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The biopharmaceutical industry is replacing fed‐batch with perfusion processes to take advantage of reduced capital and operational costs due to the operation at high cell densities (HCD) and improved productivities. HCDs are achieved by cell retention and continuous medium exchange, which is often based on the cell‐specific perfusion rate (CSPR). To obtain a cost‐productive process the perfusion rate must be determined for each process individually. However, determining optimal operating conditions remain labor‐intensive and time‐consuming experiments, as investigations are performed in lab‐scale perfusion bioreactors. Small‐scale models such as microwell plates (MWPs) provide an option for screening multiple perfusion rates in parallel in a semi‐perfusion mimic. This study investigated two perfusion rate strategies applied to the MWP platform operated in semi‐perfusion. The CSPR‐based perfusion rate strategy aimed to maintain multiple CSPR values throughout the cultivation and was compared to a cultivation with a perfusion rate of 1 RV d−1. The cellular performance was investigated with the dual aim (i) to achieve HCD, when inoculating at conventional and HCDs, and (ii) to maintain HCDs, when applying an additional manual cell bleed. With both perfusion rate strategies viable cell concentrations up to 50 × 106 cells mL−1 were achieved and comparable results for key metabolites and antibody product titers were obtained. Furthermore, the combined application of cell bleed and CSPR‐based medium exchange was successfully shown with similar results for growth, metabolites, and productivities, respectively, while reducing the medium consumption by up to 50% for HCD cultivations. [ABSTRACT FROM AUTHOR]

The promise of continuous processing to increase yields and improve product quality of biopharmaceuticals while decreasing the manufacturing footprint is transformative. Developing and optimizing perfusion operations requires screening various parameters, which is expensive and time‐consuming when using benchtop bioreactors. Scale‐down models (SDMs) are the most feasible option for high‐throughput data generation and condition screening. However, new SDMs mimicking perfusion are required, enabling experiments to be run in parallel. In this study, a method using microwell plates (MWP) operating in semi‐perfusion mode with an implemented cell bleed step is presented. A CHO cell line was cultivated in a 24‐well MWP (Vw = 1.2 mL) and grown at four high cell density (HCD) setpoints. Quasi steady‐state condition was obtained by manually performing cell bleeds followed by a total medium exchange after centrifugation. Further, two HCD setpoints were scaled up (VW = 30 mL), comparing a squared six‐well deepwell plate (DWP) to shake flasks (SF). This evaluation showed comparable results between systems (DWP vs. SF) and scales (MWP vs. DWP + SF). The results show that the well‐plate‐based methods are suitable to perform HCD and quasi steady‐state cultivations providing a robust solution to industrially relevant challenges such as cell clone and media selection. [ABSTRACT FROM AUTHOR]

This commentary reflects on the experiences of a cohort of human and physical geographers in enacting environmental collaborations during their doctoral studies. The authors identify three key structural barriers encountered whilst attempting a collaborative approach: (1) doctoral funding priorities, (2) doctoral resourcing and (3) assessing doctoral collaboration. The authors discuss how the negotiation of these encounters came to frame their understanding of collaborative approaches to environmental knowledge creation. Competitive application processes for doctoral studentships can encourage the overpromising of the impact of planned environmental collaboration, potentially co‐opting the voices of partners/communities to satisfy doctoral funding requirements. Given insufficient funding of collaborations, the authors argue that this overpromising of doctoral research's impact can later result in difficult trade‐offs between undertaking additional commitments at the expense of the career progression of the doctoral student, contributing to educational inequalities and scaling‐back the initial plans at the cost of collaborators encountering environmental crises. The trade‐off is further problematised by institutional assessment procedures that do not adequately recognise the more nuanced contributions of environmental collaborations and a prevailing culture promoting peer‐review publishing. Overall, the commentary argues that these barriers help to reproduce inequalities in the distribution of voice in environmental scholarship, undermining efforts to democratise environmental knowledge creation in doctoral research. The authors call for specific structural reforms of doctoral programmes to help address these challenges and support a broader resistance to the inadequate resourcing and evaluation of environmental collaborative research in UK higher education. This commentary reflects on the experiences of a cohort of human and physical geographers in enacting environmental collaborations during their doctoral programmes. We draw attention to the key structural barriers we encountered. Going forward, we call for specific structural reforms of doctoral programmes to help address these challenges and support wider environmental collaborative research. [ABSTRACT FROM AUTHOR]

