, need to be considered in the context of soil settlement, and require thermohydromechanical (THM) analysis. Evaporation also depends on environmental parameters, including air temperature, air relative humidity, net radiation, and wind speed. Therefore, a consideration of atmospheric coupling in predicting evaporation is also necessary. In this study, the two-dimensional model EVAP1––which numerically estimates evaporation from unsaturated soil using THM and employing a soil–atmosphere model––was used to conduct a parametric study in order to investigate the effects of variation in environmental parameters and to study the effects of climate change on potential and actual evaporation and soil settlement. We found that the evaporation rate increased nonlinearly with increases in air temperature, net radiation, and wind speed, but decreases with an increase in relative humidity. However, the effect of a change in wind speed was less than the effect of a change in the three other environmental parameters. In addition, the change in evaporation rate differed in different regions with different air temperatures. For example, the temperature change had more of an effect on the evaporation rate at higher temperatures. In addition, neglecting soil settlement led to an overestimation of evaporation, albeit the amount of evaporation was almost the same whether soil settlement was considered or not, both at the beginning of the evaporation process, when potential evaporation was dominant, and at the end, when the water content in both cases had decreased almost to the residual water content, and the evaporation was minimized. The difference in the amount of evaporation was greater in the middle of the evaporation process, when actual evaporation dominated. [ABSTRACT FROM AUTHOR]

The development of the Drag-Free and Attitude Control System is crucial for space-based gravitational wave detectors. The Drag-Free and Attitude Control System includes for spacecraft attitude control, drag-free control, moving optical subassemblies pointing control, and test masses electrostatic control during multiple phases of the mission lifetime. An accurate model of the spacecraft dynamics model is not only a precondition for the control design of Drag-Free and Attitude Control System, but also can serve as a valuable reference to assess the noise budget of gravitational wave detectors. This paper presents a generic, nonlinear mathematical model that describes the multi-body dynamics for the gravitational wave detectors. The model fully considers the couplings between the bodies involved, with a particular focus on the impact of the moving optical subassemblies on the spacecraft attitude. Additionally, a corresponding reduced model for the control design of the science mode is divided. Both models are validated numerically by Monte Carlo simulations and motion analysis, respectively. • Introduce a spacecraft dynamics model that fully describes the motion coupling. • Simscape simulation has validated the accuracy of the dynamics model. • The spacecraft dynamics model is applicable to multiple phases of motion. • Monte Carlo simulations have validated the applicability to different parameters. [ABSTRACT FROM AUTHOR]

We develop finite element models to describe the progressive damage behaviors of two types of carbon fiber reinforced epoxy (CF/EP) composite tubes triggered by three types of initiators, i.e. a flat surface plate, an outward-triggering plug and an inward-triggering cap, using a continuum damage mechanics-based approach. First, the quasi-static compression experiments were conducted to capture the load–displacement relation, failure mode, and microscopic damage of CF/EP composite tubes. These experimental results were utilized to validate the proposed finite element models. The numerical crushing response agreed well with the experimental data. Then, a comparative study on the progressive damage behaviors of two types of CF/EP composite tubes subjected to three typical initiators was performed. The failure mechanisms were systematically articulated, showing that the fiber breakages were significant due to the concentration of compressive stress caused by external loads, while the matrix and interlaminar damage behaviors were predominant under any of these initiators. [ABSTRACT FROM AUTHOR]

On 2 April 2024, an MW 7.4 earthquake struck the northern Longitudinal Valley in eastern Taiwan, about 18 km SSW of Hualien, causing damage and casualties. In this study, we investigated a comprehensive geodetic data set, employing Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) measurements to assess the rupture geometry associated with earthquake sequence. Although geodetic data can be satisfactorily reproduced by simple single‐fault models (i.e., a high‐angle E‐dipping plane related to the Longitudinal Valley Fault (LVF), or a gentle W‐dipping surface associated with the Central Range Fault, CRF), a composite model involving the rupture of different fault segments (a major CRF‐related W‐dipping fault, a deep segment of the E‐dipping LVF, and the Milun Fault) is able to explain the observations, the distribution of seismicity, and the complex structural arrangement of the northernmost sector of the Longitudinal Valley. Plain Language Summary: On 2 April 2024, a powerful magnitude 7.4 earthquake hit the northern Longitudinal Valley in eastern Taiwan, near Hualien, causing significant damage and loss of life. This study examined a detailed set of geodetic data, including measurements from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR), to understand the geometry of the earthquake's rupture. While simple models focused on single faults (like the Longitudinal Valley Fault (LVF) or the Central Range Fault) can replicate the geodetic data to some extent, a more complex model involving the rupture of different fault segments (including a major Central Range Fault segment, a deep segment of the LVF, and the Milun Fault) better explains the data, seismic activity distribution, and the intricate structural layout of the northern part of the Longitudinal Valley. Key Points: We developed a composite rupture geometry of the MW 7.4 2024 Hualien (eastern Taiwan) earthquake sequenceThe earthquake primarily ruptured a major W‐dipping fault, while secondary affecting both deep and shallow segments of an E‐dipping faultOur results support the clear partitioning of the seismic release on different structures [ABSTRACT FROM AUTHOR]

