Mobility Analysis Research Articles

Cultural mobilities and cultural heritage: concepts for an Asia-centric approach

This article sets out an original and exploratory framework for examining emerging concepts of cultural mobilities and heritage with a key focus on the infrastructure and spatialities of cultural mobilities in, of and through Asia—specifically China. To date, the scholarly analysis of mobilities has been dominated by the social sciences in relation to central themes of migration, national borders, crisis and transnational flows of objects and people. This paper seeks to expand the focus in mobilities discourses to humanities, not only to research how infrastructures and spatialities are shaped by culture and heritage but also to analyse cultural mobility through infrastructure and spatialities. We set out epistemic considerations for approaching cultural mobilities through an interdisciplinary lens that seeks to address heritage studies of diverse kinds, from environmental to religious to architectural. By centring Asian epistemes, the paper also challenges recognition and interpretation biases in the humanities and social sciences that continue to privilege Eurocentric hegemonies. Together, the co-authors examine: how Asian cultures operate through material and non-material infrastructures that defy singular location in geopolitical crossings and networks; how culture is mobilised in different ways through infrastructure; the entanglement of cultural heritage with political infrastructure and living practice as well as embedded values. The article discusses China’s Belt and Road Initiative (BRI), yoga as a cultural infrastructure between India and China, and the infrastructure and multiple spatialities of Chinatown as a evolving practice of cultural heritage.

Additive Water Uptake of the Mixtures of Urban Atmospheric HULIS and Ammonium Sulfate

AbstractThe hygroscopicity of atmospheric aerosols affect the global climate and human health. However, the hygroscopic behavior of mixtures of inorganic salts and organics in atmospheric aerosols have not been well characterized for ambient complex organic mixtures. Here, the hygroscopic growth of humic‐like substances (HULIS), which represent the complexity of organic aerosol compounds, from urban aerosols and their mixtures with ammonium sulfate were investigated with measurements from a hygroscopicity tandem differential mobility analyzer and model analyses. The Zdanovskii–Stokes–Robinson (ZSR) relationship, which assumes that the water uptake of each component is additive, was examined for the mixtures. The dependence of the hygroscopicity parameters (κ) of the mixtures on the volume fraction of HULIS is generally represented by the ZSR relationship with the deviations of κ on average of 16% (4%–27% in different mixing ratios), and 0.03 (0.02–0.04) on absolute basis. An assumption of asphericity for the dry particles reduced the deviations (14%). Approximations for the surface tension depression by surfactants and surface‐bulk partitioning were predicted to account for the deviations to a small extent. Furthermore, a thermodynamic model for simplified compound systems showed that the non‐ideality of the solutions potentially explained the deviations from the ZSR relationship. The results of this study support the use of the ZSR relationship for organic‒inorganic mixtures as a practical approximation in atmospheric models, provide a guide for the uncertainty associated with this approximation, and suggest directions for future studies.

A machine learning-based approach for automatic motion/constraint and mobility analysis of parallel robots

AbstractMotion and constraint identification are the fundamental issue throughout the development of parallel mechanisms. Aiming at meaningful result with heuristic and visualizable process, this paper proposes a machine learning-based method for motions and constraints modeling and further develops the automatic software for mobility analysis. As a preliminary, topology of parallel mechanism is characterized by recognizable symbols and mapped to the motion of component limb through programming algorithm. A predictive model for motion and constraint with their nature meanings is constructed based on neural network. An increase in accuracy is obtained by the novel loss function, which combines the errors of network and physical equation. Based on the predictive model, an automatic framework for mobility analysis of parallel mechanisms is constructed. A software is developed with WebGL interface, providing the result of mobility analysis as well as the visualizing process particularly. Finally, five typical parallel mechanisms are taken as examples to verify the approach and its software. The method facilitates to attain motion/constraint and mobility of parallel mechanisms with both numerical and geometric features.

On-Site Quantitative Visualization of Singlet Oxygen in Crops via an Organic Small Molecule-Based Ratiometric Fluorescent Probe and a Mobile Fluorescence Analysis Device.