Although the work of identifying animal and plant objects with highly similar patterns (e.g., texture, intensity, colour, etc.) to the background has recently attracted more research interest but rarely involves the military complex environment. Design an efficient camouflage small‐scale object detection algorithm that is capable of quickly discriminating the objects in open scenes from a long distance, to pre‐empt the enemy. In this work, we first recognize the fact that existing open‐source training datasets are scarce, and we have created a specific disguised people benchmark covering multiple scenes and weather conditions. Second, because the severe corruption of camouflage capabilities and chaotic scenes in the open battlefield on detailed features and generalization intensifies the challenge of feature extraction from a long‐range perspective, we propose a novel end‐to‐end Small‐scale open scene Camouflage Object Detection Network, called SM‐CODN. Inspired by the characteristics of biological brain partition, a multi‐domain partition module (MPM) with domain‐decoupling is proposed to enable specific knowledge learning for samples with obvious discrepancies in camouflage domain distribution. Concurrent with our work, we have designed a multi‐scale fusion module (MFM) to strengthen the semantic features related to small‐scale disguised objects. Moreover, due to the convergence direction of the detector in reasoning being inconsistent, a feature separation enhancement module (FSEM) is also proposed. Experimental results show that SM‐CODN surpasses many classic object detection methods and shows strong competitiveness compared with state‐of‐the‐art ones. [ABSTRACT FROM AUTHOR]

In healthcare, small‐scare data are stored with individual entities, such as hospitals, and they are not shared. However, data with one entity are not sufficient for training a machine learning model and therefore cannot be fully utilized. Given that a large amount of small‐scale data is widely distributed between hospitals/individuals, it is necessary to deploy an easy, scalable, and secure distributed computational framework. We aim to aggregate these scattered and small‐scale data to train neural networks and achieve classification and detection on coronavirus disease 2019 (COVID‐19) datasets. We propose a distributed autoencoder (AE) classifier network for this purpose. It contains a central classifier and multiple distributed AEs. The AEs are used as generators. A local generator uses an actual COVID‐19 computed tomography image as the input and outputs a synthetic image. The well‐trained generator provides an image to train the central classifier model. The central classifier network model learns information from all the generated COVID‐19 data using the distributed AE. Experiments are performed using some COVID‐19 datasets. The distributed AE classifier network outperforms all the models that use a single subset, and its performance is similar to that of a regular classifier. The proposed network solves the problem of using small‐scale and scattered COVID‐19 data to train neural networks while ensuring data privacy. The accuracy of the network is the same as that achieved using the entire data. [ABSTRACT FROM AUTHOR]

Although reconfigurable intelligent surfaces (RISs) have attracted broad attention, they usually require a large scale of elements and hundreds of tunable components, particularly for beam‐steering applications. Herein, a methodology is proposed for the compact design of a transmission‐type RIS system for beam steering. The approach leverages a customized horn feed equipped with a gradient refractive index metalens to tailor the electromagnetic fields over the horn aperture, resulting in focused amplitude and uniform phase distributions. This enables us to reduce the feed‐RIS distance and significantly decrease the RIS scale using only a small number of components while still achieving acceptable beam‐steering performance. The investigations are carried out through theoretical studies, simulations, and experiments. A prototype system is fabricated as an exemplary demonstration, which occupies a volume of 3.3λ0 × 2.6λ0 × 3.5λ0, including the feed, and contains only 3 × 5 RIS elements and 60 varactors. With every single element being controlled individually by a simple network, beam‐scanning ranges of ±30° are measured on the E‐, H‐, and 45°‐planes, respectively. With the above intriguing properties, the proposal can find potential applications where miniaturization, high portability, and low cost are required for beam‐steering functions. [ABSTRACT FROM AUTHOR]

