Performance and kinetics of rotating drum biofilter aerobic simultaneous purifying SO2 and NOx

The rotating drum biofilter (RDB) was investigated as a biological process for purifying SO2 and NOx. After 25 days of film hanging, the inlet concentration was less than 2800 mg·m-3, and the NOx inlet concentration was less than 800 mg·m-3, with greater than 90% desulphurisation and denitration efficiency. Bacteroidetes and Chloroflexi were the dominant bacteria in desulphurisation, while Proteobacteria were the dominant bacteria in denitrification. The sulphur and nitrogen in RDB were balanced when the SO2 inlet concentration was 1200 mg·m-3 and the NOx inlet concentration was 1000 mg·m-3. The best results were obtained SO2-S removal load was 28.12 mg·L-1·h-1 and NOx-N removal load was 9.78 mg·L-1·h-1. when SO2 concentration was 1200 mg·m-3, NOx concentration was 800 mg·m-3, and empty bed retention time (EBRT) was 75.36 s. The liquid phase dominated the SO2 purification process, and the experimental data fit better with the liquid phase mass transfer model. NOx purification was governed by the biological and liquid phases, with the modified biological-liquid phase mass transfer model fitting the experimental data better.

The Influence of Nonlinear High-Intensity Dynamic Processes on the Standing Wave Precession of a Non-Ideal Hemispherical Resonator

The properties of small size, low noise, high performance and no wear-out have made the hemispherical resonator gyroscope a good choice for high-value space missions. To enhance the precision of the hemispherical resonator gyroscope for use in tasks with large angular velocities and angular accelerations, this paper investigates the standing wave precession of a non-ideal hemispherical resonator under nonlinear high-intensity dynamic conditions. Based on the thin shell theory of elasticity, a dynamic model of a hemispherical resonator is established by using Lagrange's second kind equation. Then, the dynamic model is equivalently transformed into a simple harmonic vibration model of a point mass in two-dimensional space, which is analyzed using a method of averaging that separates the slow variables from the fast variables. The results reveal that taking the nonlinear terms about the square of the angular velocity and the angular acceleration in the dynamic equation into account can weaken the influence of the 4th harmonic component of a mass defect on standing wave drift, and the extent of this weakening effect varies with the dimensions of the mass defects, which is very important for steering the development of the high-precision hemispherical resonator gyroscope.

Perspectives on the adaptation of Japanese plum-type cultivars to reduced winter chilling in two regions of Spain

Japanese plum, like other temperate fruit tree species, has cultivar-specific temperature requirements during dormancy for proper flowering. Knowing the temperature requirements of this species is of increasing interest due to the great genetic variability that exists among the available Japanese plum-type cultivars, since most of them are interspecific hybrids. The reduction of winter chilling caused by climate change is threatening their cultivation in many regions. In this work, the adaptation perspectives of 21 Japanese plum-type cultivars were analyzed in two of the main plum-growing regions in Spain, Badajoz and Zaragoza, to future climate conditions. Endodormancy release for subsequent estimation of chilling and heat requirements was determined through empirical experiments conducted during dormancy at least over two years. Chill requirements were calculated using three models [chilling hours (CH), chilling units (CU) and chilling portions (CP)] and heat requirements using growing degree hours (GDH). Chilling requirements ranged 277-851 CH, 412-1,030 CU and 26-51 CP, and heat requirements ranged from 4,343 to 9,525 GDH. The potential adaption of the cultivars to future warmer conditions in both regions was assessed using climate projections under two Representative Concentration Pathways (RCP), RCP4.5 (effective reduction of greenhouse gas emissions) and RCP8.5 (continuous increase in greenhouse gas emissions), in two time horizons, from the middle to the end of 21st century, with temperature projections from 15 Global Climate Models. The probability of satisfying the estimated cultivar-specific chilling requirements in Badajoz was lower than in Zaragoza, because of the lower chill availability predicted. In this region, the cultivars analyzed herein may have limited cultivation because the predicted reduction in winter chill may result in the chilling requirements not being successfully fulfilled.

Modelling the role of glucocorticoid receptor as mediator of endocrine responses to environmental challenge

Glucocorticoid hormones (GCs) modulate acute 'stress' responses in vertebrates, exerting their actions across many physiological systems to help the organism face and overcome challenges. These actions take place via binding to the glucocorticoid receptor (GR), which determines not only the magnitude of the GC-mediated physiological response but also the negative feedback that downregulates GCs to restore homeostasis. Although GR function is assumed to determine GC regulation capacity, the associations between GR abundance and individuals' coping abilities remain cryptic. We developed a dynamic model fitted to empirical data to predict the effects of GR abundance on both plasma GC response patterns and the magnitude of GC-mediated physiological response. Individuals with higher GRs showed lower GC exposure, stronger physiological responses and greater capacity to adjust this response according to stressor intensity, which may be translated into more resilient and flexible GC phenotypes. Our results also show that among-individual variability in GR abundance challenges the detectability of the association between plasma GC measurements and physiological responses. Our approach provides mechanistic insights into the role of GRs in plasma GC measurements and function, which point at GR abundance fundamentally driving complex features of the GC regulation system in the face of environmental change. This article is part of the theme issue 'Endocrine responses to environmental variation: conceptual approaches and recent developments'.

Overcoming Dormancy in Prunus Species under Conditions of Insufficient Winter Chilling in Israel

The phenomenon of dormancy and the evolutionary causes for its development are presented together with the effects of the climatic factors: temperature and light. Shade and darkness have been found to enhance bud breaking in peach. The effects of various temperatures on chilling accumulation, chilling negation and chilling enhancement are described. The way these are computed in the face of global warming is explained, using the dynamic model. When natural chilling is less than that required, there are ways of compensation, up to a certain level. Various horticultural, physical and chemical means to achieve this are described, including bending branches, reducing vegetative vigor, shading the orchard, sprinkling to reduce daytime temperature and the application of various chemicals to break dormancy. When winter chilling is markedly reduced and temperatures increase considerably, the use of dormancy avoidance is suggested in frost-free places. This technique can induce a new growing cycle by avoiding dormancy altogether. However, the best approach is to breed high-quality cultivars requiring much less chilling. Another aspect discussed in this work, independent of the chilling requirement, is the negative effect of heat spells in winter and spring on the abnormal development of flower buds, leading to a low level of the stone fruit set and a reduced yield.

