Dynamic Growth Process Research Articles

Best Practices for Real-Time in Situ Atomic Force and Chemical Force Microscopy of Crystals

The characterization of dynamic crystal surfaces with their surroundings can be elusive because their growth and dissolution usually occur at length scales and in environments that are incompatible with most microscopy methods. Real-time in situ atomic force microscopy (AFM) and chemical force microscopy (CFM) have emerged over the past two decades as powerful tools for the investigation of crystal growth in environments of interest, enabling quantitative characterization of dynamic growth processes at the near-molecular level as well as surface adhesion. Herein, we describe protocols that we view as best practices for these measurements, which permit substantial insight into crystal growth mechanisms and phenomena that often govern aggregation and adhesion of crystals. These protocols are illustrated with a focus on soft organic crystals relevant to human health, such as the pathological crystal l-cystine, which forms kidney stones in patients suffering from cystinuria. This manuscript also describes challenges and obstacles often encountered and some tricks of the trade, while illustrating typical results and data interpretation.

Calibrated Acoustic Emission System Records M −3.5 to M −8 Events Generated on a Saw-Cut Granite Sample

Acoustic emission (AE) analyses have been used for decades for rock mechanics testing, but because AE systems are not typically calibrated, the absolute sizes of dynamic microcrack growth and other physical processes responsible for the generation of AEs are poorly constrained. We describe a calibration technique for the AE recording system as a whole (transducers + amplifiers + digitizers + sample + loading frame) that uses the impact of a 4.76-mm free-falling steel ball bearing as a reference source. We demonstrate the technique on a 76-mm diameter cylinder of westerly granite loaded in a triaxial deformation apparatus at 40 MPa confining pressure. The ball bearing is dropped inside a cavity within the sample while inside the pressure vessel. We compare this reference source to conventional AEs generated during loading of a saw-cut fault in a second granite sample. All located AEs occur on the saw-cut surface and have moment magnitudes ranging from M −5.7 down to at least M −8. Dynamic events rupturing the entire simulated fault surface (stick–slip events) have measurable stress drop and macroscopic slip and radiate seismic waves similar to those from a M −3.5 earthquake. The largest AE events that do not rupture the entire fault are M −5.7. For these events, we also estimate the corner frequency (200–300 kHz), and we assume the Brune model to estimate source dimensions of 4–6 mm. These AE sources are larger than the 0.2 mm grain size and smaller than the 76 × 152 mm fault surface.

Characteristics analysis and optimization design of entities collaboration for cloud manufacturing

SummaryBy applying the cloud manufacturing paradigm in the regional enterprise cluster, the enterprises or facilities may collaborate extensively for efficiently utilizing the manufacturing resources. It is valuable to explore and design the strategies of facilities selection for the autonomic control of the collaboration behaviors in production. In this paper, we model the collaboration relations in the regional enterprise cluster as a generalized social collaboration network and explore the dynamic growth process of the Facilities Collaboration Network for different strategies of facilities selection, including the random selection with and without preference, and the balanced selection with and without preference. With performance indexes such as network size, the distribution of node degree and act degree, clustering coefficient, the average shortest distance, and the number of n‐cliques, we present and analyze the characteristics of these strategies for cloud manufacturing. Next, based on these characteristics, we propose 2 mechanisms for self‐optimization in facilities collaboration, including the dynamic weighing of facilities and the concentrated processing of successive subtasks in the process. We also analyze the mechanisms' effects on the characteristics of Facilities Collaboration Network and the performance in manufacturing. Copyright © 2016 John Wiley & Sons, Ltd.

A field study on the dynamic uptake and transfer of heavy metals in Chinese cabbage and radish in weak alkaline soils.

Vegetables and crops can take up heavy metals when grown on polluted lands. The concentrations and dynamic uptake of heavy metals vary at different growth points for different vegetables. In order to assess the safe consumption of vegetables in weak alkaline farmlands, Chinese cabbage and radish were planted on the farmlands of Baiyin (polluted site) and Liujiaxia (relatively unpolluted site). Firstly, the growth processes of two vegetables were recorded. The growth curves of the two vegetables observed a slow growth at the beginning, an exponential growth period, and a plateau towards the end. Maximum concentrations of copper (Cu), zinc (Zn), lead (Pb), and cadmium (Cd) were presented at the slow growth period and showed a downtrend except the radish shoot. The concentrations of heavy metals (Cu, Zn, and Cd) in vegetables of Baiyin were higher than those of Liujiaxia. In the meanwhile, the uptake contents continued to increase during the growth or halted at maximum at a certain stage. The maximum uptake rates were found on the maturity except for the shoot of radish which took place at the exponential growth stages of root. The sigmoid model could simulate the dynamic processes of growth and heavy metals uptake of Chinese cabbage and radish. Conclusively, heavy metals have higher bioaccumulation tendency for roots in Chinese cabbage and for shoots in radish.

