Two-step Model Research Articles

Modernizing non-classical protein crystallization through industry 4.0: Advanced monitoring and modelling utilizing process analytical technology

Modernizing manufacturing processes of pharmaceutical drug products with advanced monitoring and modelling can aid in the transition towards Industry 4.0 with the benefit of increased productivity. This study investigated the use of process analytical technology in combination with partial least squares (PLS) regressions to create two soft sensors to predict the mass fraction of crystallised active pharmaceutical ingredient (API) and mass fraction of dissolved API during a non-classical protein crystallization with amorphous precursors. The PLS model for predicting the amount of crystalline API was based on Raman spectra, chord length distributions and turbidity data using small-angle X-ray scattering as a reference method. The model had a root mean square error on cross-validation (RMSECV) of 5 %. The model predicting mass fraction of dissolved API was based only on the Raman spectra and used high performance liquid chromatography as reference method. This model had a RMSECV of 3 % A two-step nucleation model was fitted to the predictions from the sensors and showed good agreement between data and model with a root mean square error of 2 %.

Attraction opportunities for enotourism among international travellers to Spanish wine PDO regions

We analyze why the impact of wine tourism in Spain lags behind France and Italy. We argue that a key factor is a lack of attraction of foreign enotourists, especially among tourists who visit a wine PDO region anyway. The international tourist profiles visiting such Spanish wine PDOs are identified and segmented with the ultimate objective of fine-tuning the gastro-touristic offer in these regions. In particular, an innovative two-step Latent Class Model (LCM) with covariates is developed. The first step concerns the sociodemographic profiles of potential international enotourists, which is therefore complementary to existing segmentations of actual enotourists. The second step uses touristic activities in order to profile the possible ways of reaching different types of tourists. The daily expenditure and the tourists' valuation are included as covariates, allowing to predict the probability of class membership, and thus to define priority target profiles for market positioning.

It starts at home: non-economic factors influencing consumer acceptance of battery storage in Australia

Homeowners play a critical role in the uptake of low-carbon technologies, yet little is known about the factors that underlie market acceptance of residential battery storage. This research integrates social–psychological, demographic and behavioural factors into a holistic model that predicts market acceptance. Previous research has indicated that social factors play a crucial role in the adoption of rooftop solar. Still, the influence of subjective norms on battery storage, a relatively invisible technology, has yet to be fully understood. An online survey from homeowners in Australia, a mature renewable energy market, is used to provide insights into market acceptance that are relevant to international energy markets. A two-step econometric model, using factor analysis and ordered logistic regression, was used for data analysis. The results show that subjective norms, moral emotions and an environmental self-identity are positively associated with market acceptance. Demographic factors, such as younger age and higher levels of education, predict market acceptance. Motives such as technical interest, autarky and load-shifting behaviours are also relevant. Several recommendations for policymakers and practitioners are offered to improve the acceptance of battery storage, including interventions that exploit social parameters and appeal to consumer psychology.

Effects of Polydopamine Incorporation on the Nanostructure and Electrochemical Performance of Electrodeposited Polypyrrole Films

Polypyrrole films are commonly prepared as conductive electrode surfaces for a variety of applications. Recently, there has been increasing interest in improving the adhesive properties and biocompatibility of polypyrrole electrodes via the incorporation of bioinspired polydopamine within the polymer scaffold. However, very little is currently known about the structural effects of polydopamine incorporation during the electropolymerisation of hybrid films. In this work, we combine electrochemical quartz crystal microbalance studies, fundamental electrochemical characterisation, atomic force microscopy, and a suite of spectroscopic techniques in order to correlate changes in the structure and performance of polypyrrole–polydopamine films to the structural modifications of the nanostructure induced by polydopamine incorporation. The results indicate that polydopamine incorporation greatly increases the rate of hybrid film deposition, as well as improving adhesion, surface homogeneity, and wettability, with no compromise in charge transfer properties. Polydopamine incorporation is strongly suggested to occur in non-connected domains within a predominantly polypyrrole-like scaffold. We propose a two-step model of co-polymerisation and the subsequent surface adhesion of hybrid films. Results are expected to be of broad general interest to researchers utilizing polypyrrole and polydopamine to prepare tailor-made electrodes for biosensing and catalysis.

