Diverse Behaviors Research Articles

Cooperative Advisory Residual Policies for Congestion Mitigation

Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over autonomous vehicle fleets, incur extensive installation costs for a centralized sensor ecosystem, and also fail to account for uncertainty in driver behavior. To this end, we develop a class of learned residual policies that can be used in cooperative advisory systems and only require the use of a single vehicle with a human driver. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors, particularly drivers’ reactions to instructions, to provide an improved user experience. To realize such policies, we introduce an improved reward function that explicitly addresses congestion mitigation and driver attitudes to advice. We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers. Our results show that our approaches successfully mitigate congestion while adapting to different driver behaviors, with up to 20% and 40% improvement as measured by a combination metric of speed and deviations in speed across time over baselines in our simulation tests and user study, respectively. Our user study further shows that our policies are human-compatible and personalize to drivers.

Influence of metal on the far-exploding surface on fragment deformation behavior under contact explosion

Metals exhibit diverse failure behavior under impact loading. In the context of fragment warheads, preformed fragments also undergo fracture and crushing behaviors when subjected to explosive loading, potentially diminishing the terminal effect and damage capability of the warhead. To address this issue, metal disks of varying impedance were applied to the far-exploding surface of the fragments, and their influence on fragment deformation behavior was examined. The experimental results revealed that when metal disks were attached to the far-exploding surface of the fragments, their fracture behavior changed, and the recovered fragments remained intact axially. Additionally, the axial length of the recovered fragments decreased as the impedance of the metal disk on the far-exploding surface increased. To elucidate the underlying mechanism of this experimental phenomenon, the variation in fragment pressure during the propagation process was calculated by employing theories of planar detonation waves and shock wave propagation in the study. The results indicate that when the impedance of the metal disks on the far-exploding surface is higher than that of the fragments, it leads to an increase in internal pressure and the formation of a compression zone within the fragments, thereby preventing fragment fracture. Conversely, lower impedance results in the formation of a tensile effect within the fragments. The theoretical and experimental results were consistent. Finally, based on the dimensional analysis, the dimensionless models were established to predict fragment deformation and internal pressure values influenced by the metal disk on the far-exploding surface.

Bivariate Log-Symmetric Regression Models Applied to Newborn Data

This paper introduces bivariate log-symmetric models for analyzing the relationship between two variables, assuming a family of log-symmetric distributions. These models offer greater flexibility than the bivariate lognormal distribution, allowing for better representation of diverse distribution shapes and behaviors in the data. The log-symmetric distribution family is widely used in various scientific fields and includes distributions such as log-normal, log-Student-t, and log-Laplace, among others, providing several options for modeling different data types. However, there are few approaches to jointly model continuous positive and explanatory variables in regression analysis. Therefore, we propose a class of generalized linear model (GLM) regression models based on bivariate log-symmetric distributions, aiming to fill this gap. Furthermore, in the proposed model, covariates are used to describe its dispersion and correlation parameters. This study uses a dataset of anthropometric measurements of newborns to correlate them with various biological factors, proposing bivariate regression models to account for the relationships observed in the data. Such models are crucial for preventing and controlling public health issues.

Global meta‐analysis reveals the drivers of gut microbiome variation across vertebrates

AbstractShifts in gut microbial diversity and structure are one route by which vertebrate hosts adapt to local environmental conditions. However, recent studies have mostly been limited to a single species, small sample sizes, or restricted geographic ranges. Therefore, drawing a global picture of vertebrate gut microbiome diversity, community structure, and determinants for their adaptive shifts remains to be elucidated. We here collected 6508 samples from 113 vertebrate species covering diverse classes, feeding behaviors, and host habitats based on 16S rRNA gene sequencing. The results showed that host diet pattern had a significant impact on gut microbiome variation, which might drive taxonomic and functional contents of gut microbiome across vertebrates. Of note, the phylum Fusobacteria were enriched in carnivorous vertebrate gut while herbivorous vertebrate gut selectively increased the abundance of Verrucomicrobia. Also, climate factors were strongly associated with gut microbiome variation across vertebrates. Interestingly, we found that the abundance of microbiota belonging to Bacteroidetes increased gradually while the members from Proteobacteria showed a decreasing trend from high‐ to low‐latitude zones, potentially contributing to vertebrate adaptation to local climate condition. Additionally, we comprehensively deciphered the common antibiotic resistomes and their potential mobility between terrestrial vertebrate gut microbiome (n = 487) and their sympatric soil biological environment samples (n = 203) by integrating metagenomic sequencing datasets. Particularly, potential horizontal antibiotic resistance genes (e.g., bacA) transfers were detected between vertebrates gut microbiome and their sympatric soil biological environment. Together, our findings provide new evidence of how external environmental factors affect vertebrate gut microbiome variation.