The ionosphere contains many small‐scale electron density variations that are under represented in smooth physics‐based or climatological models. This can negatively impact the results of Observation System Simulation Experiments, which use a truth model to simulate data. This paper addresses this problem by using ionosonde data to study ionospheric variability and build a new truth model with empirically driven variations. The variations are studied for their amplitude, horizontal and vertical size, and temporal extent. Results are presented for different local times, seasons, and at solar minimum and solar maximum. We find that these departures from a smooth background are often as large as 25% and are most prevalent near 250 km in altitude. They have horizontal spatial extents that vary from a few hundred to a few thousand kilometers, and typically have the largest horizontal extent at high altitudes. Their vertical extents follow the same pattern of being larger at high altitudes, but they only vary from 10s of km up to 200 km in vertical size. Temporally, these variations can last for a few hours. A procedure for using these spatial and temporal distributions to add empirically driven variance to a smooth truth model is outlined. This process is used to make a truth model with representative variations, which is compared to ionosonde data as well as Global Positioning System Total Electron Content data that was not used to inform the model. The new model resembles the data much better than the smooth models traditionally used. Plain Language Summary: The ionosphere is a region of space between 100 and 1,000 km in altitude. It contains many small variations in electron density that are not well‐described in typical models used in Observation System Simulation Experiments. This paper uses ionosonde data to study the size and duration of these variabilities, and then develops a method to add them to one of these typical models. The new model with the variabilities added more closely replicates real ionospheric measurements. Key Points: The amplitude and spatio‐temporal extent of ionospheric variabilities are quantified across altitude, local time, season, and solar cycleA method to add these variations into a smooth model is demonstrated and replicates ionospheric data wellThis noisy truth model enables more accurate Observation System Simulation Experiments [ABSTRACT FROM AUTHOR]

Untethered magnetoresponsive soft materials that can transform between various complex 3D shapes are of growing interests in diverse areas such as soft robotics, flexible electronics, and biomedical engineering. Previous approaches are difficult to precisely encode continuous 3D magnetization profiles in small‐scale 3D structures. Herein, a novel method to produce magnetoresponsive soft materials for shape‐morphing systems across different length scale is reported. The magnetization profiles are programmed by the mechanically‐guided 4D printing with high precision and time‐cost efficiency. A theoretical model and model‐based simulation method are developed to quantitatively predict shape‐morphing of printed structures under applied magnetic fields, and guide the design of complex 3D shapes. A broad set of structures with complex transformations are fabricated to illustrate the capability of the methods. Diverse typical functional applications from centimeter‐ to millimeter‐scale, including soft crawling robots, flexible grippers, bionic butterfly, and multistate magnetic switch, are further demonstrated. [ABSTRACT FROM AUTHOR]

BACKGROUND: Scale‐down models (SDM) are crucial for efficient setting of different experimental conditions in cell culture media‐ or clone‐screening applications and to predict the expected performance in larger bioreactor cultivations. However, the availability of such models for continuous bioprocessing are scarce and hampered by the requirement for proper cell retention in perfusion applications. RESULTS: In this study, perfusion scale‐down models were optimized for increased cell performance in shake tubes by a semi‐continuous operation mode without any instrumental monitoring. A 40 L perfusion bioreactor was simulated with 10 mL shake tubes by daily medium exchange with or without cell bleeding for process fine‐tuning. Specifically, the optimal cell‐specific perfusion rate (CSPR) was efficiently identified using the perfusion SDM. The cellular growth and productivity were nearly identical to those of the large‐scale experiments. The 24‐h interval of metabolite analysis identified the limits of the SDMs and underscores the need for perfusion processes in large‐scale animal cell bioprocesses. CONCLUSION: Optimized perfusion scale‐down models in semi‐continuous operation mode are highly suitable tools to define important process parameters of larger perfusion bioreactor cultures and are predictive of cellular growth and production behavior. [ABSTRACT FROM AUTHOR]