Estimation of Protein and Amino Acid Requirements in Layer Chicks Depending on Dynamic Model

Four trials were conducted to establish a protein and amino acid requirement model for layer chicks over 0-6 weeks by using the analytical factorization method. In trial 1, a total of 90 one-day-old Jing Tint 6 chicks with similar body weight were selected to determine the growth curve, carcass and feather protein deposition, and amino acid patterns of carcass and feather proteins. In trials 2 and 3, 24 seven-day-old and 24 thirty-five-day-old Jing Tint 6 chicks were selected to determine the protein maintenance requirements, amino acid pattern, and net protein utilization rate. In trial 4, 24 ten-day-old and 24 thirty-eight-day-old Jing Tint 6 chicks were selected to determine the standard terminal ileal digestibility of amino acids. The chicks were fed either a corn-soybean basal diet, a low nitrogen diet, or a nitrogen-free diet throughout the different trials. The Gompertz equation showed that there is a functional relationship between body weight and age, described as BWt(g) = 2669.317 × exp(-4.337 × exp(-0.019t)). Integration of the test results gave a comprehensive dynamic model equation that could accurately calculate the weekly protein and amino acid requirements of the layer chicks. By applying the model, it was found that the protein requirements for Jing Tint 6 chicks during the 6-week period were 21.15, 20.54, 18.26, 18.77, 17.79, and 16.51, respectively. The model-predicted amino acid requirements for Jing Tint 6 chicks during the 6-week period were as follows: Aspartic acid (0.992-1.284), Threonine (0.601-0.750), Serine (0.984-1.542), Glutamic acid (1.661-1.925), Glycine (0.992-1.227), Alanine (0.909-0.961), Valine (0.773-1.121), Cystine (0.843-1.347), Methionine (0.210-0.267), Isoleucine (0.590-0.715), Leucine (0.977-1.208), Tyrosine (0.362-0.504), Phenylalanine (0.584-0.786), Histidine (0.169-0.250), Lysine (0.3999-0.500), Arginine (0.824-1.147), Proline (1.114-1.684), and Tryptophan (0.063-0.098). In conclusion, this study constructed a dynamic model for the protein and amino acid requirements of Jing Tint 6 chicks during the brooding period, providing an important insight to improve precise feeding for layer chicks through this dynamic model calculation.

The generalized spring-loaded inverted pendulum model for analysis of various planar reduced-order models and for optimal robot leg design

This paper proposes a generalized spring-loaded inverted pendulum (G-SLIP) model to explore various popular reduced-order dynamic models' characteristics and suggest a better robot leg design under specified performance indices. The G-SLIP model's composition can be varied by changing the model's parameters, such as ground contacting type and spring property. It can be transformed into four widely used models: the spring-loaded inverted pendulum (SLIP) model, the two-segment leg model, the SLIP with rolling foot model, and the rolling SLIP model. The effects of rolling contact and spring configuration on the dynamic behavior and fixed-point distribution of the G-SLIP model were analyzed, and the basins of attraction of the four described models were studied. By varying the parameters of the G-SLIP model, the dynamic behavior of the model can be optimized. Optimized for general locomotion running at various speeds, the model provided leg design guidelines. The leg was empirically fabricated and installed on the hexapod for experimental evaluation. The results indicated that the robot with a designed leg runs faster and is more power-efficient.

An Extended Kolmogorov-Avrami-Ishibashi (EKAI) Model to Simulate Dynamic Characteristics of Polycrystalline-Ferroelectric-Gate Field-Effect Transistors

A physics-based model on polarization switching in ferroelectric polycrystalline films is proposed. The calculation results by the model agree well with experimental results regarding dynamic operations of ferroelectric-gate field-effect transistors (FeFETs). In the model, an angle θ for each grain in the ferroelectric polycrystal is defined, where θ is the angle between the spontaneous polarization and the film normal direction. Under a constant electric field for a single-crystal film with θ = 0, phenomena regarding polarization domain nucleation and wall propagation are well described by the Kolmogorov-Avrami-Ishibashi theory. Since the electric fields are time-dependent in FeFET operations and the θ values are distributed in the polycrystalline film, the model in this paper forms an extended Kolmogorov-Avrami-Ishibashi (EKAI) model. Under a low electric field, the nucleation and domain propagation proceed according to thermally activated processes, meaning that switching the time scale of a grain with the angle θ is proportional to an exponential form as exp(const./Ezcosθ) [Ez: the film-normal electric field]. Wide θ distribution makes the time response quite broad even on the logarithmic scale, which relates well with the broad switching time experimentally shown by FeFETs. The EKAI model is physics based and need not assume non-physical distribution functions in it.

Research on Dynamic Modeling Method and Flying Gait Characteristics of Quadruped Robots with Flexible Spines

In recent years, both domestic and international research on quadruped robots has advanced towards high dynamics and agility, with a focus on high-speed locomotion as a representative motion in high-dynamic activities. Quadruped animals like cheetahs exhibit high-speed running capabilities, attributed to the indispensable role played by their flexible spines during the flight phase motion. This paper establishes dynamic models of flexible spinal quadruped robots with different degrees of simplification, providing a parameterized description of the flight phase motion for both rigid-trunk and flexible-spine quadruped robots. By setting different initial values for the spine joint and calculating the flight phase results for both types of robots at various initial velocities, the study compares and analyzes the impact of a flexible spine on the flight phase motion of quadruped robots. Through comparative experiments, the research aims to validate the influence of a flexible spine during the flight phase motion, providing insights into how spine flexibility affects the flight phase motion of quadruped robots.

Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery

Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019. Data from 2009-2011 were used for initial model fitting, and data from 2012-2019 for validation and updating. We fitted four models to the data: a time-invariant logistic regression model (not updated), a logistic model which was updated every year and validated it in each subsequent year, a logistic regression model where the intercept is a function of calendar time (not updated), and a continually updating Bayesian logistic model which was updated with each new observation and continuously validated. We report predictive performance over the complete validation cohort and for each year in the validation data. Over the complete validation data, the Bayesian model had the best predictive performance.

Cyclic fatigue resistance of different nickel-titanium instruments in single and double curvature at room and body temperatures: A laboratory study

The aim of this study was to compare the cyclic fatigue resistance of nickel-titanium instruments inside single and double-curved canals at different temperatures. 160 HyFlex EDM #20.05 (HEDM), VDW.ROTATE #20.05 and #25.06, Mtwo #25.06 were randomised (n = 10) for the dynamic cyclic fatigue tests according to the curvature (i.e. single and double) at 20° ± 1°C and 35° ± 1°C. The number of cycles to fracture (NCF) was analysed by two-way ANOVA with p < 0.05. Fatigue resistance of all instruments significantly decreased at body temperature in single and double curvatures, except for HEDM in double curvature. The NCF was significantly lower in double curvature than single at both temperatures for all files, except for VDW.ROTATE #20.05 at 35° ± 1°C. Within the study limitations, temperature significantly impaired cyclic fatigue resistance of all files except HEDM #20.05 in double curvature. Similarly, double curvature had a detrimental effect on cyclic fatigue resistance of all files except for VDW.ROTATE #20.05 at body temperature.