Microenvironmental sensitivity of body weight at harvest in Nile tilapia: Genetic parameters, performance test traits, and genetic variability

Uniformity of body weight at harvest is a trait of great economic importance in Nile tilapia production. Studying the genetic aspects of microenvironment sensitivity proves to be a valuable instrument for enhancing the uniformity of production. The microenvironmental sensitivity of a given genotype can be quantified by analyzing the degree of genetic heterogeneity of environmental variance. This study involved the analysis of a database comprising phenotypic records of body weight at harvest for 10,207 individuals. It aimed to estimate genetic parameters, scrutinize the effects of direct selection, and evaluate how selection impacts the genetic variability within a Nile tilapia breeding program. Estimates of genetic parameters were obtained by Bayesian procedures using a two-step animal model in GIBBS1F90 (BLUPF90 family) software. The coefficients of variation for residual variance (0.29) and genetic correlation (−0.43) between body weight at harvest and microenvironmental sensitivity were deemed moderately favorable. Direct selection for microenvironmental sensitivity resulted in a 41% increase in the coancestry coefficient. The group chosen for low microenvironmental sensitivity exhibited a significant difference of 19.08% in the coefficient of variation from the group selected for high microenvironmental sensitivity. No significant differences in the coefficient of variation were observed between tilapia of the commercial line selected for growth rate and those selected for low microenvironmental sensitivity. Selection for microenvironmental sensitivity has the potential to enhance production uniformity. Significantly, directional breeding for performance achieves a comparable improvement in uniformity. Nevertheless, opting for direct selection for microenvironmental sensitivity could result in a decrease in genetic variability within Nile tilapia breeding programs.

Enhancing the accuracy of China's electricity consumption forecasting through economic cycle division: An MSAR-OPLS scenario analysis

Accurate electricity consumption (EC) forecasting is vital for ensuring efficient energy supply and underpinning long-term economic stability. Prior research has often overlooked the intricate interplay between the nuanced dynamics of economic growth and EC. In contrast, this study innovatively incorporates the economic cycle into the analytical framework of EC, providing a more comprehensive perspective. Firstly, we construct the MSAR-OPLS two-step model to discern the factors influencing EC across various cycles. We select the most fitting stabilization cycle as the foundation for forecasting. By integrating both dynamic and static scenario analyses, we offer a comprehensive and precise evaluation of future EC trends. This study ensures both the reliability and accuracy of our forecasts. Projections suggest that China's EC will escalate to 11,780 billion kWh by 2030, reflecting an average annual growth rate of 3.96% from 2023 to 2030. Furthermore, through rigorous robustness and sensitivity analyses, the study validates the forecast's accuracy. Beyond emphasizing the role of economic phases in ensuring energy security, this research furnishes policymakers with insights on embedding foresightful elements into strategic economic and energy planning.

Assessment of the cerebroplacental ratio and uterine arteries in low-risk pregnancies in early labour for the prediction of obstetric and neonatal outcomes

BackgroundThe evidence-based management of human labor includes the antepartum identification of patients at risk for intrapartum hypoxia. However, available evidence has shown that most of the hypoxic-related complications occur among pregnancies classified at low-risk for intrapartum hypoxia, thus suggesting that the current strategy to identify the pregnancies at risk for intrapartum fetal hypoxia has limited accuracy. ObjectiveTo evaluate the role of the combined assessment of the cerebroplacental ratio (CPR) and uterine arteries (UtA) Doppler in the prediction of obstetric intervention (OI) for suspected intrapartum fetal compromise (IFC) within a cohort of low-risk singleton term pregnancies in early labor. MethodsProspective multicentre observational study conducted across four tertiary Maternity Units between January 2016 and September 2019. Low-risk term pregnancies with spontaneous onset of labor were included. A two-step multivariable model was developed to assess the risk of OI for suspected IFC. The baseline model included antenatal and intrapartum characteristics, while the combined model included antenatal and intrapartum characteristics plus Doppler anomalies such as CPR MoM < 10th percentile and mean UtA Doppler PI MoM ≥ 95th percentile. Predictive performance was determined by receiver–operating characteristics curve analysis. Results804 women were included. At logistic regression analysis, CPR MoM < 10th percentile (aOR 1.269, 95 % CI 1.188–1.356, P < 0.001), mean UtA PI MoM ≥ 95th percentile (aOR 1.012, 95 % CI 1.001–1.022, P = 0.04) were independently associated with OI for suspected IFC. At ROC curve analysis, the combined model including antenatal characteristics plus abnormal CPR and mean UtA PI yielded an AUC of 0.78, 95 %CI(0.71–0.85), p < 0.001, which was significantly higher than the baseline model (AUC 0.61, 95 %CI(0.54–0.69), p = 0.007) (p < 0.001). The combined model was associated with a 0.78 (95 % CI 0.67–0.89) sensitivity, 0.68 (95 % CI 0.65–0.72) specificity, 0.15 (95 % CI 0.11–0.19) PPV, and 0.98 (0.96–0.99) NPV, 2.48 (95 % CI 2.07–2.97) LR + and 0.32 (95 % CI 0.19–0.53) LR- for OI due to suspected IFC. ConclusionsA predictive model including antenatal and intrapartum characteristics combined with abnormal CPR and mean UtA PI has a good capacity to rule out and a moderate capacity to rule in OI due to IFC, albeit with poor predictive value.