ABSTRACT The current study involved production, comprehensive structural analysis, and physicochemical characterizing of two distinctive complexes namely, Cu(TBH) and Zn(TBH); TBH donor ligand: N′‐(1‐(3,6‐dihydro‐4‐hydroxy‐2,6‐dioxo‐2H‐1,3‐thiazin‐5‐yl)ethylidene)‐2‐hydroxybenzohydrazide) using a wide range of analytical methods, including elemental analysis, UV–Vis, FT‐IR, and 1HNMR spectrometry, molar conductivity and magnetic susceptibility measurements, and thermal analysis. According to the findings, chelation can occur through O, N, and O donor atoms of monoanionic chelator for generating mono‐nuclear chelates with tetrahedral geometry for Cu (II) and octahedral geometry for Zn (II). The anticarcinogenic ability of TBH chelator and its coordinated compounds against human liver cancer (HepG‐2) was investigated. The Cu(TBH) complex was found to have selective and promising anticancer activity against a liver cancer cell line, with lower IC50 (liver carcinoma cell line) and higher IC50 (normal human cell line) values than other produced compounds and standard drugs. Utilizing DFT computations, the molecular structures of the TBH and its complexes were verified, offering a detailed understanding of their quantum chemical characteristics. A quantitative structure–activity relationship (QSAR) model, which illustrates the association between DFT‐computed descriptors and biological activities pIC50, was also created using multiple linear regression (MLR) on the synthesized compounds as anticancer medicines. Additionally, there are no issues with the produced compounds' oral bioavailability according to (ROF) Lipinski's rule of five. Furthermore, docking studies of the synthesized TBH chelator and its chelates with CDK2 kinase have been performed to validate the biological findings. According to our findings, the novel Cu(TBH) nanodrug exhibits considerable cytotoxic activity, prompting further study into its pharmacological profile and exploring its potential in drug development. [ABSTRACT FROM AUTHOR]

This study applies both stationary and nonstationary generalized extreme value (GEV) models to analyze annual extreme temperature patterns in four stations of Southern Highlands region of Tanzania: Iringa, Mbeya, Rukwa, and Ruvuma over a 30‐year period. Parameter estimates reveal varied distribution characteristics, with the location parameter μ ranging from 28.98 to 33.44, and shape parameter ξ indicating both bounded and heavy‐tailed distributions. These results highlight the potential for extreme temperature conditions, such as heatwaves and droughts, particularly in regions with heavy‐tailed distributions. Return level estimates show increasing temperature extremes, with 100‐year return levels reaching 33.95 °C in Ruvuma. Nonstationary models that incorporate time‐varying location and scale parameters significantly improve model fit, particularly in Mbeya, where such a model outperforms the stationary model (p value = 0.0092). Trend analyses identify significant temperature trends in Mbeya (p value = 0.0123) and Ruvuma (p value = 0.0015), emphasizing the need for adaptive climate strategies. These findings underscore the importance of accounting for nonstationarity in climate models to better understand and predict temperature extremes. [ABSTRACT FROM AUTHOR]

Bovine viral diarrhea (BVD) represents a serious threat to the cattle sector in Chile, indicating the need for a regionally defined control program. Ex-ante evaluations of program options using simulation modeling have proven to be a successful approach in providing decision‐makers with relevant supporting insights in that respect. Given the complexity of bovine viral diarrhea virus (BVDV) infection dynamics, simulation of BVD spread in a metapopulation requires detailed consideration of both within and between herd transmission dynamics. The aims of the study are (i) to investigate the dynamics of BVDV transmission in cattle herds in southern Chile by linking a within‐herd transmission model (WHM) that accounts for the BVDV's unique characteristics with a between‐herd model (BHM) that meets the demands for further regional control strategy evaluation; (ii) to suggest and discuss criteria for evaluation of the model approach and plausibility for later research and for support decision‐making. This resulted in bringing forth a modeling rationale for complex disease spread simulation in metapopulations. BHM simulations under this approach show outcomes that agree with BVDV's known situation in Chile; dairy herds prevalence at endemic equilibrium reaches and maintains 75%, which agrees with estimations of BVDV active infection in dairy herds in southern Chile (77%). For the entire herd population, the infection always reaches endemic levels with a large proportion of infected herds (median = 60%), where herd prevalence was higher in the dairy herd class than in the remaining categories. Transmission probability variation affects the new infections picked, prevalence at endemic levels, and the velocity in which the infection spreads between herds. The fact that the presented approach was able to model a complex infection dynamic such BVDV, with sufficient confidence, provides evidence that this approach can be used to explore mitigation strategies to control BVDV in southern Chilean herds. [ABSTRACT FROM AUTHOR]