Investigating materials formation with liquid-phase and cryogenic TEM

The recent advent of liquid-phase transmission electron microscopy (TEM) and advances in cryogenic TEM are transforming our understanding of the physical and chemical mechanisms underlying the formation of materials in synthetic, biological and geochemical systems. These techniques have been applied to study the dynamic processes of nucleation, self-assembly, crystal growth and coarsening for metallic and semiconductor nanoparticles, (bio)minerals, electrochemical systems, macromolecular complexes, and organic and inorganic self-assembling systems. New instrumentation and methodologies that are currently on the horizon promise new opportunities for advancing the science of materials synthesis. Applications of liquid-phase transmission electron microscopy (TEM) and cryogenic TEM to investigations of dynamic processes in diverse materials systems — both inorganic and organic — are transforming our understanding of the mechanisms underlying the formation of materials in synthetic, biological and geochemical environments.

Reconstruction and Visualization of Coordinated 3D Cell Migration Based on Optical Flow.

Animal development is marked by the repeated reorganization of cells and cell populations, which ultimately determine form and shape of the growing organism. One of the central questions in developmental biology is to understand precisely how cells reorganize, as well as how and to what extent this reorganization is coordinated. While modern microscopes can record video data for every cell during animal development in 3D+t, analyzing these videos remains a major challenge: reconstruction of comprehensive cell tracks turned out to be very demanding especially with decreasing data quality and increasing cell densities. In this paper, we present an analysis pipeline for coordinated cellular motions in developing embryos based on the optical flow of a series of 3D images. We use numerical integration to reconstruct cellular long-term motions in the optical flow of the video, we take care of data validation, and we derive a LIC-based, dense flow visualization for the resulting pathlines. This approach allows us to handle low video quality such as noisy data or poorly separated cells, and it allows the biologists to get a comprehensive understanding of their data by capturing dynamic growth processes in stills. We validate our methods using three videos of growing fruit fly embryos.

High-Temperature Carbon Deposition on Oxide Surfaces by CO Disproportionation.

Carbon deposition due to the inverse Boudouard reaction (2CO → CO2 + C) has been studied on yttria-stabilized zirconia (YSZ), Y2O3, and ZrO2 in comparison to CH4 by a variety of different chemical, structural, and spectroscopic characterization techniques, including electrochemical impedance spectroscopy (EIS), Fourier-transform infrared (FT-IR) spectroscopy and imaging, Raman spectroscopy, and electron microscopy. Consentaneously, all experimental methods prove the formation of a more or less conducting carbon layer (depending on the used oxide) of disordered nanocrystalline graphite covering the individual grains of the respective pure oxides after treatment in flowing CO at temperatures above ∼1023 K. All measurements show that during carbon deposition, a more or less substantial surface reduction of the oxides takes place. These results, therefore, reveal that the studied pure oxides can act as efficient nonmetallic substrates for CO-induced growth of highly distorted graphitic carbon with possible important technological implications especially with respect to treatment in pure CO or CO-rich syngas mixtures. Compared to CH4, more carbon is generally deposited in CO under otherwise similar experimental conditions. Although Raman and electron microscopy measurements do not show substantial differences in the structure of the deposited carbon layers, in particular, electrochemical impedance measurements reveal major differences in the dynamic growth process of the carbon layer, eventually leading to less percolated islands and suppressed metallic conductivity in comparison to CH4-induced graphite.

Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China

A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an urgent need for good methods to facilitate such delineations. This paper presents a machine learning CA model (termed MachCA) with nonlinear transition rules based on least squares support vector machines (LS-SVM) to simulate such urban growth. By projecting the input dataset into a high dimensional space using the LS-SVM method, an optimal hyper-plane is constructed to separate the complex boundaries between urban and nonurban land, thus enabling the retrieval of nonlinear CA transition rules. In the MachCA model, the transition rules are yes–no decisions on whether a cell changes its state or not, the rules being dynamically updated for each iteration of the model implementation. The application of the MachCA for simulating urban growth in the Shanghai Qingpu–Songjiang area in China reveals that the spatial configurations of rural–urban patterns can be modeled. A comparison of the MachCA model with a conventional CA model fitted by logarithmic regression (termed LogCA) shows that the MachCA model produces more hits and less misses and false alarms due to its capability for capturing the spatial complexity of urban dynamics. This results in improved simulation accuracies, although with only less than 1 % deviation between the overall errors produced by the MachCA and LogCA models. Nevertheless, the way MachCA model use in retrieving the transition rules provides a new method for simulating the dynamic process of urban growth.