Data-Driven Distributionally Robust Optimization-Based Coordinated Dispatching for Cascaded Hydro-PV-PSH Combined System

The increasing penetration of photovoltaic (PV) and hydroelectric power generation and their coupling uncertainties have brought great challenges to multi-energy’s coordinated dispatch. Traditional methods such as stochastic optimization (SO) and robust optimization (RO) are not feasible due to the unavailability of accurate probability density function (PDF) and over-conservative decisions. This limits the operational efficiency of the clean energies in cascaded hydropower and PV-enriched areas. Based on data-driven distributionally robust optimization (DRO) theory, this paper tailors a joint optimization dispatching method for a cascaded hydro-PV-pumped storage combined system. Firstly, a two-step model for a Distributed Renewable Optimization (DRO) dispatch is developed to create the daily dispatch plan, taking into account the system’s complementary economic dispatch cost. Furthermore, the inclusion of a complementary norm constraint is implemented to restrict the confidence set of the probability distribution. This aims to identify the optimal adjustment scheme for the day-ahead dispatch schedule, considering the adjustment cost associated with real-time operations under the most unfavorable distribution conditions. Utilizing the MPSP framework, the Column and Constraint Generation (CCG) algorithm is employed to resolve the two-stage dispatch model. The optimal dispatch schedule is then produced by integrating the daily dispatch plan with the adjustive dispatch scheme. Finally, the numerical dispatch results obtained from an actual demonstration area substantiate the effectiveness and efficiency of the proposed methodology.

Two-Step Relaxation of Non-Equilibrium Electrons in Graphene: The Key to Understanding Pump–Probe Experiments

In the majority of experiments targeting nonlinear optical phenomena, the application of high-intensity pulses drives electrons in graphene into a strongly non-equilibrium state. Under these conditions, conventional perturbation theory falls short in explaining graphene’s intricate optical response because of significant deviations in electron distribution over energy states from the equilibrium Fermi-Dirac one. In this work, we present a two-step relaxation model capable of predicting the transient dynamics of graphene’s carriers out of equilibrium, from the generation of spectrally narrow populations of non-thermalized electrons and holes to the establishment of a hot-electron gas and its subsequent cooling toward equilibrium with the crystal lattice. By comparing our model calculations to experimental results, we demonstrate its reliability and relevance to pump–probe experiments, providing insights into the pivotal role of hot electrons in comprehending ultrafast dynamics in graphene.

Beekeepers’ resilience as a key to ecosystem sustainability, empirical evidence from Hungary

Abstract The beekeeping sector plays an important role in maintaining biodiversity and the ecosystem, in addition to its production function. The European beekeeping sector is facing a number of constraints that make it difficult to operate, and the Covid-19 pandemic has posed new challenges for beekeepers. The purpose of the study is to examine the resilience of beekeeping businesses to the most important problems affecting beekeepers and the Covid-19 pandemic. The research analyses the role of marketing and profitability and how they influence the resilience. Variables obtained from online questioning of a representative sample of Hungarian beekeepers (N = 297). Cluster analysis was used to validate the results. Using a Two-step clustering model, three clusters of beekeepers were identified: a non-resilient; a resilient, but not proactive and not very profitable and a resilient, proactive and profitable cluster. According to the research, the resilience of beekeepers is shaped by several factors. Well-trained, long-established beekeepers who seek to differentiate their products from their competitors’ overcome the difficulties caused by the pandemic and other beekeeping problems much more easily. Very important factors resulting in remarkable business resilience are the number of used subsidies and the number of cooperations.