AbstractThe transition of a material from macro- to Nano-scale brings about significant changes in electron conductivity, optical absorption, mechanical properties, chemical reactivity, and surface morphology. These changes present opportunities for creating innovative composite mixtures. As there is a growing need for improved infrastructure, it becomes crucial to develop new, high-performance materials. To enhance the performance of concrete mixtures, various methods have been explored, including the utilization of nanoparticles (NPs). Incorporating NPs aims to improve the fresh and mechanical properties of self-compacting concrete (SCC) while also enhancing the permeability and absorption capacity of the composite by introducing extremely fine particles to fill micro-pores and voids. Numerous initiatives have been implemented to explore the mechanical characteristics of SCC. Typically, compressive strength (CS) serves as a crucial mechanical parameter for assessing concrete quality. Conventional methods for determining SCC’s CS are costly, time-intensive, and restrictive due to the intricate interplay of various mixing proportions and curing processes. Thus, this investigation employs machine learning techniques, including artificial neural network (ANN), multi-expression programming (MEP), full quadratic (FQ), and linear regression (LR), to predict self-compacting mortar’s CS. Approximately 292 CS values from the literature were extracted and analyzed to facilitate model development. Six influential variables were used as input parameters and one as an output during the modeling process. Four statistical metrics gauged model performance, and sensitivity analysis was conducted. Results indicate that the ANN model outperformed other models in predicting self-compacting mortar’s CS. Meanwhile, the water-to-binder ratio, nanoparticle dosage, and concrete age significantly influence self-compacting mortar’s CS. [ABSTRACT FROM AUTHOR]

ABSTRACT The investigation encompassed an examination of the drying durations, modeling, and quality attributes (color, rehydration capacity, microstructural features, total soluble solid [TSS], and pH values) of strawberry slices subjected to diverse drying methodologies, namely electrohydrodynamic (EHD), EHD‐hot air, and hot air processes. Furthermore, 10 distinct thin‐layer drying models were applied, and their goodness‐of‐fit was assessed to identify the most suitable model for the drying process. This analysis encompassed applying two distinct temperatures (50°C and 55°C), and voltage levels (20 and 30 kV). The EHD method yielded the lengthiest drying durations for the strawberry samples, with hot air and EHD‐hot air drying techniques subsequent in descending order of drying time. The outcomes of the modeling analyses demonstrated that the thin‐layer drying behaviors of used drying methods were best described by the Midilli et al. and Wang and Singh models in terms of their goodness‐of‐fit. A decline in the L* values was noted with the elevation of temperature in the hot air drying method and with the escalation of voltage in the EHD drying. The minimum a* value was detected in the hot air drying method conducted at 55°C. The rehydration capacity of strawberry samples subjected to the EHD and EHD‐hot air combination drying methods (except 20 kV‐55°C) did not exhibit any statistically significant variation in response to different voltage settings. Although the structure of strawberry samples dried with 20 kV application was observed as smoother, cracks occurred on the product surface in other drying applications. In hot air and EHD methods, varying temperature and volt applications did not show a significant effect on TSS and pH values. As a result, it has been seen that EHD technology, which is a promising drying method used in this study, is a suitable method in terms of processing efficiency and consumer acceptability of dried strawberry products. [ABSTRACT FROM AUTHOR]

