Vertical ozone formation mechanisms resulting from increased oxidation on the mountainside of Mount Tai, China.

The vertical distribution of ozone (O3) within the boundary layer (BL) and its ground-level effects have been extensively studied. However, observational limitations in obtaining high-resolution, real-time data on O3 and its precursors, especially volatile organic compounds (VOCs), have led to a scarcity of research on O3 formation sensitivity and mechanisms. Online measurements for O3, nitrogen oxides (NO x ), and VOCs were made on the mountainside of Mount Tai (∼550 m a.s.l.) in China during the summer of 2022 and were compared with the data from a ground-level site. The Master Chemical Mechanism (V3.3.1) was used to uncover a positive correlation between NO x and photochemical reaction rates on the mountainside, marking it as a NO x -limited regime in contrast to the VOC-limited regime identified at surface. On the mountainside, lower NO levels limited hydroxyl radicals (OH) recycling reactions, resulting in earlier O3 peaks and higher concentrations of hydroperoxy radicals (HO2) and organic peroxy radicals (RO2). The arrival of fresh air masses rich in NO accelerated OH radical cycling, enhanced atmospheric oxidization, and significantly impacted surface O3 concentrations though vertical transport. Moreover, NO x reduction scenario simulations show that when considering vertical transport, the peak O3 production rate at the surface is lower due to differences in O3 formation sensitivity vertically. This study highlights the significant sensitivity of O3 formation to NO within the BL, underscoring the potential impact of vertical in situ O3 formation above the ground on surface-level O3 concentrations through vertical exchange, particularly in cities with mountainous terrain.

Molecular-level insight into the effect of fertilization regimes on the chemodiversity of dissolved organic matter in tropical cropland.

Fertilization is a critical agronomic measure for croplands in tropical regions, owing to their low fertility. However, the effects of fertilization on the quantity and chemodiversity of latosolic dissolved organic matter (DOM) in tropical regions remains largely unknown. Therefore, in this study, the variations in latosol DOM concentrations and chemodiversity induced by inorganic fertilization and the co-application of inorganic fertilization with straw return, sheep manure, biochar, and vermicompost fertilizers at a molecular level were systematically investigated using multispectral techniques and ultrahigh-resolution mass spectrometry. In line with our expectations, the results showed that combined inorganic-organic fertilization improved soil quality by increasing soil organic carbon content compared to that under inorganic fertilization. However, as the most active and bioavailable organic carbon pool, dissolved organic carbonconcentrations between the fertilization treatments were not significantly different (p = 0.07). However, the dissolved organic carbon concentrations under combined inorganic-organic fertilization treatment (NPK plus straw return, 263.45±37.51 mg/kg) were lower than those under inorganic fertilization treatment (282.10±18.57 mg/kg). Spectral analysis showed that the DOM in the combined inorganic-organic fertilization treatments had a higher degree of humification and lower autogenetic contributions. Furthermore, Fourier transform ion cyclotron resonance mass spectrometry analysis indicated that the combined inorganic-organic fertilization increased the chemodiversity of latosolic DOM and promoted the production of large, oxidized, and stable molecules, including lignin, aromatic, and tannin compounds, which potentially benefits soil carbon sequestration in tropical regions. This study could provide a theoretical basis for elucidating on the potentially relevant ecological functions and environmental effects of DOM under fertilization regimes.

Differentiation and destandardization of family life courses: Theoretical and empirical links to sociopolitical regime types.

Building on research on cohort-specific trends in life-course differentiation and destandardization, our study seeks to explore how patterns of family-life-course diversity relate to characteristics of sociopolitical regimes. Applying sequence analysis, we analyse cohort-specific family life courses from nine European countries and Canada, classifying them into five sociopolitical regimes (Nordic, Anglo-Saxon, Mediterranean, Continental, Eastern). We use data from the Harmonized Histories and the German National Educational Panel Survey. Our results indicate that sociopolitical regimes differentiate patterns of life-course diversity quite well in the Nordic, Anglo-Saxon, and Eastern European countries but to a lesser degree in the Mediterranean and Continental countries. In methodological terms, our results suggest that distinguishing patterns of diversity between and within life courses helps to create a more nuanced empirical account of changing life-course patterns, particularly in a cross-national perspective.

