Large Scale Research Articles

Development of Learning Models to Enhance Students’ Creative Thinking: A Systematic Literature Review

Creative thinking is an essential component in education that drives innovation, problem-solving, and the development of students’ critical thinking skills. This study aims to review various learning models designed to enhance students’ creative thinking abilities through a systematic literature review (SLR). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this research identifies, screens, and evaluates relevant articles from the Scopus database, resulting in eight studies that meet the inclusion criteria. The results indicate that, following innovative learning models such as play-based activities, digital-based virtual classrooms (VCLE), basic movement through play approaches, and project-based mathematics learning are effective in improving creativity, problem-solving, collaboration, and student learning motivation. The integration of technology, STEAM approaches, and creative game design also show positive effects in enhancing students’ creative thinking skills. The higher distribution of research at the tertiary education level highlights the need for greater focus on other educational levels, particularly secondary education, to ensure the benefits of learning innovation are evenly distributed. These findings provide practical implications for educators and policymakers, emphasizing the importance of using innovative teaching methods, integrated technology, and the role of teachers as facilitators to develop a conducive learning environment for student creativity. The study also identifies several limitations, including small trial scales and short research durations, indicating the need for further research with larger scales and longer durations. By continually exploring and developing effective learning models, it is expected that students can achieve their maximum potential in dynamic and diverse learning environments.

Cross-industry innovation for sustainable repositioning through cluster collaboration

ABSTRACT Recent literature on sustainable transitions seeks to bridge macro – and micro-level dynamics; this study examines how agents at different levels interact, and what implications these lower-level dynamics have on sustainable repositioning. A new normal characterized by frequent industry crises, coupled with green policy initiatives, creates push and pull opportunities for sustainable repositioning; thus, incumbent firms must continuously adjust to changing conditions. Cross-industry innovation (CII) has emerged as a favorable option for sustaining competitiveness. To illustrate this development, this study provides insights into how firms from the oil and gas industry attempt CII in the aquaculture industry based on cluster-to-cluster collaboration and facilitation. Our findings indicate a form of tandem development for both industry clusters and incumbent firms, with the cluster responding to bottom-up changes in its firms. Moreover, cluster collaboration lowers initial CII obstacles in terms of knowledge and entry points and offers a gateway for incumbent firms looking to enter different industries. This facilitates an outbound and coupled CII that emerges to maintain a knowledge continuum for incumbent actors within sustainable repositioning. On a larger scale, these cluster initiatives can act as agents for stepwise sustainability transitions and further evolve into a cross-industry cluster.

Design and Implementation of Line Follower Robot for Automatic Watering of Orchid Plants

This study studies how the line follower robot is made and used for the automation of watering orchid plants. The purpose of using this robot is to reduce the involvement of human hands in the watering process and improve the efficiency of water use. This robot, which is made with an Arduino microcontroller, has an infrared sensor that can detect paths and has an automatic water pump for water distribution. The tests were carried out in a greenhouse where the orchids were well cared for. The results show that the robot can effectively water plants by saving up to 35% water compared to manual methods and increasing productivity by 20%. This invention can be applied on a larger scale in the horticultural industry.

Electrochemical Methods for Nutrient Removal in Wastewater: A Review of Advanced Electrode Materials, Processes, and Applications

In response to the increasing global water demand and the pressing environmental challenges posed by climate change, the development of advanced wastewater treatment processes has become essential. This study introduces novel electrochemical technologies and examines the scalability of industrial-scale electrooxidation (EO) methods for wastewater treatment, focusing on simplifying processes and reducing operational costs. Focusing on the effective removal of key nutrients, specifically nitrogen and phosphorus, from wastewater, this review highlights recent advancements in electrode materials and innovative designs, such as high-performance metal oxides and carbon-based electrodes, that enhance efficiency and sustainability. Additionally, a comprehensive discussion covers a range of electrochemical methods, including electrocoagulation and electrooxidation, each evaluated for their effectiveness in nutrient removal. Unlike previous studies, this review not only examines nutrient removal efficiency, but also assesses the industrial applicability of these technologies through case studies, demonstrating their potential in municipal and industrial wastewater contexts. By advancing durable and cost-effective electrode materials, this study emphasizes the potential of electrochemical wastewater treatment technologies to address global water quality issues and promote environmental sustainability. Future research directions are identified with a focus on overcoming current limitations, such as high operational costs and electrode degradation, and positioning electrochemical treatment as a promising solution for sustainable water resource management on a larger scale.

