Diverse Behaviors Research Articles

Synchronization of extraction and chromatographic conditions of diverse physicochemical behavior of pseudoephedrine, loratadine and desloratadine for their simultaneous determination in human plasma: Application to pharmacokinetic study

A selective, sensitive, and rapid LC-MS/MS method has been developed, optimized, and validated for the simultaneous determination of pseudoephedrine (PSE), loratadine (LOR), and LOR active metabolite, desloratadine (DES) in K3EDTA human plasma. A simple liquid–liquid extraction method was adopted for extraction of the 3 analytes and their internal standards; chlorpheniramine (CLP), loratadine D4 (LOR-D4), and desloratadine D4 (DES-D4), respectively, giving consistent recovery results (78.38––88.23 %) using diethyl ether: dichloromethane (80:20, v/v) as the extraction solvent. The chromatographic separation was challenging due to the polarity differences between PES, which is more polar, and LOR and DES, which are less polar that affected their physicochemical properties including the retention of the drugs on C18 column. The separation was achieved using of isocratic elution of 20 mM ammonium formate and 2 mM trifluoracetic acid ammonium salt: acetonitrile containing 0.15 % formic acid (12:88, v/v) as a mobile phase pumped onto a Supelco Discovery® C18 (4.6 mm x 100 mm, 5 µm) at 35 °C with a flow rate of 0.8 mL/min. The use of trifluoracetic acid ammonium salt as an ion pair reagent in the selected mobile phase allowed good retention of the polar PSE on the used C18 column with a reasonable retention time. Positive Electrospray ionization was employed followed by detection using a tandem mass spectrometer in multiple reactions monitoring (MRM) mode. The sample analysis of this study was conducted in compliance with ICH guideline M10 on bioanalytical method validation and study sample analysis. The method was linear over the concentration range of (4–1200) ng/mL for PSE, (50–15000) pg/mL for both LOR, and DES. The developed method was accurate (91.14–110.30 %), precise (0.53–7.86 %), and was applied to study the pharmacokinetic profile of the cited drugs in the plasma of healthy human volunteers after single oral administration of the fixed-dose combination Claritin D® tablet, (240/10 mg) of PSE, and LOR, respectively.

The mechanism of selective separation of stibnite and arsenopyrite by Cu2+ coordination assembly KBX collector

The similar physicochemical properties of stibnite and arsenopyrite resulted in the difficult separation by traditional collectors in flotation. Hence, this study utilized Cu2+ and potassium butyl xanthate (KBX) coordinate assembly to form a novel Cu-KBX complex collector, investigating its properties, conformation, and its role in flotation separation of stibnite and arsenopyrite, as well as its diverse adsorption behavior on mineral surfaces. The stable Cu-KBX complex solution was formed when the molar ratio of Cu2+ to KBX was 1:2, exhibiting a network-like structure at that time. The micro-flotation results showed that at pH 5, the grade of Sb in the concentrate was as high as 61.08 %, with As content at only 4.65 %, enabling effective separation of stibnite and arsenopyrite. Furthermore, the Cu-KBX complex exhibited a reticulated structure at the stibnite surface, while it adhered in a granular fashion on the arsenopyrite surface. FTIR analysis confirmed stronger chemisorption of Cu-KBX onto stibnite compared to arsenopyrite. XPS results indicated that Cu-S was the main collecting component. However, a weak Fe(II)-S substance was found on the arsenopyrite interface, likely due to Fe3+ oxidizing in the slurry with the Cu-KBX complex. Therefore, this disruption of the Cu-KBX complex structure by Fe3+ in arsenopyrite sharply reduced its adsorption on arsenopyrite, enhancing the selective separation of stibnite and arsenopyrite.

Tailored graphene nanoparticles for bio-medical application: Preliminary in vitro characterization of the functionality in model cell lines