Learning as an epistemic activity / El aprendizaje como actividad epistémica

The life-long acquisition of knowledge is a major research topic in educational psychology. Many approaches based on the idea of learning as information processing treat learning primarily as the expansion of knowledge and the enrichment of existing knowledge structures with new information. In this article, we argue for a slightly different conceptualization of learning as an epistemic activity. Our proposal builds on the classical cognitive view of learning as information processing but relies on a stronger notion of knowledge, according to which knowledge is construed as justified true belief. Understanding and evaluating arguments is central to learning as an epistemic activity, particularly as many learning materials are structured around arguments. Furthermore, learning as an epistemic activity requires using source information for credibility evaluations, and the avoidance of belief biases in the selection, comprehension and evaluation of information during learning. We discuss examples of learning as an epistemic activity within the framework of the Two-Step Model of Validation and outline how this perspective on learning can guide future research.

Returnee directors and green innovation

This study examines the effect of returnee directors, defined as local directors who have studied or worked in foreign countries, on a firm’s green innovation. Utilising robust econometric modelling, we examine a large sample of A-share nonfinancial firms listed on the Shenzhen and Shanghai stock exchanges. Our findings reveal a positive and significant relationship between returnee directors and green innovation. Interestingly, this association remains consistent across both environmentally sensitive and non-sensitive industries. However, it becomes more pronounced when returnee directors are non-executive, function within state-owned enterprises, number more than one on the board, or operate in firms with poor internal and external corporate governance environments. Furthermore, we demonstrate that returnee directors, armed with foreign education or work experience, increase green innovation. The results withstand a series of robustness checks, including the Heckman two-step sample selection model and propensity score matching. Overall, the results suggest that returnee directors drive green innovation.

Chemical evolution of local post-starburst galaxies: implications for the mass–metallicity relation

ABSTRACT We use the stellar fossil record to constrain the stellar metallicity evolution and star-formation histories of the post-starburst (PSB) regions within 45 local PSB galaxies from the MaNGA survey. The direct measurement of the regions’ stellar metallicity evolution is achieved by a new two-step metallicity model that allows for stellar metallicity to change at the peak of the starburst. We also employ a Gaussian process noise model that accounts for correlated errors introduced by the observational data reduction or inaccuracies in the models. We find that a majority of PSB regions (69 per cent at &amp;gt;1σ significance) increased in stellar metallicity during the recent starburst, with an average increase of 0.8 dex and a standard deviation of 0.4 dex. A much smaller fraction of PSBs are found to have remained constant (22 per cent) or declined in metallicity (9 per cent, average decrease 0.4 dex, standard deviation 0.3 dex). The pre-burst metallicities of the PSB galaxies are in good agreement with the mass–metallicity (MZ) relation of local star-forming galaxies. These results are consistent with hydrodynamic simulations, which suggest that mergers between gas-rich galaxies are the primary formation mechanism of local PSBs, and rapid metal recycling during the starburst outweighs the impact of dilution by any gas inflows. The final mass-weighted metallicities of the PSB galaxies are consistent with the MZ relation of local passive galaxies. Our results suggest that rapid quenching following a merger-driven starburst is entirely consistent with the observed gap between the stellar mass–metallicity relations of local star-forming and passive galaxies.

Improving Cointegration-Based Pairs Trading Strategy with Asymptotic Analyses and Convergence Rate Filters

AbstractA pairs trading strategy (PTS) constructs a mean-reverting portfolio whose logarithmic value moves back and forth around a mean price level. It makes profits by longing (or shorting) the portfolio when it is underpriced (overpriced) and closing the portfolio when its value converges to the mean price level. The cointegration-based PTS literature uses the historical sample mean and variance to establish their open/close thresholds, which results in bias thresholds and less converged trades. We derive the asymptotic mean around which the portfolio value oscillates. Revised open/close thresholds determined by our asymptotic mean and standard derivations significantly improve PTS performance. The derivations of asymptotic means can be extended to construct a convergence rate filter mechanism to remove stock pairs that are unlikely to be profitable from trading to further reduce trading risks. Moreover, the PTS literature oversimplifies the joint problem of examining a stock pair’s cointegration property and selecting the fittest vector error correction model (VECM). We propose a two-step model selection procedure to determine the cointegration rank and the fittest VECM via the trace and likelihood ratio tests. We also derive an approximate simple integral trading volume ratio to meet no-odd-lot trading constraints. Experiments from Yuanta/P-shares Taiwan Top 50 Exchange Traded Fund and Yuanta/P-shares Taiwan Mid-Cap 100 Exchange Traded Fund constituent stock tick-by-tick backtesting during 2015–2018 show remarkable improvements by adopting our approaches.