Computational Multi-Scale Modeling of Drug Delivery into an Anti-Angiogenic Therapy-Treated Tumor

The present study develops a numerical model, which is the most complex one, in comparison to previous research to investigate drug delivery accompanied by the anti-angiogenesis effect. This paper simulates intravascular blood flow and interstitial fluid flow using a dynamic model. The model accounts for the non-Newtonian behavior of blood and incorporates the adaptation of the diameter of a heterogeneous microvascular network derived from modeling the evolution of endothelial cells toward a circular tumor sprouting from two-parent vessels, with and without imposing the inhibitory effect of angiostatin on a modified discrete angiogenesis model. The average solute exposure and its uniformity in solid tumors of different sizes are studied by numerically solving the convection-diffusion equation. Three different methodologies are considered for simulating anti-angiogenesis: modifying the capillary network, updating the transport properties, and considering both microvasculature and transport properties modifications. It is shown that anti-angiogenic therapy decreases drug wash-out in the periphery of the tumor. Results show the decisive role of microvascular structure, particularly its distribution, and interstitial transport properties modifications induced via vascular normalization on the quality of drug delivery, such that it is improved by 39% in uniformity by the second approach in R = 0.2 cm.

Which demographic processes control competitive equilibria? Bayesian calibration of a size-structured forest population model

In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, that is, the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short time series from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.

Milling Stability Prediction: A New Approach Based on a Composited Newton-Cotes Formula

Based on a composited Newton-Cotes formula, this paper proposes a numerical method to predict milling stability considering regenerative chatter and focusing on rate and prediction accuracy. First, the dynamic model of milling motion is expressed as state-space equations considering regenerative chatter, with the tooth passing period divided into a set of time intervals. Second, a composited Newton-Cotes formula is introduced to calculate the transition function map for each time interval. Third, the state transition matrix is constructed based on the above-mentioned transition function, and the prediction stability boundary is determined by the Floquet theory. Finally, simulation analysis and experimental verification are conducted to verify the effectiveness of the proposed method. The simulation results demonstrate that, for the milling model with a single degree of freedom (DOF), the convergence rate and prediction accuracy of the proposed method are higher than those of the comparison method. The experimental results demonstrate that, for the milling model with two DOFs, the machining parameters below the prediction stability boundary can avoid the chatter as much as possible, ensuring the machined surface quality.

A High-Efficient Reinforcement Learning Approach for Dexterous Manipulation

Robotic hands have the potential to perform complex tasks in unstructured environments owing to their bionic design, inspired by the most agile biological hand. However, the modeling, planning and control of dexterous hands remain unresolved, open challenges, resulting in the simple movements and relatively clumsy motions of current robotic end effectors. This paper proposed a dynamic model based on generative adversarial architecture to learn the state mode of the dexterous hand, reducing the model's prediction error in long spans. An adaptive trajectory planning kernel was also developed to generate High-Value Area Trajectory (HVAT) data according to the control task and dynamic model, with adaptive trajectory adjustment achieved by changing the Levenberg-Marquardt (LM) coefficient and the linear searching coefficient. Furthermore, an improved Soft Actor-Critic (SAC) algorithm is designed by combining maximum entropy value iteration and HVAT value iteration. An experimental platform and simulation program were built to verify the proposed method with two manipulating tasks. The experimental results indicate that the proposed dexterous hand reinforcement learning algorithm has better training efficiency and requires fewer training samples to achieve quite satisfactory learning and control performance.

A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library

Introduction

Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies.

Methods

Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values.

Results

We demonstrate the utility of such low-complexity models with few parameters by fitting them to longitudinal datasets from the simulation platform APSIM-Wheat involving in silico biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation.

Discussion

A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.

Size-Dependent Polymeric Nanoparticle Distribution in a Static versus Dynamic Microfluidic Blood Vessel Model: Implications for Nanoparticle-Based Drug Delivery

Nanoparticles (NPs) have been widely investigated in the nanomedicine field. One of the main challenges is to accurately predict the NP distribution and fate after administration. Microfluidic platforms acquired huge importance as tools to model the in vivo environment. In this study, we leveraged a microfluidic platform to produce FITC-labeled poly(lactide-co-glycolide)-block-poly(ethylene glycol) (PLGA-PEG) NPs with defined sizes of 30, 50, and 70 nm. The study aimed to compare the ability of NPs with differences of 20 nm in size to cross an endothelial barrier using static (Transwell inserts) and dynamic (microfluidic perfusion device) in vitro models. Our results evidence a size-dependent NP crossing in both models (30 > 50 > 70 nm) and highlight the bias deriving from the static model, which does not involve shear stresses. The permeation of each NP size was significantly higher in the static system than in the dynamic model at the earliest stages. However, it gradually decreased to levels comparable with those of the dynamic model. Overall, this work highlights clear differences in NP distribution over time in static versus dynamic conditions and distinct size-dependent patterns. These findings reinforce the need for accurate in vitro screening models that allow for more accurate predictions of in vivo performance.

Design and Analysis of a Bionic Gliding Robotic Dolphin

In this paper, we focus on the design and analysis of a bionic gliding robotic dolphin. Inspired by natural dolphins, a novel bionic gliding robotic dolphin is developed. Different from the existing ones, the gliding robotic dolphin developed in this work is specially introduced with a yaw joint to connect its three oscillating joints to improve maneuverability in both dolphin-like swimming and gliding motion. Consequently, the gliding robotic dolphin can realize several flexible motion patterns under the coordination of its flippers, yaw joint, oscillating joints, and buoyancy-driven modular. Thereafter, relying on the Newton-Euler method, a hybrid-driven dynamic model is constructed to further analyze the propulsive performance in both dolphin-like swimming and gliding motions. Finally, various simulations and experiments, including forward swimming, gliding, and turning in both dolphin-like swimming and gliding modes, are carried out to validate the effectiveness of the developed gliding robotic dolphin.

Hot-topics cross-propagation and opinion-transfer dynamics in the Chinese Sina-microblog social media: a modeling study

On social media platforms, hot topics often contain several pieces of related information that can influence internet users, generating either positive or negative opinion orientation. Some of them will choose to retain or change their original opinions after exposure to multiple related messages. To describe the opinion-transfer transient and collective behaviors in this scenario, this paper proposes an opinion-transfer susceptible-forwarding-immunized (OT-SFI) information cross-propagation model. Real multiple information in messages with opinions obtained from the Chinese Sina microblog is used for data fitting to illustrate how model parameters can be estimated and used to predict the accumulative numbers of users with a particular view. The study attempts to relate changes in group views in the network to initial opinion distribution and individuals' opinion choices at the macro level. Furthermore, the model parameters at the micro level are used to measure the probability of "retention" and "reversal" of views in events, as well as the extent to which the masses are influenced by new information views. The result illustrates that the viewpoint distribution of the initial message and the opinion selection of the new message opinion leaders play crucial roles in promoting attention to the topic and driving for a desired collective opinion.

