Examples Of Such Areas Research Articles

Under-reporting by surgical pathologists in tissue removed during revision surgery for metal-on-metal arthroplasties.

Although adverse local tissue reactions (ALTR) have been reported for metal-on-metal implants (MoM) requiring early revision surgery, no study has looked at the accuracy of surgical pathologists in diagnosing ALTR. This study aims to investigate the accuracy of reporting adverse local tissue reactions in tissue samples following revision surgery from metal-on-metal implants. The authors reviewed histology glass slides as well as the original pathology reports of tissue processed in revision arthroplasties in 23 cases. These samples were microscopically analyzed for tissue necrosis and cystic degeneration, the presence of metal particles, corrosion byproducts, membrane formation, histiocytic cells, lymphocytic cells, and vascular pathology. The authors' findings were then compared to their corresponding original pathology reports. The authors found consistent under-reporting of the tissue findings. Most importantly, 18 samples showed evidence of metal present compared to 2 samples on original pathology reporting. The authors found that 15 samples showed evidence of pathological membranous tissue compared to just 6 on original pathology reporting. While just 3 of the original pathology reports indicated the presence of areas of predominantly lymphocytic inflammatory cells, the authors found 13 examples of such areas. Although ALTR reactions have been described as a sequala of failed MoM, the authors' data suggest that ALTR may occur more frequently than previously described. Under-reported findings of ALTR deprive both the patient and orthopaedic surgeon of important information that can help guide further follow-up.

Application of synanthropic plants in the design of green spaces in Warsaw (Poland)

Abstract The aim of the study was to summarize the use of synanthropic vegetation in public green areas of selected Polish cities. The study included issues related to phytosociology and landscape architecture. For this purpose, a literature study was performed. Part 1 identified possible ways of using synanthropic vegetation in the design of public green areas, Part 2 showed examples of such areas from Warsaw. The authors suggested species that can help enrich urban lawns, part 3 contains proposition of plant species composition for more natural lawns in city parks, including synanthropic plants typical for Poland (prepared by the authors). The process of gradual introduction of synanthropic vegetation in Poland was summarized and compared to the trends in Western European countries. The urban policy of Warsaw regarding the mowing of lawns and the use of flower meadows was briefly described, which influences the presence of synanthropic species in the urban environment. A watershed moment for the use of synanthropic species in Poland was identified, as well as the authors‘ predictions for their future use.

Content and Methodological Features of Preparing Bachelor Students for the Activities of a Class Teacher

The relevance of training bachelor students to implement the goals, objectives and functions of the class teacher is associated with modern trends and government requests in the field of ensuring the implementation of educational activities in educational organizations. The purpose of the article is to characterize the content and methodological features of preparing bachelor students at a pedagogical university to implement the activities of a class teacher. Methodology. The study was conducted using an activity-based approach, which made it possible to determine the educational and socializing role of students’ participation in the extracurricular activities of the university and its life, and to consider them as a factor in the formation of professional competencies; ideas of a multi-subjective approach that help determine the potential of relations between subjects of educational activity in the development of the subjectivity of bachelor students and their personal and professional qualities. The research methods are the analysis of scientific literature and normative documentation on the topic, which helps determine the actual content and elements of preparation (curricular and extracurricular activities, university life, internship) of bachelor students for the activities of a class teacher; a method of specification that allows us to characterize the versatility of the content of bachelor’s student training. Research results. An important task in preparing students to perform the functions of a class teacher is the formation of the subjectivity of bachelor students in the course of involving them in educational and extracurricular activities at a pedagogical university, involving them in the life of the university. The article presents the features of the organization of extracurricular activities at the Novosibirsk State Pedagogical University using the example of such areas as: student associations, university-wide and departmental events, artistic and scientific activities. The substantive aspects of preparing students for the activities of a class teacher when they master the discipline “Technology and Organization of Educational Practices (Classroom Management) and the practice program “Industrial Pedagogical Practice (Classroom Management)” are built in accordance with the requirements of federal state educational standards and an exemplary work program of education. In conclusion, it is concluded that when preparing bachelor students for the activities of a class teacher, it is important to provide a complex of elements, which include a variety of extracurricular activities and the life of the university; implementation of academic discipline and practice programs; approaches to the implementation of educational activities at a pedagogical university (event-activity, humanistic, systemic).