English

Crop models known to be based on the theory of crop physiology for describing the dynamic process of crop growth are recently explored for their uncertainties in model application under resource limited conditions. The aim of this study was to test Environmental Policy Integrated Climate (EPIC), on upland land rice production by taking into account seasonal variability in Guinean and Guinean-Sudanian zones in Benin and Nigeria (West Africa). A range of data available under farmer or experimental conditions in rainfed agriculture were measured or used from literature. The results show the accuracy of the model to simulate LAI, total above ground biomass and grain yield of upland rice for 2 NERICA rice cultivars. After calibration, the model showed average mean relative error between 0.06 and 0.15 with the model efficiency up to 0.98% in the case of LAI. The assessment of the model performances about sensitivity to N or P fertilizer application is also discussed under Ultisols. Large root mean square (RMSE) in calibration and the validation (>100) process suggested that robustness of the model became restrictive under severe environmental conditions such as in drought or flooding condition. Performance of the model at large scale should be executed of with land marginality classification. Key words: Environmental Policy Integrated Climate (EPIC), modeling, upland rice, West Africa.

Mechanical model of geometric cell and topological algorithm for cell dynamics from single-cell to formation of monolayered tissues with pattern.

Geometric and mechanical properties of individual cells and interactions among neighboring cells are the basis of formation of tissue patterns. Understanding the complex interplay of cells is essential for gaining insight into embryogenesis, tissue development, and other emerging behavior. Here we describe a cell model and an efficient geometric algorithm for studying the dynamic process of tissue formation in 2D (e.g. epithelial tissues). Our approach improves upon previous methods by incorporating properties of individual cells as well as detailed description of the dynamic growth process, with all topological changes accounted for. Cell size, shape, and division plane orientation are modeled realistically. In addition, cell birth, cell growth, cell shrinkage, cell death, cell division, cell collision, and cell rearrangements are now fully accounted for. Different models of cell-cell interactions, such as lateral inhibition during the process of growth, can be studied in detail. Cellular pattern formation for monolayered tissues from arbitrary initial conditions, including that of a single cell, can also be studied in detail. Computational efficiency is achieved through the employment of a special data structure that ensures access to neighboring cells in constant time, without additional space requirement. We have successfully generated tissues consisting of more than 20,000 cells starting from 2 cells within 1 hour. We show that our model can be used to study embryogenesis, tissue fusion, and cell apoptosis. We give detailed study of the classical developmental process of bristle formation on the epidermis of D. melanogaster and the fundamental problem of homeostatic size control in epithelial tissues. Simulation results reveal significant roles of solubility of secreted factors in both the bristle formation and the homeostatic control of tissue size. Our method can be used to study broad problems in monolayered tissue formation. Our software is publicly available.

Spatial segmentation of multi/hyperspectral imagery by fusion of spectral-gradient-textural attributes

We propose an unsupervised algorithm that utilizes information derived from spectral, gradient, and textural attributes for spatially segmenting multi/hyperspectral remotely sensed imagery. Our methodology commences by determining the magnitude of spectral intensity variations across the input scene, using a multiband gradient detection scheme optimized for handling remotely sensed image data. The resultant gradient map is employed in a dynamic region growth process that is initiated in pixel locations with small gradient magnitudes and is concluded at sites with large gradient magnitudes, yielding a map comprised of an initial set of regions. This region map is combined with several co-occurrence matrix-derived textural descriptors along with intensity and gradient features in a multivariate analysis-based region merging procedure that fuses the regions with similar characteristics to yield the final segmentation output. Our approach was tested on several multi/hyperspectral datasets, and the results show a favorable performance in comparison with state-of-the-art techniques.