Modeling of Rapid Response Characteristics of Piezoelectric Actuators for Ultra-Precision Machining

Piezoelectric actuators are characterized by high positioning accuracy, high stiffness and a fast response and are widely used in ultra-precision machining technologies such as fast tool servo technology and ultrasonic machining. The rapid response characteristics of piezoelectric actuators often determine the overall quality of machining. However, there has been little research on the fast response characteristics of piezoelectric actuators, and this knowledge gap will lead to low precision and poor quality of the final machining. The fast response characteristics of a piezoelectric actuator were studied in this work. Firstly, the piezoelectric actuator was divided into a no-load state and a load state according to the working state. A fast response analysis and output characteristic analysis were carried out, the corresponding dynamic model was established, and then the model was simulated. Finally, an experimental system was established to verify the dynamic model of the piezoelectric actuator's fast response by conducting an experiment in which the piezoelectric actuator bounces a steel ball. The experimental results verify the correctness of the model and show that the greater the cross-sectional area and height of the piezoelectric actuator, the higher the bouncing height of the ball, and the better the dynamic performance of the piezoelectric actuator. It is believed that this study has guiding significance for the application of the dynamic characteristics of piezoelectric actuators in the machining field.

Modeling the economic impacts of mobility scores in dairy cows under Irish spring pasture-based management

Moderate to severe forms of suboptimal mobility on dairy cows are associated with yield losses, whereas mild forms of suboptimal mobility are associated with elevated somatic cell count and an increased risk to be culled. Although the economic consequences of severe forms of suboptimal mobility (also referred as clinical lameness) have been studied extensively, the mild forms are generally ignored. Therefore, the aim of the current study was to determine the economic consequences associated with varying prevalence and forms of suboptimal mobility within spring calving, pasture-based dairy herds. A new submodel predicting mobility scores was developed and integrated within an existing pastured-based herd dynamic model. Using a daily timestep, this model simulates claw disorders, and the consequent mobility score of individual cows. The impact of a cow having varying forms of suboptimal mobility on production and reproduction was simulated. The economic impact was simulated including treatment costs, as well as the production and reproductive impacts of varying levels of suboptimal mobility. Furthermore, different genetic predispositions for mobility issues and their interaction with herd-level management associated with each level of suboptimal mobility were simulated. Overall, 13 scenarios were simulated, representing a typical spring calving, pasture-based dairy herd with 100 cows. The first scenario represents a perfect herd wherein 100% of the cows had mobility score 0 (optimal mobility) throughout the lactation. The remaining 12 scenarios represent a combination of (1) 3 different herd-management levels, and (2) 4 different levels of a genetic predisposition for suboptimal mobility. The analysis showed that a 17% decrease in farm net profit was achieved in the worst outcome (wherein just 5% of the herd had optimal mobility) compared with the perfect herd. This was due to reduced milk yield, increased culling, and increased treatment costs for mobility issues compared the ideal scenario.

Modelling the impact of the Omicron BA.5 subvariant in New Zealand

New Zealand experienced a wave of the Omicron variant of SARS-CoV-2 in early 2022, which occurred against a backdrop of high two-dose vaccination rates, ongoing roll-out of boosters and paediatric doses, and negligible levels of prior infection. New Omicron subvariants have subsequently emerged with a significant growth advantage over the previously dominant BA.2. We investigated a mathematical model that included waning of vaccine-derived and infection-derived immunity, as well as the impact of the BA.5 subvariant which began spreading in New Zealand in May 2022. The model was used to provide scenarios to the New Zealand Government with differing levels of BA.5 growth advantage, helping to inform policy response and healthcare system preparedness during the winter period. In all scenarios investigated, the projected peak in new infections during the BA.5 wave was smaller than in the first Omicron wave in March 2022. However, results indicated that the peak hospital occupancy was likely to be higher than in March 2022, primarily due to a shift in the age distribution of infections to older groups. We compare model results with subsequent epidemiological data and show that the model provided a good projection of cases, hospitalizations and deaths during the BA.5 wave.

Quantifying the Leaping Motion Using a Self-Propelled Bionic Robotic Dolphin Platform

Kinematic analysis of leaping motions can provide meaningful insights into unraveling the efficient and agile propulsive mechanisms in dolphin swimming. However, undisturbed kinematic examination of live dolphins has been very scarce due to the restriction of close-up biological observation with a motion capture system. The main objective of this study is to quantify the leaping motion of a self-propelled bionic robotic dolphin using a combined numerical and experimental method. More specifically, a dynamic model was established for the hydrodynamic analysis of a changeable submerged portion, and experimental data were then employed to identify hydrodynamic parameters and validate the effectiveness. The effects of wave-making resistance were explored, indicating that there is a varying nonlinear relationship between power and speed at different depths. In addition, the wave-making resistance can be reduced significantly when swimming at a certain depth, which leads to a higher speed and less consumed power. Quantitative estimation of leaping motion is carried out, and the results suggest that with increase of the exiting velocity and angle, the maximum height of the center of mass (CM) increases as well; furthermore, a small exiting angle usually requires a much larger exiting velocity to achieve a complete exiting motion. These findings provide implications for optimizing motion performance, which is an integral part of underwater operations in complex aquatic environments.

Non Linear Control System for Humanoid Robot to Perform Body Language Movements

In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve this, the use of closed-loop control techniques that consider the complex nonlinear dynamics and disturbances inherent in these systems is required. In this paper, an implementation of a nonlinear controller for the tracking of trajectories and a profile of speeds that execute the movements of the arms and head of a humanoid robot based on the mathematical model is proposed. First, the design and implementation of the arms and head are initially presented, then the mathematical model via kinematic and dynamic analysis was performed. With the above, the design of nonlinear controllers such as nonlinear proportional derivative control with gravity compensation, Backstepping control, Sliding Mode control and the application of each of them to the robotic system are presented. A comparative analysis based on a frequency analysis, the efficiency in polynomial trajectories and the implementation requirements allowed selecting the non-linear Backstepping control technique to be implemented. Then, for the implementation, a centralized control architecture is considered, which uses a central microcontroller in the external loop and an internal microcontroller (as internal loop) for each of the actuators. With the above, the selected controller was validated through experiments performed in real time on the implemented humanoid robot, demonstrating proper path tracking of established trajectories for performing body language movements.