A generalized outcome-adaptive sequential multiple assignment randomized trial design.

A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power.

Understanding fire regimes: A biogeographical perspective.

Fire regimes are often considered to be either driven by climate, fuel load or human activities. A significant proportion of fires across various ecosystems occur via large fire events. Recently, suggestions have been made that fires are becoming more severe and frequent as a consequence of current climate change. Although there are many factors influencing fire events, scientists have not found a suitable framework that can provide for understanding fires at the macroscale level. This review article proposes a new conceptual framework to better understand fire regimes. The proposed framework relies on a biogeographical perspective of fire regimes that include characteristics that have been underestimated in previous frameworks and to mitigate time as well as spatial scale issues at the macrolevel.

Modelling alternative harvest effects on soil CO2 and CH4 fluxes from peatland forests.

Over the last century, many peatlands in northern Europe have been drained for forestry. Forest management with different harvesting regimes has a significant impact on soil water status and consequently on greenhouse gas emissions from peat soils. In this paper, we have used the process-based JSBACH-HIMMELI model to simulate the effects of alternative harvesting regimes, namely non-harvested (NH), selection harvesting (SH; 70 % of stem volume harvested) and clear-cutting (CC; 100 % of stem volume harvested), on soil CH4 and CO2 fluxes in peatland forests. We modified the model to account for the specific characteristics of peatland forests, where the water level (WL) is generally low and is regulated by the amount of aboveground vegetation through evapotranspiration. Multi-year measurements before and after the forest harvesting in a nutrient-rich peatland forest in southern Finland were used to constrain the model. The results showed that the modified model was able to reproduce the seasonal dynamics of water level, soil CH4 and soil CO2 fluxes under alternative harvesting regimes with reasonable accuracy. The averaged Pearson's r (Pearson correlation coefficient) and RMSE (Root Mean Square Error) between the model and the measurement were 0.75 and 7.3 cm for WL, 0.75 and 0.23 nmol m-2 s-1 for soil CH4 flux, 0.73 and 0. 88 µmol m-2 s-1 for soil CO2 flux. The modified model successfully reproduced soil CH4 uptake at both NH and SH sites and soil CH4 emission at the CC site, as observed in the measurements. Our study showed that increasing harvesting intensity (NH â†’ SH â†’ CC) in the model increased soil CH4 emission and decreased soil CO2 emission on an annual basis, but the magnitude of the decreased soil CO2 emission was much larger than that of the increased soil CH4 emission when comparing their global warming potentials. Therefore, in the short term as in our study (first three years after the harvest), the climate impacts of the soil GHG was reduced more in CC than in SH, which yet can be fundamentally different when considering in the long term.

Review of techniques, challenges, and gaps in the subsurface gas release knowledge base.

Underground pipelines serve as critical infrastructure for gas transmission, strategically buried for safety, environmental, and economic considerations. Despite their importance, operational challenges and external interferences can lead to underground gas leaks with potentially catastrophic consequences for both human safety and the environment. The presence of a protective soil bed introduces complexities in understanding subsurface transport phenomena and quantifying gas releases accurately. Herein, this review presents a systematic analysis of published research in the field of underground gas releases, with an emphasis on interdisciplinary approaches that connect the lithosphere and atmosphere. The analysis highlights the broad spectrum of employed methods, including theoretical models based on fundamental principles, empirical formulations derived from experimental data, and sophisticated computational tools. A clear fundamental understanding and computational analysis, and to a lesser extent experimental, have been established to describe the migration regime. In contrast, more empirical research has addressed the crater formation regime, though focus was given to the far-field modelling following the soil ejection rather than the transient phenomena leading to the formation of the crater. Additionally, this review touches upon practical and conceptual topics, such as detection and localization techniques, and flow regimes in other gaseous flows through soil and powder beds, putting into question the applicability of some presumed granulated concepts to the flowing behavior expected beyond migration. The research landscape predominantly focuses on investigating the influence of release parameters on the release phenomena only from the atmospheric or soil domain perspective. This work provides insights that aim to first transcend both domains and then bridge the three distinct flow regimes-migration, uplift, and crater formation-despite the limited acknowledgment of the necessity of addressing all regimes concurrently through a universal approach. This review serves as a valuable resource for engineers to develop innovative solutions for the management of risks associated with underground gas leaks.