Thanks to an environmentally friendly physical treatment of high purity graphite, a good control of the structure of graphene nanoparticles (GNPs) has been obtained with the production of stable and reproducible GNPs water dispersions. The preparation protocol entailed ball-milling of synthetic graphite followed by sonication in water and centrifugation/separation procedures. This way, two different GNPs samples with slightly different structural characteristics were harvested: TOP60, showing an average lateral size of the graphene layers < L> = 70 nm and average number of stacked layers < N> = 4, and BOTTOM60, with < L> = 120 nm and < N> = 6. A detailed structural characterization of GNPs was performed as mandatory pre-requisite to build reliable structure/properties correlations, in terms of both bio-medical efficacy and toxicity, aiming at a rationale design of tailored materials for applications in biological environments.To this end, in this study GNPs were thoroughly characterized, focusing on cytotoxicity, cellular up-take, and inflammatory response, by testing their effect in different cell lines. BOTTOM60 GNPs in culture medium and in the presence of cells showed a tendency to form big aggregates, phenomenon that was probably responsible for their cytotoxicity at high concentrations. On the other hand, TOP60 GNPs showed a diverse behavior depending on the cell type under investigation. Indeed, the nanoparticles were internalized by cells specialized in endo/phagocytosis, such as astrocytoma cells, but not by carcinoma cells of epithelial origin. Moreover, TOP60 GNPs caused a reduction of proliferation only at high concentration and did not trigger an inflammatory response in THP-1-derived macrophages.The evidence here collected paves the way for further investigations towards the development of GNPs-based drug delivery systems.

SPathDB: a comprehensive database of spatial pathway activity atlas.

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.

Furniture Design of Smart Art Classroom Based on Interactive Analysis of Intelligent Sensing and ITIAS

This paper explores the design and optimization of smart art classroom furniture, addressing the evolving needs of modern art education environments. With the integration of intelligent technologies and sensor networks, the research aims to enhance the functionality, flexibility, and user experience of classroom furniture. It highlights the limitations of traditional classroom furniture and the need for adaptive, multifunctional designs that accommodate diverse learning behaviors and physical requirements. Through a combination of ITIAS interactive analysis and mathematical modeling, the study proposes a framework for developing furniture that supports privacy protection, dynamic updates, and better engagement in art education. The findings emphasize the importance of incorporating ergonomic, aesthetic, and technological considerations to improve teaching and learning outcomes in art classrooms.

RBIO-04. OUTCOMES OF RADIOSURGERY FOR WHO GRADE 2 MENINGIOMAS: THE ROLE OF KI-67 INDEX IN GUIDING THE TUMOR MARGIN DOSE

Abstract The management of WHO grade 2 meningiomas is complicated by their diverse clinical behaviors. Stereotactic radiosurgery (SRS) can be an effective management option. Literature on SRS dose selection is limited, but suggests a higher dose is better for tumor control. We aimed to characterize the predictors of post-SRS outcomes that can help guide planning and management. We reviewed a cohort of consecutive patients with pathologically-proven WHO grade 2 meningiomas who underwent SRS at a single institution between 2011 and 2023. Ninety-nine patients (median age 62 years) underwent SRS, eleven of whom received hypofractionated SRS in 5 fractions. Twenty-two patients had received prior irradiation. The median follow-up was 49 months. The median overall survival (OS) was 119 months (95%CI 92–NA) with estimated 5- and 10-year survival of 83% and 27% respectively. The median progression-free survival (PFS) was 40 months (95%CI 32–62) with 3- and 5-year rates at 54% and 35% respectively. The median locomarginal PFS was 63 months (95%CI 51.8-NA) with 3- and 5-year rates at 65% and 52%. Nine (9%) patients experienced adverse events, 2 CTCAE grade 3 and 7 grade 2, consisting of worsening neurologic deficit from edema. In the single-session SRS cohort, Ki-67 was a significant predictor of both OS and intracranial PFS. Tumors with Ki-67 &amp;gt;10% had ~10 times risk of locomarginal progression compared to Ki-67 ≤10% (p&amp;lt;0.001). In tumors with Ki-67 &amp;gt;10%, margin dose ≥14 Gy was associated with significantly better tumor control, but not for tumors with Ki-67 ≤ 10%. Other significant predictors of tumor control were prescription dose, tumor volume, and location. The management of WHO grade 2 meningiomas requires a multi-modality approach. This study demonstrated the value of a targeted SRS approach in patients with limited disease, and further establish predictive biomarkers that can guide planning through a personalized approach.

Outcomes of Radiosurgery for WHO Grade 2 Meningiomas: The Role of Ki-67 Index in Guiding the Tumor Margin Dose.