Amorphous silica production from Colombian rice husk: demonstration in scaled-up process Products

Introduction: the agroindustry generates significant waste, posing environmental, health, and economic challenges. Among these, rice husk, a byproduct of the food industry, stands out due to its potential as a source of silicon. Due to its silicon content, rice husk offers a unique opportunity for sustainable energy production and the extraction of high-value products, such as amorphous silicon dioxide (SiO2). However, optimizing processes for its efficient conversion remains a challenge.Objective: the aim of this study was to optimize the nitric acid concentration for the pretreatment of Colombian rice husk in order to produce high-purity amorphous SiO2 and demonstrate the feasibility of scaling up the process.Methods: a two-stage process was developed, which involved treating rice husk with nitric acid, followed by calcination at 620 °C. The nitric acid concentration was optimized to achieve the highest SiO2 purity. Material characterization was performed using thermogravimetric analysis (TGA), X-ray diffraction (XRD), X-ray fluorescence (XRF), and nitrogen adsorption-desorption. To assess the scalability of the process, the treatment was replicated on a larger scale using the optimized acid concentration.Results: the optimized process using a nitric acid concentration of 0.2 M yielded amorphous SiO2 with a purity of 94.9% and a surface area of 298 m²/g. When scaled up, the process achieved SiO2 with a purity of 95.5%, confirming the feasibility of the methodology for industrial applications. Conclusions: the treatment of rice husk with nitric acid followed by calcination proves to be an effective and scalable approach for producing high-purity amorphous SiO2. This process not only holds potential for industrial applications but also provides a sustainable solution for valorizing agroindustrial waste, contributing to the circular economy.

A Well‐Coupled Supramolecular System Accelerates Photophosphorylation

AbstractSupramolecular assembly allows multiple chemical/bio‐components integrated as one system for cascade biochemical reactions. Herein the graphitic carbon nitrides (g‐C3N4) as photocatalyst trapped in a dipeptide hydrogel covering adenosine triphosphate (ATP) synthase accelerates the photophosphorylation through ATP synthesis. Self‐assembled N‐fluorenylmethoxycarbonyl diphenylalanine (Fmoc‐FF) as nanofibrils to allow g‐C3N4 nanosheets are embedded as a complex Fmoc‐FF/g‐C3N4 hydrogel. Fmoc‐FF gel exhibits good electronic coupling with g‐C3N4, which enables a photo‐induced proton generation. The transmembrane proton gradient can be established by ATP synthase‐lipid reconstituted on the surface of the Fmoc‐FF/g‐C3N4 hydrogel to enhance the ATP synthesis. It indicates that the Fmoc‐FF/g‐C3N4/ATP synthase‐lipid film can possess a longer‐term ATP production capability and allow repeated immersion for sustained ATP production. Such a hydrogel‐supported ATP synthesis platform is achieved by a procedure at a larger scale.

Uncovering migration systems through spatio-temporal tensor co-clustering

A central problem in the study of human mobility is that of migration systems. Typically, migration systems are defined as a set of relatively stable movements of people between two or more locations over time. While these emergent systems are expected to vary over time, they ideally contain a stable underlying structure that could be discovered empirically. There have been some notable attempts to formally or informally define migration systems. However, they have been limited by being hard to operationalize and defining migration systems in ways that ignore origin/destination aspects and fail to account for migration dynamics over time. In this work, we propose to employ spatio-temporal tensor co-clustering—that stems from signal processing and machine learning theory—as a novel migration system analysis tool. Tensor co-clustering is designed to cluster entities exhibiting similar patterns across multiple modalities and thus suits our purpose of analyzing spatial migration activities across time. To demonstrate its effectiveness in describing stable migration systems, we first focus on domestic migration between counties in the US from 1990 to 2018. We conduct three case studies on domestic migration, namely, (i) US Metropolitan Areas, (ii) the state of California, and (iii) Louisiana, in which the last focuses on detecting exogenous events such as Hurricane Katrina in 2005. In addition, we also examine a case study at a larger scale, using worldwide international migration data from 200 countries between 1990 and 2015. Finally, we conclude with a discussion of this approach and its limitations.

