Search

Purpose: In this study, we compared the influence of movement specificity during velocity–load jump testing to predict on-ice acceleration performance in elite speed skaters. Methods: Elite long-track speed skaters (N = 27) performed velocity–load testing with 3 external loads during unilateral horizontal jumping, lateral jumping, and bilateral vertical countermovement jumping. For the unilateral tests, external load conditions were set to 10 N, 7.5% and 15% of external load relative to body weight. For the countermovement jumping, load conditions were body weight and 30% and 60% of external load relative to body weight. On-ice performance measures were obtained during maximal 50-m accelerations from a standing start, including maximal skating speed, maximal acceleration capacity, and maximum horizontal power. The 100-m split time from a 500-m race was also obtained. Regularized regression models were used to identify the most important predictors of on-ice acceleration performance. In addition to regularized regression coefficients, Pearson correlation coefficients (r) were calculated for all variables retained by the model to assess interrelationships between single predictors and on-ice performance measures. Results: The countermovement jump with 30% of body mass demonstrated the strongest association with maximal skating speed, maximum horizontal power, and 100-m time (regularized regression coefficient =.16−.49, r =.84−.97, P <.001). Horizontal jump with 15% of body mass was the strongest predictor of maximal acceleration capacity performance (regularized regression coefficient =.08, r =.83, P <.001). Conclusions: The findings of this study suggest that mechanical specificity rather than movement specificity was more relevant for predicting on-ice acceleration performance. [ABSTRACT FROM AUTHOR]

Musculoskeletal modeling, typically implemented using marker-based systems in laboratory environments, is commonly used for noninvasive estimations of loads. Inertial measurement units (IMUs) have become an alternative for the evaluation of kinematics. However, estimates of spine joint contact forces using IMUs have yet to be thoroughly evaluated. Dynamics tasks and static postures from activities of daily living were captured on 11 healthy subjects using both systems simultaneously. Spine kinematics obtained from IMU- and marker-based systems and L4–L5 joint contact forces were compared. Lateral bending resulted in a weak agreement with significant differences between the 2 systems (P =.02, average root mean-squared error = 4.81), whereas flexion–extension and axial rotation exhibited the highest agreement with no significant differences (P <.05, average root mean-squared error = 5.51 and P <.31, average root mean-squared error = 5.08, respectively). All tasks showed excellent correlations (R2 =.76–.99) in estimated loads between systems. Differences in predicted loads at the L4–L5 were only observed during flexion–extension (1041 N vs 947 N, P =.0004) and walking with weights (814 N vs 727 N, P =.004). Different joint reaction force outcomes were obtained in 2 of the 8 tasks between systems, suggesting that IMUs can be robust tools allowing for convenient and less expensive evaluations and for longitudinal assessments inside and outside the laboratory setting. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a theory on the frequent observation that cyclists prefer cadences (RPMs) higher than those considered most economical at submaximal exercise intensities via modeling and simulation of its mathematical description. Methods: The theory combines the parabolic power-to-velocity (v) relationship, where v is defined by crank length, RPM-dependent ankle velocity, and gear ratio, RPM effects on the maximal lactate steady state (MLSS), and lactate-dependent carbohydrate oxidation (CHO). It was tested against recent experimental results of 12 healthy male recreational cyclists determining the v-dependent peak oxygen uptake (VO2PEAKv), MLSS (MLSSv), corresponding power output (PMLSSv), oxygen uptake at PMLSSv (VO2MLSSv), and CHOMLSSv-management at 100 versus 50 per minute, respectively. Maximum RPM (RPMMAX) attained at minimized pedal torque was measured. RPM-specific maximum sprint power output (PMAXv) was estimated at RPMs of 100 and 50, respectively. Results: Modeling identified that MLSSv and PMLSSv related to PMAXv (IPMLSSv) promote CHO and that VO2MLSSv related to VO2PEAKv inhibits CHO. It shows that cycling at higher RPM reduces IPMLSSv. It suggests that high cycling RPMs minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. Conclusions: The present theory-guided modeling approach is exclusively based on data routinely measured in high-performance testing. It implies a higher performance reserve above IPMLSSv at higher RPM. Cyclists may prefer high cycling RPMs because they appear to minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. [ABSTRACT FROM AUTHOR]