New AIEgens based on thieno[2,3-d]pyrimidine derivatives: Synthesis, DFT calculations, and molecular docking

In this paper, new thieno[2,3-d]pyrimidine analogs have been synthesized and characterized through different spectroscopic techniques. The photophysical properties of the molecules (7b-7f) and the quantum chemical calculations have been investigated. These molecules exhibited aggregation-induced emission behavior and showed efficient emission in both solid and solution states. A shift toward shorter wavelengths (hypsochromic shift) was observed when transitioning from the solution to the solid state, which could be explained by photoinduced intramolecular charge transfer (ICT) processes. DFT calculations confirmed that the diverse behavior of the thieno[2,3-d]pyrimidine analogs could be attributed to variations in molecular packing and rigidity. This study illustrates how luminophores aggregation can occur without significantly impacting the excited state, emphasizing the unconventional behavior resulting from minor alterations in terminal substituents. These findings hold implications for the advancement of luminescent materials. Additionally, theoretical calculations were conducted to compare the excitations and emissions of the new AIEgens with practical observations. Furthermore, a molecular docking study of the five AIEgens suggested their potential utility as anticancer drugs. Compound 7c exhibited a high affinity for human cyclin-dependent kinase 2 (CDK-2) protein, with a binding energy of 9.6 kcal/mol, indicating its potential as an inhibitor for this specific protein.

A study of the effect of team diversity on employee innovative behavior: The mediating role of psychological capital

In today's extremely competitive market environment, employee innovative behavior determines the innovation ability of the enterprise, and the innovation ability of the enterprise determines whether the enterprise can survive and develop under brutal market competition; thus, enhancing employee innovative behavior becomes critical for the long-term survival and development of the enterprise.This paper delves into the theoretical mechanisms by which team diversity and psychological capital affect employee innovative behavior. It leverages social identity theory and conservation of resources theory to provide a comprehensive understanding of these research. The study suggests that psychological capital acts as a mediator in this relationship, serving as a key factor that connects the elements of team diversity with the outcomes of employee innovative behavior. To empirically validate the proposed research model, which connects team diversity, psychological capital, and employee innovative behavior, the study gathered data from a national survey, resulting in 387 valid responses. The analysis employed PLS-SEM to conduct reliability and validity assessments, as well as tests for direct and mediating effects. The research results show that team information diversity can directly and significantly affect employee innovative behavior, team underlying diversity does not directly influence employee innovative behavior; team informational diversity significantly impacts employees' self-efficacy, optimism, hope, and resilience, team underlying diversity significantly affects self-efficacy, optimism, and resilience, but not hope; Self-efficacy and hope do not influence employee innovative behavior, resilience and optimism significantly influence employee innovative behavior. optimism and resilience play a partially mediating role between team information diversity and employee innovative behavior; and optimism and resilience play a completely mediating role between team underlying diversity and employee innovative behavior.

The effect of experimental warming on reproductive performance and parental care in the burying beetle Nicrophorus nepalensis.

Rising temperatures can adversely affect parental care and reproductive performance across a range of taxa. However, the warming impact is contingent upon understanding how temperature affects the spectrum of parental behaviours and their interplay. Here, we assessed how temperature affects parental care and reproductive success in the burying beetle, Nicrophorus nepalensis, which exhibits complex parental care behaviours. We exposed breeding pairs of N. nepalensis, to three temperature regimes (18°C, 20°C and 22°C) and assessed changes in parental care, and the subsequent development and growth of their offspring. Our findings show that 22°C disrupts carcass nest building by the parents and results in smaller clutches. Moreover, no eggs successfully hatched in the 22°C treatment. A milder increase to 20°C did not affect the hatching rate but resulted in smaller broods and lighter offspring, even when considering brood size, suggesting a change in post-hatching care quality. Our research suggests that warming may weakly affect parental care but has strong detrimental effects on offspring performance. These findings highlight the necessity of investigating the effect of ambient temperature across a diversity of traits and behaviours and across a range of life-history stages to fully assess species vulnerability in the face of future climate change.

Comparative biophysical study of clinical surfactants using constrained drop surfactometry.

