Dimension Of Aggregates Research Articles

Low-temperature treatment optimization for diesel-contaminated kaolin: Mutual impacts of generated pyrolytic carbon and particle agglomeration

Efficiency improvement in low-temperature treatment for diesel-contaminated sites is urgent because changes in soil properties and the generation of new substance during the remediation process can influence the duration and energy utilization. This paper focuses on low-temperature treatment optimization based on the mutual impacts of pyrolytic carbon and kaolin aggregation. Results reveal that the peak mass loss rate occurred between 100–150°C, with minimal loss beyond 200°C. Samples thermally treated at 150–200°C exhibited darker colors, indicating pyrolytic carbon formation, corroborated by x-ray photoelectron spectroscopy (XPS), fourier transform infrared spectroscopy (FTIR), and three-dimensional fluorescence spectrum (3D-EEM) analyses. Additionally, diesel contamination influenced the fractal dimension of aggregates by influencing adhesion forces (<10000 mg/kg) and forming liquid bridges (≥10000 mg/kg) in untreated kaolin, resulting in an initial increase and subsequent fall in fractal dimension with increasing concentration. Decline rates of pollutant gas concentration were closely correlated with fractal dimension changes under thermal conditions due to pollutant volatilization and pyrolytic carbon formation. Based on the consistency between fractal dimension and decline rate, two critical remediation concentrations (C0) and temperatures (T0) indices were identified to optimize the low-temperature remediation.

Power imbalance in hospital dual management system and its impact on turnover.

With the deepening of the healthcare reform in China, the competition in the sector is becoming fiercer leading to several unintended consequences such as talent turnover, which highly affect hospital development. Taking the case of a specialized university-affiliated stomatology hospital in Guangzhou, this thesis aims to analyze the main factors that contribute to talent turnover in this type of hospitals. The loss of talents in the field of stomatology is not unique to China and represents a significant problem in both developed and developing countries. The data for the study were collected from post facto live interviews conducted with 21 resigners to understand their feelings and motivations after the passage of time. The data were coded and concepts refined using MAXQDA software to form 18 sub-categories and six aggregate dimensions leading to the construction of two theoretical models. The balance of power embedded in the dual leadership system characteristic of Chinese organizations and corresponding leadership style and behavior, deeply affect the turnover of talents. The more unbalanced the power, the greater the impact on turnover. Based on the situation studied, a power imbalance allowed for individual behaviors that determined the development of the hospital and directly affected institutional fairness, culture, working atmosphere and ultimately led to turnover.

Surface engineered novel cationic surfactants with enhanced surface adsorption for environmental applications

Gemini Cationic Surfactants (GCS) have garnered significant attention due to their distinctive molecular structure, featuring two hydrophilic head groups associated with a hydrophobic spacer. This distinctive configuration enables diverse applications across various fields. Hence, the current study introduces a novel series of GCSs, ethanediyl-1,2-bis[di(2-hydroxypropyl) alkylammonium] dibromide (Cn-C2-Cn[IsoPr(OH)2; n = 9, 12 and 14) and comprehensively explore their properties, highlighting their promising antimicrobial activity. The synthesis of GCSs was validated using 1H NMR and IR spectroscopy. Dynamic Light Scattering was employed to determine the dimensions of Gemini surfactant aggregates in aqueous solutions. Surface tension analysis determined critical concentrations of micelle (CMC), minimum surface area (Amin) and concentration of surface excess (Γmax). Physicochemical properties were assessed through conductivity and surface tension analyses in aqueous solutions. The CMC, Amin and Γmax values decrease as alkyl chain length extends. Surface tension data revealed strong adsorption of GCSs at interfaces. π values (mN/m) ranged from 36.7 (C14C2C14[Iso-Pr(OH)]2) to 45.3 (C9C2C9[Iso-Pr(OH)]2). Conductivity and surface tension measurements further characterized Gemini surfactants in aqueous environments at 298 K. The size of the micelle aggregates varied between C9C2C9[Iso-Pr(OH)]2 (1.2885 nm) and C14C2C14[Iso-Pr(OH)]2 (1.9210 nm), highlighting the impact of chain length on micelle size. Hence, the study highlights significant effect of the chain lengths on Gemini cationic surfactants’ self-assembly in dilute aqueous environments. Antimicrobial tests demonstrated significant activity of Cn-C2-Cn[IsoPr(OH)]2 GCSs at varying concentrations. Antimicrobial tests showed the significant antimicrobial potential of these GCS compounds, suggesting their potential and promising applications in medical, hygiene, environmental sanitation and advanced materials sectors.