The Establishment and Optimization of a Chicken Primordial Germ Cell Induction Model Using Small-Molecule Compounds.

In recent years, inducing pluripotent stem cells to differentiate into functional primordial germ cells (PGCs) in vitro has become an important method of obtaining a large number of PGCs. However, the instability and low induction efficiency of the in vitro PGC induction system restrict the application of PGCs in transgenic animal production, germplasm resource conservation and other fields. In this study, we successfully established a two-step induction model of chicken PGCs in vitro, which significantly improved the formation efficiency of PGC-like cells (PGCLCs). To further improve the PGC formation efficiency in vitro, 5025 differentially expressed genes (DEGs) were obtained between embryonic stem cells (ESCs) and PGCs through RNA-seq. GO and KEGG enrichment analysis revealed that signaling pathways such as BMP4, Wnt and Notch were significantly activated during PGC formation, similar to other species. In addition, we noted that cAMP was activated during PGC formation, while MAPK was suppressed. Based on the results of our analysis, we found that the PGC formation efficiency was significantly improved after activating Wnt and inhibiting MAPK, and was lower than after activating cAMP. To sum up, in this study, we successfully established a two-step induction model of chicken PGCs in vitro with high PGC formation efficiency, which lays a theoretical foundation for further demonstrating the regulatory mechanism of PGCs and realizing their specific applications.

Enhancing RDX Thermal Decomposition in Al@RDX Composites with Co Transition Metal Interfacial Layer

In this study, an Al/Co@RDX composite was meticulously prepared through a combination of planetary high-energy ball-milling and a spray-drying technique. The thermal reactivity of these Al/Co@RDX composites was comprehensively investigated and compared using the TG/DSC technique. It is shown that the initial decomposition temperature of RDX in the DSC curve was decreased by 26.3 °C in the presence of Al/Co, which could be attributed to the nano-sized Co transition metal catalyzing the decomposition reaction of nitrogen oxides in RDX decomposition products. The decomposition peak temperature of RDX and the heat released by the thermal decomposition of RDX in the Al/Co@RDX composite were decreased by 26.3 °C and increased by 74.5 J·g−1, respectively, in comparison with those of pure RDX. The types of major gaseous products released from Al/Co@RDX were found to be identical to those of pure RDX, encompassing N2O, CH2O, CO2 and HCN. However, the concentrations of those gaseous products for Al/Co@RDX were higher than those observed for pure RDX, which may owe to the fact that the Al/Co composite can interact with the –CH2 and –NO2 within RDX molecules, which leads to the weakening of the C-N and N-N bonds. In addition, the decomposition of RDX in the Al/Co@RDX composite was observed as a one-step process with an apparent activation energy (Ea) of 115.6 kJ·cm−3. The decomposition mechanism of the RDX in the Al/Co@RDX composite was identified to follow the chain scission model (L2), whereas the two-step decomposition physical models observed for pure RDX were found to closely resemble the L2 and autocatalytic models.

Numerical investigation on pyrolysis and ignition of ammonia/coal blends during co-firing