지식성장의 영향요인에 관한 연구-분산기억중심으로

Abstract As an empirical study focused on how transactive memory concept influence the four processes of knowledge growth, this study aimed at explaining the function of transactive memory in the dynamic process of knowledge growth. To verify the research model, this study carried out a path analysis of 130 team memebers andas a result, part of the team transactive memory measurements does affect the knowledge growth. Consequentially, transactive memory especially based on team level, is relevant with knowledge growth. According to team level, this study targeted at the team members, and by adopting the team transactive memory concept in Cognitive Psycology, theoretically explained and analysed how to approach personal knowledge in internal organizations. To accelerate theknowledge process, the work should be modified collaboratively by trusting the team members' duty relations morethan specialized knowledge. Furthermore, managers had better assign team members the work where they can make the most of their personal knowledge, and this study presented that the whole team performance could be improvedby doing that.

Complexity, selectivity and asymmetry in the conformation of the power phenomenon. Analysis of Chilean society

In this work we analyzed the relationships between powerful politicians and businessmen of Chile in order to study the phenomenon of social power. We developed our study according to Complex Network Theory but also using traditional sociological theories of Power and Elites. Our analyses suggest that the studied network displays common properties of Complex Networks, such as scaling in connectivity distribution, properties of small-world networks, and modular structure, among others. We also observed that social power (a proposed metric is presented in this work) is also distributed inhomogeneously. However, the most interesting observation is that this inhomogeneous power and connectivity distribution, among other observed properties, may be the result of a dynamic and unregulated process of network growth in which powerful people tend to link to similar others. The compatibility between people, increasingly selective as the network grows, could generate the presence of extremely powerful people, but also a constant inequality of power where the difference between the most powerful is the same as among the least powerful. Our results are also in accordance with sociological theories.

Optimized growth of graphene on SiC: from the dynamic flip mechanism.

Thermal decomposition of single-crystal SiC is one of the popular methods for growing graphene. However, the mechanism of graphene formation on the SiC surface is poorly understood, and the application of this method is also hampered by its high growth temperature. In this study, based on the ab initio calculations, we propose a vacancy assisted Si-C bond flipping model for the dynamic process of graphene growth on SiC. The fact that the critical stages during growth take place at different energy costs allows us to propose an energetic-beam controlled growth method that not only significantly lowers the growth temperature but also makes it possible to grow high-quality graphene with the desired size and patterns directly on the SiC substrate.

Estimates of Biomass Yield for Perennial Bioenergy Grasses in the USA

Perennial grasses, such as switchgrass (Panicum viragatum) and Miscanthus (Miscanthus × giganteus), are potential choices for biomass feedstocks with low-input and high dry matter yield per hectare in the USA and Europe. However, the biophysical potential to grow bioenergy grasses varies with time and space due to changes in environmental conditions. Here, we integrate the dynamic crop growth processes for Miscanthus and two different cultivars of switchgrass, Cave-in-Rock and Alamo, into a land surface model, the Integrated Science Assessment Model (ISAM), to estimate the spatial and temporal variations of biomass yields over the period 2001–2012 in the eastern USA. The validation with observed data from sites across diverse environmental conditions suggests that the model is able to simulate the dynamic response of bioenergy grass growth to changes in environmental conditions in the central and south of the USA. The model is applied to identify four spatial zones characterized by their average yield amplitude and temporal yield variance (or stability) over 2001–2012 in the USA: a high and stable yield zone (HS), a high and unstable yield zone (HU), a low and stable yield zone (LS), and a low and unstable yield zone (LU). The HS zones are mainly distributed in the regions with precipitation larger than 600 mm, and mean temperature range 292–294 K during the growing season, including southern Missouri, northwestern Arkansas, southern Illinois, southern Indiana, southern Ohio, western Kentucky, and parts of northern Virginia. The LU yield zones are distributed in southern parts of Great Plains with water stress conditions and higher temporal variances in precipitation, such as Oklahoma and Kansas. Three bioenergy grasses may not grow in the LS yield zones, including western parts of Great Plains due to extreme low precipitation and poor soil texture, and upper part of north central, northeastern, and northern New England due to extreme cold conditions.

Editorial: Targeting Neuregulin1 and HER Receptor Tyrosine Kinases for Therapy of Breast Cancer and Heart Failure