Effect of CO2 Nanobubble Water on the Fracture Properties of Cemented Backfill Materials under Different Aggregate Fractal Dimensions

In order to cope with climate change and achieve the goal of carbon neutrality, the use of carbonization technology to enhance the performance of cement-based materials and achieve the purpose of carbon sequestration has become a very promising research direction. This paper considers the use of CO2NBW as mixing water for cement-based materials, aiming to improve the carbonization efficiency of materials to achieve the goal of carbon neutrality. This time, the effect of CO2NBW on cementitious filling materials under different aggregate fractal dimensions was studied through uniaxial compression tests and acoustic emission technology. The effect of CO2NBW on the mechanical properties and crack evolution of the material was discussed. The results showed that CO2 nanobubbles significantly improved the strength of cemented filling materials under different fractal dimensions, and the uniaxial compressive strength was most significantly improved by 23.04% when the fractal dimension was 2.7824. In addition, the characteristics of acoustic emission ring counts and energy parameters indicate that CO2 nanobubbles help improve the overall pore structure of the sample, affecting the macroscopic strength. However, the addition of CO2 nanobubbles reduces the limit energy storage ratio of elastic strain energy, which indicates that excessive CO2 concentration may affect the hydration reaction of the cementing material.

Unveiling the assessment process behind an integrated flood risk management plan

In the context of the European Floods Directive, flood risk assessment is a critical component for the definition of an integrated management plan that operates within a multidimensional landscape shaped by intricate interactions. This study explores this complex interplay using a comprehensive framework, aimed at enlightening the non-linear pathways that flood risk assessments can traverse. It adopts the Gioia Methodology within the Grounded Theory approaches, enabling a nuanced exploration of flood risk assessment dynamics. Utilizing data from an Italian case study in the Po River District, this study unveils the flood risk assessment process framework by identifying 13 first-order codes, 6 s-order themes and 3 aggregate dimensions. It introduces a qualitative self-assessment tool to facilitate integration across dimensions and enhance Directive alignment, offering valuable insights for future flood risk assessment implementations.

Digital Twin Stakeholder Communication: Characteristics, Challenges, and Best Practices

Digital Twins (DT) encompass virtual models interconnected with a physical system through data links. Although DTs hold significant potential for positive organisational impact, their successful adoption in industrial practice remains limited. Whereas existing research predominantly focuses on technical challenges, more recent studies underscore the importance of addressing organisational and human factors to overcome implementation barriers. One central aspect in this context is stakeholder communication, especially given the ambiguous nature of the term DT in academic and industrial discussions. To expand the limited understanding of the factors causing challenging DT stakeholder communications, this article presents findings from an extensive exploratory study. It involves 27 in-depth interviews and two focus groups with highly experienced DT professionals. By employing grounded theory and the Gioia methodology, a grounded model for DT stakeholder communication challenges is derived. This model reveals the complex communication dynamics within DT projects, emphasising the emergence of novel stakeholder communication patterns that heavily rely on multidisciplinary collaboration. In total, 28 communication challenges were identified, grouped into eight theoretical themes and categorised into two aggregate dimensions: human- and organisation-centric challenges. Additionally, the study identified 15 practices, e.g., defining clear objectives, and starting small and building gradually, that organisations are following to mitigate these challenges. As a result, this article provides the theoretical groundwork for a comprehensive understanding of DT stakeholder communication and its associated challenges by revealing distinctive features and offering practical guidance to overcome critical challenges in DT projects.

Morphology, aspect ratio, and surface elemental composition of primary aerosol particles at urban region of India.