Convergence of residential property prices in the Szczecin agglomeration in the context of the COVID-19 pandemic

The outbreak of the COVID-19 pandemic has left a great mark on all of humanity. In addition to the immediate threat to health and life, the virus in a way also infected other spheres of human life. The lockdowns, which lasted for many weeks, had a very big impact on various sectors of the economy. Most of them experienced a severe slowdown or recession. However, there were areas that benefited from the fact that the people in many countries stayed at home and worked remotely. Examples of such areas include the computer and tablets market, online stores, and delivery companies. This proves that even in difficult circumstances like a pandemic, we are not facing a total global recession. In such a context, it is worth looking at the impact of the COVID-19 pandemic on the real estate market. This area of the economy can experience the impact of the virus in different ways. On the one hand, a general increase in global uncertainty can cause certain investment projects to freeze or be abandoned. Lockdowns cause difficulties in accessing workers at construction sites. Populations losing their jobs during a pandemic have difficulty paying rent or mortgage payments. On the other hand, rising inflation and low interest rates on bonds and bank deposits may encourage people to protect their savings by buying property. Remote working increases the demand for larger apartments that need to serve as offices in addition to residential functions. Remote work also means not having to commute to workplaces, which may encourage people to move to cheaper locations without having to give up a well-paying job in a larger but also more expensive city. These two groups of impulses are contradictory and may have a completely different impact on the shape of the real estate market understood as the structure of prices and the number of transactions concluded. The study is part of a broader research effort to assess the impact of the COVID-19 pandemic on various areas of the economy. Previous research conducted in the Szczecin agglomeration proved the existence of the price divergence phenomenon. The main city of the agglomeration showed a tendency to pull in the economic potential from the region, which translated into a faster increase in real estate prices in the capital of the region in comparison with the surrounding cities. The aim of this study is to verify whether the pandemic and the socio-economic processes triggered by it, such as the development of remote working, changed the process of divergence into a process of convergence, but whether the faster growth of residential property prices in the main city of the agglomeration, which started years ago, has not been stopped. For this purpose, α-convergence and σ-convergence models were used. The study covers the period 2015-2021. The study area is the city of Szczecin and the four largest cities adjacent to the capital of the West Pomeranian Voivodeship in Poland.

Categorisation in Forest Areas: A Study of the ‘OTFDs’ in the Forest Rights Act

There are certain demarcated tribal areas in our country where the Scheduled Tribes (STs) have special community rights to live their lives according to their customs and maintain control over local natural resource management. The Sixth Schedule and Fifth Schedule are examples of such areas, and after the enactment of the Forest Rights Act, (FRA), 2006, there are crucial preferential provisions for the STs in forest areas of the whole country too. This article probes the historical development of categorisations in India, particularly in the context of forest-dwelling communities, and attempts to examine constitutional provisions and the provisions of different laws passed by the Parliament to evaluate the situation of other minority communities, particularly dalits, living in ‘forest areas’. In this context, the article primarily focuses on the genesis and practice of the Other Traditional Forest Dwellers (OTFDs) category. Based on the field study of the Taungya village, the article shows the problem of categorisation in forest areas and marginalisation of dalits due to this process and emphasises the need for a more dialogical and democratic process of categorisation in India.

MORPHOTECTONIC EVALUATION OF SIRCH MOUNTAINS USING WATER BASIN GEOMORPHIC INDICES

Morphotectonic indices are useful instruments for investigating the effect of tectonic activity in a certain region. Calculation of these indicators using the advantages of Geographical Information System, GIS, in a large area is useful for detecting potential disorders related to active tectonics. This method is useful in regions on which few morphotectonic studies have been conducted. An example of such areas can be the watersheds of the Sirch mountainous region. The watersheds of the Sirch region located in Central Iran’s zone in the southeast of Iran are a very ideal zone to evaluate the concepts of these indicators in the prediction of the relative tectonic activity according to the investigation of drainage systems or mountain fronts. Based on the values of the calculated indices including Form Factor (FF), Compaction Coefficient (CC), Ratio of Circularity (RC), Ratio of Elongation (RE), relative Basin Height (BH), and Ruggedness Number (RN), a final indicator called Active Tectonic Indicator (ATI) is obtained. The latter is a combination of the abovementioned indicators and evaluates the morphotectonic activity based on the shape of the watersheds. By measuring and combining these indicators, the tectonic activity stage of the Sirch region was determined by analyzing the watersheds of this region.