Reconstructing blood flow in data-poor regimes: a vasculature network kernel for Gaussian process regression.

Blood flow reconstruction in the vasculature is important for many clinical applications. However, in clinical settings, the available data are often quite limited. For instance, transcranial Doppler ultrasound is a non-invasive clinical tool that is commonly used in clinical settings to measure blood velocity waveforms at several locations. This amount of data is grossly insufficient for training machine learning surrogate models, such as deep neural networks or Gaussian process regression. In this work, we propose a Gaussian process regression approach based on empirical kernels constructed by data generated from physics-based simulations-enabling near-real-time reconstruction of blood flow in data-poor regimes. We introduce a novel methodology to reconstruct the kernel within the vascular network. The proposed kernel encodes both spatiotemporal and vessel-to-vessel correlations, thus enabling blood flow reconstruction in vessels that lack direct measurements. We demonstrate that any prediction made with the proposed kernel satisfies the conservation of mass principle. The kernel is constructed by running stochastic one-dimensional blood flow simulations, where the stochasticity captures the epistemic uncertainties, such as lack of knowledge about boundary conditions and uncertainties in vasculature geometries. We demonstrate the performance of the model on three test cases, namely, a simple Y-shaped bifurcation, abdominal aorta and the circle of Willis in the brain.

Interactions between vegetation, sedimentation and flood inundation levels in wetlands.

Wetlands produce key ecosystem services to mitigate the impacts of peak flows caused by pluvial or fluvial floods or storm surges. Sediment floods were characterized by a peak flow flowing over a simulated wetland, populated with two natural species. Floods have been drawn as flows of height H, into waters of height h, where H > h. Peak flow along the flume passed through: peak flow adjustment; peak flow; drag-dominated peak flow; and gravity current regimes. For high inundation wetland levels, settling rates of coarse and fine sediment were similar during the peak flow regime. At larger distances, sedimentation decreased monotonically, with higher sedimentation of fine particles. For low inundation levels, the sedimentation rate during the drag-dominated peak flow regime was higher for coarse particles. Vegetation decreased the inundation level needed for enhancing sedimentation. Our study then adds practical knowledge at considering that the synergies between the vegetation and the inundation level may enhance wetland services such as the mitigation of pluvial, fluvial or coastal floodings.

Subnational authority and political alignment in African states.

This paper examines the importance of, and variations in, political alignment within African regimes. Political alignment is how leaders establish sufficient political support across elites: we posit that formal appointments are the primary way that leaders manipulate political coalitions in order to secure their collective authority and tenure. Appointments, individually and collectively, can take on multiple characteristics: they can create inclusive or exclusive coalitions, transactional or loyal support, volatile or stable elite networks. Appointment powers have greater salience since governments institutionalized and formalized in governance systems across democratic and autocratic states. Manipulating who holds and secures power at the subnational and national levels, rather than repressive control or state capacity, underpins the stability, security, and survival of modern African regimes.

The Continuum of Violence and Interstices in the Journeys and Bodies of Women on the Move From West Africa.

On the migration route from Western and Central Africa to Europe, aggression toward women's bodies has played a prominent role. In this study, we analyze this route as a continuum of violence and also study the interstices to confront it. Using a biographical narrative methodology, we analyze the narratives of 52 migrant women, through life stories or creative narrative workshops carried out in Mali, Morocco, and Spain. The results allow us to systematize the continuum of violence that the women report, understood as a pedagogy of cruelty but also the possible spaces to build a safe landscape.