The PM2.5 and PM10 particles were characterized in terms of morphology (size and shape) and surface elemental composition at two different (traffic and industrial) locations in urban region of India and further linked to different morphological defining parameters. The overall PM2.5 and PM10 showed significant daily variability indicating higher PM10 as compared to PM2.5. PM2.5/PM10 ratio was found to be 0.58 ± 0.10 indicating the abundance of PM2.5. Soot aggregates, aluminosilicates, and brochosomes particles were classified based on morphology, aspect ratio (AR), and surface elemental composition of single particles. The linear regression analysis indicates the significant correlation between area equivalent (Daeq) and feret diameter (Dfd) (R2 0.86-0.98). Higher aspect ratio (1.48 ± 0.87-1.43 ± 0.50) was noted at traffic site as compared to industrial site (1.33 ± 0.58-1.29 ± 0.30), while circularity showed the opposite trend. Fractal dimension (Df) of soot aggregates estimated by the soot parameters method (SPM) were found to be 1.70, 1.72, and 1.88, mainly attributed to vehicular emissions, biomass, and industrial emission/coal burning, respectively. This further inferred that freshly emitted soot particles exhibited lacey in nature with spherical shape (Df 1.70) at traffic site, while at industrial location, they were different with compact shapes (Df 1.88) due to particle aging processes. This study inferred the synoptic changes in mass, chemical characteristics, and morphology of aerosol particles which provide the new insights into individual atmospheric particle and their dynamic nature.

Research on the enhancement material and culture method of soil aggregates composed of feldspathic sandstone and sand

The Mu Us Sandy Land is a region characterized by wind-blown sand and soil erosion in northern China. To enhance the soil quality of this area, various organic materials were incorporated into the mixed soil at a volume ratio of 1:2 for feldspathic sandstone to sand. Culture was conducted in the field and under constant temperature conditions in laboratory culture chambers. Four treatments were established in the experiment, each calculated based on weight ratio and controlled (with no organic material added, CK); single application of straw (5% straw, P1); single application of biochar (5% biochar, P2); combined application of biochar and straw (5% biochar + 5% straw, P3). After 90 days of culture, soil samples were collected for analysis of various indicators such as soil aggregate particle size distribution, water stability of soil aggregates, mean weight diameter, mean geometric diameter, and fractal dimension using dry sieving and wet sieving methods. The objective is to establish a scientific basis and provide technical support for addressing the challenges associated with compound soil and implementing rational fertilization measures. The research results indicate that: (1) The quantity of aggregates > 0.25 mm under different treatments follows the order CK < P1 < P2 < P3, and the differences between treatments are significant (P < 0.05); (2) Soil water stability, mean weight diameter (MWD), mean geometric diameter (GMD), and fractal dimension of soil aggregates in compound soil with different organic material additions are superior to the control, and the effect of biochar on improving soil aggregates is better than that of corn straw. The combined application of both significantly improves the effect compared to single applications. In both culture modes, under wet sieving, the P3 treatment shows the highest MWD and GMD of soil aggregates, with an increase ranging from 3.45% to 85% and 4.55% to 38.46%, respectively, compared to other treatments. (3) The trend of fractal dimension among treatments is consistent: P3 < P2 < P1 < CK, and the differences between treatments are significant (P < 0.05). Moreover, there is a good negative correlation linear equation relationship between the fractal dimension (y) and WR > 0.25 (x) of compound soil, with a correlation coefficient of up to 0.9851. In conclusion, the incorporation of organic materials can effectively enhance the proportion of macroaggregates in compound soil consisting of Feldspathic sandstone and sand, thereby improving soil stability and erosion resistance. The optimal outcome is achieved through the combined application of biochar and straw. Indoor culture proves to be more effective than field culture, while wet sieving accurately reflects the structural characteristics of compound soil under both dry and wet sieving treatments.

New Insights into the Formation of Aggregates of Bidisperse Nano- and Microplastics in Water Based on the Analysis of In Situ Microscopy and Molecular Simulation.