Kinesin-5 motors play an essential role during mitotic spindle assembly in many organisms 1,2,3,4,5,6,7,8,9,10,11 : they crosslink antiparallel spindle microtubules, step toward plus ends, and slide the microtubules apart. 12,13,14,15,16,17 This activity separates the spindle poles and chromosomes. Kinesin-5s are not only plus-end-directed but can walk or be carried toward MT minus ends, 18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34 where they show enhanced localization. 3,5,7,27,29,32 The kinesin-5 C-terminal tail interacts with and regulates the motor, affecting structure, motility, and sliding force of purified kinesin-5 35,36,37 along with motility and spindle assembly in cells. 27,38,39 The tail contains phosphorylation sites, particularly in the conserved BimC box. 6,7,40,41,42,43,44 Nine mitotic tail phosphorylation sites were identified in the kinesin-5 motor of the fission yeast Schizosaccharomyces pombe , 45,46,47,48 suggesting that multi-site phosphorylation may regulate kinesin-5s. Here, we show that mutating all nine sites to either alanine or glutamate causes temperature-sensitive lethality due to a failure of bipolar spindle assembly. We characterize kinesin-5 localization and sliding force in the spindle based on Cut7-dependent microtubule minus-end protrusions in cells lacking kinesin-14 motors. 39,49,50,51,52 Imaging and computational modeling show that Cut7p simultaneously moves toward the minus ends of protrusion MTs and the plus ends of spindle midzone MTs. Phosphorylation mutants show dramatic decreases in protrusions and sliding force. Comparison to a model of force to create protrusions suggests that tail truncation and phosphorylation mutants decrease Cut7p sliding force similarly to tail-truncated human Eg5. 36 Our results show that C-terminal tail phosphorylation is required for kinesin-5/Cut7 sliding force and bipolar spindle assembly in fission yeast. [Display omitted] • Phosphorylation site mutations in the kinesin-5 tail affect motor function • Phosphorylation site mutants show impaired mitotic spindle assembly and elongation • Kinesin-5 sliding force is reduced in phosphorylation mutants • Computational modeling reproduces kinesin-5 accumulation in protrusions Jones et al. show that phosphorylation of the kinesin-5 C-terminal tail is required for proper spindle assembly in fission yeast. Mutations in multiple tail phosphorylation sites impair both spindle function and motor sliding force, similarly to full tail truncations of fission yeast and (by comparison to a model of force) human kinesin-5s. [ABSTRACT FROM AUTHOR]

The insertion of finer Z‐pins is an effective method to reduce the microstructure damage and maintain the in‐plane performance of composite laminates. In this paper, an ultrasound guided insertion process is proposed to achieve the insertion of fine Z‐pins and the influence of Z‐pin material on the mechanical properties of Ø0.11 mm Z‐pin reinforced composite laminates is revealed based on experiments and simulations. For in‐plane mechanical properties, stainless steel Z‐pins help to reduce the loss of compression modulus and compression strength. For the interlaminar mechanical properties, stainless steel Z‐pins and carbon fiber Z‐pins exhibit bilinear and trilinear bridging laws respectively, resulting in the mode‐I maximum load and fracture toughness of carbon fiber Z‐pin reinforced composites being greater than that of stainless steel Z‐pin reinforced composites. By adjusting the insertion spacing, it is found that for unidirectional composite laminates reinforced by Ø0.11 mm carbon fiber Z‐pins with an insertion spacing of 2 mm, the compression strength reduction is only 3.39% while the mode‐I fracture toughness can be improved by 14.4 times in comparison with the unpinned specimens, achieving a better balance between in‐plane performance loss and interlaminar reinforcement effect. Highlights: The effect of Z‐pin material on fine Z‐pin reinforced composites is studied.The compression properties are simulated by the RVE model.The mode‐I fracture properties are simulated by the DCB unit strip model. [ABSTRACT FROM AUTHOR]

AbstractThis paper presents a novel capacitive microelectromechanical system comprising an array of elastomeric micro-pillars, which has been developed through the introduction of a coupled set of three nonlinear equations. The results have demonstrated that the new model utilizing polydimethylsiloxane is effective in reducing the actuation voltage, although this efficiency is reduced when the temperature is decreased. Moreover, the impact of frequency-dependent permittivity and its relaxation time on the vibrations of the moving electrode has been examined. The provided model and procedure can be utilized to analyze the effects of other materials on the system’s performance and to enhance its sensitivity. [ABSTRACT FROM AUTHOR]

Does inventing representations in open-ended computational environments contradict the goal of acquiring canonical scientific knowledge? We analyze the computational models of electric conductors that N = 35 eight-grade students built with a NetLogo-based microworld. Students used the microworld to sketch structures that represent models of electric conductors and then observed the resulting electric current. We use data from classroom discourse, computer log files and questionnaires, to identify different ways through which students used the platform, and how their engagement in construction, explains the variance in their learning gains. We found differences in the number and types of models that students constructed in the microworld: while most students built the models that were intended by the instructors, some added unconventional doodling models, that deviated from the intended structures. We found that students who built more models made larger gains in conceptual knowledge about electric current. However, we also found that students who constructed more unconventional models, produced lower learning gains on items that assessed the particle-level mechanism of electrons in conductors. [ABSTRACT FROM AUTHOR]