To understand the extent of anthropogenic noise in the ocean, it is essential to compare the differences between modern noise environments and their pre-industrial equivalents. The Santa Barbara Channel, off the coast of Southern California, is a corridor for the transportation of goods to and from the busiest shipping ports in the Western hemisphere. Commercial ships introduce high levels of underwater noise into the marine environment. To quantify the extent of noise in the region, we modeled pre-industrial ocean noise levels, driven by wind, and modern ocean noise levels, resulting from the presence of both ships and wind. By comparing pre-industrial and modern underwater noise levels, the low-frequency (50 Hz) acoustic environment was found to be degraded by more than 15 dB. These results can be used to identify regions for noise reduction efforts, as well as to model scenarios to identify those with the greatest potential to support marine conservation efforts.

Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a "top 10" list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Purpose: To evaluate statistical models developed for predicting medal-winning performances for international swimming events and generate updated performance predictions for the Paris 2024 Olympic Games. Methods: The performance of 2 statistical models developed for predicting international swimming performances was evaluated. The first model employed linear regression and forecasting to examine performance trends among medal winners, finalists, and semifinalists over an 8-year period. A machine-learning algorithm was used to generate time predictions for each individual event for the Paris 2024 Olympic Games. The second model was a Bayesian framework and comprised an autoregressive term (the previous winning time), moving average (past 3 events), and covariates for stroke, gender, distance, and type of event (World Championships vs Olympic Games). To examine the accuracy of the predictions from both models, the mean absolute error was determined between the predicted times for the Budapest 2022 World Championships and the actual results from said championships. Results: The mean absolute error for prediction of swimming performances was 0.80% for the linear-regression machine-learning model and 0.85% for the Bayesian model. The predicted times and actual times from the Budapest 2022 World Championships were highly correlated (r =.99 for both approaches). Conclusions: These models, and associated predictions for swimming events at the Paris 2024 Olympic Games, provide an evidence-based performance framework for coaches, sport-science support staff, and athletes to develop and evaluate training plans, strategies, and tactics, as well as informing resource allocation to athletes, based on their potential for the Paris 2024 Olympic Games. [ABSTRACT FROM AUTHOR]

There is a powerful global trend toward deeper integration of digital twins into modern life driven by Industry 4.0 and 5.0. Defense, agriculture, engineering, manufacturing, and urban planning sectors have thoroughly incorporated digital twins to great benefit across their respective product lifecycles. Despite clear benefits, a digital twin framework for health and medical sectors is yet to emerge. This paper proposes a digital twin framework for precision neuromusculoskeletal health care. We build upon the International Standards Organization framework for digital twins for manufacturing by presenting best available computational models within a digital twin framework for clinical application. We map a use case for modeling Achilles tendon mechanobiology, highlighting how current modeling practices align with our proposed digital twin framework. Similarly, we map a use case for advanced neurorehabilitation technology, highlighting the role of a digital twin in control of systems where human and machine are interfaced. Future work must now focus on creating an informatic representation to govern how digital data are passed to, from, and within the digital twin, as well as specific standards to declare which measurement systems and modeling methods are acceptable to move toward widespread use of the digital twin framework for precision neuromusculoskeletal health care. [ABSTRACT FROM AUTHOR]