Microplastics (MPs) and nanoplastics (NPs) in water pose a global threat to human health and the environment. To develop efficient removal strategies, it is crucial to understand how these particles behave as they aggregate. However, our knowledge of the process of aggregate formation from primary particles of different sizes is limited. In this study, we analyzed the growth kinetics and structures of aggregates formed by polystyrene MPs in mono- and bidisperse systems using in situ microscopy and image analysis. Our findings show that the scaling behavior of aggregate growth remains unaffected by the primary particle size distribution, but it does delay the onset of rapid aggregation. We also performed a structural analysis that reveals the power law dependence of aggregate fractal dimension (df) in both mono- and bidisperse systems, with mean df consistent with diffusion-limited cluster aggregation (DLCA) aggregates. Our results also suggest that the df of aggregates is insensitive to the shape anisotropy. We simulated molecular forces driving aggregation of polystyrene NPs of different sizes under high ionic strength conditions. These conditions represent salt concentration in ocean water and wastewater, where the DLVO theory does not apply. Our simulation results show that the aggregation tendency of the NPs increases with the ionic strength. The increase in the aggregation is caused by the depletion of clusters of ions from the NPs surface.

Organizational resilience during COVID-19 pandemic: individual, group and organizational responses

PurposeThis paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.Design/methodology/approachThis briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.Findings25 first-order concepts and 8 second-order themes are categorized into 4 aggregate dimensions at different unit levels; organizational, leadership, operational and individual.Originality/valueThe briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Artificial intelligence in talent acquisition: exploring organisational and operational dimensions

PurposeWith the recent proliferation of AI, organisations are transforming not only their organisational design but also the input and output operational processes of the hiring process. The purpose of this paper is to explore the organisational and operational dimensions resulting from the deployment of AI during talent acquisition process.Design/methodology/approachThe authors conducted semi-structured interviews and meetings with human resources (HRs) professionals, recruiters and AI hiring platform providers in Sweden. Using an inductive data analysis rooted in the principles of grounded theory, the study uncovered four aggregate dimensions critical to understanding the role of AI in talent acquisition.FindingsWith insights from algorithmic management and ambidexterity theory, the study presents a comprehensive theoretical framework that highlights four aggregate dimensions describing AI’s transformative role in talent recruitment. The results provide a cautionary perspective, advising against an excessive emphasis on operational performance driven solely by algorithmic management.Research limitations/implicationsThe study is limited in scope and subject to several constraints. Firstly, the sample size and diversity are restricted, as the findings are based on a limited number of semi-structured interviews and meetings with HRs professionals, recruiters, and AI hiring platform providers. Secondly, the rapid evolution of AI technologies means that the study’s findings may quickly become outdated as new advancements and applications emerge.Practical implicationsThe results provide managers with actionable information that can lead to more precise and strategic management practices, ultimately contributing to improved organizational performance and outcomes. Plus, enhancing their ability to make informed decisions, optimize processes and address challenges effectively.Social implicationsThe results signal both positive and negative impacts on employment opportunities. On the positive side, AI can streamline recruitment processes, making it easier for qualified candidates to be identified and hired quickly. However, AI systems can also perpetuate existing biases present in the data they are trained on, leading to unfair hiring practices where certain groups are systematically disadvantaged.Originality/valueBy examining the balance between transactional efficiency and relational engagement, the research addresses a crucial trade-off that organizations face when implementing AI in recruitment. The originality lies in its critique of the prevailing emphasis on e-recruiting.

Evaluating strategies to persist for digital platform firms in a post-digitalization era: an empirical evidence from Indian platform firms

PurposeThe dynamic and evolving nature of the market calls for attention to digital platform firms' survival strategies, building agility for persistence in a continuously changing business environment. In India, the government’s adoption of the Digital Policy is one such change in the business environment for the firms that impact almost all sectors. Such policies cause a disruption wherein digital platform firms must be agile and create a strategic response that will endure any changes. The present study attempts to gain insight into the competitive strategies adopted by the digital platform firms of the consumer durables industry in India, which are implemented to facilitate their growth.Design/methodology/approachThe entire study is conducted in two phases. Phase one includes identifying strategies sampled digital platform firms adopted in response to the digitalization policy, and the second phase evaluates the significance of the adopted plans to persist.FindingsWhile clubbing the 42 strategic responses to a few aggregate dimensions, the study found four types of responses adopted by the digital platform firms in the consumer durable industry to persist in the market. Using a two-step system, the Generalized Method of Moments (GMM) approach, the study found that all four dimensions are statistically significant, positively impacting these firms' profitability.Practical implicationsThe study contributes to the knowledge base of strategic responses to persist for the incumbent platform firms in a dynamic business environment.Originality/valueThe study answers the pertinent research question of how such strategic decisions may be informed in favor of profitability.