The management of World Health Organization (WHO) grade 2 meningiomas is complicated by their diverse clinical behaviors. Stereotactic radiosurgery (SRS) can be an effective management option. Literature on SRS dose selection is limited but suggests that a higher dose is better for tumor control. We characterize the predictors of post-SRS outcomes that can help guide planning and management. We reviewed a cohort of consecutive patients with pathologically-proven WHO grade 2 meningiomas who underwent SRS at a single institution between 2011 and 2023. Ninety-nine patients (median age 62 years) underwent SRS, 11 of whom received hypofractionated SRS in 5 fractions. Twenty-two patients had received previous irradiation. The median follow-up was 49 months. The median overall survival was 119 months (95% CI 92-NA) with estimated 5- and 10-year survival of 83% and 27%, respectively. The median progression-free survival (PFS) was 40 months (95% CI 32-62), with 3- and 5-year rates at 54% and 35%, respectively. The median locomarginal PFS was 63 months (95% CI 51.8-NA) with 3- and 5-year rates at 65% and 52%. Nine (9%) patients experienced adverse events, 2 Common Terminology Criteria for Adverse Events grade 3 and 7 grade 2, consisting of worsening neurologic deficit from edema. In the single-session cohort, Ki-67 significantly predicted both overall survival and intracranial PFS. Tumors with Ki-67 >10% had 2.17 times the risk of locomarginal progression compared with Ki-67 ≤10% (P = .018) adjusting for covariates. Sex, prescription dose, tumor volume, and location also predicted tumor control. In tumors with Ki-67 >10%, margin dose ≥14 Gy was associated with significantly better tumor control but not for tumors with Ki-67 ≤10%. The management of WHO grade 2 meningiomas requires a multimodality approach. This study demonstrates the value of a targeted SRS approach in patients with limited disease and further establishes predictive biomarkers that can guide planning through a personalized approach.

FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics

Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across various learning settings, little research has been conducted to evaluate these systems in authentic learning contexts, particularly regarding students' perceived fairness, accountability, transparency, and ethics (FATE). Understanding these perceptions is essential to using MMLA effectively without introducing ethical complications or negatively affecting how students learn. This study aimed to address this gap by assessing the FATE of MMLA in an authentic, collaborative learning context. We conducted semi-structured interviews with 14 undergraduate students who used MMLA visualizations for post-activity reflection. The findings highlighted the significance of accurate and comprehensive data representation to ensure visualization fairness, the need for different levels of data access to foster accountability, the imperative of measuring and cultivating transparency with students, and the necessity of transforming informed consent from dichotomous to continuous and measurable scales. While students value the benefits of MMLA, they also emphasize the importance of ethical considerations, highlighting a pressing need for the LA and MMLA communities to investigate and address FATE issues actively.

Behavioral Modeling of Drivers near Speed Control Cameras: A Dual Perspective from Micro and Macro Data

The present study investigates the efficacy of speed cameras in managing vehicle speeds and their influence on various driver demographics within the operational context of the cameras. The research, conducted as a case study in Iran, comprises both macroscopic and detailed analyses, quantifying vehicle speeds at different spatial points relative to the speed camera location—upstream, adjacent to, and downstream of the camera—using a manual laser speedometer. Furthermore, the study examines diverse driver behavior metrics, including vehicle type, speeds at different locations, lane changes, braking actions, and measures such as obscuring license plates. Statistical analyses show that speed cameras effectively reduce speeds across different user groups and lead to increased braking and lane-changing behaviors, particularly among light vehicle categories. Moreover, the study reveals distinct behavioral patterns between indigenous and non-indigenous drivers. In addition, a multi-layer perceptron neural network model successfully approximates user speed selection behaviors within the operational range of speed cameras. Overall, this research provides valuable insights into the effectiveness of speed cameras and their impact on diverse driver demographics, contributing to a deeper understanding of advanced speed control mechanisms.

Adopting Non-invasive Approaches into Precision Colorectal Cancer Screening.