Singlet oxygen (1O2) plays imperative roles in a variety of biotic or abiotic stresses in crops. The change of its concentration within a crop is closely related to the crop growth and development. Accordingly, there is an urgent need to develop an efficient analytical method for on-site quantitative detection of 1O2 in crops. Here, we judiciously constructed a novel ratiometric fluorescent probe, SX-2, for the detection of 1O2 in crops. Upon treating with 1O2, probe SX-2 displayed highly selective ratiometric fluorescence response, which is favorable for the quantitative detection of 1O2. Concurrently, the fluorescence solution color of probe SX-2 was varied, obviously from blue to yellow, indicating that the probe is beneficial for on-site detection by the naked eye. Sensing reaction mechanism studies showed that the 2,3-diphenyl imidazole group in SX-2 could function as a new selective recognition group for 1O2. Probe SX-2 was utilized for the detection of photoirradiation-induced 1O2 and endogenous 1O2 in living cells. The changes in the 1O2 level in zebrafish were also tracked by fluorescence imaging. In addition, the production of 1O2 in crop leaves under a light source of different wavelengths was studied. The results demonstrated more 1O2 were produced under a light source of 365 nm. Furthermore, to achieve on-site quantitative detection, a mobile fluorescence analysis device has been made. Probe SX-2 and mobile fluorescence analysis device were capable of on-site quantitative detecting of 1O2 in crops. The method developed herein will be convenient for the on-site quantitative measurement of 1O2 in distinct crops.

Returns to education in Nepal: an analysis of educational attainment, employability and social mobility

ABSTRACT This study examines the impact of educational attainment on employment and earnings in Nepal. Using the Nepal Labour Force Survey 2017–2018 cycle, we employ an extended earnings function derived from the foundational Mincer equation to estimate returns to education. The results underscore the overall positive impact of education (1.76, 3.73, 7.68 and 11.00% increase in earnings by primary, secondary, bachelor’s and master’s degrees, respectively), which is lower than the average observed in other low– and middle– income countries, except in higher education. The results also indicate persisting disparities for females and disadvantaged groups. The study emphasizes the need for strategic interventions, improved educational infrastructure and inclusive policies to foster equitable opportunities.

A comparative approach to GIS modelling of terrestrial mobility in archaeological sites. The iron age hillfort of Villasviejas del Tamuja as a study case

The archaeological analysis of Historical mobility is an increasingly studied topic thanks to new geographic information technologies. This paper proposes a modelling exercise of the spatial behaviour of a Second Iron Age community in the Middle Tagus Valley: the hillfort of Villasviejas del Tamuja (Botija, Cáceres). Based on our knowledge of the configuration of the site and the surrounding settlements, we propose a heuristic use of a series of GIS tools to understand how the spatial relationship between both elements was structured. More specifically, we compare the results obtained with different calculation methods that combine two essential variables to address this issue: mobility and visibility relations. On the one hand, we evaluate the results with the application of an already developed methodology: the MADO analysis. On the other hand, we present a complementary procedure for the calculation of Least Cost Paths (LCP), considering the visibility as a key element in the mobility. The methodology uses the same data, in a paradigmatic case study for comparing results. The differences obtained through the use of different tools are thus evaluated in order to weigh up the additional or complementary knowledge that they can provide us with to investigate archaeological research questions such as the defensive architecture of the hillfort or the distribution of other nearby settlements.

A New Method for Size‐Resolved Aerosol CCN Activity Measurement at Low Supersaturation in Pristine Atmosphere

AbstractUnderstanding aerosol‐cloud interactions under pristine atmospheric conditions is crucial for meaningful global climate change predictions. However, ability of aerosol particles to act as cloud condensation nuclei (CCN) activity of aerosols in pristine atmosphere remains limited, mainly because of the difficulty of measuring it in pristine environments. Given the extremely low CCN number concentration under low supersaturations (SSs) and the complex impact of the kinetic limitation in CCN measurements, size‐resolved CCN activity measurements at low SSs cannot be achieved with traditional methods using differential mobility analyzers and a CCN counter (CCNC) because of the low sample efficiency and the impact of particles with multiple charges. To overcome this difficulty, we propose a new method for measuring size‐resolved CCN activity at low aerosol number concentrations. The new method is based on a combined system comprising an aerodynamic aerosol classifier (AAC) and a CCNC (AAC‐CCNC system). A control program was developed to achieve high‐resolution scans of the SS and particle size within a reasonable time frame. A data inversion scheme, including corrections for the transfer function and kinetic limitation, was developed to obtain accurate size‐resolved CCN activity. In July‐August 2022, the new method was used to measure the size‐resolved CCN activity on the Tibetan Plateau. This new method can advance the size‐resolved CCN measurements, thereby enhancing our understanding of aerosol‐cloud interactions in pristine atmosphere.