Study on the influence of fractal dimension and size effect of coarse aggregate on the frost resistance of hydraulic concrete

In cold regions, hydraulic concrete structures are exposed to harsh environmental conditions that greatly impact their durability and safety. The role of aggregates in influencing concrete performance is paramount. This study aims to investigate how the fractal distribution and size of aggregates affect the frost resistance of hydraulic concrete, with air void characteristics, mass loss rate, Elastic Modulus of dynamic, and strength serving as primary references. Two experiment series, fractal distribution influence and aggregate size influence of concrete frost resistance, were conducted to analyze the frost resistance of hydraulic concrete with different aggregates. Based on the fractal theory, four grading fractal distributions were utilized to fractalize aggregate gradation, aiming to investigate the influence of aggregate fractal dimension on the frost resistance of hydraulic concrete. Simultaneously, concrete specimens of fully graded aggregate and wet-screened aggregate were also constructed to simultaneous freeze-thaw cycle tests, the impact of aggregate size on concrete's frost resistance performance was also studied by analyzing the air void structure in the frost-resistant concrete specimens. Results indicate that the specimens exhibit varying frost resistance performance under different aggregate gradation fractals, ranking from strongest to weakest as follows: D=2.5, D=2.3, D=2.1, D=2.7, which is attributed to the differences of air void spacing factors. Increasing aggregate size leads to greater internal structural heterogeneity in concrete, resulting in an increase in defects and weak points. This causes microcracks to develop more rapidly in the transition zone of fully graded concrete during freeze-thaw cycles, leading to more severe damage. The findings of this study can serve as a fundamental scientific basis for the designing and constructing of hydraulic concrete structures in cold areas.

Characterization of rutting damage based on two-dimensional image analysis of changes in mesoscopic aggregate properties of asphalt mixtures

Accurately predicting the rutting development law of asphalt pavement is of great significance for preventing and controlling pavement diseases. The aggregate skeleton of the asphalt mixture is the main load-bearing part and plays an important role in its anti-rutting performance. However, quantitative and mesoscopic evaluation indicators and standards for aggregate skeletons have not been established. This study aims to reveal the relationship between the mesostructured characteristics of asphalt mixtures and rutting damage. This paper uses two-dimensional image technology to analyze the dynamic change process of internal skeleton information of two graded asphalt mixtures under cyclic wheel load. According to the fractal theory, the fractal dimension and multifractal spectrum of the full-depth aggregate were calculated. The results indicate that the linear regression model between the mesoscopic skeleton information of asphalt mixture and the average rutting depth during the rutting test can be used to quantify the rutting performance. There is an obvious correlation between the fractal dimension of the section aggregate and the loading times, and there is a certain linear relationship between the multifractal spectrum indexes and the loading times. It indicates that the fractal theory index of aggregate can reflect the change of the macro performance of loading times.

An Overview of the Recent Evolution of the Socioeconomic Dimension of Inequality. The Case of CEE Countries

Abstract This research explores the evolution of several socioeconomic indicators belonging to the aggregate dimension of inequality (income, health, life expectancy, education, and sustainable development) in 5 CEE countries spanning 2010-2019. A special focus is dedicated to Romania’s national and regional framework due to mixed evidence concerning inequality’s impact on economic growth, poverty and human development. Our main results show a high value of this group’s recorded human development index (HDI), indicating an elevated level of the population’s well-being. Nevertheless, further analyses reflect severe disparities among countries and regions in terms of productivity, earnings, and social inclusion, corroborated by losses in education and welfare due to increased inequality.