The Neuregulin1 proteins and HER receptor tyrosine kinases are pivotal for function and disease development of multiple organ systems, including breast cancer and the heart [1, 2]. The HER2 receptor is mutated or overexpressed in about 20-30% of metastatic breast cancers [3]. Therefore, it is a major drug target of breast cancer therapy. Trastuzumab, a monoclonal antibody that directly binds and blocks the HER2 receptor, is among the first drugs approved by the US Food and Drug Administration for targeted cancer therapy, which proven to be effective for improving the overall survival of breast cancer patients [4]. However, Trastuzumab can cause severe heart failure in patients, especially when used in combination with the chemotherapy drug doxorubicin [4]. Clinical studies show that the New York Heart Association class III/IV heart failure is significantly increased in breast cancer patients treated concurrently with Trastuzumab and doxorubicin compared to doxorubicin alone (16 vs. 4 %), suggesting the HER2 receptor is necessary for protecting the heart from doxorubicin-induced cardiotoxicity [4]. This clinical finding led to a decade of vigorous research on the HER signaling in heart failure, as well as the identification of Neuregulin1 as a cardio-protective factor [5-10]. Clinical trials are ongoing to test the therapeutic effects of using recombinant Neuregulin1 for the treatment of heart failure [11]. With the success of inhibiting the HER2 signals in cancer and using Neuregulin1 for heart failure, major clinical challenges remain which are: how to inhibit the HER signaling for cancer therapy while sparing the heart, and whether Neuregulin1 proteins can be safely used in patients without exacerbating the existing cancer burden or increasing the risk of cancer. In this issue, scientists from both the oncology and cardiology fields are getting together to review the current knowledge of the Neuregulin1-HER signaling in breast cancer and the heart. In the paper “Neuregulin Signaling in Pieces – Evolution of the Gene Family”, Mark Marchionni utilizes gene structure and protein sequence searches and alignments, as well as construction of phylogenetic tree to illustrate how Neuregulins and HER receptor tyrosine kinases evolve during evolution to form a complex signaling network that is currently known. In the paper “Heregulin in Breast Cancer: Old Story, New Paradigm”, Ruth Lupu et al. review Neuregulins’ biological role in the development, progression and maintenance of breast cancer. In the paper “Breast Cancer Biomarkers: Risk Assessment, Diagnosis, Prognosis, Prediction of Treatment Efficacy and Toxicity, and Recurrence”, Adedayo Onitilo et al. review the biomarker development for assisting breast cancer diagnosis, prediction of prognosis, therapeutic response and toxicity. In the paper “Cardiovascular Effects of Neuregulin1-1/ErbB Signaling: Role in Vascular Signaling and Angiogenesis”, Kerry Russell et al. review the role of Neuregulin-HER signaling in endothelial cell angiogenesis, vascular smooth muscle cell proliferation and migration, and maintenance of vascular structure and function. This paper also reviews the cross-talk between cardiac endothelial cells and cardiomyocytes, Neuregulin1 and VEGF signaling, as well as Neuregulin1 and inflammatory cytokines. In the paper “The Developing Role of Neuregulin1 in Cardiac Regenerative Stem Cell Therapy”, James Morgan et al. review the findings of using Neuregulin1 proteins to direct stem cell differentiation into the cardiac lineage in cell culture and discuss the potential use of Neuregulin1 to improve the therapeutic efficacy of stem cell therapy for heart failure. In the paper “Anti-HER2 Cancer Therapy and Cardiotoxicity”, Xinhua Yan et al. review the current anti-HER2 therapies, the preclinical and clinical data on cardiotoxicity induced by anti-HER2 therapy and the mechanisms of how HER2 signaling protects the heart from stress. In the paper “Tumor Dormancy and the Angiogenic Switch: Possible Implications of Bone Marrow-Derived Cells”, Nava Almog et al. review the molecular and cellular regulators, including growth stimulatory signals for angiogenic switch in dormant tumors. In the paper “Mathematical Modeling of Tumor Growth and Treatment”, Heiko Enderling et al. discuss how to use mathematic modeling to simulate the dynamic biological process of tumor growth, tumor-host interactions and to predict treatment response.

Bimodality of woody cover and biomass across the precipitation gradient in West Africa

Abstract. Multiple states of woody cover under similar climate conditions are found in both conceptual models and observations. Due to the limitation of the observed woody cover data set, it is unclear whether the observed bimodality is caused by the presence of multiple stable states or is due to dynamic growth processes of vegetation. In this study, we combine a woody cover data set with an aboveground biomass data set to investigate the simultaneous occurrences of savanna and forest states under the same precipitation forcing. To interpret the results we use a recently developed vegetation dynamics model (the Balanced Optimality Structure Vegetation Model), in which the effect of fires is included. Our results show that bimodality also exists in aboveground biomass and retrieved vegetation structure. In addition, the observed savanna distribution can be understood as derived from a stable state and a slightly drifting (transient) state, the latter having the potential to shift to the forest state. Finally, the results indicate that vegetation structure (horizontal vs. vertical leaf extent) is a crucial component for the existence of bimodality.