Summary Characteristic of all conifer needles, the transfusion tissue mediates the radial transport of water and sugar between the endodermis and axial vasculature. Physical constraints imposed by the needle's linear geometry introduce two potential extravascular bottlenecks where the opposition of sugar and water flows may frustrate sugar export: one at the vascular access point and the other at the endodermis. We developed a network model of the transfusion tissue to explore how its structure and composition affect the delivery of sugars to the axial phloem. To describe extravascular transport with cellular resolution, we construct networks from images of Pinus pinea needles obtained through tomographic microscopy, as well as fluorescence and electron microscopy. The transfusion tissue provides physically distinct pathways for sugar and water, reducing resistance between the vasculature and endodermis and mitigating flow constriction at the vascular flank. Dissipation of flow velocities through the transfusion tissue's branched structure allows for bidirectional transport of an inbound diffusive sugar flux against an outbound advective water flux across the endodermis. Our results clarify the structure–function relationships of the transfusion tissue under conditions free of physiological stress. The presented model framework is also applicable to different transfusion tissue morphologies in other gymnosperms. [ABSTRACT FROM AUTHOR]

The fuel cell market, which utilizes hydrogen as a fuel for zero-carbon power generation to address global warming, continues to grow steadily. In the building sector, where small-scale fuel cells of 10 kW or less have been predominant, there is increasing interest in scaling up fuel cells to increase deployment and reduce system costs. The multi-stack fuel cell system (MFCS), which consists of connecting multiple modules, is one method of configuring large-scale fuel cell systems, offering advantages in system efficiency, durability, and cost. The objective of this paper is to compare and analyze the behavior of the MFCS according to different operation strategies. In particular, it compares the Equivalent operation method, which behaves similarly to the single-stack fuel cell system (SFCS), with others (Daisy-Chain, Electrical Optimization). Unlike previous studies that only focused on the electrical efficiency of MFCS, this study analyzed thermal energy, which is indispensable in building applications, to maximize the effects of MFCS. Simulation of operation strategies based on the electricity and heat demand of a specific residence showed that MFCS outperformed the SFCS in terms of electrical and heat efficiency, hydrogen consumption, degradation, and operating costs. • The Multi-Stack Fuel Cell System (MFCS) considers both electrical and heat efficiency. • Application of MFCS in residential environments with demand for both power and heat. • MFCS offers advantages in terms of efficiency, durability, and operating costs. [ABSTRACT FROM AUTHOR]

Modeling of various phenomena in engineering work is always a kind of simplification of real processes, aiming at a model where a certain level of mathematical theory and computational procedures is sufficient. If the complexity of the required theory corresponds to the general mathematical competence of engineers, then technical problems can be treated separately in engineering (or physical) models without regard to the mathematical background. However, in some advanced engineering fields, the harmonized development of engineering and mathematical models and toolboxes is necessary to find efficient solutions. For example, modeling variable structure systems in ideal sliding mode requires a mathematical toolbox that goes far beyond general engineering competence through the theory of discontinuous right-hand-side differential equations. Although sliding mode control is popular in practice and the concept of sliding mode allows a significant reduction of model complexity, its exact mathematical description is rarely encountered. The problem of friction compensation of a micro-telemanipulator using sliding mode control demonstrates a harmonized application of the mathematical and engineering approaches. Based on Filippov's theory, the ideal sliding mode can be discussed. Although an ideal system cannot be implemented in reality, the real systems can be kept close enough to it; therefore, the discussion of the solution of the ideal model is important for practical applications. Although several elements of the topic are available in the literature, in this paper a unique complex approach is given for users of sliding mode control with experimental considerations, different engineering models, and codes. The paper concludes that sliding mode control is a case where engineering and mathematical modeling are inseparable and requires the competence of both fields. [ABSTRACT FROM AUTHOR]

The use of potentially beneficial microorganisms in agriculture (microbial inoculants) has rapidly accelerated in recent years. For microbial inoculants to be effective as agricultural tools, these organisms must be able to survive and persist in novel environments while not destabilizing the resident community or spilling over into adjacent natural ecosystems. Despite the importance of propagule pressure to species introductions, few tools exist in microbial ecology to predict the outcomes of agricultural microbial introductions. Here, we adapt a macroecological propagule pressure model to a microbial scale and present an experimental approach for testing the role of propagule pressure in microbial inoculant introductions. We experimentally determined the riskrelease relationship for an IAA-expressing Pseudomonas simiae inoculant in a model monocot system. We then used this relationship to simulate establishment outcomes under a range of application frequencies (propagule number) and inoculant concentrations (propagule size). Our simulations show that repeated inoculant applications may increase establishment, even when increased inoculant concentration does not alter establishment probabilities. Applying ecological modeling approaches like those presented here to microbial inoculants may aid their sustainable use and provide a monitoring tool for microbial inoculants. [ABSTRACT FROM AUTHOR]