Rolling resistance and its relation to operating conditions: A literature review

For at least 50 years, the interest in understanding and reducing the rolling resistance of pneumatic tyres has been growing. This interest is driven by the need to reduce vehicle fuel consumption and CO2-emissions, for environmental and economic reasons. The amount of rolling resistance generated depends on the vehicle type, tyre properties and operating conditions. The main objective of this literature review is to provide an overview of the most influential operating conditions with respect to rolling resistance, their effects and their connection to different measurement techniques. The examined operating conditions are the inflation pressure, the temperature, the curvature of the test surface, the load, road surface, speed, torque, slip angle and camber angle. In addition, the definition of rolling resistance is investigated, which shows lack of harmony in the literature. There are important areas where little research can be found and where further research would be valuable. Examples of such areas are effects of the torque, slip angle and camber angle on rolling resistance, thorough comparison between flat-surface and drum measurements, effects of temperature difference between laboratory measurements and actual driving on rolling resistance and evaluation of Unrau’s formula for temperature correction of rolling resistance measurements.

A Deep Learning Approach to Ancient Egyptian Hieroglyphs Classification

Nowadays, advances in Artificial Intelligence (AI), especially in machine and deep learning, present new opportunities to build tools that support the work of specialists in areas apparently far from the information technology field. One example of such areas is that of ancient Egyptian hieroglyphic writing. In this study, we explore the ability of different convolutional neural networks (CNNs) to classify pictures of ancient Egyptian hieroglyphs coming from two different datasets of images. Three well-known CNN architectures (ResNet-50, Inception-v3 and Xception) were taken into consideration and trained on the available images. The paradigm of transfer learning was tested as well. In addition, modifying the architecture of one of the previous networks, we developed a specifically dedicated CNN, named Glyphnet, tailoring its complexity to our classification task. Performance comparison tests were carried out and Glyphnet showed the best performances with respect to the other CNNs. In conclusion, this work shows how the ancient Egyptian hieroglyphs identification task can be supported by the deep learning paradigm, laying the foundation for information tools supporting automatic documents recognition, classification and, most importantly, the language translation task.

Land Cover Change and Landscape Transformations (2000–2018) in the Rural Municipalities of the Upper Silesia-Zagłębie Metropolis

Dynamic changes in the landscape have been observed in recent years. They are particularly visible in areas with a high degree of anthropopressure. An example of such areas is metropolitan regions and their immediate rural surroundings. The purpose of this article is to identify changes in land cover in the rural municipalities within metropolises and detect the processes of landscape transformation in rural areas, which are extremely sensitive to anthropopressure. The dynamics of land cover changes in the years 2000–2018 were determined using a change index (ChI), and their directions were determined using the indicator of changes in types of land cover. Corine Land Cover for level 2 groups (1.1–4.2) was used as research material, and the Upper Silesia-Zagłębie Metropolis was selected as the model area. The greatest changes in the landscape were observed in built-up areas, industrial areas, meadows and mining areas. This is due to the disappearance of the mining industry that was traditional for this region and the ongoing suburbanization process, as well as the re-industrialization of modern industry and the abandonment of arable land in rural areas.

Long-term studies reveal major environmental factors driving zooplankton dynamics and periodicities in the Black Sea coastal zooplankton.