Effective screening is essential to reducing CRC incidence and mortality by detecting the disease at early stages and identifying non-invasive precursors. While colonoscopy remains the most sensitive modality to visualize and remove neoplastic lesions thereby reducing CRC and the related death, its high cost and invasive nature limit its widespread use. The fecal immunochemical test (FIT), which offers a non-invasive alternative with higher public acceptance and comparable cost-effectiveness to colonoscopy, has become the preferred screening method in many regions. Newer non-invasive tests, such as multitarget stool DNA or RNA tests, have shown improved sensitivity for CRC and advanced adenomas, although their high costs and lower specificity present challenges for large-scale implementation. Blood-based circulating cell-free DNA test also offer promise but still require optimization to be cost-effective. The heterogeneity of the screening population further complicates the effectiveness of CRC screening programs. Variations in non-communicable disease risk factors, such as metabolic syndrome, lifestyle habits, and comorbidities, can significantly influence CRC risk and screening outcomes. Moreover, diverse screening behaviors, including inconsistent adherence to recommended screening intervals and the interchangeable use of different screening modalities, add complexity to achieving uniform effectiveness across populations. This variability underscores the need for personalized screening strategies that consider individual risk profiles and screening behaviors, as well as the application of cutting-edge technologies such as big data analytics, artificial intelligence, and digital twin approaches to evaluate its effectiveness. This article reviews the current CRC screening strategies, the advantages of non-invasive methods, and the potential of fecal hemoglobin concentration, to tailor screening intervals and improve risk stratification. It also discusses the emerging role of real-world data and advanced technologies in enhancing CRC screening accuracy and effectiveness, particularly in complex real-world scenarios where traditional methods may fall short. Before novel non-invasive approaches, such as ctDNA tests or polygenic risk scores, are validated and proven cost-effective, exploring the clinical utility of FIT and its quantitative measurement in both screening and surveillance by integrating real-world clinical big data seems a feasible direction for achieving sustained development in population screening.

A Function of Board Diversity? Does Diversity Equip Nonprofit Performance

Nonprofit boards receive plenty of advice about what they “should” do, but many fail to heed the guidance—perhaps due to limited resources, a failure to prioritize, or not realizing the critical linkage between board performance and organizational performance. This research examines diversity in board membership by linking board diversity to organizational performance. We utilize data from a large, representative national survey of nonprofit executives in the United States to examine the diversity of nonprofit boards in terms of visible diversity and inclusive behaviors and practices. Using regression analysis, we find diverse boards have more inclusive governance practices, and board diversity practices are positively related to nonprofit performance. These findings offer fresh evidence about why diversity matters relative to how boards function and their impact on fulfilling their organizational mission. This research contributes to how we think of diversity and inclusion in the nonprofit sector and why it matters beyond representational value.

Confidence interval estimation for reliability of a two parameter Weibull distribution under complete and censored samples

Estimating the reliability function of the two parameter Weibull distribution is a critical task in various fields, providing essential insights into product longevity and optimizing maintenance strategies. Due to its adaptable parameters namely scale parameter α and shape parameter β, the Weibull distribution models diverse failure behaviours, from early-life defects to wear-out periods. By evaluating reliability at key time phases namely early life (t &lt; 0.1α), useful life (0.1α ≤ t ≤ α), and end-of-life (t &gt; α), this estimation process supports quality control, operational forecasting, and end-of-life planning. Such reliability assessments help minimize operational disruptions, improve cost management, and support strategic planning across industries where dependability and lifecycle management are essential, such as in manufacturing, healthcare, and technology sectors. Although it holds practical significance, the estimation of confidence interval (CI) for the reliability function of two parameter Weibull distribution has been relatively underexplored in the literature. This paper presents a new approach for constructing CI for the reliability function of the Weibull distribution using the generalized variable (GV) technique, applicable to both complete samples and type II singly right-censored samples. The empirical evaluation of this method indicates that it provides coverage probabilities that are closely aligned with the nominal level, even when dealing with small uncensored samples (as small as 5) and censored samples where the proportion of censored observations can reach 70%. In comparison, traditional methods for the Weibull distribution tend to yield less reliable or widely varied coverage probabilities for complete samples. The findings are demonstrated through practical examples. Keywords: Reliability, Confidence interval, Generalized Variable approach, Censoring.

Changes in Adolescent Heterosexual Behaviors from the 1980s to the Present in Various Western Countries: A Systematic Review

Adolescence is a stage of significant intrapersonal and interpersonal changes, influenced by cultural and historical shifts. This study aims to analyze the changes in heterosexual behaviors among adolescents in Western countries over the past 50 years approximately. For this purpose, we conducted a systematic review following the PRISMA model, utilizing the online databases EBSCO, WoS, and Scopus, covering the period from 1980 to 2024, across ten European countries. The review focused on studies published in scientific journals with regional or national samples. In addition to the 30 selected studies, three more were included due to their relevance as cited in the selected articles. Despite the considerable methodological heterogeneity, the results showed a relative stabilization of the differences in sexual behaviors of boys and girls throughout time. Those differences were more evident in Southern European countries. Some changes were also noted, such as the delay in the initiation of the first sexual intercourse, a trend observed over the last decade. The findings are discussed in terms of the sexual script theory, highlighting the importance of these mental frameworks in the heteronormative sexual socialization of boys and girls. Future research should emphasize the diversity of heterosexual behaviors, their significance, and the emotional experiences that accompany them.