Robotic arms have received attention in manufacturing due to their high precision and productivity. As their demand rises, energy consumed by robotic arms in manufacturing applications has become a critical issue. Predicting energy usage of industrial robotic arms is therefore necessary to assess and reduce their energy consumption and costs. However, existing power estimation methods require too many mechanical inputs or focus on only one single robotic arm without considering operational perspectives such as interactions between robotic arms and machines. To tackle these issues, this study proposes a simplified power estimation model for robotic arms and provides energy simulation models in which machines and robotic arms work together. For more accurate results, power data are collected through experiments of central composite design (CCD) using a 7-axis robotic arm. Then, regression models are built to estimate power demand of the robotic arm as three simple inputs: starting point, ending point, and payload. The regression analyses of this study consistently show that the power demand estimation models are very accurate with high R2 values between 0.95 and 0.98. Then, a manufacturing system with milling machines supported by robotic arms is simulated. By applying the proposed method, the activities of robotic arms and machines are simulated with various operational factors, and the time series power is obtained. Further analyses suggest that energy performance of robotic arms can be improved at a maximum of 22% by optimizing two operational parameters: the base and idle positions of robotic arms. The proposed method facilitates energy modeling and simulation processes for robot-assisted manufacturing and enables practitioners and researchers to readily adjust operational parameters and evaluate energy performance for robot applications in manufacturing. [ABSTRACT FROM AUTHOR]

This paper concerns the modeling of smart health sensors for data monitoring, where the data are eventually noisy, with correlated noise. In this work, we applied Clifford-based wavelets/multiwavelets for correlated noise in multi-sensors health data monitoring by estimating the set of sensor nodes minimizing an eventual error computed by the signal values at the selected nodes mostly caused by the correlated noise. Instead of directly minimizing the estimation error, we focused on evaluating a multi-level scheme based on multiwavelets for the estimation of the error between the parameter vector and its sub-vector of those nodes. Numerical simulations are provided with a comparison to some recent existing works. This model showed a performance and a fast time execution compared to those existing works. This model exceeds these models by the non-necessity to assume a priory structure of the data. Wavelets are capable to detect, localize, and eliminate the noise, even correlated, efficiently via the independent uncorrelated multiwavelets’ components. [ABSTRACT FROM AUTHOR]

The utilization of pesticides has experienced a substantial rise in order to enhance both the quantity and caliber of the produced goods. Agricultural runoff, a major source of nonpoint pollution, adversely impacts surface water, groundwater, soil and sediment quality in the watershed. Especially, organochlorine pesticides have adverse effects on the environment because of their toxicity, persistence and bioaccumulation. In this study, the presence of organochlorine pesticides (DDTs- dichlorodiphenyltrichloroethane and HCHs- hexachlorocyclohexane) in the environment of the dryland agricultural watershed, was determined. For this purpose, soil, sediment and water samples from the landscape of pond were collected. The concentrations of organochlorine pesticides in water, sediment, and soil ranged from 0.0005 to 0.0138 μg/L, 0.03 to 1.02 μg/kg, and 0.54 to 52 μg/kg, respectively. The persistence of these pesticides in soil and sediment poses long-term risks to both terrestrial and aquatic ecosystems. Sediment, as a significant reservoir for these contaminants, can act as a secondary source of pollution, releasing pesticides back into the water column under certain environmental conditions. Similarly, soil contamination not only affects plant health and productivity but also leads to further leaching and runoff into water bodies. The behavior of a toxic substance in water, soil and sediment environment, which is organochlorine pesticide, in a well-mixed lake watershed, was modeled using mass balances in the lake. Statistical analysis, including a paired t-test and calculation of the Nash-Sutcliffe index, confirms the consistency and reliability of the model’s predictions compared to observed data. Simulated concentrations closely matched observed values, and the distribution patterns and trends remained consistent across different months. Moreover, the Risk Quotient (RQ) for DDTs and HCHs is less than 0.1, indicating low ecological risk. [ABSTRACT FROM AUTHOR]

Many researchers have focused on molecular electronics, and this field can progress faster by applying semiconductors to molecular modeling. In this work, two middle rings of a bipyridine–biborinine molecular diode are rotated from 25∘ to 90∘ such that the current along the molecule is reduced. The current is minimum and almost zero at 90∘, while it is maximum at 25∘. Accordingly, considering these two angles, the molecule can act as a switching device when the two middle rings are rotated. The bipyridine–biborinine molecular diode is modeled considering the effect of rotation by including two resistors in the model. Two parallel diodes are used in this modeling for pyridine and borinine rings. The ideality factors of these diodes are varied based on the electronegativity of pyridine and borinine rings. This model is then applied to consider different molecular logic gates such as NAND, NOR, NOT, OR and AND gates, as well as molecular logic circuits (half adder and full adder), using the unique capabilities of the LTspice software. [ABSTRACT FROM AUTHOR]