Modelling thermal reaction norms for development and viability in Drosophila suzukii under constant, fluctuating and field conditions.

Phenological models for insect pests often rely on knowledge of thermal reaction norms. These may differ in shape depending on developmental thermal conditions (e.g. constant vs. fluctuating) and other factors such as life-stages. Here, we conducted an extensive comparative study of the thermal reaction norms for development and viability in the invasive fly, Drosophila suzukii, under constant and fluctuating thermal regimes. Flies, were submitted to 15 different constant temperatures (CT) ranging from 8 to 35 °C. We compared responses under CT with patterns observed under 15 different fluctuating temperature (FT) regimes. We tested several equations for thermal performance curves and compared various models to obtain thermal limits and degree-day estimations. To validate the model's predictions, the phenology was monitored in two artificial field-like conditions and two natural conditions in outdoor cages during spring and winter. Thermal reaction norm for viability from egg to pupa was broader than that from egg to adult. FT conditions yielded a broader thermal breadth for viability than CT, with a performance extended towards the colder side, consistent with our field observations in winter. Models resulting from both CT and FT conditions made accurate predictions of degree-day as long as the temperature remained within the linear part of the developmental rate curve. Under cold artificial and natural winter conditions, a model based on FT data made more accurate predictions. Model based on CT failed to predict adult's emergence in winter. We also document the first record of development and adult emergence throughout winter in D. suzukii. Population dynamics models in D. suzukii are all based on summer phenotype and CT. Accounting for variations between seasonal phenotypes, stages, and thermal conditions (CT vs. FT) could improve the predictive power of the models.

Bioaccumulation of Cadmium in Muscle and Liver Tissues of Juvenile Yellowfin Tuna (Thunnus albacares) from the Indian Ocean.

The present study evaluated the cadmium (Cd) levels and temporal variation of Cd in dark muscle, white muscle, and liver of juvenile Thunnus albacares. 72 individuals (Standard length: 50-67 cm; weight: 0.8-2.5 kg) were collected from Indian Oceanic water around Sri Lanka during the period between April 2021 to May 2022. Total Cd levels were analyzed using an Inductively Coupled Plasma Mass Spectrophotometer. The mean Cd levels (mean ± SD mg kg-1 dry weight) in different tissues varied with significantly higher levels in the liver (13.62 ± 0.98, p < 0.05), compared to dark muscle (0.52 ± 0.05), and white muscle (0.42 ± 0.04). Cd levels in liver tissues were positively correlated (p < 0.05) with the fish weight. The Cd levels reported in dark muscles, white muscles, and liver tissues were significantly higher (p < 0.05) during 2nd inter-monsoon than in the other monsoonal regimes and exceeded the maximum permissible level (0.1 mg kg-1 wet weight) set by the European Union (EU). However, the measured Cd levels in white and dark muscles were below the maximum permissible level (0.2 mg kg-1 wet weight) set by FAO/WHO. The Cd levels in all the liver tissues were above the levels set by the EU and FAO/WHO. Accordingly, people should avoid the consumption of liver tissues of T. albacares from the Indian Ocean. A human with a body weight of 60 kg can consume white muscles up to 4.667 kg per week without exceeding the Provisional Tolerable Weekly Intake.

Differentially Private Outcome-Weighted Learning for Optimal Dynamic Treatment Regime Estimation.

Precision medicine is a framework for developing evidence-based medical recommendations that seeks to determine the optimal sequence of treatments tailored to all of the relevant patient-level characteristics which are observable. Because precision medicine relies on highly sensitive, patient-level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting. Outcome-Weighted Learning (OWL) is a family of techniques for estimating optimal DTRs based on observational data. OWL techniques leverage support vector machine (SVM) classifiers in order to perform estimation. SVMs perform classification based on a set of influential points in the data known as support vectors. The classification rule produced by SVMs often requires direct access to the support vectors. Thus, releasing a treatment policy estimated with OWL requires the release of patient data for a subset of patients in the sample. As a result, the classification rules from SVMs constitute a severe privacy violation for those individuals whose data comprise the support vectors. This privacy violation is a major concern, particularly in light of the potentially highly sensitive medical data which are used in DTR estimation. Differential privacy has emerged as a mathematical framework for ensuring the privacy of individual-level data, with provable guarantees on the likelihood that individual characteristics can be determined by an adversary. We provide the first investigation of differential privacy in the context of DTRs and provide a differentially private OWL estimator, with theoretical results allowing us to quantify the cost of privacy in terms of the accuracy of the private estimators.