Aims: Vegetation sampling typically involves the use of quadrats to estimate species cover abundance. Such surveys do not generally record small‐scale vegetation patch structure at sub‐quadrat scales. Here we test a simple method to quantify patch structure that complements conventional techniques. We compare the two methods, and analyse metrics derived from small‐scale patch surveys with environment/management data. Location: Northumberland, United Kingdom. Methods: We recorded cover abundance of all species in an upland moor with 1‐m2 quadrats. These were divided into 100 'sub‐quadrats', 10 cm × 10 cm, and the dominant and subdominant species identified. Patch metrics (number, area and shape) for individual species recorded as dominant or subdominant in the subquadrat survey were analysed using multi‐variate generalised linear models with environmental and management data. Sub‐quadrat data were also aggregated for each quadrat, to create species composition data. The two sets of compositional data, from whole‐quadrat and sub‐quadrat aggregations, were compared via Procrustes rotation of ordination scores. Results: Patch number, area and shape for dominant and subdominant species were all significantly affected by soil pH, soil water content, slope and elevation. Effects of proximity to sheep tracks and drainage ditches were less consistent amongst species. Ordinations of vegetation data from conventional and sub‐quadrats were similar, with significant Procrustes R2 of 67% and 70% for dominant and subdominant species respectively. Conclusions: Sub‐quadrat surveys can easily be used to complement existing whole‐quadrat surveys at little cost in time or resources. Their patch metrics can provide additional insights into the environmental and management drivers that may affect the growth of individual plants or clumps, potentially in relation to plant traits, and thus alter the overall community composition. The methods we describe can readily be adapted to other sizes of quadrats and sub‐quadrats in a wide range of vegetation communities. [ABSTRACT FROM AUTHOR]

Understanding compliance with conservation rules is key for biodiversity conservation. Here, we assess compliance and its underlying motivations in a small‐scale fishery in Chile. We adapt a framework originally developed for forestry to unpack compliance motivations at within‐individual and between‐individuals levels while accounting for contextual factors. We find that 92–100% fishers comply with temporal or gear rules, while only 3% comply with the quota limit. Legitimacy‐based motivations are more important in explaining why individual fishers comply with temporal/gear rules than they are for compliance with the quota. At the between‐individuals level, we find that normative motivations are significantly related to the degree of non‐compliance with the quota. Contextual factors such as quota levels are key in explaining broader non‐compliance patterns. Our results suggest that considering compliance at appropriate analytical levels is necessary to unpack motivations, guide local and national natural resource management policies, and move toward a better theory of compliance. [ABSTRACT FROM AUTHOR]

Background and objectives: Durum breeders are interested in being able to remove weak gluten genotypes from their crossing populations. Although numerous tests exist to measure gluten strength, methods that can be used in early generations, use minimal sample with little processing and have good discrimination are lacking. The purpose of this work was to evaluate several small‐scale methods to predict gluten strength in durum wheat with emphasis on the gluten aggregation behavior measured using a GlutoPeak device. Findings: Using as little as 9 g of wholemeal (or semolina), a reproducible GlutoPeak aggregation curve is developed at the end of 5 min. Wholemeal gave higher precision than semolina and is more convenient to prepare when screening breeders’ material. The GlutoPeak Area and Peak Mixing Time (PMT) are good predictors of mixograph and gluten index (GI), which are traditional measures of gluten strength. Good correlations between these parameters were obtained using a diverse set of 145 durum genotypes. GlutoPeak Area gave the best prediction of GI from wholemeal samples allowing a breeder to remove weak‐moderate GI samples from a population using as little as 9 g of wholemeal. The GlutoPeak test showed higher correlations with mixograph and GI than the small‐scale biochemical methods, was easier to perform and uses no chemicals. However, GlutoPeak is not suitable when only mg quantities of sample are available. Conclusions: The utility of the GlutoPeak device has been further validated in this work for screening durum wheat lines for gluten strength. Wholemeal is a suitable sample (9 g minimum) and provides good precision and reproducibility with a short analysis time of 5 min and requires minimal operator skill. It is suited to screening breeders’ populations for gluten strength and is preferred over SIG, HPLC determination of UPP and IP% determination using a turbidity assay. Significance and novelty: The GlutoPeak can be used to effectively screen out low‐intermediate gluten strength in a durum wheat breeders’ population using 9 g of wholemeal with a 5 min run time. [ABSTRACT FROM AUTHOR]