Time-varying drainage basin development and erosion on volcanic edifices

Abstract. The erosional state of a landscape is often assessed through a series of metrics that quantify the morphology of drainage basins and divides. Such metrics have been well explored in tectonically active environments to evaluate the role of different processes in sculpting topography, yet relatively few works have applied these analyses to radial landforms such as volcanoes. We quantify drainage basin geometries on volcanic edifices of varying ages using common metrics (e.g., Hack's law, drainage density, and number of basins that reach the edifice summit, as well as basin hypsometry integral, length, width, relief, and average topographic slope). Relating these measurements to the log-mean age of activity for each edifice, we find that drainage density, basin hypsometry, basin length, and basin width quantify the degree of erosional maturity for these landforms. We also explore edifice drainage basin growth and competition by conducting a divide mobility analysis on the volcanoes, finding that young volcanoes are characterized by nearly uniform fluvial basins within unstable configurations that are more prone to divide migration. As basins on young volcanoes erode, they become less uniform but adapt to a more stable configuration with less divide migration. Finally, we analyze basin spatial geometries and outlet spacing on edifices, discovering an evolution in radial basin configurations that differ from typical linear mountain ranges. From these, we present a novel conceptual model for edifice degradation that allows new interpretations of composite volcano histories and provides predictive quantities for edifice morphologic evolution.

62°S Witnesses the Transition of Boundary Layer Marine Aerosol Pattern Over the Southern Ocean (50°S–68°S, 63°E–150°E) During the Spring and Summer: Results From MARCUS (I)

AbstractThe Atmospheric Radiation Measurement Mobile Facility‐2 was installed onboard the research vessel Aurora Australis to measure aerosol properties during the 2017–2018 Measurement of Aerosols, Radiation, and CloUds over the pristine Southern ocean (MARCUS) Experiment, providing unique data on aerosols latitudinal and seasonal variation, including south of 60°S where previous observations are scarce. Data from a Cloud Condensation Nuclei (CCN) counter and Ultra‐High‐Sensitivity Aerosol Spectrometer show that both the number concentration (NCCN) and size distribution of CCN‐active aerosols, with diameters (D) between 60 nm < D < 1,000 nm are different over the North Southern Ocean (NSO) (50°S–60°S) and the South Southern Ocean (SSO) (62°S–68°S). The average NSO NCCN at 0.2% and 0.5% supersaturation were 28% and 49% less than that over the SSO. This increase of CCN over the SSO is caused by the increase of aerosols with 60 nm < D < 200 nm, consistent with calculations of Aerosol Scattering Angstrom Exponents derived from a nephelometer. Aerosol hygroscopicity growth factor measured by the Hygroscopic Tandem Differential Mobility Analyzer stayed close to 1.41 for aerosols with 50 nm < D < 250 nm over the SSO, but increased from 1.30 to 1.67 over the NSO, indicating different chemical compositions. Both CCN and Ice Nucleating Particles (INPs) showed a stronger variation with season than with latitude. The variation of heat‐labile and presumably proteinacous INPs suggests an increase of ice nucleating‐active microbes in summer.

Analisis Mobilisasi Dini Post Sectio Caesaria dengan Proses Penyembuhan Luka Operasi di Rumah Sakit Umum Ulfuadi Kota Binjai

World Health Organization (WHO) data estimates that every year there are 230 million major caesarean section operations performed worldwide, one for every 25 people alive (Haynes, 2015). WHO recommends caesarean section operations for only around 10-15% of the number of births. WHO's recommendation is certainly based on an analysis of the risks that arise due to caesarean section, both risks for the mother and the baby (Sudiharjani, 2014). In 2016, the demand for caesarean section was 50%, and currently more than 60% of pregnant women want caesarean section operations for various reasons. This study aims to conduct an Analysis of Early Mobilization Post-Caesarean Section With the Healing Process of Surgical Wounds at Ulfuadi General Hospital, Binjai City. The population in this study were all mothers who gave birth by caesarean section. The number of mothers who gave birth by caesarean section at Ulfuadi General Hospital, Binjai City was 135 people. The research sample was 43 people. Analysis of early mobilization with the process of healing post-section caesarean wounds. According to researchers, the results of this study indicate that by carrying out early mobilization, post-section caesarean patients can improve blood circulation which can help repair wound tissue better. The faster the post-section caesarean mother moves, the faster she can go home and the faster the wound heals. Wound healing is faster when compared to not doing early mobilization. The results of the chi-square test have a p value of 0.001 where the p value <0.05, it can be concluded that the Analysis of Early Mobilization Post-Cesarean Sectio with the Process of Healing Surgical Wounds at Ulfuadi General Hospital, Binjai City.