Development of a dynamic model for prediction of energy in milk protein, lactose, fat, and enteric methane emissions in goats based on energy balance and indirect calorimetry studies

Feed costs are overwhelmingly the largest expense for dairy producers. Thus, improving milk production efficiency (milk fat and protein are the main incomes for farmers) is of great economic importance in the dairy industry. The main objective of this study was to develop a dynamic energy partitioning model to describe and quantify how dietary energy from carbohydrate, protein, and fat is transferred to milk (protein, lactose, and fat) in dairy goats. In addition, due to increasing worldwide concerns regarding livestock contribution to global warming, methane (CH4) emission was quantified. For modeling purposes, 158 individual goat observations were used and randomly split into 2/3 for model development and 1/3 for internal evaluation. For external evaluation, 20 different energy balance studies from the literature (77 observations) were evaluated. The Root Mean Square Prediction Error (RMSPE) was 13.2% for loss of energy in CH4, 16.8% for energy in fat, 19.4% for energy in protein, and 22.3 energy in lactose. Mean bias was around zero for all variables and the slope bias was zero for milk energy in lactose, close to 1% for milk fat (1.01%), and around 3% and 10% for protein and CH4, respectively. Random bias was greater than 85% for energy in CH4 and milk energy components indicating non-systematic errors and that the equation in the model fitted the data properly. Analyses of residuals appeared to be randomly distributed around zero. Slopes of regression lines for residuals vs. predicted were positive for milk fat energy, zero for lactose, and negative for milk energy in protein and CH4. This model suggested for use with mixed diets and by-products to obtain balanced macronutrient supply, methane emissions, and milk performance during mid lactation could be an interesting tool to help farmers simulate scenarios that increase milk fat and protein, evaluate CH4 emissions, without the costs of running animal trials.

[Development of Prediction Methods for Drug-Drug Interactions Based on the Construction of Physiologically Based Pharmacokinetic Models for Hepatic OATP Substrate Drugs and Endogenous Substrates]

Quantitative prediction of the potential for drug-drug interaction (DDI) is essential to guarantee the safety and efficacy of drugs. DDI screening, modeling, and prediction is standard practice in the pharmaceutical industry. This review describes our work on (1) the establishment of a standard framework for determining physiologically based pharmacokinetic (PBPK) model structures and parameters useful for quantitatively analyzing DDIs via hepatic organic anion transporting polypeptides (OATPs). By analyzing clinically observed DDIs involving several statins as substrates, and cyclosporin A and rifampicin as inhibitors, similar in vivo inhibition constants for OATPs by each inhibitor were obtained, regardless of the substrate. (2) We took a PBPK modeling-based approach to define rate-determining processes in hepatic elimination of several OATPs and CYP3A dual substrates using our clinical DDI data with specific inhibitors for OATPs and CYP3A. Essential in vivo parameters (the passive diffusion/active transport ratio in the uptake, and the fraction of intrinsic clearance in the total drug elimination from the hepatocytes) dominating the rate-determining process in hepatic elimination were estimated quantitatively. (3) Finally, using our clinical DDI data with rifampicin, we established a PBPK model for coproporphyrin I (CP-I), which is expected to act as an endogenous substrate (biomarker) supporting the prediction of DDI involving hepatic OATPs. Our PBPK modeling-based approach with several in vitro experiments using CP-I and OATP probe substrates (statins) demonstrated the usefulness of the translation of the effect of an OATP inhibitor on CP-I pharmacokinetics into that on OATP probe substrates in drug discovery and development.

Experimental Investigation of the Spall Propagation Mechanism in Bearing Raceways

This article investigates the spall propagation mechanism for ball bearing raceways by focusing on an experimental investigation of cracks that evolve in the vicinity of the spall edge. Understanding the spall propagation mechanism is an important step towards developing a physics-based prognostic tool for ball bearings. This research reflects an investigation of different spall sizes that propagate naturally both in laboratory experiments and in the field. By using a combined model of a rigid body dynamic model and a finite element model that simulates the rolling element-spall edge interaction, our results shed light on the material behavior (displacements, strains, and stresses) that creates an environment for crack formation and propagation. With the support of the experimental results and the rolling element-spall edge interaction model results, three stages of the mechanism that control fragment release from the raceway were identified. In Stage one, sub-surface cracks appear underneath the spall trailing edge. In Stage two, cracks appear in front of the trailing edge of the spall and, in Stage three, the cracks propagate until a fragment is released from the raceway. These stages were observed in all the tested bearings. In addition, other phenomena that affect the propagation of the cracks and the geometry of the fragment were observed, such as blistering and plastic deformation. We include an explanation of what determines the shape of the fragments.

Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration

BACKGROUND

We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19.

METHODS

We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including >40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 h. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration.

RESULTS

Overall, 3706 patients (2587 in the development and 1119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79-0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78.

CONCLUSIONS

Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors.

Analysis of instantaneous brain interactions contribution to a motor imagery classification task

The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself.

Method of Changing Running Direction of Cheetah-Inspired Quadruped Robot

The rapid change of motion direction during running is beneficial to improving the movement flexibility of the quadruped robot, which is of great relevance to its research. How to make the robot change its motion direction during running and achieve good dynamic stability is a problem to be solved. In this paper, a method to change the running direction of the cheetah-inspired quadruped robot is proposed. Based on the analysis of the running of the cheetah, a dynamic model of the quadruped robot is established, and a two-level stability index system, including a minimum index system and a range index system, is proposed. On this basis, the objective function based on the stability index system and optimization variables, including leg landing points, trunk movement trajectory, and posture change rule, are determined. Through these constraints, the direction changes with good dynamic stability of the cheetah-inspired quadruped robot during running is realized by controlling the leg parameters. The robot will not roll over during high-speed movement. Finally, the correctness of the proposed method is proven by simulation. This paper provides a theoretical basis for the quadruped robot's rapid change of direction in running.

Modeling and Design Optimization of a New Piezoelectric Inchworm Actuator with Screw Clamping Mechanisms

A new piezoelectric inchworm actuator with screw clamping mechanisms has been developed recently for the wing folding mechanism of a small unmanned aircraft where the actuator power density is a great concern. Considering that the prototype actuator was designed just with engineering intuition and the performance optimization through experimental developments would take a vast amount of cost and time, a mathematical model was developed to investigate the actuator's critical design parameters and optimize its presently undesirable performance. Based on the lumped parameter method reported previously, and taking full account of the detailed modeling of the complex actuator housing and the actual nonlinear behaviors from the high-force contact and friction occurring at the screw-nut interface, as well as the output performance of the main drive elements including the piezoelectric stack and hollow ultrasonic motors (HUSMs), this model was built and then was experimentally verified for its accuracy and availability. Finally, nine design parameters were studied for their individual effect on the actuator's output using the proposed model. The simulation results indicate that the performance can be considerably improved by performing a slight modification to the prototype, and the dynamic modeling and parameter optimization methods used in this study can also serve as a useful reference for the design of similar piezoelectric inchworm actuators with intermittent clamping behaviors.