By experimental characterization and numerical modeling, the dependence of thermophysical properties on time and temperature as well as its influence on the thermomechanical behavior of alumina–magnesia castables has been studied in this work. The thermal conductivity, thermal expansion coefficient, and Young's modulus are the most important parameters, which evolve with temperature and piecewise change with the thermal processes of drying, preheating, and service. It is mainly related to the sintering degree and amount of formed calcium aluminate and spinel phases. The sensitivity analysis of parallel simulations for multilayer steel ladle with different numbers of temperature‐dependent thermophysical parameters demonstrates that the thermal conductivity is the most influential parameter, as it dominates the temperature distribution and affects the succeeding thermal expansion as well as deformation. The increase of thermal expansion coefficient and decrease of Young's modulus with temperature counterbalance the deformation of linings under thermal process. Meanwhile, compared with the simulation using temperature‐independent parameters, the model with temperature‐dependent thermophysical parameters exhibits more severe damage in outer surface of both the working lining and the permanent lining. This gives new insight into the adoption of temperature‐dependent parameters for actual thermomechanical behavior evaluation of refractories. [ABSTRACT FROM AUTHOR]

While blast furnaces remain the dominant ironmaking technology, there is growing interest in using hydrogen to partially replace coke and pulverized coal to reduce CO2 emissions in this hard‐to‐decarbonize process. However, significant discrepancies in modeling the tuyere injection of hydrogen are noted in the literature, which can challenge the scale‐up from modeling predictions and pilot trials to actual blast furnace operations. To address this, the impact of uncertainties in the assumptions of the temperature in the thermal reserve zone and the ratio of H2 and CO utilization on the results of blast furnace process modeling are evaluated using a 1D steady‐state zonal model. The study indicates that variations in the assumed value of thermal reserve zone temperature significantly impact the estimation of optimal hydrogen injection rate, coke replacement ratio, and direct CO2 emissions reductions. In contrast, the same parameters are much less affected by the variations in the assumed value of the proportion between H2 and CO utilization ratios, which, however, affect the top gas composition. A need for further research to determine the range of the difference in temperatures of gas and solid in the thermal reserve zone tolerable for smooth blast furnace operation is highlighted. [ABSTRACT FROM AUTHOR]

The turbulent airflow in a rectangular channel with two porous vertical baffles of 25%, 50%, and 75% porosity is investigated using the finite volume method and Fluent software. The k-ε turbulence model analyzes turbulence dynamics, adjusting boundary conditions for varying inlet velocities. Key parameters, including turbulent kinetic energy (TKE), dynamic pressure (DP), temperature variation (T), friction coefficient (Cf), Nusselt number (Nu), and thermal enhancement factor (TEF) are evaluated at Reynolds numbers of 28,000 and 56,000. Findings indicate that at a Reynolds number of 28,000, the 25% porous ratio maximizes energy transfer, while the friction coefficient peaks at the same ratio for a Reynolds number of 56,000. Notably, the TEF reaches 1.4 at a 75% porous ratio for a Reynolds number of 28,000. This research uniquely demonstrates the pronounced impact of porous baffles on heat transfer and friction under different Reynolds numbers, particularly highlighting the significance of baffle porosity in lower Reynolds number regimes. [ABSTRACT FROM AUTHOR]

In this study, a mathematical model was formulated to characterize the functioning of third‐generation enzyme‐based amperometric biosensors. This study utilized a horseradish peroxidase (HRP)‐based, screen‐printed biosensor. The model parameters were determined using the Environment for Modeling Simulation and Optimization (EMSO), validated with experimental data, and exhibited high determination coefficients, indicating the accuracy of the models in capturing biosensor behavior. Simulations based on this model emphasize diffusion as a limiting factor. Further sensitivity analysis was conducted using simulated response surfaces and biosensor performance optimization, which confirmed that higher concentrations of HRP in the thinnest polymeric film resulted in enhanced biosensor sensitivity. [ABSTRACT FROM AUTHOR]