Exploring environmental equity and visitation disparities in peri-urban parks: A mobile phone data-driven analysis in Tokyo

Peri-urban parks play a crucial role in enhancing urban living conditions and promoting contact with nature. However, assessing environmental equity and visitor disparities in peri-urban parks requires a thorough understanding of visitation patterns, which has been lacking in previous research. To bridge the gap, this study utilizes mobile phone big data from over 40,000 visitors to peri-urban parks in Tokyo. We applied Local Moran's I, Lorenz Curve, Gini coefficient, and K-means clustering methods to scrutinize accessibility and disparities among residents of Tokyo's 23 special wards and within distinct visitor groups. The findings reveal significant insights: Firstly, mobile-based indicators expose disparities, underscoring the relevance of human activities in assessing peri-urban park accessibility, variations in these indicators highlight the need for a multi-dimensional approach. Secondly, Gini coefficient analysis of mobile-based and two-step floating catchment area (2SFCA) indicators suggest that extending the service radius beyond 10 km could mitigate environmental inequity. Furthermore, visitation disparities are more distinctly illustrated through mobile-derived visitor subgroups compared to age-demographic groups. These findings offer valuable insights for decision-makers in park planning policy, enabling the development of strategies that address accessibility inequity while establishing effective classifications for peri-urban park visitor groups.

Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph

Career mobility analysis aims at understanding the occupational movement patterns of talents across distinct labor market entities, which enables a wide range of talent-centered applications, such as job recommendation, labor demand forecasting, and company competitive analysis. Existing studies in this field mainly focus on a single fixed scale, investigating either individual trajectories at the micro-level or crowd flows among market entities at the macro-level. Consequently, the intrinsic cross-scale interactions between talents and the labor market are largely overlooked. To bridge this gap, we propose UniTRep , a novel unified representation learning framework for cross-scale career mobility analysis. Specifically, we first introduce a trajectory hypergraph structure to organize the career mobility patterns in a low-information-loss manner, where market entities and talent trajectories are represented as nodes and hyperedges, respectively. Then, for learning the market-aware talent representations , we attentively propagate the node information to the hyperedges and incorporate the market contextual features into the process of individual trajectory modeling. For learning the trajectory-enhanced market representations , we aggregate the message from hyperedges associated with a specific node to integrate the fine-grained semantics of trajectories into labor market modeling. Moreover, we design two auxiliary tasks to optimize both intra-scale and cross-scale learning with a self-supervised strategy. Extensive experiments on a real-world dataset clearly validate that UniTRep can significantly outperform state-of-the-art baselines for various tasks.

Aza-Prilezhaev Aziridination-Enabled Multidimensional Analysis of Isomeric Lipids via High-Resolution U-Shaped Mobility Analyzer-Mass Spectrometry.

Unsaturated lipids constitute a significant portion of the lipidome, serving as players of multifaceted functions involving cellular signaling, membrane structure, and bioenergetics. While derivatization-assisted liquid chromatography tandem mass spectrometry (LC-MS/MS) remains the gold standard technique in lipidome, it mainly faces challenges in efficiently labeling the carbon-carbon double bond (C═C) and differentiating isomeric lipids in full dimension. This presents a need for new orthogonal methodologies. Herein, a metal- and additive-free aza-Prilezhaev aziridination (APA)-enabled ion mobility mass spectrometric method is developed for probing multiple levels of unsaturated lipid isomerization with high sensitivity. Both unsaturated polar and nonpolar lipids can be efficiently labeled in the form of N-H aziridine without significant side reactions. The signal intensity can be increased by up to 3 orders of magnitude, achieving the nM detection limit. Abundant site-specific fragmentation ions indicate C═C location and sn-position in MS/MS spectra. Better yet, a stable monoaziridination product is dominant, simplifying the spectrum for lipids with multiple double bonds. Coupled with a U-shaped mobility analyzer, identification of geometric isomers and separation of different lipid classes can be achieved. Additionally, a unique pseudo MS3 mode with UMA-QTOF MS boosts the sensitivity for generating diagnostic fragments. Overall, the current method provides a comprehensive solution for deep-profiling lipidomics, which is valuable for lipid marker discovery in disease monitoring and diagnosis.