Cultural and Behavioral Insights into European Social Media Users: Platform Preferences and Personality Types

The study examines European social media personalities based on age, gender, platform preference, and country of origin. The research sample, which consists of respondents from 19 European countries (Albania, Bosnia &amp; Herzegovina, Bulgaria, Czech Republic, Cyprus, France, Georgia, Germany, Greece, Italy, North Macedonia, Poland, Russia, Serbia, Slovenia, Spain, Sweden, Turkey, and the UK) reflects the general population ratio and predominantly includes middle-aged individuals ensuring representative findings. Almost two-thirds of respondents engage with social media daily, highlighting its pervasive role in European life. Analysis of platform preferences reveals that Facebook and Instagram are the most popular, while TikTok and Snapchat are less favored, particularly among older demographics. The study shows that gender differences are significant, with females favoring Facebook, Instagram, TikTok, and Snapchat, while males prefer LinkedIn and Twitter. This delineation underscores the varying social media consumption habits between genders. The study identifies three primary social media personalities: Lurkers, who passively consume content; Ultras, who exhibit obsessive usage; and Deniers, who balance regular use without dependency. Additional personalities include Ranters, Informers, and Peacocks, each representing unique motivations and engagement styles. This diversity in user behaviors highlights the multifaceted nature of social media engagement in Europe. A country-specific analysis reveals cultural and regional differences in social media use, with distinct preferences and behaviors emerging across Europe. For instance, Germans exhibit unique tendencies with a notable presence among Approval Seekers, Changelings, and Ghosts. Such variations underscore the importance of considering cultural contexts in social media strategies. The findings offer valuable insights for stakeholders, including marketers, policymakers, and platform developers. By understanding these diverse usage patterns, more effective campaigns can be designed, privacy and digital literacy issues can be addressed, and user experiences can be enhanced. This study underscores the integral role of social media in European life and provides a foundation for future research and strategy development.

STGlow: A Flow-Based Generative Framework With Dual-Graphormer for Pedestrian Trajectory Prediction.

The pedestrian trajectory prediction task is an essential component of intelligent systems. Its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting their future trajectory is challenging. Existing approaches commonly adopt generative adversarial networks (GANs) or conditional variational autoencoders (CVAEs) to generate diverse trajectories. However, GAN-based methods do not directly model data in a latent space, which may make them fail to have full support over the underlying data distribution. CVAE-based methods optimize a lower bound on the log-likelihood of observations, which may cause the learned distribution to deviate from the underlying distribution. The above limitations make existing approaches often generate highly biased or inaccurate trajectories. In this article, we propose a novel generative flow-based framework with a dual-graphormer for pedestrian trajectory prediction (STGlow). Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Besides, our method has clear physical meanings for simulating the evolution of human motion behaviors. The forward process of the flow gradually degrades complex motion behavior into simple behavior, while its reverse process represents the evolution of simple behavior into complex motion behavior. Furthermore, we introduce a dual-graphormer combined with the graph structure to more adequately model the temporal dependencies and the mutual spatial interactions. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.

The Effect of Digital Marketing-Upgrading and Product Virality on the Sustainability of MSMEs

This study aims to understand the role of consumer behavior in mediating the influence of virality, digital marketing, and product enhancement on the desires of MSMEs in East Java. This study expands our understanding of the impact of digital marketing, product enhancement, and virality on the desires of MSMEs in East Java, focusing on diverse consumer behavior. With a quantitative approach, this study uses a survey method with a questionnaire as the main instrument, involving 160 respondents from various cities in East Java, including Surabaya, Malang, and Mojokerto. The variables explained include digital marketing (X1) consisting of social media indicators, websites, and mobile marketing; product enhancement (X2) such as product quality and innovation; and virality (X3) which includes content creativity and the use of influencers. Consumer behavior (Y) focuses on satisfaction, loyalty, and interest, as well as MSME desires (Z) which are measured through profitability and public acceptance. The results of the analysis show that digital marketing has a positive effect on consumer behavior, which has an impact on increasing customer loyalty and satisfaction. In addition, product enhancement and virality significantly affect the desire of MSMEs, with consumers more interested in modern and popular products.