Co-firing ammonia with coal is a promising and feasible technology for reducing coal-related carbon emissions. Pyrolysis and ignition of ammonia-coal blended fuels are the key steps for flame stability and boiler operation safety throughout the conversion process but remain unclear. In this work, an extended Euler–Lagrange framework coupled to detailed solid-phase pyrolysis kinetics and gas-phase reactions mechanism is introduced and validated against experimental results for ammonia-coal co-firing in a two-stage flat flame burner. First, the CRECK-S coal pyrolysis model was validated with TGA experiments and the CPD model at different heating rates to determine parameters for a competing two-step model for CFD simulations. Second, a detailed gas-phase mechanism with 116 species and 1513 elementary reactions was derived from the volatile and ammonia reaction mechanisms. Thirdly, the co-firing simulations were validated for the ignition delay time for various ammonia co-firing ratios. The results show that increasing the co-firing ratios from 0.0 to 1.0 results in gradually increasing ignition delay times in a low-oxygen atmosphere. Further analysis demonstrates that adding ammonia accelerates the coal particle heating rate due to the reduced coal particle number density and ammonia reaction induced gas-phase temperature increase, and thus the coal devolatilization rate is increased. The latter plays a dominant role. Hydrogen produced from ammonia pyrolysis is negligible, so ammonia predominantly participates in the ignition. Time scale analysis shows that homogeneous ignition is dominant during the ignition process. The presence of ammonia inhibits the inward oxygen diffusion and causes the reaction zone to move away from the pulverized coal particle flow. The oxygen diffusion inhibition and low reactivity of ammonia compared to volatiles lead to an increase in ignition delay time for ammonia co-firing, even though pulverized coal devolatilization is accelerated.

Scheduling of automated ore terminal operations based on fixed inflow rhythm

To facilitate transportation, some mines construct specialized ore terminals for outbound shipments. However, these terminals frequently encounter challenges when synchronizing their schedules with the production plans of the mines. The primary issue arises due to the inherent deviation between vessel demand and mine production schedules. The terminals must efficiently arrange storage positions for diverse cargo types and berthing orders of vessels to reduce the time vessels spend at the terminal and operation time. In this study, we propose a comprehensive two-step model framework that considers various customer priorities to optimize the operational plans for each operation. In the first step, we propose a column-generation approach to generate candidate berth plans. Subsequently, we evaluate the feasibility of berth plans and construct an infeasible CUT to modify the model. We implement the developed framework in real-world case studies to demonstrate its practicality and potential. Computational outcomes underscore the method’s efficacy in resolving coordination challenges between mines and terminals. Furthermore, it exhibits a noteworthy capability to diminish the terminal’s spatial requirements, enhancing its pragmatic value.

Nutritional Status and Physical Exercise Are Associated with Cognitive Function in Chinese Community-Dwelling Older Adults: The Role of Happiness.

We aim to assess the relationship between nutrition status, physical exercise, and cognitive function and particularly examine how happiness modifies and mediates the relationship, among 699 seniors aged 60 and above in Shanghai, China. Linear regression models were used to validate the effects of nutrition and exercise on cognitive function and to test their interaction effects with happiness. When the interactions were significant, stratified analyses in sub-groups were conducted. Mediation effects of happiness were examined using two-step causal mediation models. We confirmed that better nutrition (p < 0.001) and exercise (p = 0.009) were significantly associated with less cognitive decline. Furthermore, the effects of nutrition and exercise on cognitive decline were significant in the unhappy (happiness < 20) (p < 0.001) and younger (age < 74) sub-groups (p = 0.015). Happiness partially mediated 11.5% of the negative association of cognitive decline with nutrition (p = 0.015) and 23.0% of that with exercise (p = 0.017). This study suggests that happiness moderates and partially mediates the effects of exercise and nutrition on cognitive status. The beneficial effects of exercise and nutrition were stronger in less happy or younger seniors. Future intervention studies are required to confirm this path relationship.

A maximum of two readily releasable vesicles per docking site at a cerebellar single active zone synapse.

Recent research suggests that in central mammalian synapses, active zones contain several docking sites acting in parallel. Before release, one or several synaptic vesicles (SVs) are thought to bind to each docking site, forming the readily releasable pool (RRP). Determining the RRP size per docking site has important implications for short-term synaptic plasticity. Here, using mouse cerebellar slices, we take advantage of recently developed methods to count the number of released SVs at single glutamatergic synapses in response to trains of action potentials (APs). In each recording, the number of docking sites was determined by fitting with a binomial model the number of released SVs in response to individual APs. After normalization with respect to the number of docking sites, the summed number of released SVs following a train of APs was used to estimate of the RRP size per docking site. To improve this estimate, various steps were taken to maximize the release probability of docked SVs, the occupancy of docking sites, as well as the extent of synaptic depression. Under these conditions, the RRP size reached a maximum value close to two SVs per docking site. The results indicate that each docking site contains two distinct SV-binding sites that can simultaneously accommodate up to one SV each. They further suggest that under special experimental conditions, as both sites are close to full occupancy, a maximal RRP size of two SVs per docking site can be reached. More generally, the results validate a sequential two-step docking model previously proposed at this preparation.