BackgroundThe development and management of shelf-sea ecosystems require a holistic understanding of the factors that influence the zooplankton structure and ecosystem functions. The Black Sea is an example of such areas influenced by eutrophication, overfishing, climate variability, invasions of the ctenophores Mnemiopsis leidyi followed by Beroe ovata. Thus, there is a set of principal factors which may influence and explain periodicities in the Black Sea ecosystem.MethodsWe analysed a total of 918 samples taken from 1991 to 2017 with intervals of 10 days. Taxa were identified to species, their abundance and biomass were calculated. We tested 12 environmental factors, which may explain zooplankton distribution: temperature, productivity-linked factors (surface chlorophyll as a proxi), wind, turbidity, lowest winter temperature, and concentration of the ctenophore M. leidyi. We used canonical correspondence analyses to find the dominant environmental factors and further regression analyses to retrieve dependences of plankton biomass on the major factors. Periodicities were assessed with the use of the Continuous wavelet transform and tested with use of One-way ANOSIM and PERMANOVA. The distances between ecosystem states in different years were assessed using non-metric multidimensional scaling.ResultsCurrently, temperature and productivity are the major environmental factors driving zooplankton dynamics. Not long ago, before 1999, abundance of M. leidyi was one of the major factors explaining the zooplankton variance. Spectral analysis of species abundances revealed a 4-year transitional period in 1999–2002 (not reported before) when ecosystem adapted to a new invader B. ovata. Statistically robust 2- and 3-year periodicities were retrieved for most plankton taxa and some benthic larvae. We found robust correlations between temperature and surface chlorophyll concentration on one side and plankton abundances and biomass on the other, and retrieved multivariate regressions, which may have a prognostic value.

Neural network based study of PV panel performance in the presence of dust

Neural networks are utilised to study PV solar panels' performance dependence on accumulated dust and tilt angle. For every location in the world there is an optimum tilt angle of dust-free PV panels for best performance. However, in some areas of the world dust is an obstacle for PV panels operation. Examples of such areas are the Middle East and the Arabian Gulf countries. Accumulation rate of dust depends on the location as well as the tilt angle of the PV panel. In this paper, relevant data are collected for two identical sets of PV panels in the city of Ras Al Khaimah in UAE where significant amounts of dust are present. Each set includes four different PV panels with various tilt angles. One set is kept clean while the other set is left unclean. The data includes information about PV panel cleaning condition, tilt angle, date and output power. Available data are used to train a neural network model which predicts the PV panel power under the given conditions. Furthermore, the neural network model serves as a key for adjusting the tilt angle of the PV panels at an optimum value for best performance taking into account the dust factor.

Neural network based study of PV panel performance in the presence of dust

Neural networks are utilised to study PV solar panels' performance dependence on accumulated dust and tilt angle. For every location in the world there is an optimum tilt angle of dust-free PV panels for best performance. However, in some areas of the world dust is an obstacle for PV panels operation. Examples of such areas are the Middle East and the Arabian Gulf countries. Accumulation rate of dust depends on the location as well as the tilt angle of the PV panel. In this paper, relevant data are collected for two identical sets of PV panels in the city of Ras Al Khaimah in UAE where significant amounts of dust are present. Each set includes four different PV panels with various tilt angles. One set is kept clean while the other set is left unclean. The data includes information about PV panel cleaning condition, tilt angle, date and output power. Available data are used to train a neural network model which predicts the PV panel power under the given conditions. Furthermore, the neural network model serves as a key for adjusting the tilt angle of the PV panels at an optimum value for best performance taking into account the dust factor.

Building a multilingual ultrasound corpus

This paper presents a corpus of three dimensional ultrasound data focused on tongue shape during speech sound articulation. Our ultimate goal is to collect data pertinent to phonetic structures from as many languages as possible (at present upwards of 20 languages are represented) and to curate these data in an open access corpus that is freely available for use by other researchers. In this presentation, we review the structure of the corpus and present a series of case studies illustrating the ways in which three-dimensional data are being used to address questions of phonetic interest. Examples of such areas include: examining the articulation of laterals; determining the point of articulation for dorsal consonants; analyzing consonant and vowel coarticulation patterns; and elucidating how coupling of the tongue body with the tongue blade and root affect place and manner of articulation. Like the work of Allard Jongman and Joan Sereno, this project is inherently collaborative and includes ample opportunity for using guided investigation of targeted research questions to ease novice scholars into the research process.