Impact of Organic Farming in Economic Development in Agriculture

Organic farming is valued for its self-sustaining, environmentally friendly methods that protect surrounding ecosystems of plants and animals. It reduces weed growth through techniques like crop rotation and integrates traditional knowledge alongside natural processes. This approach optimizes inputs, strengthens regenerative abilities, and ensures effective conservation of resources and nutrients. The cost-benefit ratio in sustainable agriculture is notably better, as it requires fewer primary inputs compared to conventional methods, which often incur high costs for fertilizers and pesticides, lowering profit margins. Additionally, conventional farming's heavy reliance on chemicals can lead to significant health risks, both mental and physical. Organic farming, by following natural principles and using non-synthetic, traditional practices, creates a balanced environment that allows crops to thrive. This approach helps counteract the negative impacts of chemical-dependent agriculture, increasing production sustainably and supporting population growth without harming the ecosystem. Core principles of organic farming include health, ecology, fairness, and care. The benefits range from natural pest and disease resistance, specialization potential, healthier soil, support for pollinators, no GMO concerns, safer work conditions for farmers, and on-farm input production. Farmers and companies must meet stringent, government-approved standards, undergo regular certification, and comply with inspections to ensure the high quality of organic products produced with renewable resources. This paper aims to delve into the origins of organic farming, its environmental, social, economic, and agricultural advantages, and underscore its importance for the future. Similarly, on a larger scale, it can provide rural populations with greater earnings, better resource management and more chances to generate revenue Keywords: Organic Farming, Sustainability, Farmers, Health, Ecology.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

A modeling approach to forecast local demographic trends in metapopulations.

Predicting animal population trajectories into the future has become a central exercise in both applied and fundamental ecology. Because demographic models classically assume population closure, they tend to provide inaccurate predictions when applied locally to interconnected subpopulations that are part of a larger metapopulation. Ideally, one should explicitly model dispersal among subpopulations, but in practice this is prevented by the difficulty of estimating dispersal rates in the wild. To forecast the local demography of connected subpopulations, we developed a new demographic model (hereafter, the two-scale model) that disentangles two processes occurring at different spatial scales. First, at the larger scale, a closed population model describes changes in metapopulation size over time. Second, total metapopulation size is redistributed among subpopulations, using time-varying proportionality parameters. This two-step approach ensures that the long-term growth of every subpopulation is constrained by the overall metapopulation growth rate. It implicitly accounts for the interconnectedness among subpopulations and avoids unrealistic trajectories. Using realistic simulations, we compared the performance of this new model with that of a classical closed population model at predicting subpopulations' trajectories over 30 years. While the classical model predicted future subpopulation sizes with an average bias of 30% and produced predictive errors sometimes >500%, the two-scale model showed very little bias (<3%) and never produced predictive errors >20%. We also applied both models to a real dataset on European shags (Gulosus aristotelis) breeding along the Atlantic coast of France. Again, the classical model predicted highly unrealistic growths, as large as a 200-fold increase over 30 years for some subpopulations. The two-scale model predicted very sensible growths, never larger than a threefold increase over the 30-year time horizon, which is more in accordance with this species' life history. This two-scale model provides an effective solution to forecast the local demography of connected subpopulations in the absence of data on dispersal rates. In this context, it is a better alternative than closed population models and a more parsimonious option than full-dispersal models. Because the only data required are simple counts, this model could be useful to many large-scale wildlife monitoring programs.

The VST ATLAS Quasar Survey – II. Halo mass profiles of galaxies, LRGs and galaxy clusters via quasar and CMB lensing

ABSTRACT We cross-correlate a low-contamination subset of the VST ATLAS $g\lt 22.5$ quasar catalogue with $g\lt 21.5$ galaxy clusters, $r\lt 21$ galaxies and $r\lt 19.5$ luminous red galaxies (LRGs) to probe their halo mass profiles via quasar magnification bias caused by weak lensing. In the case of galaxy clusters, we find that at small scales their mass profiles are well fitted by Navarro, Frenk, and White models with masses within the expected range. For the galaxies, we find consistency with previous Sloan Digital Sky Survey-based results for the galaxy–quasar cross-correlation and the galaxy auto-correlation functions. Disagreement as to whether the cross-correlation results are in tension with $\Lambda$cold dark matter appears due to different assumptions as to whether galaxies trace mass. We conclude that halo occupation distribution (HOD) models fit the galaxy–quasar lensing results better than models where galaxies trace the mass. We further test the cluster and galaxy HOD models in the 2-halo range using the Planck cosmic microwave background (CMB) lensing map, finding that the cross-correlation with both the poorest clusters and the galaxies may be marginally overpredicted by the above HOD models. Finally, we measure the magnification bias of LRGs using both quasar and CMB lensing and find that the observed quasar lensing amplitude may be ${\approx} 2\times$ too high and, on larger scales, the CMB lensing amplitude may be too low to be explained by a standard LRG HOD model.