Fertilization regime changes rhizosphere microbial community assembly and interaction in Phoebe bournei plantations.

Fertilizer input is one of the effective forest management practices, which improves soil nutrients and microbial community compositions and promotes forest productivity. However, few studies have explored the response of rhizosphere soil microbial communities to various fertilization regimes across seasonal dynamics. Here, we collected the rhizosphere soil samples from Phoebe bournei plantations to investigate the response of community assemblages and microbial interactions of the soil microbiome to the short-term application of four typical fertilizer practices (including chemical fertilizer (CF), organic fertilizer (OF), compound microbial fertilizer (CMF), and no fertilizer control (CK)). The amendments of organic fertilizer and compound microbial fertilizer altered the composition of rhizosphere soil bacterial and fungal communities, respectively. The fertilization regime significantly affected bacterial diversity rather than fungal diversity, and rhizosphere fungi responded more sensitively than bacteria to season. Fertilization-induced fungal networks were more complex than bacterial networks. Stochastic processes governed both rhizosphere soil bacterial and fungal communities, and drift and dispersal limitation dominated soil fungal and bacterial communities, respectively. Collectively, these findings demonstrate contrasting responses to community assemblages and interactions of rhizosphere bacteria and fungi to fertilizer practices. The application of organic fertilization strengthens microbial interactions and changes the succession of key taxa in the rhizosphere habitat. KEY POINTS: • Fertilization altered the key taxa and microbial interaction • Organic fertilizer facilitated the turnover of rhizosphere microbial communities • Stochasticity governed soil fungal and bacterial community assembly.

Enhancing hydrodynamic forces through miniaturized control of square cylinders using the lattice Boltzmann method.

This study investigates the influence of small control cylinders on the fluid dynamics around a square cylinder using the Lattice Boltzmann Method (LBM). Varying the gaps (L) between the main and control cylinders from 0 to 6, four distinct flow regimes are identified: the solo body regime (SBR), shear layer reattachment (SLR), suppressed fully developed flow (SFDF), and intermittent shedding (IS). The presence of control cylinders results in significant reductions in flow-induced forces, with drag coefficient (CD) and root mean square values of drag and lift coefficients (CDrms and CLrms) decreasing by approximately 31%, 90%, and 81%, respectively. The SFDF flow regime exhibits the lowest fluid forces compared to other regimes. The effects of tiny control cylinders on the fluid flow characteristics of a square cylinder are examined using the Lattice Boltzmann Method (LBM) in this research work. The gaps (L) between the main and control cylinders are varied in the range from 0 to 6. The size of each control cylinder is equal to one-fifth of the primary cylinder. According to the findings, there are four distinct flow regimes as the gap spacing varies: solo body regime (SBR), shear layer reattachment (SLR), suppressed fully developed flow (SFDF), and intermittent shedding (IS) for gap spacing ranges 0 ≤ L ≤ 0.2, 0.3 ≤ L ≤ 0.9, 1 ≤ L ≤ 3, and 3.2 ≤ L ≤ 6, respectively. Additionally, it has been noted that the amplitude of variable lift force is reduced when the gap separation between the main and control cylinders is increased. When compared to solo cylinder values, it is found that the presence of small control cylinders in the flow field results in a considerable reduction of flow-induced forces. The SFDF flow regime was determined to have the lowest fluid forces compared to the other flow regimes studied. Our findings highlight the efficacy of small control cylinders in mitigating flow-induced forces and controlling flow characteristics. The LBM proves to be a valuable computational technique for such fluid flow problems.