Approaching Dynamic Behaviors of Life through Systems Chemistry.

The intricate interplay of metabolic reactions and molecular assembly in living systems enables spatiotemporally organization and gives rise to diverse dynamic behaviors that characterize life. Over the last decades, research efforts have increasingly focused on replicating the remarkable properties and characteristics of living systems, driving the rapid growth of systems chemistry. This young discipline which generally studies interacting molecular networks and emergent system-level properties, behaviors, and functions, offers new concepts and tools to tackle the complexity of life. In this review paper, we have explored seminal research and recent advancements in recreating dynamic behaviors of life with systems chemistry. We believe that the recreation of the dynamic behaviors of life through systems chemistry would set the initial steps to obtain synthetic life de novo.

Choreographing multi-degree of freedom behaviors in large-scale crowd simulations

This study introduces a novel framework for choreographing multi-degree of freedom (MDoF) behaviors in large-scale crowd simulations. The framework integrates multi-objective optimization with spatio-temporal ordering to effectively generate and control diverse MDoF crowd behavior states. We propose a set of evaluation criteria for assessing the aesthetic quality of crowd states and employ multi-objective optimization to produce crowd states that meet these criteria. Additionally, we introduce time offset functions and interpolation progress functions to perform complex and diversified behavior state interpolations. Furthermore, we designed a user-centric interaction module that allows for intuitive and flexible adjustments of crowd behavior states through sketching, spline curves, and other interactive means. Qualitative tests and quantitative experiments on the evaluation criteria demonstrate the effectiveness of this method in generating and controlling MDoF behaviors in crowds. Finally, case studies, including real-world applications in the Opening Ceremony of the 2022 Beijing Winter Olympics, validate the practicality and adaptability of this approach.

Photoswitchable AIE‐Active Supramolecular Self‐Assemblies for Biosensing and Bioimaging

AbstractPhotoswitchable fluorescent self‐assemblies exhibiting aggregation‐induced emission (AIE) properties have garnered widespread attention. Due to their great adaptiveness and responsiveness towards different wavelengths required by photoreaction, photo‐isomerization, and photoluminescence excitation, these fluorescent self‐assemblies show diverse emission behavior in a spatial, temporal, and polychromatic manner. Integrating organic photoswitches and AIE fluorophores, these photoluminescent nano‐assemblies have been proven to be an effective approach, allowing the development of sophisticated systems for drug release and cell regulation that can be precisely manipulated through light or other stimuli. This review systematically encapsulates recent advances in photoswitchable fluorescent self‐assemblies under multi‐scenario biological applications. The construction of photoswitchable AIE fluorophores through covalent and non‐covalent interactions and their multimodal regulation are detailed, along with their biological applications in biosensing, bioimaging, and drug therapy.

Measuring Multi-Dimensional Mobile Behavior Effect on Inclusive Finance: Evidence from China

Credit Invisible is one key area that many countries put much effort to solve in decades. According to the 2020 World Bank statistics, for example, there are over 500 million Chinese and 45 million American, classified as credit invisible who don’t have banking and finance history in bank or credit bureau, making them difficult to borrow money from financial institution. Previous studies adopted different non-financial information to evaluate one’s credit worthiness and status to address this issue. However, they provide little information about how real mobile user interactions can be used to solve this issue in inclusive finance. This paper proposes a novel data generative framework to fusion APP data, call detail record data and SMS data with a total of 4,689 attributes derived from a large-scale mobile dataset. We then construct a unique set of mobile behavior-driven credit risk factors based on statistical diversity, intensity, consistency, and regularity of mobile user behavior characterizing user preferences, attitudes, geolocation, and temporal patterns. Empirical analysis demonstrates that the newly discovered mobile behavior factors are useful as new inputs for credit scoring and proves the factors representing new source of positive and negative credit information. Decision tree analysis and Quantile regression are conducted to validate effect of these factors to credit default. It facilitates credit assessment based on non-financial data for the credit invisible people, which promoting inclusive finance to larger community in society. We also analyze implications of mobile user characterization findings in relation to credit default which helps decision makers to optimize credit policy and product design.