Multirobot Assembly Sequence Planning of a Large Space Telescope

As space structures trend toward larger scales and modularization, their on-orbit assembly presents a significant challenge. Assembly sequence planning is crucial in minimizing vibration and enhancing the efficiency of on-orbit construction. This paper explores the multirobot assembly sequence planning algorithm for a large space telescope. A dynamic model is firstly developed to analyze the vibration of segmented mirror pieces and the main satellite during assembly, taking into account the flexibility of connections at interfaces. The dynamic model is able to provide criteria for time efficiency and satellite attitude disturbances. Considering constraints during the assembly process, a modified multiobjective ant colony optimization algorithm capable of planning multirobot assembly sequences is implemented. The simulation results demonstrate that the proposed method not only facilitates parallel planning of multirobot assembly sequences but also leads to a reduction in assembly time and attitude disturbances of the main satellite. The results of assembly sequence planning provide dependable guidance for the subsequent planning levels, such as task planning and trajectory planning, of the on-orbit assembly task for large space telescopes.

Remotely Sensed Variables Predict Grassland Diversity Better at Scales Below 1,000 km as Opposed to Abiotic Variables That Predict It Better at Larger Scales

AbstractGlobal spatial patterns of vascular plant diversity have been mapped at coarse grain based on climate‐dominated environment–diversity relationships and, where possible, at finer grain using remote sensing. However, for grasslands with their small plant sizes, the limited availability of vegetation plot data has caused large uncertainties in fine‐grained mapping of species diversity. Here we used vegetation survey data from 1,609 field sites (&gt;4,000 plots of 1 m2), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water‐ and energy‐related, characterizing climate‐dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m2 across the Mongolian Plateau at 500 m resolution. Combining all variables yielded a predictive accuracy of 69% compared with 64% using remotely sensed variables or 65% using abiotic variables alone. Among remotely sensed variables, functional traits showed the highest predictive power (55%) in species richness estimation, followed by productivity and phenology (48%), spectral diversity (48%) and habitat heterogeneity (48%). When considering spatial autocorrelation, remotely sensed variables explained 52% and abiotic variables explained 41%. Moreover, Remotely sensed variables provided better prediction at smaller grain size (&lt;∼1,000 km), while water‐ and energy‐dominated macro‐environment variables were the most important drivers and dominated the effects of remotely sensed variables on diversity patterns at macro‐scale (&gt;∼1,000 km). These findings indicate that while remotely sensed vegetation characteristics and climate‐dominated macro‐environment provide similar predictions for mapping grassland plant species richness, they offer complementary explanations across broad spatial scales.

How the Galaxy–Halo Connection Depends on Large-scale Environment

We investigate the connection between galaxies, dark matter halos, and their large-scale environments at z = 0 with Illustris TNG300 hydrodynamic simulation data. We predict stellar masses from subhalo properties to test two types of machine learning (ML) models: explainable boosting machines (EBMs) with simple galaxy environment features and E(3) -invariant graph neural networks (GNNs). The best-performing EBM models leverage spherically averaged overdensity features on 3 Mpc scales. Interpretations via SHapley Additive exPlanations also suggest that in the context of the TNG300 galaxy–halo connection, simple spherical overdensity on ∼3 Mpc scales is more important than cosmic web distance features measured using the DisPerSE algorithm. Meanwhile, a GNN with connectivity defined by a fixed linking length, L, outperforms the EBM models by a significant margin. As we increase the linking length scale, GNNs learn important environmental contributions up to the largest scales we probe (L = 10 Mpc). We conclude that 3 Mpc distance scales are most critical for describing the TNG galaxy–halo connection using the spherical overdensity parameterization, but that information on larger scales, which is not captured by simple environmental parameters or cosmic web features, can further augment these models. Our study highlights the benefits of using interpretable ML algorithms to explain models of astrophysical phenomena, and the power of using GNNs to flexibly learn complex relationships directly from data while imposing constraints from physical symmetries.