In this review, we elaborate on how musculoskeletal (MSK) modeling combined with dynamic movement simulation is gradually evolving from a research tool to a promising in silico tool to assist medical doctors and physical therapists in decision making by providing parameters relating to dynamic MSK function and loading. This review primarily focuses on our own and related work to illustrate the framework and the interpretation of MSK model-based parameters in patients with 3 different conditions, that is, degenerative joint disease, cerebral palsy, and adult spinal deformities. By selecting these 3 clinical applications, we also aim to demonstrate the differing levels of clinical readiness of the different simulation frameworks introducing in silico model-based biomarkers of motor function to inform MSK rehabilitation and treatment, with the application for adult spinal deformities being the most recent of the 3. Based on these applications, barriers to clinical integration and positioning of these in silico technologies within standard clinical practice are discussed in the light of specific challenges related to model assumptions, required level of complexity and personalization, and clinical implementation. [ABSTRACT FROM AUTHOR]

POREnet, a novel approach to model effective properties of thin porous media, TPM, is presented. The methodology allows the extraction of local effective property tensors by volume averaging from discrete pore networks, PNs, built on the tessellated continuum space of a TPM. The gradient theorem is used to describe 3D transport in bulk tessellated space, providing an appropriate metric to normalize network fluxes. Implemented effective transport properties include diffusivity, permeability, solid-phase conductivity, and entry capillary pressure and contact angle under two-phase conditions, considering multi-component materials with several solid phases and local contact resistances. Calculated property tensors can be saved on 3D image stacks, where interfacial and sub-CV scale features can be added before exporting data to CFD meshes for simulation. Overall, POREnet provides a general-purpose, versatile methodology for modeling TPM in an ample range of conditions within a single CFD framework. Among other advantages, coupling of PN and continuum models at TPM-channel interfaces is simplified, interfacial contact resistances can be included using robin boundary conditions, and transient multiphysics simulations can be implemented more easily using CFD. The code is tested against a miscellaneousness of examples extracted from electrochemical applications.

Arid and semi-arid regions of the world are particularly vulnerable to greenhouse gas-driven hydroclimate change. Climate models are our primary tool for projecting the future hydroclimate that society in these regions must adapt to, but here, we present a concerning discrepancy between observed and model-based historical hydroclimate trends. Over the arid/semi-arid regions of the world, the predominant signal in all model simulations is an increase in atmospheric water vapor, on average, over the last four decades, in association with the increased water vapor-holding capacity of a warmer atmosphere. In observations, this increase in atmospheric water vapor has not happened, suggesting that the availability of moisture to satisfy the increased atmospheric demand is lower in reality than in models in arid/semi-arid regions. This discrepancy is most clear in locations that are arid/semi-arid year round, but it is also apparent in more humid regions during the most arid months of the year. It indicates a major gap in our understanding and modeling capabilities which could have severe implications for hydroclimate projections, including fire hazard, moving forward.

Triangular cross-section color center photonics in silicon carbide is a leading candidate for scalable implementation of quantum hardware. Within this geometry, we model low-loss beam splitters for applications in key quantum optical operations such as entanglement and single-photon interferometry. We consider triangular cross-section single-mode waveguides for the design of a directional coupler. We optimize parameters for a 50:50 beam splitter. Finally, we test the experimental feasibility of the designs by fabricating triangular waveguides in an ion beam etching process and identify suitable designs for short-term implementation.

Abstract: The aim of this study is to enable the hydrogen economy and decarbonize various sectors in our environment that requires less expensive and more durable water electrolyzers, which can meet the Hydrogen-Shot target. The key is to improve the ionomer interfaces in low-temperature water electrolyzers as rapidly as possible, but to do so, it requires a systematic and holistic campaign combining both experiments and theory. In this perspective, we discuss the issues of electrolyzers and needs for translational science. We then present the approach that the Energy EarthShot Research Center: Center for Ionomer-based Water Electrolysis is taking in hopes of inspiring the community with this approach that can be leveraged to multiple problems and technologies. Graphical abstract: (Figure presented.) Highlights: One way to achieve the Hydrogen-Shot goal of low-cost, clean hydrogen, is advancing research and development on the interfaces of water electrolyzers for both performance and lifetime. The Center for Ionomer-based Water Electrolysis is exploring new techniques and strategies to not only interrogate interfacial phenomena in water electrolyzers to increase efficiency and durability, but also a new paradigm related to synergistic, cojoined experimental and theoretical research. Discussion: Catalyst\ionomer interfaces are complex and not fully understood, but through investigating different interfaces and utilizing digital and physical twins, we can elucidate key mechanisms and understanding. Understanding the dynamic double layer in electrochemical systems that use solid electrolytes is crucial to identifying and mitigating the controlling phenomena to enable increased performance and durability at the technology level. Studying the time and length scales of interfacial changes can be a powerful tool to understand reaction mechanisms and changes in the electrolyzer performance and durability.