Mobile network traffic analysis based on probability-informed machine learning approach

The paper proposes an approach to the joint use of statistical and machine learning (ML) models to solve the problems of the precise reconstruction of historical events, real-time detection of ongoing incidents, and the prediction of future quality of service-related occurrences for prospective development of the modern networks. For forecasting, a regression version of the deep Gaussian mixture model (DGMM) is introduced. First, the preliminary clustering based on the finite normal mixtures is performed. This information is then used as an input for some supervised ML algorithm. It is the basic concept of the probability-informed ML approach in the field of telecommunications networks. Using the real-world datasets from a Portuguese mobile operator as well as public cellular traffic data, the article compares this approach with methods such as random forests, support vector machine regression, gradient boosting and LSTM. Vector autoregression, informed by the parameters of the generalized gamma (GG) distribution, which has also been successfully used to reconstruct past traffic patterns, is also used as a benchmark. We demonstrate that DGMM-based regression is 6.82–22.8 times faster than LSTM for the dataset. Moreover, DGMM-based regression can achieve better results for the most important traffic characteristics (average and total traffic, the number of users). For metrics MAPE and RMSE, it surpasses the results of statistical methods up to 46.7% (RMSE) and 91.5% (MAPE) (median increases are 28.0% and 80.1%, respectively), as well as for ML methods up to 13.0% (RMSE) and 35.7% (MAPE) (median increases are 0.39% and 2.5%, respectively). Thus, the use of a probability-informed approach for telecommunication data seems optimal for the computational speed and accuracy trade-off. Also, we introduce a novel statistical hypothesis testing method based on GG distribution for detecting suspected anomalies in traffic.

International mobility between the UK and Europe around Brexit: a data-driven study

Among the multiple effects of Brexit, changes in migration and mobility across Europe were expected. Several studies have analysed these aspects, mostly from the point of view of perceptions, motivations, economic effects, scenarios, and changes in migration from Central and Eastern European countries. In this study we propose an analysis of migration and cross-border mobility using an integrated data-driven approach. We investigate official statistics from Eurostat, together with non-traditional data, to give a more complete view of the changes after Brexit, at EU and regional level. Specifically, we employ scientific publication and Crunchbase data to study highly-skilled migration, Twitter and Air Passenger data to investigate monthly trends. While main trends are preserved across datasets, with a general decrease in migration towards the UK immediately after the referendum approval, we are able to also observe more fine grained trends specific to some data or regions. Furthermore, we relate the changes in mobility observed from Air Passenger data with attention to Brexit from Google Trends data.

OneFlowTraX: a user-friendly software for super-resolution analysis of single-molecule dynamics and nanoscale organization.

Super-resolution microscopy (SRM) approaches revolutionize cell biology by providing insights into the nanoscale organization and dynamics of macromolecular assemblies and single molecules in living cells. A major hurdle limiting SRM democratization is post-acquisition data analysis which is often complex and time-consuming. Here, we present OneFlowTraX, a user-friendly and open-source software dedicated to the analysis of single-molecule localization microscopy (SMLM) approaches such as single-particle tracking photoactivated localization microscopy (sptPALM). Through an intuitive graphical user interface, OneFlowTraX provides an automated all-in-one solution for single-molecule localization, tracking, as well as mobility and clustering analyses. OneFlowTraX allows the extraction of diffusion and clustering parameters of millions of molecules in a few minutes. Finally, OneFlowTraX greatly simplifies data management following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We provide a detailed step-by-step manual and guidelines to assess the quality of single-molecule analyses. Applying different fluorophores including mEos3.2, PA-GFP, and PATagRFP, we exemplarily used OneFlowTraX to analyze the dynamics of plant plasma membrane-localized proteins including an aquaporin, the brassinosteroid receptor Brassinosteroid Insensitive 1 (BRI1) and the Receptor-Like Protein 44 (RLP44).