Green Energy Transition: Evaluating Carbon-To-Liquid Technologies for 2050 Climate Goals and Energy Security

China's coal-to-liquid (CTL) sectors must quickly adjust to a low-carbon economy to meet the energy demand while mitigating environmental and energy security challenges. Similarly, China's climate goals for 2050 are gravely jeopardized if the industry's enormous ecological impact is not addressed. This research develops a conceptual framework for environment-security-economic evaluation of the high-carbon energy sector. Moreover, it constructs a system dynamics material model to predict the upgrade plan's industrial capacity and carbon emission scale. Finally, it calculates the dynamic optimal deployment scale at various epochs based on the sequential decomposition goal of "carbon peak" and "carbon neutrality." These measures are needed to anticipate the development of China's CTL industry. The road map predicts that by 2025, China's CTL industry will be able to meet at least 5% of the country's total oil demand. The adoption of scale A process will improve the economic and environmental performance of the CTL industry due to the cost savings and efficiency gains at a larger scale. In addition, the industry's overall energy efficiency will rise in 2023 and exceed 50% after 2035. Findings indicate that the CTL sector will approach a carbon peak in 2029 and neutrality and transition to hydrogen generation by 2050.

Defect dynamics modeling of mesoscale plasticity

The collective motion of defects and their interaction are the basic building blocks for plastic deformation and corresponding mechanical behaviors of crystalline metals. Especially, dislocations among various defects are the “carrier” of plastic deformation in many crystalline materials, particularly ductile materials. To get a fundamental understanding of plastic deformation mechanisms, it calls for an integrated computational platform to simultaneously capture detailed defects characteristics across several length scales together with corresponding macroscopic mechanical response. In this paper, we present a three-dimensional mesoscale defect dynamics model to directly couple the three dimensional discrete dislocation dynamics model with continuum finite element method, aiming at capturing both size dependent plasticity at micron-, and submicron scale and constitutive behaviors at larger scales where such size-dependence disappear. Using non-singular dislocation theories, our model could accurately consider both short- and long-range elastic interactions between multiple dislocation segments with even higher computational efficiency than traditional dislocation dynamics simulations, together with the careful consideration of crystal/material rotation in the coupled framework. In addition, our model could directly model dislocation nucleation from stress concentrators such as a void, crack and indentor tip, which could allow us to investigate various defects’ motion and their mutual interactions, predicting macroscopic mechanical response of complex structures. The developed concurrently coupled model could also consider multiphysical phenomena by solving coupled governing equations in finite element framework, which could shed light on complex defect behaviors under various physical environments.

Context-Tailored Food-Based Nutrition Education and Counselling for Pregnant Women to improve Birth Outcomes: A Cluster-Randomized Controlled Trial in Rural Malawi

BackgroundInadequate maternal dietary intakes remain a public health challenge in low-income countries like Malawi and can cause adverse birth outcomes. ObjectiveTo improve maternal dietary intakes and thus reduce the prevalence of adverse birth outcomes in rural Malawi. MethodsWe performed a two-armed (1:1) cluster-randomized controlled trial in Southern Malawi enrolling pregnant women at gestational age 12-18 weeks. Twenty villages (clusters) were randomized to an intervention or a control group. A nutrition education and counselling (NEC) intervention consisted of education sessions followed by cooking demonstrations and counselling sessions. The women were encouraged to use locally available nutrient-dense foods to enhance dietary adequacy and -diversity. We applied linear programming to identify food combinations which could increase micronutrient-intakes. The control group received standard antenatal health education. ResultsAmong the 311 women recruited, 187 (60%) completed the trial. We found no significant difference in mean birth weights recorded within 1 or 24 hours of birth between the intervention and control groups. Intervention infants had greater birth length (P = 0.043) and abdominal circumference (P = 0.007) compared to controls whereas other birth outcomes did not differ significantly. Notably, a quantile analysis revealed that the NEC intervention favored birth weight among mothers with height below the mean height of the participant sample (156 cm) (P interaction=0.043). ConclusionsTailoring NEC in food insecure communities did not result in significant difference in birth weight among infants of the participating mothers, but mean birth length and abdominal circumference were greater in the intervention group compared to controls. We noted that the NEC intervention favored birth weight among mothers with a lower height than the mean sample height. Our results warrant further investigation into offering tailored NEC early in pregnancy and on a larger scale. The study was registered with Clinical trials.gov ID: NCT03136393.