Climate change has rapidly altered marine ecosystems and is expected to continue to push systems and species beyond historical baselines into novel conditions. Projecting responses of organisms and populations to these novel environmental conditions often requires extrapolations beyond observed conditions, challenging the predictive limits of statistical modeling capabilities. Bioenergetics modeling provides the mechanistic basis for projecting climate change effects on marine living resources in novel conditions, has a long history of development, and has been applied widely to fish and other taxa. We provide our perspective on 4 opportunities that will advance the ability of bioenergetics-based models to depict changes in the productivity and distribution of fishes and other marine organisms, leading to more robust projections of climate impacts. These are (1) improved depiction of bioenergetics processes to derive realistic individual-level response(s) to complex changes in environmental conditions, (2) innovations in scaling individual-level bioenergetics to project responses at the population and food web levels, (3) more realistic coupling between spatial dynamics and bioenergetics to better represent the local- to regional-scale differences in the effects of climate change on the spatial distributions of organisms, and (4) innovations in model validation to ensure that the next generation of bioenergetics-based models can be used with known and sufficient confidence. Our focus on specific opportunities will enable critical advancements in bioenergetics modeling and position the modeling community to make more accurate and robust projections of the effects of climate change on individuals, populations, food webs, and ecosystems.

Understanding factors that affect the clustering and association of antibodies molecules in solution is critical to their development as therapeutics. For 19 different monoclonal antibody (mAb) solutions, we measured the viscosities, the second virial coefficients, the Kirkwood-Buff integrals, and the cluster distributions of the antibody molecules as functions of protein concentration. Solutions were modeled using the statistical-physics Wertheim liquid-solution theory, representing antibodies as Y-shaped molecular structures of seven beads each. We found that high-viscosity solutions result from more antibody molecules per cluster. Multi-body properties such as viscosity are well predicted experimentally by the 2-body Kirkwood-Buff quantity, G22, but not by the second virial coefficient, B22, and well-predicted theoretically from the Wertheim protein-protein sticking energy. Weakly interacting antibodies are rate-limited by nucleation; strongly interacting ones by propagation. This approach gives a way to relate micro to macro properties of solutions of associating proteins.

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]

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]

This article presents examples of the use of complex sheet metal in everyday life and the course of modeling a sheet bent simultaneously in two planes. The element selected to describe the modelling process is a fragment of the side wall of a lever hydroelectric power plant that operates on the Bystrzyca River in Lublin. The shape of the element is conditioned by the need to achieve a gentle change in the cross-section of the water stream during the flow between the steering system and the outflow area. The segment through which the water flows is clearly lower and wider than the other modules of the power plant, which is due to the average water level in the Bystrzyca River. Such a shape is necessary because the inflow and outflow parts of the power plant must be submerged under water all the time. This is due to the principle of operation of the power plant, which works by obtaining a vacuum created by pumping air from the inside. The article also presents the results of the strength analysis, which was performed for the segment exposed to the highest loads. The analysis and the entire design were performed in Autodesk Inventor, and the results obtained were as expected and even in the most critical places no values were reached that would require changes to the design. [ABSTRACT FROM AUTHOR]