Revealing the influence of additional structure on the flow field characteristics and flocculation performance in thickener feedwell through PIV experiments

Cemented paste backfill (CPB) is a widely used method for resource utilization of tailings. The thickener feedwell is the key equipment for CPB to realize solid-liquid separation. Flocculation is a significant process in treating slurry thickening and dehydration. To reveal the flow field variation in the flocculation process, a range of feedwells with additional structures were devised, containing the guide chute feedwell, guide shelf feedwell, and no-additional structure feedwell. The effects of hydrodynamics on flocculation performance were investigated with particle image velocimetry. The velocity and energy distribution were used to describe the variation of the flow field. The size and fractal dimension of aggregates for the flocculation reaction were studied. Furthermore, the turbidity was compared to evaluate the flocculation performance. The results indicated that additional structure can improve flocculation performance, ultimately achieving efficient solid-liquid separation. The flocculation process in feedwell incorporates the mixing-collision section and settling-growth section along the flow direction, with bounding by additional structure. The ideal high-efficiency feedwell should produce a symmetrical and uniform flow field, providing matched shear strength for two sections. The guide chute feedwell exhibited superior performance, resulting in significantly larger aggregate sizes while also experiencing remarkable compactness compared to the alternative additional structure. The reason is that tailings slurry and flocculant obtain high mixing intensity and residence time in the mixing-collision section, causing the effective collision of micro aggregates, further increasing their size and compactness in the settling-growth section. Meanwhile, gradually decaying energy distribution avoids fragmentation of aggregates. This work is expected to provide theoretical and technical support for the design and optimization of feedwells, finally achieving deep dewatering of tailings slurry, and reducing solid waste pollution in mines.

Contributing Dimension Structure of Deep Feature for Coreset Selection

Coreset selection seeks to choose a subset of crucial training samples for efficient learning. It has gained traction in deep learning, particularly with the surge in training dataset sizes. Sample selection hinges on two main aspects: a sample's representation in enhancing performance and the role of sample diversity in averting overfitting. Existing methods typically measure both the representation and diversity of data based on similarity metrics, such as L2-norm. They have capably tackled representation via distribution matching guided by the similarities of features, gradients, or other information between data. However, the results of effectively diverse sample selection are mired in sub-optimality. This is because the similarity metrics usually simply aggregate dimension similarities without acknowledging disparities among the dimensions that significantly contribute to the final similarity. As a result, they fall short of adequately capturing diversity. To address this, we propose a feature-based diversity constraint, compelling the chosen subset to exhibit maximum diversity. Our key lies in the introduction of a novel Contributing Dimension Structure (CDS) metric. Different from similarity metrics that measure the overall similarity of high-dimensional features, our CDS metric considers not only the reduction of redundancy in feature dimensions, but also the difference between dimensions that contribute significantly to the final similarity. We reveal that existing methods tend to favor samples with similar CDS, leading to a reduced variety of CDS types within the coreset and subsequently hindering model performance. In response, we enhance the performance of five classical selection methods by integrating the CDS constraint. Our experiments on three datasets demonstrate the general effectiveness of the proposed method in boosting existing methods.

Morphological and optical properties of carbonaceous aerosol particles from ship emissions and biomass burning during a summer cruise measurement in the South China Sea