Dynamic model of respiratory infectious disease transmission by population mobility based on city network

In modern societies, newly emerging infectious diseases spread rapidly between regions owing to frequent contact between people, causing considerable social and economic impacts. In this study, first, a scale-free city network was established, and then the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset and hospitalization, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterized after summarizing the information. Finally, the reliability of the model was verified. The number of citizens and tourists in every city remained stable during this time. The results indicated that a larger basic reproduction number (R ₀) and population outflow rate signify a faster growth rate of infected people in each city in the network. Compared with small and medium-sized cities, the epidemic spread faster in central cities. Population mobility was the decisive factor causing the spread of the epidemic to other areas. Therefore, the rapid spread of epidemics can be prevented by swiftly reducing the flow of people between cities.

Estimating the Effectiveness of Shielding during Pregnancy against SARS-CoV-2 in New York City during the First Year of the COVID-19 Pandemic

Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population.

Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

An efficient method of evaluating multiple concurrent management actions on invasive populations

Evaluating the efficacy of management actions to control invasive species is crucial for maintaining funding and to provide feedback for the continual improvement of management efforts. However, it is often difficult to assess the efficacy of control methods due to limited resources for monitoring. Managers may view effort on monitoring as effort taken away from performing management actions. We developed a method to estimate invasive species abundance, evaluate management effectiveness, and evaluate population growth over time from a combination of removal activities (e.g., trapping, ground shooting) using only data collected during removal efforts (method of removal, date, location, number of animals removed, and effort). This dynamic approach allows for abundance estimation at discrete time points and the estimation of population growth between removal periods. To test this approach, we simulated over 1 million conditions, including varying the length of the study, the size of the area examined, the number of removal events, the capture rates, and the area impacted by removal efforts. Our estimates were unbiased (within 10% of truth) 81% of the time and were correlated with truth 91% of the time. This method performs well overall and, in particular, at monitoring trends in abundances over time. We applied this method to removal data from Mingo National Wildlife Refuge in Missouri from December 2015 to September 2019, where the management objective is elimination. Populations of feral swine on Mingo NWR have fluctuated over time but showed marked declines in the last 3-6 months of the time series corresponding to increased removal pressure. Our approach allows for the estimation of population growth across time (from both births and immigration) and therefore, provides a target removal rate (above that of the population growth) to ensure the population will decline. In Mingo NWR, the target monthly removal rate is 18% to cause a population decline. Our method provides advancement over traditional removal modeling approaches because it can be applied to evaluate management programs that use a broad range of removal techniques concurrently and whose management effort and spatial coverage vary across time.

Study of the Dynamic Model and Vibration Performance of Pot-Shaped Metal Rubber

Metal rubber has been extensively used in recent years due to its several unique properties, especially in adverse environments. Although many experimental studies have been conducted, theoretical research on metal rubber is still in its infancy. In this work, a dynamic model for the nonlinear characteristics of pot-shaped metal rubber is established on the basis of the asymmetric dynamic model of the wire rope shock absorber and the trace method model. In addition, the corresponding parameters in the model are identified based on the parameter-separation method. The theoretical hysteresis loop obtained using the model and the measured hysteresis loop agree with each other. The results show that the asymmetric dynamic model can better describe the asymmetric dynamic characteristics of pot-shaped metal rubber. Furthermore, a pot-shaped metal rubber vibration reduction system is built to further verify the correctness of the model. This study provides an experimental reference and a theoretical basis for the practical application of pot-shaped metal rubber in the field of three-dimensional vibration reduction.

Predicting changes in molluscan spatial distributions in mangrove forests in response to sea level rise

Mollusks are an important component of the mangrove ecosystem, and the vertical distributions of molluscan species in this ecosystem are primarily dictated by tidal inundation. Thus, sea level rise (SLR) may have profound effects on mangrove mollusk communities. Here, we used dynamic empirical models, based on measurements of surface elevation change, sediment accretion, and molluscan zonation patterns, to predict changes in molluscan spatial distributions in response to different sea level rise rates in the mangrove forests of Zhenzhu Bay (Guangxi, China). The change in surface elevation was 4.76–9.61 mm year⁻¹ during the study period (2016–2020), and the magnitude of surface‐elevation change decreased exponentially as original surface elevation increased. Based on our model results, we predicted that mangrove mollusks might successfully adapt to a low rate of SLR (2.00–4.57 mm year⁻¹) by 2100, with mollusks moving seaward and those in the lower intertidal zones expanding into newly available zones. However, as SLR rate increased (4.57–8.14 mm year⁻¹), our models predicted that surface elevations would decrease beginning in the high intertidal zones and gradually spread to the low intertidal zones. Finally, at high rates of SLR (8.14–16.00 mm year⁻¹), surface elevations were predicted to decrease across the elevation gradient, with mollusks moving landward and species in higher intertidal zones blocked by landward barriers. Tidal inundation and the consequent increases in interspecific competition and predation pressure were predicted to threaten the survival of many molluscan groups in higher intertidal zones, especially arboreal and infaunal mollusks at the landward edge of the mangroves, resulting in a substantial reduction in the abundance of original species on the landward edge. Thus, future efforts to conserve mangrove floral and faunal diversity should prioritize species restricted to landward mangrove areas and protect potential species habitats.

Do Chief Executives Matter in Corporate Financial and Social Responsibility Performance Nexus? A dynamic Model Analysis of Chinese Firms

This study examined the relationship between Corporate Financial Performance (CFP) and Corporate Social Responsibility Performance (CSRP). Furthermore, it explored the effectiveness of chief executive characteristics as a moderator in the CFP-CSRP nexus. We employed a dynamic sysGMM regression model on 2,439 firm-year observations of Chinese firms. The results reveal that CFP (market-based) has a significant positive impact on CSRP. However, CFP (historical) is significantly negatively related to CSRP. Furthermore, the study found that CEO turnover and CEO duality negatively moderate the CFP-CSRP relationship, while CEO as CFO positively moderates this relationship. The findings have substantial implications for all stakeholders, including investors, CEOs, corporate regulators, and policymakers.