The significant production of sewage sludge by wastewater treatment plants on a global scale and the lack of correspondence between housing development and the expansion of sanitation infrastructure indicate a genuine concern regarding environmental preservation. This study addresses the crucial issue of effective sewage sludge management and its environmental impact. In the context of searching for new drying methods that optimize energy use and effectively stabilize sewage sludge, this work investigated the drying behavior of sewage sludge from treatment plants in two northern Moroccan cities using a prototype of an indirect forced convection solar dryer. The drying experiments enabled the determination of drying kinetics as well as highlighted the influence of temperature and humidity on the drying rate. The characteristic drying curve (CDC) and its mathematical expression were determined using Van Meel’s formalism. Thermal diffusivity of wastewater sludge during drying was also investigated. Using Fick’s diffusion model, diffusion coefficients ranged between 0.59 × 10-9 m²/s and 1.43 × 10-9 m²/s, demonstrating an increase in effective diffusivity with rising temperature. The Arrhenius equation provided activation energy values of 16.80 kJ/mol for Oujda samples and 19.72 kJ/mol for Nador samples, indicating the effect of temperature on effective diffusivity. A new equation based on the Midilli-Kucuk model was proposed to predict the drying behavior under untested aerothermal conditions, considering drying temperature and the initial dryness. This study offers a comprehensive analysis of the drying kinetics and effective diffusivity of sewage sludge, providing valuable insights for designing large dryers for sludge management in WWTPs. This approach presents an optimal solution for drying and stabilizing sludge, contributing to environmental preservation efforts. [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]

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]

Although the recent drop in melanoma mortality has been attributed to the introduction of newer therapies, the impact of ongoing public efforts remains unknown. Characterize and model melanoma mortality trends before the era of molecular and immune therapies (1969-2014) in the U.S. and Australia. Differential time series analysis based on population-ascertained melanoma mortality rates from the U.S. and Australia. Mortality rates were modeled and compared to the trajectories of ten other cancers. Melanoma mortality rates have been significantly decelerating since the 1970s in both the U.S. (P <.0001) and Australia (P =.0021). Zero acceleration occurred around 2001 (95% CI: 1996, 2008) for the U.S. and 2004 (95% CI: 1999, 2011) for Australia. Male mortality rates decelerated 3x-4x faster than females in both countries. Melanoma mortality followed a similar quadratic function (R2 > 0.9) to 10 other cancers, albeit with a later inflection point (1986 vs 2001) and broader focal width. Absolute mortality data used without further stratification or considering cancer incidence or covariates. Melanoma deaths have been decelerating for the past 5 decades, reaching an inflection point around 2001, suggesting that mitigating campaigns were already afoot in both the U.S. and Australia before the advent of modern therapies. [ABSTRACT FROM AUTHOR]

Estimation of fault severity throughout bearing life is required for bearing prognostics and remaining useful life estimation. The useful life limit varies based on the function and criticality of the bearing and the machine. Most research has focused on the initial stages when faults are small. This paper presents novel severity categories of a spall-like fault located on a deep groove bearing outer race by investigating the dynamic behavior. Different stages of fault severity, classified as small, medium, and large faults, are characterized from fault initiation to faults extending to angles corresponding to several balls interacting simultaneously with the fault. This research combines dynamic modeling and experiments. The dynamic model is used to understand dynamic behavior, and the experiments are used to validate the results. The dynamic behavior is used to analyze and explain new unique patterns in the vibration signatures of different fault severities. This study enhances the capabilities for estimating fault severity in ball bearings, providing valuable insights that contribute to the development of more accurate and effective maintenance strategies. This is particularly relevant in scenarios where the system can operate efficiently even with large faults exceeding the "small" category. [ABSTRACT FROM AUTHOR]

Forest and biomass crops for bioenergy and bioproducts can promote a sustainable bioeconomy while effectively reducing greenhouse gas (GHG) emissions to mitigate global warming. One of the most concerning issues is selecting and using appropriate modeling and analytical technologies to optimize the benefits of multi-feedstock biomass supply chains, including logistics. Machine learning (ML) has been used to solve increasingly complex supply chain problems, providing powerful tools for sustainable forest management and biomass resource development. Existing research is extensive and spans many different ML techniques, but synthesis is needed to help guide the adoption of these rapidly evolving tools. This review summarizes ML applications in forest and biomass supply chain management in terms of data, algorithms, and process examples, with an emphasis on direct application to supply chain management. ML is a viable technique to support strategic, operational, and tactical planning and decision-making in this field and can enhance the environmental and economic performance of diverse forest and biomass supply chains. [ABSTRACT FROM AUTHOR]