Improving data transfer efficiency in a gas field wireless telemetry system

Effective organisation of communication channels in autonomous information and measurement systems (AIMS) is a burning issue. It is particularly challenging for areas where, for a number of reasons (primarily unprofitability or immaturity of the wired infrastructure), telecommunications can rely only on wireless technologies, i.e., radio channels. Arctic regions of the Russian Federation, where most of Russia’s gas and gas condensate deposits are located, constitute a typical example of such areas. The key challenges during construction of wireless communication channels are associated with the fixed range of frequencies that can be used without a licence. For the purposes of radio traffic, the frequency used by AIMS transmitters and receivers depends on the frequency of the quartz crystal resonators used in such devices. The stability of this frequency determines both the number of radio channels that can be used and the efficiency of data transfer. Key factors affecting the quartz frequency include temperature and “ageing” of quartz crystals. Known methods for increasing the frequency stability generally allow compensation for the temperature drift of the quartz frequency. In addition, such methods are increasingly energy-consuming, which is unacceptable in the Extreme North. This article suggests using GPS receiver data for frequency adjustment. With a minor increase in energy consumption, this technique enables full compensation for quartz crystal resonator frequency drift, no matter what the cause of such drift, eventually allowing operation of more radio channels within the authorised bandwidth with preserved channel separation. In general, it helps increase the efficiency of data transfer in the telemetry systems of gas field operations.

Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery

Association rule mining is a data mining technique for discovering useful and novel patterns or relationships from databases. These rules are simple to infer and intuitive and can be easily used for classification in any domain that requires explanation for and investigation into how the classification works. Examples of such areas are medicine, agriculture, education, etc. For such a system to find wide adoptability, it should give output that is correct and comprehensible. The amount of data has been growing very fast and so has the search space of these problems. So we need to change traditional methods. This paper discusses a rule mining classifier called DA-AC (dynamic adaptive-associative classifier) which is based on a Dynamic Particle Swarm Optimizer. Due to its seeding method, exemplar selection, adaptive parameters, dynamic reconstruction of regions and velocity update, it avoids premature convergence and provides a better value in every dimension. Quality evaluation is done both for individual rules as well as entire rulesets. Experiments were conducted over fifteen benchmark datasets to evaluate performance of proposed algorithm in comparison with six other state-of-the-art non associative classifiers and eight associative classifiers. Results demonstrate competitive performance of proposed DA-AC while considering predictive accuracy and number of mined patterns as parameters. The method was then applied to predict life expectancy of post operative thoracic surgery patients.

Análise da vulnerabilidade natural da paisagem em relação aos diferentes níveis de ocupação da bacia hidrográfica do Rio Vermelho, estado de Goiás

A bacia hidrográfica do Rio Vermelho, situada no Estado de Goiás, possui uma elevada geodiversidade, com terrenos que vão do Arqueano ao Holocêno, e um proeminente registro tectônico impresso. Da mesma forma, ao longo de seu processo histórico de ocupação, tem sido impactada por variadas atividades de utilização do solo e do subsolo, o que acarretou tanto o empobrecimento da biodiversidade, quanto a modificação do seu aspecto físico, sendo expressiva a ocorrência de processos erosivos e acúmulos de sedimentos em razão da dinâmica de entrada e saída de energia e matéria do sistema natural. Neste trabalho, o qual teve por base a elaboração e análise do mapa de entropia do relevo para toda a área da bacia (~ 11.000 km2), foi possível identificar áreas mais suscetíveis à atuação de processos morfogenéticos e, portanto, mais vulneráveis à ocupação, à exemplo das áreas próximas à foz da bacia, cujos valores de entropia são coerentes com a diversidade de usos e intervenções sobre o complexo sistema fluvial no entorno do canal principal do Rio Vermelho. Em que pese a necessidade de análises adicionais, à escalas de maior detalhe, nossos resultados demonstram o potencial da análise da entropia do relevo como ferramenta importante para identificar áreas com energia potencial disponível para realização de trabalho geomorfológico e, portanto, mais vulneráveis à ocupação (i.e. sujeitas a enchentes, inundações e deslizamentos de terras).

Dynamic fragmentation of blast mitigants

Experimental evidence from a wide range of sources shows that the expanding cloud of explosively disseminated material comprises “particles” or fragments which have different dimensions from those associated with the original material. Powders and liquids have often been used to surround explosives to act as blast mitigants, and this is the main driver for our research. There are also many other areas of interest where an initially intact material surrounding an explosive charge is dynamically fragmented into a distribution of fragment sizes. Examples of such areas include fuel air explosives and enhanced blast explosives as well as quasi-static pressure mitigation systems, and our studies are thus also relevant to these applications. In this paper, we consider the processes occurring as an explosive interacts with a surrounding layer of liquid or powder and identify why it is important to model these processes as a multiphase material problem as opposed to a single phase, single material velocity problem. We shall present results from this class of numerical modelling. In this paper we shall explore what determines the particle or fragment size distribution resulting from explosive dissemination of a layer of material and discuss reasons why clouds from disseminated liquids and powders look similar. We shall support our analysis with results from recent explosives trials and introduce early results from some ongoing small scale explosive mitigation experiments.