Dynamic Models of Within-Herd Transmission and Recommendation for Vaccination Coverage Requirement in the Case of African Swine Fever in Vietnam

African swine fever (ASF) is a highly contagious disease that is caused by the ASF virus (ASFV) with a high fatality rate in domestic pigs resulting in a high socio-economic impact. The pig business in Vietnam was recently affected by ASF for the first time. This study thus aimed to develop a disease dynamic model to explain how ASFV spreads in Vietnamese pig populations and suggest a protective vaccine coverage level required to prevent future outbreaks. The outbreak data were collected from ten private small-scale farms within the first wave of ASF outbreaks in Vietnam. Three methods were used to estimate the basic reproduction number (R₀), including the exponential growth method, maximum likelihood method, and attack rate method. The average R₀ values were estimated at 1.49 (95%CI: 1.05–2.21), 1.58 (95%CI: 0.92–2.56), and 1.46 (95%CI: 1.38–1.57), respectively. Based on the worst-case scenario, all pigs in a herd would be infected and removed within 50 days. We suggest vaccinating at least 80% of pigs on each farm once a commercially approved ASF vaccine is available. However, an improvement in biosecurity levels in small-scale farms is still greatly encouraged to prevent the introduction of the virus.

Zonal model for predicting contaminant distribution in stratum ventilated rooms

Accurate prediction of the non-uniform contaminant distribution under stratum ventilation (SV) is crucial for optimal design for reducing contaminant exposure risks. Compared with experiments and computational fluid dynamics, zonal models are convenient to implement. This study proposes a zonal model for predicting dynamic non-uniform contaminant distribution in the stratum ventilated room. The zoning method is based on the unique airflow pattern under SV, and the room is divided into the jet zone, entrainment zone, and the mixing zone. The interzonal airflow rate is derived from the profile of the supply air jet. The results show that the proposed zonal model can predict the dynamic contaminant distribution in the stratum ventilated room. Compared with the experimental measurement, the predictions show good accuracy with the mean absolute error (MAE) at 0.51-2.36 ppm and root mean squared error (RMSE) at 0.64-2.53 ppm. The error of the proposed zonal model is influenced by the degree of mixing in each subzone. The proposed zonal model shows better accuracy for non-uniform air distribution under stratum ventilation compared with the existing zonal model.

Trajectory Control for Vibrating Screen with Magnetorheological Dampers

The article presents a method of vibrating screen trajectory control based on MR (magnetorheological) dampers applied in a screen suspension. A mathematical description of the dynamic screen model was derived, and parameters of this model were estimated based on experimental data from a semi-industrial vibrating screen. The investigated screen included a single mechanical exciter with unbalanced masses, generating a circular vibration trajectory and operating with over-resonant frequency close to 19 Hz. It was experimentally tested in several phases of operation: start-up, nominal operation at a target vibration frequency and shutdown. The implemented screen model was further extended and included several MR dampers oriented horizontally and vertically in the form of Bouc-Wen models. The Bouc-Wen model was identified based on experiments carried out for an MR damper subjected to harmonic excitations generated by the MTS (material testing system). Dominant frequencies of excitation varied by up to 20 Hz during experiments. The main novelty of the reported solution is that according to the proposed control algorithm, the desired forces generated by MR dampers emulate an additional virtual mechanical exciter of the vibrating screen. In turn, it interacts with the available exciter, resulting in conversion of the trajectory from circular to linear, which was validated in the presented study. For the purpose of simulation accuracy, the desired control force was additionally limited within the simulator by MR damper dissipative domain, which maps the constraints of a semi-active damper. The presented approach allows one to obtain a close to linear trajectory with only one exciter and with semi-active control of suspension stiffness. The results were successfully repeated with different configurations of desired trajectory, indicating that the effectiveness of the desired linear trajectory generation depends on its orientation. The reported findings may lead to the design of new vibrating screen constructions, taking advantage of the semi-active control of a suspension in the attenuation of disturbance resulting from varying processed material parameters.

Predicting Influenza Epidemic for United States

Influenza causes repeat epidemics and huge loss of lives and properties. To predict influenza epidemics, we proposed an infectious disease dynamic prediction model with control variables (SEIR-CV), which considers the characteristics of the influenza epidemic transmission, seasonal impacts, and the intensity changes of control measures over time. The critical parameters of the model were inversed using an adjoint method. When using the surveillance data of the past 15 weeks to invert the parameters, the epidemic in the next 3 weeks in the United States can be accurately predicted. In addition, roll predictions from 26 September 2016 to 27 September 2018 were implemented. The correlation coefficient between the predicted values and the surveillance values was greater than 0.975, and the overall relative error of the predictions was less than 10%. These good model performances demonstrated the practicability and feasibility of SEIR-CV for influenza and corresponding infectious disease prediction.

Male Meiosis as a Biomarker for Endo- to Ecodormancy Transition in Apricot

Dormancy is an adaptive strategy in plants to survive under unfavorable climatic conditions during winter. In temperate regions, most fruit trees need exposure to a certain period of low temperatures to overcome endodormancy. After endodormancy release, exposure to warm temperatures is needed to flower (ecodormancy). Chilling and heat requirements are genetically determined and, therefore, are specific for each species and cultivar. The lack of sufficient winter chilling can cause failures in flowering and fruiting, thereby compromising yield. Thus, the knowledge of the chilling and heat requirements is essential to optimize cultivar selection for different edaphoclimatic conditions. However, the lack of phenological or biological markers linked to the dormant and forcing periods makes it difficult to establish the end of endodormancy. This has led to indirect estimates that are usually not valid in different agroclimatic conditions. The increasing number of milder winters caused by climatic change and the continuous release of new cultivars emphasize the necessity of a proper biological marker linked to the endo- to ecodormancy transition for an accurate estimation of the agroclimatic requirements (AR) of each cultivar. In this work, male meiosis is evaluated as a biomarker to determine endodormancy release and to estimate both chilling and heat requirements in apricot. For this purpose, pollen development was characterized histochemically in 20 cultivars over 8 years, and the developmental stages were related to dormancy. Results were compared to three approaches that indirectly estimate the breaking of dormancy: an experimental methodology by evaluating bud growth in shoots collected periodically throughout the winter months and transferred to forcing chambers over 3 years, and two statistical approaches that relate seasonal temperatures and blooming dates in a series of 11-20 years by correlation and partial least square regression. The results disclose that male meiosis is a possible biomarker to determine the end of endodormancy and estimate AR in apricot.