Nonwoven fabrics, especially spunlace varieties, find application across diverse fields. This study presents an analytical model that utilizes the energy method, factoring in bending and torsion strain energies. This model is integrated with the dynamic recursive splitting (DYRES) algorithm to predict the flexural rigidity by estimating fiber-to-fiber contacts within spunlace nonwoven fabrics. The input parameters for this proposed model encompass the number of bending couples, the bending stiffness of fibers, and the orientation characteristics of spunlace nonwoven fabrics. To validate the predictive capability of the model, six samples with varying fiber types, basis weights, and thicknesses were fabricated. A comparison between experimental and theoretical values reveals a consistent trend but with overestimated values in both the machine direction (MD) and cross-machine direction (CD). Through the utilization of two coefficients (0.7854 and 0.7823) to refine the presented model, an average relative error of 4.22% and 8.92% was achieved for the MD and CD directions, respectively. This research proposes a theory that utilizes fewer parameters compared to other studies in the field of nonwoven textiles. The proposed approach offers a more convenient and accessible method for obtaining these parameters, as well as determining the bending stiffness of the fabric. Moreover, advanced and more suitable techniques, including knot theory and image processing, have been employed to obtain accurate parameter measurements. This work is believed to lay the foundation for future investigations into the mechanical behavior of nonwoven fabrics. [ABSTRACT FROM AUTHOR]

Computer simulation of energy resources has led to significant achievements in the interdisciplinary fields of energy and environment. Apart from renewable resources, fossil-fuel power generation plants can be made cleaner to satisfy the future climate targets while keeping secure and stable grid. Clean coal power plants are still among the dominant options for power generation, which are committed through energy-efficient operation, carbon capture and storage, or combination of both strategies. On the other hand, machine learning and deep learning techniques have a leading integrity in the field of simulation. This paper presents accurate models of a cleaner coal-fired supercritical (SC) unit using two types of artificial neural network, which are Elman neural network (ENN) and generalized regression neural network (GRNN). The models newly embed higher coverage range and more accurate results than previously published models. Each subsystem of the models has been structured as a multi-input single-output (MISO) component to predict the behavior of significant variables in the plant, mainly the supercritical pressure in MPa, the steam temperature in °C and the production in MW. Those variables have been intentionally selected as they are clear indicators for the energy-efficient and cleaner production. Simulation results of four sets of data have indicated satisfactory performance of both models with a bit higher superiority of the GRNN that has given negligible or zero Mean Squared Error (MSE) for all outputs, whereas the minimum MSE of the deep ENN is 3.131 × 10−3. [ABSTRACT FROM AUTHOR]

The present work constitutes a generalization of the hydrodynamic model used to predict the pressures and the rolling speeds during the hot rolling of aluminum and copper strips. The hydrodynamic model with a linear behavior (Newton viscous) of the materials shows good predictions in the literature but its applicability is questionable in non-linear cases, when the materials exhibit viscoplastic or plastic behavior. This work extends the model to accommodate non-linear cases commonly encountered in rolling models (viscoplastic and plastic behaviors). The obtained results are in good agreement with experimental data from the literature. The validated model can, therefore, be considered an enhanced hydrodynamic model for predicting pressures and velocities during both hot and cold rolling of thin strips. [ABSTRACT FROM AUTHOR]