In estimating the number of species per quadrat with a given area, we usually need much time and labor, because we have no simple ways to easily count it using hardware. We devised here a software method to estimate the mean number of species by counting them only partially in the survey, and estimating it mathematically. We classify quadrats into three classes composed of k, k + 1 and more than k + 1 species; or classify quadrats into four classes composed of k, k + 1, k + 2, and more than k + 2 (referred to as “censored sampling”), where k is the minimum number of species per quadrat. We do not need to count more than k + 2 or k + 3 species, respectively. Using only these 3 or 4 numerals we can estimate the mean of number of species per quadrat, the 95% confidence intervals for the mean and know the spatial pattern value such as aggregated, random or uniform pattern of the number of species per quadrat. Using this method, 10-20% time and labor in counting will be saved compared to the full census. The estimates are obtained through the maximum likelihood calculation. Computer programs to estimate the mean number of species per quadrat are attached as ESMs. [ABSTRACT FROM AUTHOR]

Gilthead sea bream ( Sparus aurata L.) aquaculture has rapidly spread around the Mediterranean Sea. Fish escapes from sea cages are one of the major problems for the industry and also represent a threat to the marine environment. Fishery catches are also influenced by escape events, being mirrored in increased landings of cultured species. However, within the Mediterranean, studies shedding light on the incidence of escaped individuals in fisheries landings are scarce. Firstly, the present study describes a simple scale-examination methodology to distinguish escaped from wild S. aurata landed by coastal fisheries. Secondarily, the incidence of escapees is assessed within landings of the species in the western Mediterranean. The scale readings revealed a monthly incidence of escapees between 11.2 and 20.8%, representing 6.8% of the total income derived from these landings. Moreover, morphological differences between wild fish captured near and far from fish farms point towards escape events as a source of potential genetic admixture between cultured and wild genotypes. Socio-economic and ecological implications of escapees are discussed, and a set of management guidelines proposed, to prevent and/or mitigate the negative influences of escaped fish on the ecosystem and traditional users of the coast such as fisheries. [ABSTRACT FROM AUTHOR]

South Africa's sugar industry has long been distinguished by its large number of small-scale sugarcane growers ( SSGs) farming on 'communal' land and its peculiar privately administered regulatory structure. In recent years, however, the numbers of small-scale growers have declined precipitously. This paper argues that the relationship between the rise and fall of SSG production and the industry's governing regulatory structure is closer than usually appreciated. The emergence of SSG production in the late 1970s and the 1980s can be traced to industry-subsidized initiatives, disguised as small-scale credit, to bring commercially inalienable Bantustan land into cane production with strong miller oversight. From the late 1980s to 1990s, however, the elimination of these subsidies encouraged millers to subcontract support to farmers, while simultaneously instigating an increase in SSG numbers by removing restrictions on grower registration. Although low rainfall is a central proximal factor in the rapid decline of the SSGs in the 2000s, their rapid increase was structurally fragile. [ABSTRACT FROM AUTHOR]

This paper synthesises published literature on run-of-river hydropower, highlighting its potential to affect both the physical and ecological conditions of river systems. The paper considers the limited number of direct studies and reviews a wider literature on the two principal impacts of such schemes on river systems: the introduction or maintenance of in-channel barriers and water abstraction/flow regime alteration. We outline how river systems are likely to be impacted by such schemes and identify the key issues arising from their continued development. Potential mitigation approaches are highlighted and the areas of future research required to adequately address current knowledge gaps are identified. [ABSTRACT FROM AUTHOR]