Abstract. Carbonaceous aerosols constitute a crucial component of atmospheric marine aerosols among which black carbon (BC) and brown carbon (BrC) are important contributors to light absorption and hence the positive climatic radiative forcing in the marine atmosphere. We conducted month-long (5 May–9 June 2021) onboard sample collections and online measurements of carbonaceous aerosols to characterize their morphological and optical properties during a ship cruise in the South China Sea (SCS), covering a marine region of 11.9–24.5° N, 111.1–118.2° E. Single particles were collected by a single-particle sampler, and offline analyses were performed using a transmission electron microscope (TEM) coupled with energy-dispersive X-ray spectroscopy (EDS). Online measurements of BC in PM2.5 were made by a seven-wavelength aethalometer, and organic carbon (OC) and elemental carbon (EC) mass concentrations were measured by a semi-online OC/EC analyzer. Feret diameters of the single particles during navigation and stop showed size distributions with the lognormal fitting peaks at 307 and 325 nm, respectively. The fresh (without coating) and aged BC particles (after removal of coating by the electron beams in the TEM) showed same median fractal dimensions (1.61), in contrast to their different median lacunarities (0.53 vs. 0.59). The aged BC particles showed narrower Feret diameters (229–2557 nm) during navigation than those (78–2926 nm) of freshly emitted BC from the ship's own exhaust during stop. Moreover, tar balls, as one important component of single particles from ship emissions and as the tracer of biomass burning, were identified with geometrical diameters of 160–420 nm in the TEM images. The EDS analyses showed those tar balls are mainly mixed with sea salt, organics, BC, and sulfate. We also found a significant fraction of aged BC in various mixing states (core–shell, embedded) with other components of the aerosol particles after long-range transport. The campaign was further divided into several periods (the before-monsoon period, BMP; transition monsoon period, TMP; after-monsoon period, AMP; and ship pollution period, SPP) according to the wind direction during monsoon and the ship's own pollution. The median absorption Ångström exponent (AAE) values derived from all wavelengths were 1.14, 1.02, 1.08, and 1.06 for BMP, TMP, AMP, and SPP, respectively. Particularly, a median AAE value of 1.93 was obtained during two significant biomass burning events. These results showed that biomass burning (BB) and fossil fuel (FF) combustion contributed to 18 %–22 % and 78 %–82 % of all the BC light absorption without the two intense biomass burning events, during which BB and FF accounted for 42 % and 58 %, respectively. The two BB events originated from the Philippines and Southeast Asia before and after the summer monsoon. Our results demonstrated that BC can serve as the core of aged particles, but the fractal dimensions of BC aggregates were subject to little variation; moreover, such BC particles become much more aggregated after aging in the marine atmosphere, which further affects the light absorption of the BC particles in the SCS.

AI in medical diagnosis: AI prediction & human judgment

AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, ‘explainability’, and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.

Study on the morphology, nanostructure and fragmentation properties of diesel and diesel-DMM soot particles oxidized in the air/air-NO environment

As an alternative fuel for diesel engines, dimethoxymethane (DMM) has attracted wide attention in recent years, but its impact on the post-treatment process of diesel exhaust particles has been rarely studied. To get a better understanding of the oxidation kinetics of soot in diesel particulate filters (DPFs), the oxidation process of soot emitted from a modern compression ignition (CI) fueled by diesel (D100) and its blend with 11 vol% dimethoxymethane (DMM11) at the same working conditions were investigated by a thermogravimetric analyzer in the air and air-NO atmospheres in this study, respectively. Soot samples at different oxidation degrees (0 %, 20 %, 50 % and 80 % mass loss) were tested by a high-resolution transmission microscopy to characterize the variations of physical properties (morphology, fractal dimension, primary particle diameter and nanostructure) and oxidation-induced fragmentation properties over the oxidation process. The results showed that DMM11 soot had higher oxidation reactivity than D100 soot, and the oxidation reactivity of soot was enhanced by NO involved in the air atmosphere. During the oxidation process, the fractal dimension of D100 and DMM11 soot aggregates gradually increased, and the number and diameter of the primary particles in an aggregate became smaller. This variation trends became more obvious with the presence of NO in the air environment. The nanostructures of D100 and DMM11 soot particles gradually become ordered (characterized by longer fringe length, narrower separation distance and less tortuosity) as the oxidation proceeds. While, when NO added in the air atmosphere the possibility of microcrystals oxidation inside the particles decrease to slow down the getting-ordered rate of soot. Moreover, the fragmentation probability of D100 and DMM11 soot aggregates decreases gradually as oxidation proceeds. During the oxidation process, the probability of primary particle fragmentation for D100 soot increases continuously, while the probability of DMM11 primary soot particles fragmentation showed an increase-then-decrease trend. Compare with D100 soot, DMM11 soot are more likely to occur the fragmentation behavior over the oxidation process. It is worth noting that the addition of NO to the air atmosphere increases the possibility of aggregate fragmentation, while it decreases the possibility of primary particle fragmentation. This work can provide theoretical basis for the development of high efficiency purification technology for diesel exhaust particles.