Learning from spatial variability: Aspen persistence in the Centennial Valley, Montana

Abstract Quaking aspen ( Populus tremuloides ) is commonly believed to be declining throughout western North America. A literature review of recent local-scale aspen studies, however, indicates that regional-scale spatial variability in aspen dynamics is greater than previously recognized and there are areas of successful aspen regeneration and recruitment. This study examines 20th century aspen recruitment and regeneration patterns along a 15 km long lower forest–grassland ecotone in the Centennial Valley, Montana to determine if the Centennial Valley ecotone aspen stands support the observation that there are areas of successful recruitment and regeneration and provide another example of such areas. A total of 525 adult aspen stems were cored and 8924 aspen suckers were counted and mapped in 106 rectangular plots randomly distributed along the ecotone. Aspen-age distribution was constructed at a decadal time scale to examine 20th century recruitment patterns. Sucker density was examined to evaluate aspen regeneration. The tree-age distribution patterns indicated successful aspen recruitment during the 20th century, while the sucker density assessment suggested successful regeneration. The Centennial Valley ecotone aspen stands appear to support the observation that aspen recruitment and regeneration are spatially variable and there are areas of aspen persistence. Learning about the underlying factors that enable successful aspen regeneration and recruitment in such areas can help management and restoration activities at other locales. Recognizing the regional-scale spatial variability in aspen dynamics can inform regional-level policy and management.

Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing

This paper attempts to summarise the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from non-destructive testing (NDT). Problems in the rail transport domain are mainly discussed. However, a majority of the emphasis in this paper is laid on rail inspection problems, since it was believed that the review would provide a perfect ground to the authors in pursuing further work within the rail inspection area. NDT is a broad name for a variety of methods and procedures concerned with all aspects of uniformity, quality and serviceability of materials and structures, without causing damage to the material that is being inspected. During the past several years, problems concerning the automatic interpretation of data from NDT have received good attention and have stimulated interests in other areas like transportation, for making key assessments within some of its subject areas. Rail, air and marine industries together with bridge inspection and pavement maintenance are good examples of such areas where a considerable amount of work has been done. Such work neatly splits into two schools. The first school investigates the classical usage of data by an experienced human operator to determine the condition of the inspected material or structure. The other school focuses attention on the automatic interpretation of NDT data using AI techniques, in determining the result of inspection. The scope of this paper is only limited to the automatic interpretation of data from NDT, with the goal of assessing embedded flaws as quickly and accurately as possible in a cost effective fashion. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in the area. A secondary goal was to provide an insight into possible research methods concerning railway sleeper inspection by automatic interpretation of data. A brief introduction is provided for the benefit of the readers unfamiliar with the techniques.

Reduction of Irreversible Protein Adsorption on Solid Surfaces by ProteinEngineering for IncreasedStability

The influence of protein stability on the adsorption and desorption behavior to surfaces with fundamentally different properties (negatively charged, positively charged, hydrophilic, and hydrophobic) was examined by surface plasmon resonance measurements. Three engineered variants of human carbonic anhydrase II were used that have unchanged surface properties but large differences in stability. The orientation and conformational state of the adsorbed protein could be elucidated by taking all of the following properties of the protein variants into account: stability, unfolding, adsorption, and desorption behavior. Regardless of the nature of the surface, there were correlation between (i) the protein stability and kinetics of adsorption, with an increased amplitude of the first kinetic phase of adsorption with increasing stability; (ii) the protein stability and the extent of maximally adsorbed protein to the actual surface, with an increased amount of adsorbed protein with increasing stability; (iii) the protein stability and the amount of protein desorbed upon washing with buffer, with an increased elutability of the adsorbed protein with increased stability. All of the above correlations could be explained by the rate of denaturation and the conformational state of the adsorbed protein. In conclusion, protein engineering for increased stability can be used as a strategy to decrease irreversible adsorption on surfaces at a liquid-solid interface.