Developments such as the Common Agricultural Policy reform, growth of the bio-based economy, increasing energy prices, increasing sustainability demands, and expected growth of global sugar demand change the environment in which the sugar producing industry operates. In order to remain competitive and profit from this, the traditional large-scale sugar producing industry can adapt. The aim of this study was to address sustainability and energy issues of the traditional sugar production process and to provide opportunities for improving the process and value chain. The methodological approach included evaluating function and resource usage of the unit operations. More sustainable alternative unit operations and processes were identified and studied. The results indicate that the current sugar production and by-product valorization focuses on centralized processing and has been individually optimized per sector and industry based on relatively inexpensive transportation and energy without a focus as such on the bio-based economy. For incorporation of the opportunities, a process for targeting new bio-based markets and supplementing large-scale sugar production was designed. It was found that small-scale biorefineries as an alternative and/or supplementation of the traditional large-scale process have the ability to increase the overall sustainability of sugarbeet processing, for example reduce energy usage and carbon footprint, by reducing transportation movements. Moreover, it provides opportunities for leaving out certain unit operations and using less capital-intensive technologies. With a holistic approach throughout the value chain, the introduction of small-scale biorefineries can help meet the challenges of the sugar producing industry, while simultaneously benefitting people, planet, and profit. [ABSTRACT FROM AUTHOR]

Marine soft sediments are often considered to be vast, homogeneous expanses of mud or sand; however, most exhibit small-scale biogenic structures. Generally such structures result from short-term processes, but mollusk shell debris can last for centuries, providing a settlement surface for plants and animals, which in turn stabilize the debris. This study sampled patches of shell debris and the surrounding sediment at increasing distances from the patches to determine the role of the patches in driving beta biodiversity. Sampling was conducted at three similar sites within 1 km in a marine reserve in New Zealand. The shell debris significantly affected beta diversity at all sites, although the magnitude of the effect was dependent on patch size, density, and debris particle size. These results have important implications for estimating and mapping biodiversity. They also have implications for marine conservation. The role of shell debris, suggested by this study, in increasing and maintaining biodiversity, emphasizes a need for reducing disturbance regimes and actively managing seafloor habitats in areas not previously considered for such. [ABSTRACT FROM AUTHOR]

Many applications in urban areas require high‐resolution rainfall measurements. Typical operational weather radars can provide rainfall intensities at 1‐km2 grid cells every 5 min. Opportunistic sensing with commercial microwave links yields path‐averaged rainfall intensities (typically 0.1–10 km) within urban areas. Additionally, large amounts of urban in situ rainfall measurements from amateur weather observers are obtainable in real‐time. The accuracy of these three techniques is evaluated for an urban study area of 20 × 20 km, taking into account their respective network layouts and sampling characteristics. We use two simulated rainfall events described in terms of drop size distributions on a 100‐m grid and with a temporal resolution of 30 s. Accurate radar rainfall estimation with the Z‐R relationship relies heavily on an appropriate choice of parameters, and a dual‐polarization strategy is more suitable for higher intensities. Under ideal measurement conditions, the weather station network is the most promising, with a Pearson correlation coefficient above 0.86 and a relative bias below 4% for 100‐m rainfall estimates at 5‐min resolution. Microwave link rainfall observations contain the largest error, shown by a consistently larger coefficient of variation. The accuracy of all techniques improves when considering rainfall at larger scales, especially by increasing time intervals, with the strongest improvements found for microwave links for which errors are largely caused by their temporal sampling. Sparser networks are examined, showing that the decline in measurement accuracy only becomes significant when the link and station network density are reduced to less than half their levels in Amsterdam. Key Points: Assuming perfect measurement accuracy, the personal weather station network captures small‐scale rainfall dynamics best in AmsterdamMeasurement accuracy increases at larger temporal and spatial scales, most significantly for commercial microwave linksWith current network layouts, similar accuracy is achieved by half the number of commercial microwave links and personal weather stations [ABSTRACT FROM AUTHOR]