In this study, the ability of regression-based methods, namely conventional regression analysis (CRA) and multivariate adaptive regression splines (MARS), and artificial neural networks (ANNs) method was investigated to model the river dissolved oxygen (DO) concentration. Daily average data for discharge and water-quality (WQ) indicators, which include DO concentration, temperature, specific conductance, and pH, were provided for the monitoring stations USGS 14210000 (upstream) and USGS 14211010 (downstream) in the Clackamas River, Oregon, USA. Eight models were established using different combinations of the input parameters and tested to determine the contribution of each parameter used in the modeling to the performance of the models. The results of the models and methods were compared with each other using several performance statistics. Although the performances of the methods were quite close to each other, the highest estimation performance was obtained from the ANNs method in the testing data sets. According to the performance statistics, Model 8, in which all WQ indicators were included as input parameters, was selected as the optimal model to estimate DO concentration of different periods of the same stations. However, when estimating the DO concentration from one station to another, the highest performance statistics were obtained from Model 8 for upstream and Model 1 for downstream station using the CRA method. For the ANNs method, Model 1 having the single input for both stations was the best model. [ABSTRACT FROM AUTHOR]

Copyright of Boletín Latinoamericano y del Caribe de Plantas Medicinales y Aromáticas is the property of Universidad de Santiago de Chile and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to analyze the needs of the elderly at the signalized intersection and evaluate the service level of the elderly pedestrians, a service level evaluation model based on subjective evaluation and intersection traffic characteristics was established. The traffic characteristic parameters of 33 signal-controlled crosswalks were obtained through field observation and video analysis. A truncated questionnaire was used to obtain the subjective value of pedestrian service level evaluation of the elderly crossing the street with synchronous conditions. The initial selection was based on nonlinear, linear and fuzzy linear regression models. Based on the data analysis of 30 crosswalks, Pearson correlation coefficient and Spearman correlation coefficient were used to identify the key factors affecting the service level of the elderly crossing the street. Taking the key factors as input, comparing the nonlinear, linear and fuzzy linear regression models, the results showed that the multivariate nonlinear regression model of elderly pedestrian service level evaluation was better in terms of fit and error, and the data of 3 crosswalks in the verification group further confirm the applicability of the model. The research results could provide some reference for the study of traffic safety improvement strategies in the aging trend. [ABSTRACT FROM AUTHOR]

This study aims to optimize chitin extraction conditions by reducing water and chemical consumption using a twolevel factorial design approach. Two variables were studied for the demineralization process: the volume of hydrochloric acid (400 to 500 mL) and the demineralization time (1 to 1.5 hours) at room temperature. For deproteinization, the variables examined were NaOH concentration (0.6 to 1 mol/L) and deproteinization time (1 to 1.5 hours) at 70-80 °C. The ash and protein contents were measured to evaluate the efficiency of the demineralization and deproteinization processes, respectively. Minitab 18 software was used for the experimental design. The results showed that the best conditions for extracting high-quality chitin are a demineralization time of 1 hour, an HCl volume of 500 mL, a NaOH concentration of 1 M, and a deproteinization time of 1 hour. These optimal conditions reduce the amount of water, time, and chemicals required for chitin extraction. Compared to previous studies, we significantly reduced the amount of water and chemicals as well as the extraction duration. These results are innovative as they offer a more efficient and sustainable solution for chitin extraction, thus filling a gap in current research by proposing an optimized method. By providing these new data and demonstrating their effectiveness, our research makes a significant contribution to improving chitin extraction techniques, addressing an unmet need in the industrial field. [ABSTRACT FROM AUTHOR]

Nanosatellites, as a standardized Space research project with fast and cost-effective development possibilities, have enabled universities in a growing number of emerging countries to develop a wide range of space missions in Low Earth Orbit (LEO). This paper aims to contribute to research efforts to improve the success of university nanosatellite missions using Model-Based Systems Engineering (MBSE) to facilitate the initial project management and design phases. The proposed model-based approach is the result of experience gained from the MASAT1 project, a Moroccan university nanosatellite, and it is based on an architectural framework that allows the system to be described via a set of views. The management and design process is based on a reference framework based on existing standards such as ESA's European Cooperation for Spatial Standardization (ECSS). The choice fell on the Systems Modeling Language (SysML) for the MBSE implementation with the future goal of developing executable SysML Modeling approaches. [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]