Machine learning for antidepressant treatment selection in depression.

Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.

Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea.

This study analyzed spatiotemporal variation and long-term trends in water quality indicators and trophic state conditions in an Asian temperate reservoir, Juam Reservoir (JR), and developed models that forecast algal chlorophyll (CHL-a) over a period of 30 years, 1993-2022. The analysis revealed that there were longitudinal gradients in water quality indicators along the reservoir, with notable influences from tributaries and seasonal variations in nutrient regimes and suspended solids. The empirical model showed phosphorus was found to be the key determinant of algal biomass, while suspended solids played a significant role in regulating water transparency. The trophic state indices indicated varying levels of trophic status, ranging from mesotrophic to eutrophic. Eutrophic states were particularly observed in zones after the summer monsoons, indicating a heightened risk of algal blooms, which were more prevalent in flood years. The analysis of trophic state index deviation suggested that phosphorus availability strongly influences the reservoir trophic status, with several episodes of non-algal turbidity at each site during Mon. Increases in non-algal turbidity were more prevalent during the monsoon in flood years. This study also highlighted overall long-term trends in certain water quality parameters, albeit with indications of shifting pollution sources towards non-biodegradable organic matter. According to the machine learning tests, a random forest (RF) model strongly predicted CHL-a (R2 = 0.72, p < 0.01), except for algal biomass peaks (>60 µg/L), compared to all other models. Overall, our research suggests that CHL-a and trophic variation are primarily regulated by the monsoon intensity and predicted well by the machine learning RF model.

Mechanical properties of low calcium alkali activated binder system under ambient curing conditions.

These days, the construction industry is facing sustainability issues, leading to the selection of greener, low-carbon, alkali-activated materials. This study examines a low calcium alkali activated system composed of three constituents (ceramic brick, metakaolin waste, and phosphogypsum). The AAB compositions consist of the primary precursor, waste ceramic brick, which is increasingly (20-100 wt%) replaced with waste metakaolin. The alkaline solution was made of sodium hydroxide and water; dosage depended on the Na2O/Al2O3 ratio (1.00-1.36). The AAB specimens were inspected by using XRD (X-ray diffraction) and FT-IR (Fourier transform infrared spectroscopy) methods for the evaluation of mineral composition, accompanied by SEM-EDS (scanning electron microscopy & energy dispersive X-ray spectroscopy) for the analysis of the microstructure. The compressive strength after 7, 28 and 90 days, along with water absorption and softening coefficient were determined. Also, mixture calorimetry was established. The results have shown that the initial materials are suitable for producing medium-strength alkali-activated binder under ambient temperature. The maximum compressive strength was reached by using the combination of 80% CBW and 20% MKW (13.9 and 21.2 MPa after 28 and 90 days respectively). The compressive strength development was linked with the formation N-A-S-H gel and faujasite type zeolite. A higher level of geopolymerization in composition with metakaolin waste led to lower compressive strength. Consequently, binding materials with low demand of high final and especially early compressive strength could be produced under ambient temperature curing, making them more sustainable.

Crystallization of the ß-Form of Polypropylene from the Melt with Reduced Entanglement of Macromolecules.

The influence of decreasing the entanglement density of macromolecules on the crystallization of the ß-form of polypropylene was investigated. Polypropylene with seven times less entanglement was obtained from a solution in xylene, and its properties were compared with those of fully entangled polypropylene. To obtain a high ß-phase content, the polymer was nucleated using calcium pimelate. In non-isothermal crystallization studies, accelerated growth of ß-crystals was found, increasing the crystallization temperature. Also, the isothermal crystallization was fastest in the nucleated, partially disentangled polypropylene. Increased growth rate of spherulites and enhanced nucleation activity in the presence of more mobile macromolecules were responsible for the high rate of melt conversion to crystals in the disentangled polypropylene. It was also observed that the equilibrium melting temperature of ß-crystals is lower after disentangling macromolecules. Better conditions for crystal building after reduction of entanglements resulted in enhanced crystallization according to regime II.