Regionalized ensemble estimation of wave periods for assessing wave energy resources across Canada. Part I: Improved wave-period modelling methodology

Large-scale estimations of wave periods are desired for wave energy assessment, ocean engineering, and wave climate research. Long-term global wave data from satellite altimeters are routinely applied to the estimations. However, this is challenged by uncertainties in wave-period models (WPMs), inaccuracies in data, and simplifications in modeling. Additionally, there exists a gap in the comprehensive examination of the variational mechanisms governing wave periods or model performances. As an effort to address them, we innovate a macroscale regionalized ensemble wave-period modeling (MREWPM) method by optimizing four wave-period models, driven by enhanced altimeter-based REWS (regionalized ensemble wave simulation) estimates of wave heights and wind speeds, within a regionalization framework in macroscale water environments. Results show that MREWPM driven by REWS dataset outperforms existing methods and performs better at larger scales (e.g., in eliminating local-scale overestimation). WPMs are more accurate over remote, deep, and windy regions in cool seasons under metrics-, scale- and data-dependent variations of performances with driving factors (mainly geographical features). This study serves as a foundational contribution towards the enhancement of wave-period simulations, the advancement of understanding wave-period dynamics, and the scientific evaluation of wave energy at macroscales.

Genome reduction improves octanoic acid production in scale down bioreactors.

Microorganisms in large-scale bioreactors are exposed to heterogeneous environmental conditions due to physical mixing constraints. Nutritional gradients can lead to transient expression of energetically wasteful stress responses and as a result, can reduce the titres, rates and yields of a bioprocess at larger scales. To what extent these process parameters are impacted is often unknown and therefore bioprocess scale-up comes with major risk. Designing platform strains to account for these intermittent stresses before introducing synthesis pathways is one strategy for de-risking bioprocess development. For example, Escherichia coli strain RM214 is a derivative of wild-type MG1655 that has had several genes and whole operons removed from its genome based on their metabolic cost. In this study, we engineered E. coli strain RM214 (referred to as WG02) to produce octanoic acid from glycerol in batch-flask and fed-batch bioreactor cultivations and compared it to an octanoic acid-producing E. coli MG1655 (WG01). In batch flask cultivations, the two strains performed similarly. However, in carbon limited fed-batch bioreactor cultivations, WG02 provided a greater than 22% boost to biomass compared to WG01 while maintaining similar titres of octanoic acid. Reducing the biomass accumulation of WG02 with nitrogen limited fed-batch cultivation resulted in a 16% improvement in octanoic acid titre over WG01. Finally, in a scale-down system consisting of a stirred tank reactor (representing a well-mixed zone) and plug flow reactor (representing an intermittent carbon starvation zone), WG02 again improved octanoic acid titre by almost 18% while maintaining similar biomass concentrations as WG01.

Radio–FIR correlation: A probe into cosmic ray propagation in the nearby galaxy IC 342

Resolved studies of the correlation between the radio and far-infrared (FIR) emission from galaxies at different frequencies can unveil the interplay between star formation and the relativistic interstellar medium (ISM). Thanks to the LOFAR LoTSS observations combined with VLA, Herschel, and WISE data, we study the role of cosmic rays and magnetic fields in the radio–FIR correlation on scales of ≳200 pc in the nearby galaxy IC 342. The thermal emission traced by the 22 μm emission, constitutes about 6%, 13%, and 30% of the observed radio emission at 0.14, 1.4, and 4.8 GHz, respectively, in star-forming regions and less in other parts. The nonthermal spectral index becomes flatter at frequencies lower than 1.4 GHz (αn = −0.51 ± 0.09, Sν ∝ ναn) than between 1.4 and 4.8 GHz (αn = −1.06 ± 0.19) on average, and this flattening occurs not only in star-forming regions but also in the diffuse ISM. The radio–FIR correlation holds at all radio frequencies; however, it is tighter at higher radio frequencies. A multi-scale analysis shows that this correlation cannot be maintained on small scales due to diffusion of cosmic ray electrons (CREs). The correlation breaks at a larger scale (≃320 pc) at 0.14 GHz than at 1.4 GHz (≃200 pc), indicating that the CREs traced at lower frequencies have diffused a longer path in the ISM. We find that the energy index of CREs becomes flatter in star-forming regions, in agreement with previous studies. Cooling of CREs due to the magnetic field is evident globally only after compensating for the effect of star formation activity that both accelerates CREs and amplifies magnetic fields. Compared with other nearby galaxies, we show that the smallest scale of the radio–FIR correlation is proportional to the propagation length of the CREs on which the ordered magnetic field has an important effect.