Superwetting of TiO2 by light-induced water-layer growth via delocalized surface electrons.

Titania, which exhibits superwetting under light illumination, has been widely used as an ideal material for environmental solution such as self-cleaning, water-air purification, and antifogging. There have been various studies to understand such superhydrophilic conversion. The origin of superwetting has not been clarified in a unified mechanism yet, which requires direct experimental investigation of the dynamic processes of water-layer growth. We report in situ measurements of the growth rate and height of the photo-adsorbed water layers by tip-based dynamic force microscopy. For nanocrystalline anatase and rutile TiO2 we observe light-induced enhancement of the rate and height, which decrease after O2 annealing. The results lead us to confirm that the long-range attraction between water molecules and TiO2, which is mediated by delocalized electrons in the shallow traps associated with O2 vacancies, produces photo-adsorption of water on the surface. In addition, molecular dynamics simulations clearly show that such photo-adsorbed water is critical to the zero contact angle of a water droplet spreading on it. Therefore, we conclude that this "water wets water" mechanism acting on the photo-adsorbed water layers is responsible for the light-induced superwetting of TiO2. Similar mechanism may be applied for better understanding of the hydrophilic conversion of doped TiO2 or other photo-catalytic oxides.

Climate-induced yield variability and yield gaps of maize (Zea mays L.) in the Central Rift Valley of Ethiopia

There is a high demand for quantitative information on impacts of climate on crop yields, yield gaps and their variability in Ethiopia, yet, quantitative studies that include an indication of uncertainties in the estimates are rare. A multi-model crop growth simulation approach using the two crop models, i.e. Decision Support System for Agro-Technology (DSSAT) and WOrld FOod STudies (WOFOST) was applied to characterize climate-induced variability and yield gaps of maize. The models were calibrated and evaluated with experimental data from the Central Rift Valley (CRV) in Ethiopia. Subsequently, a simulation experiment was carried out with an early maturing (Melkassa1) and a late maturing (BH540) cultivar using historical weather data (1984–2009) of three locations in the CRV. Yield gaps were computed as differences among simulated water-limited yield, on-farm trial yields and average actual farmers’ yields.The simulation experiment revealed that the potential yield (average across three sites and 1984–2009) is 8.2–9.2 and 6.8–7.1Mg/ha for the late maturing and early maturing cultivars, respectively; ranges indicate mean differences between the two models. The simulated water-limited yield (averaged across three sites and 1984–2009) is 7.2–7.9Mg/ha for the late maturing and 6.1–6.7Mg/ha for the early maturing cultivar. The water-limited yield shows high inter-annual variability (CV 36%) and about 60% of this variability in yield is explained by the variation in growing season rainfall. The gap between average farmers yield and simulated water-limited yield ranges from 4.7 to 6.0Mg/ha. The average farmers’ yields were 2.0–2.3Mg/ha, which is about 1.1–3.1Mg/ha lower than on-farm trial yields. In relative terms, average farmers’ yields are 28–30% of the water-limited yield and 44–65% of on-farm trial yields. Analysis of yield gaps for different number of years to drive average yields indicates that yield gap estimation on the basis of few years may result in misleading conclusions. Approximately ten years of data are required to be able to estimate yield gaps for the Central Rift Valley in a robust manner.Existing yield gaps indicate that there is scope for significantly increasing maize yield in the CRV and other, similar agro-ecological zones in Africa, through improved crop and climate risk management strategies. As crop models differ in detail of describing the complex, dynamic processes of crop growth, water use and soil water balances, the multi-model approach provides information on the uncertainty in simulating crop–climate interactions.

Knowledge Shift Model of Strategic Alliance Based on Prisoner's Dilemma and Snowdrift Game

According to the development trend of the act of China's current strategic alliance, this paper elaborates the reason why the success rate of strategic alliance is not high from game theory. The mathematical model based on prisoner's dilemma game and snowdrifts game; synthesize the relevant factors of impacting the revenue sharing of enterprise knowledge alliance. Through analysis, it finds that the strategic alliance is a dynamic process of growth. In each stage of the process, because internal and external environment, business processes, customers, the market reaction and other external conditions as well as internal conditions, such as bearing and sharing mechanisms of the risks and benefits between alliance partners change, which brings enormous difficulties to maintain and develop strategic alliance . Due to the different strategic objectives, work efficiency, learning ability and other conditions, the alliance partners need to pay attention to coordination to make flexible and creative reforms. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4504 Full Text: PDF