Changes in neighbourhood walkability and body mass index: An analysis of residential mobility from a longitudinal multilevel study in Brisbane, Australia

This study examined associations between changes in neighbourhood walkability and body mass index (BMI) among 1041 residents who relocated within Brisbane, Australia between 2007 and 2016 over five waves of the HABITAT study. Measures included spatially-derived neighbourhood walkability (dwelling density, street connectivity, and land use mix) and self-reported height and weight. No associations were found between any neighbourhood walkability characteristics and BMI. Examining these associations over the life course, and the impact of residential relocation in the younger years, remains a priority for future research.

A Comparative Analysis of Malware Detection Methods Traditional vs. Machine Learning

Mobile devices have been the target of malicious software since their beginnings. Two known types
 of malware threats can intrude into mobile, independently injected applications and fraudulent
 applications that are developed to breach the security of mobile devices. Mostly these fraudulent
 applications access data using API calls and permission requests. API calls and permission requests
 are important for smooth conversation between mobile devices and database servers. This research
 proposes an efficient classification model that concatenates API calls and permission requests to
 detect malicious applications. We have used a dataset that contained more than 15 thousand Android
 devices’ malware. We have divided data into three groups to differentiate between the malicious
 permission requests and malicious API calls with normal permission requests and normal API calls.
 To increase the probability of recognizing Android malware applications, we develop three distinct
 grouping strategies for selecting the most valuable API calls that are obscure, critical, and obstreperous and are chosen because Android apps extensively use several application programming interfaces (APIs). According to the results, malware applications require authorizations to access confidential information very frequently than normal Android applications do. Also, malicious Android
 applications raise a diverse set of API calls to access sensitive data, evidenced by malware applications making a distinct set of API calls. Our proposed method attains an F-score of 94.04%, which suggests that it is efficient at discovering mobile malware applications. Our model can be of significant assistance in conducting mobile application analysis and forensic investigations into malware.

Quantifying thiolated chemical additives for copper electroplating process

BackgroundCopper foil, a thin layer of high-purity metallic copper, having excellent conductivity, ductility, and corrosion resistance, is extensively applied in various electronic applications. Thiolated (SH-containing) chemical additives (i.e., accelerator and inhibitor) in copper electroplating solution are known to be critical for optimizing the copper foil manufacturing processes. Due to the high ionic strength and acidity of copper electroplating solution, proper and accurate characterization of the thiolated chemical additives is a critical concern. ResultsIn this study, a facile, accurate approach is developed for quantitative characterization of thiolated additives in the copper electroplating solution. Firstly, gold nanoparticles (AuNPs) were employed as an adsorbent for separating the thiolated chemical additives, namely, poly(ethylene glycol) methyl ether thiol (PEG-SH) as inhibitor, and 3-mercaptopropionic acid (MPA) as accelerator from other interfering chemicals present in the copper electroplating solution. Subsequently, quantitative analysis of the AuNPs in the form of thin particle film was performed using attenuated total reflection-Fourier transform infrared spectroscopy. Electrospray-differential mobility analyzer was employed orthogonally for the quantitative analysis of the amount of thiolated additives adsorbed on AuNP. Interestingly, the results indicated that the detection concentration ranges of 5 μM–100 μM for PEG-SH and 10 μM–200 μM for MPA, respectively. SignificanceOverall, this work demonstrates a successful separation and analysis methodology for the thiolated chemical additives in copper electroplating solution which enables the precise control over the copper foil manufacturing process.

Characterization of size-resolved charge distributions for triboelectrically charged microparticles via electrical mobility analysis coupled to optical particle spectrometry

In printing applications, microparticles are commonly triboelectrically charged with larger carrier particles. Here, we develop an impaction-differential mobility analyzer-optical particle spectrometer (DMA-OPS) system to examine the size-dependent charge distributions of toner particles in the 0.3 – 10 μm range resulting from triboelectric charging via acrylic-coated ferrite carrier. The toner-carrier blend is aerosolized, and toner particles are released from the carrier via inertial impaction of toner-carrier complexes. The toner particles are then classified by electrical mobility and their optical diameters are determined. We can produce toner particle size-dependent charge distributions, which can be converted to surface potentials, and charge-to-mass ratios.