This study examines the influence of the reinforced-concrete frame topology and crack opening effects on the structural behavior under a column removal scenario. For this purpose, a variant of discretization of the finite element model has been proposed, which takes into account additional rotations during the formation of discrete cracks. As a criterion of the bearing capacity of structures, the equilibrium point at the force-displacement curve was adopted. Verification of the proposed method has been performed using experimental data for a U-shaped frame. The paper provides a comparative analysis of the calculation results for 3-story, 5-story and 9-story reinforced-concrete frames using structural elements and the proposed method. It is found that accounting for the discrete nature of cracks had practically no effect on the magnitudes of axial forces in the elements. However, for bending moments, the proposed method showed a decrease compared to the traditional approach. The increase of axial tensile forces in the support sections of the beam above the zone of local failure has been detected for reinforced-concrete frames with 3 - 5 stories, which can lead to an increase in the influence of buckling for the columns to which such a beam is adjacent. [ABSTRACT FROM AUTHOR]

Leaf respiratory carbon loss decreases independent of temperature as the night progresses. Detailed nighttime measurements needed to quantify cumulative respiratory carbon loss at night are challenging under both lab and field conditions. We provide a simple yet accurate approach to represent variation in nighttime temperature‐independent leaf respiratory CO2 efflux in environments with both stable and fluctuating temperatures, which requires no detailed measurements throughout the night. We demonstrate that the inter‐ and intraspecific variation in the cumulative leaf respiratory CO2 efflux at constant temperature, at any length of night, scales linearly with the inter‐ and intraspecific variation in initial measurement of leaf respiratory CO2 efflux at the same temperature at the beginning of the night. This approach informs large‐scale predictions of cumulative leaf respiratory CO2 efflux, which is needed to understand plant carbon economy in global change studies as well as in global modeling and eddy covariance monitoring of the land–atmosphere exchange of CO2. [ABSTRACT FROM AUTHOR]

Glacial isostatic adjustment (GIA) describes the response of the solid Earth, oceans, and gravitational field to the spatio‐temporal evolution of ice sheets during a glacial cycle. Present‐day vertical displacements and sea‐level changes vary throughout eastern North America in response to the melting of the Laurentide Ice Sheet following the Last Glacial Maximum. We use the open‐source software SELEN4.0 (a SealEveL EquatioN solver) to investigate the influence of GIA on vertical land motions and sea‐level changes in eastern North America. Further, we evaluate the uncertainties associated with the lithospheric thickness and viscosity structure using an ensemble modeling approach (129,956 total simulations). We identify the best‐fitting rheological profiles by comparing modeled vertical displacements to vertical velocity rates derived from Global Positioning System (GPS). We find a general pattern of subsidence (causing accelerated relative sea‐level rise) in the eastern United States region and uplift (causing relative sea‐level fall) in the eastern Canada region consistent with previous studies for two tested ice sheet models (ICE‐6G(VM5a) and ICE‐7G(VM7)). Overall, we find lower rates of modeled vertical displacement using ICE‐6G(VM5a) compared with ICE‐7G(VM7), which produces lower residuals when compared with the GPS‐derived vertical velocity rates. Our ensemble analysis identifies adjustments to the nominal VM5a and VM7 viscosity models that improve fits to the GPS‐imaged vertical velocity rates throughout eastern North America and on the North American Atlantic Coast. The differences in our best‐fitting models for inland versus coastal regions highlight the importance of exploring lateral viscosity variations for GIA modeling throughout North America and elsewhere. Plain Language Summary: Approximately 19,000–26,000 years ago a massive ice sheet covered Canada and a portion of the northern United States. We investigate the Earth's surface movements following the melting of the ice sheet throughout eastern North America. Through computational modeling, we predict rates of vertical land motion and relative sea‐level change throughout eastern North America. We see a general pattern of downward movements (causing relative sea‐level rise) in the eastern United States region and upward movements (causing relative sea‐level fall) in the eastern Canada region. However, modeled rates vary depending on the assumptions about Earth's internal structure. We test different models of Earth structure, based upon variations of the Earth structure profiles paired with two published models of ice sheet history. Our different models vary the lithospheric thickness and radial viscosity structure (viscosity varies by depth only). By comparing the rates of Earth's surface movements generated by these different models to published data of observed vertical land motions, we determine which Earth structure models produce the best fits to the published observations. Differences in the best‐fitting Earth structure for different regions of eastern North America indicate the importance of exploring Earth structures that vary both radially and laterally. Key Points: Modeled present‐day vertical displacement and sea‐level change rates fit observed data better using ICE‐6G(VM5a) compared with ICE‐7G(VM7)Ensemble analysis suggests adjustments to VM5a and VM7 viscosity models improve fits to geodetic data throughout eastern North AmericaRegional differences in best‐fitting Earth structure support the adoption of lateral viscosity in glacial isostatic adjustment modeling [ABSTRACT FROM AUTHOR]