Simulation of COVID-19 spread through family feast gatherings in a complex network

Family feasting during the Spring Festival is a Chinese tradition. However, close contact during this period is likely to promote the spread of coronavirus disease 2019 (COVID-19). This study developed a dynamic infectious disease model in which the feast gatherings of families were considered the sole mode of transmission. The model simulates COVID-19 transmission via family feast gatherings through a social contact network. First, a kinship-based, virtual social contact network was constructed, with nodes representing families and connections representing kinships. Families in kinship with each other comprised of the largest globally coupled network, also known as a clique, in which a feast gathering was generated by randomly selecting two or more families willing to gather. The social contact network in the model comprised of 215 cliques formed among 608 families with 1517 family members. The modelling results indicated that when there is only one patient on day 0, the number of new infections will reach a peak on day 29, and almost all families and their members in the social contact network will be infected by day 60. This study demonstrated that COVID-19 can spread rapidly through continuous feast gatherings through social contact networks and that the disease will run rampant throughout the network.

Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

Design and Analysis of a Pneumatic Spring Testing System for Precision Manufacturing

The vibration isolation effect of the pneumatic spring determines the precision of the manufacturing. In this paper, in order to detect the performance of a pneumatic spring, a multi frequency band testing system with different payload is designed and developed. First, the pneumatic spring structure is analyzed, and the stiffness of the pneumatic spring is obtained based on the ideal gas model, Kelvin–Voigt model, and finite element method. Then, to verify the reliability of the system, a dynamic model of the vibration platform is established. Through an analysis of the simulation using the Simulink environment, critical parameters are determined, and the effective conditions of the vibration isolation are obtained. Based on the results from the simulation and experiment, the transmission rate is around 20% under 40 Hz vibration, and 12% under 100 Hz vibration. The pneumatic spring proves to be effective under vibrations beyond 7 H. This achievement will become an important basis for future research concerning precision manufacturing.

Comparison of Molly and Karoline models to predict methane production in growing and dairy cattle

Numerous empirical and mechanistic models predicting methane (CH₄) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH₄ production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH₄ production were not included in the data set. The relationships between observed and predicted CH₄ (pCH₄) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH₄ (g/d) = 0.94 (±0.022) × pCH₄ (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH₄ (g/d) = CH₄ (g/d) = 0.98 (±0.019) × pCH₄ (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RMSPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, respectively. The residuals of CH₄ predictions were more strongly related to factors associated with CH₄ production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH₄ yield with the Molly model. It was concluded that both models could be used for prediction of CH₄ production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.

Land Tenure and Cotton Farmers' Land Improvement: Evidence from State-Owned Farms in Xinjiang, China

The land system of state-owned farms in China is different from that in rural areas. Whether the land tenure of state-owned farms can play a role in protecting cultivated land is an important issue for the high-quality development of state-owned agriculture in China. This article develops a dynamic model to examine how land tenure influences farmers' decisions on land improvement. It then analyzes this relationship based on cotton farmers' household-level data from state-owned farms of Xinjiang in China. We applied methods that take into account the possible endogeneity of the land tenure. The results reveal that the stability of land tenure in the past will not affect the current behavior of farmers for they have a relatively stable expectation of current land tenure and a high degree of trust in the government and its policies. The intergenerational transfer of land tenure is not the key factor that affects farmers' land conservation, and the relatively long-term duration of land tenure (possibly five years or more) during their careers is more important. Our findings also reveal that non-property factors, such as government intervention (e.g., technology promotion) that alleviates the limited rationality of farmers, cannot be ignored because they played a crucial role in past land improvement when land tenure was less stable.

Modeling the public health impact of different meningococcal vaccination strategies with 4CMenB and MenACWY versus the current toddler MenACWY National Immunization Program in Chile

Invasive meningococcal disease (IMD) is an uncommon yet unpredictable, severe, and life-threatening disease with the highest burden in young children. In Chile, most IMD is caused by meningococcal serogroup B (MenB) and W (MenW) infection. In response to a MenW outbreak in 2012, a toddler vaccination program was implemented using quadrivalent meningococcal conjugate vaccine against serogroups A, C, W and Y (MenACWY). The vaccine program, however, does not protect infants or other unvaccinated age groups and does not protect against MenB IMD. Since 2017, MenB IMD cases are becoming increasingly prevalent. Using a dynamic transmission model adapted for Chile, this analysis assessed the public health impact (reduction in IMD cases, long-term sequelae, deaths, and quality-adjusted life-years) of six alternative vaccination strategies using MenACWY and/or the four-component MenB (4CMenB) vaccine in infants, toddlers, and/or adolescents compared to the National Immunization Program (NIP) implemented in 2014. Strategies that added infant 4CMenB to MenACWY in toddlers or adolescents would prevent more IMD than the current NIP, observed within the first 5 years of the program. Replacing the NIP by an adolescent MenACWY strategy would prevent more IMD in the longer term, once herd immunity is established to protect unvaccinated infants or older age groups. The strategy that maximized reduction of IMD cases and associated sequelae in all age groups with immediate plus long-term benefits included infant 4CMenB and MenACWY in both toddlers and adolescents. This analysis can help policymakers determine the best strategy to control IMD in Chile and improve public health. A set of audio slides linked to this manuscript can be found at https://doi.org/10.6084/m9.figshare.16837543.

What is the context?

Invasive meningococcal disease (IMD) is a severe, sometimes fatal, unpredictable disease with highest rates in infants, young children, and adolescents. It is caused by different serogroups of Neisseria meningitidis bacteria. Most cases in Chile are due to meningococcal serogroups B (MenB) and W (MenW). Following a MenW IMD outbreak in 2012, vaccination was introduced, leading to the current National Immunization Program (NIP) in toddlers with quadrivalent meningococcal conjugate vaccine (MenACWY) (protecting against IMD caused by MenA, C, W, and Y).

What is new?

A disease model to predict the impact of vaccination strategies in the Chilean population compared six alternative strategies, using the multi-component MenB (4CMenB) vaccine for infants (protecting against MenB, with potential cross-protection against MenW and Y IMD) and/or the MenACWY vaccine for toddlers and/or adolescents.

What is the impact?

Results, compared to the NIP, show that: Strategy 1 (a program targeting only infants with 4CMenB) would reduce more MenB cases but fewer MenA, C, W and Y cases resulting in a lower reduction of total IMD cases in the long term; Strategy 3 (a program targeting only adolescents with MenACWY) would have a similar effect to the NIP in the short term but a far greater IMD reduction in the long term (as vaccinating this age group eventually reduces transmission to other age groups, reducing their risk of disease); all the other strategies targeted more than one age group, further reducing numbers of IMD cases compared with the NIP. The greatest benefits were seen with infant 4CMenB vaccination combined with toddler and adolescent MenACWY vaccination. Results can help policymakers determine the best IMD strategy to maximize the benefits of available meningococcal vaccines.