Surfactant replacement therapy is crucial in managing neonatal respiratory distress syndrome (RDS). Currently licensed clinical surfactants in the United States and Europe, including Survanta, Infasurf, Curosurf, and Alveofact, are all derived from bovine or porcine sources. We conducted a comprehensive examination of the biophysical properties of these four clinical surfactant preparations under physiologically relevant conditions, using constrained drop surfactometry (CDS). The assessed biophysical properties included the adsorption rate, quasi-static and dynamic surface activity, resistance to surfactant inhibition by meconium, and the morphology of the adsorbed surfactant films. This comparative study unveiled distinct in vitro biophysical properties of these clinical surfactants and revealed correlations between their chemical composition, lateral film structure, and biophysical functionality. Notably, at 1 mg/mL, Survanta exhibited a significantly lower adsorption rate compared with the other preparations at the same surfactant concentration. At 10 mg/mL, Infasurf, Curosurf, and Survanta all demonstrated excellent dynamic surface activity, whereas Alveofact exhibited the poorest quasi-static and dynamic surface activity. The suboptimal surface activity of Alveofact is found to be correlated with its unique monolayer-predominant morphology, in contrast to other surfactants forming multilayers. Curosurf, in particular, showcased superior resistance to biophysical inhibition by meconium compared with other preparations. Understanding the diverse biophysical behaviors of clinical surfactants provides crucial insights for precision and personalized design in treating RDS and other respiratory conditions. The findings from this study contribute valuable perspectives for the development of more efficacious and fully synthetic surfactant preparations.NEW & NOTEWORTHY A thorough investigation into the biophysical properties of four animal-derived clinical surfactant preparations was conducted through constrained drop surfactometry under physiologically relevant conditions. This comparative study unveiled unique in vitro biophysical characteristics among these clinical surfactants, establishing correlations between their chemical composition, lateral film structure, and biophysical functionality. The acquired knowledge offers essential insights for the precise and personalized design of clinical surfactant for the treatment of respiratory distress syndrome and other respiratory conditions.

Variability and stellar pulsation incidence in Am and Fm stars using TESS and Gaia data

Aims. We aim to study chemically peculiar Am and Fm stars, distinguished by their unique abundance patterns, which are crucial for studying mixing processes in intermediate-mass stars. These stars provide a window into the atomic diffusion in their stellar envelopes, the evolution-dependent changes in mixing, and the resulting effects on pulsation mechanisms. Methods. This study examines the pulsation characteristics of the Am and Fm star group. Our analysis encompasses 1276 stars (available as catalogues on GitHub), utilising data from TESS and Gaia and focussing on stars from the Renson catalogue. Results. In our sample, 51% of stars (649) display no variability, and are thus categorised as constant stars. Among those that remain, 25% (318 stars) are pulsating Am, Fm, and ρ Puppis stars, including 20% (261 stars) that are exclusively Am and Fm stars. Additionally, 17% of stars (210) show variability indicative of binarity and/or rotational modulation and 7% (93 stars) are eclipsing binaries. Of the pulsating stars, 10% (32 stars) are γ Doradus type, 54% (172 stars) δ Scuti type, and 36% (114 stars) are hybrids, underlining a diverse pulsational behaviour of Am and Fm stars. Conclusions. Our findings indicate that pulsating stars predominantly occupy positions near the red edge of the classical instability strip, allowing us to ascertain the incidence of pulsations in this stellar population.

Investigation of the Physicochemical Behaviour of Homoeopathic Dilutions of Glycerol (Glycerinum) at the Temperatures (293.15 - 318.15) K: Volumetric, Acoustic and ViscometricStudy

The physicochemical studies of homoeopathic medicines provide better understanding regarding the presence and mechanism of their action in ultra-dilutions. The physicochemical behaviour of glycerol dilutions homoeopathic dilutions has been investigated from the measurements of densities, , ultrasonic speeds, u and viscosities,  of pure ethanol control (91% ethanol in water) and 33 dilutions of glycerol dilutions of potencies ranging from 1C to 200C at ambient temperatures and atmospheric pressure. From the experimental data, a number of physicochemical parameters, viz., the isentropic compressibilities, s , intermolecular free length, Lf , acoustic impedance, Z, relative association, RA, relaxation time, , ultrasonic absorption, /f 2 , pseudoGrüneisen parameter, , deviations in isentropic compressibility, s , deviations in intermolecular free length, Lf , deviations in acoustic impedance, Z, deviations in viscosity,  and deviations in pseudo-Grüneisen parameter, have been evaluated. These parameters showed diverse behaviour at certain potencies of these homoeopathic dilutions. The results have been qualitatively discussed in terms of prevailing interactions of these glycerol dilutions dilutions. The results indicated that that even in extreme dilutions (50C, 110C, 150C and 160C) the molecules of glycerol dilutions may be present in these homoeopathic formulations

Tracking animal movements via collaborative acoustic telemetry networks: Multiscale habitat use, phenology, and management insights.

Estuaries support diverse fish and invertebrate communities, including resident species that rely on estuarine habitats year-round and transient migratory species. The unique movement patterns of these animals connect habitats within and far beyond the estuary and are integrally linked to fisheries management objectives. With a focus on Chesapeake Bay, this study leveraged data from collaborative acoustic telemetry networks in the northwest Atlantic to assess habitat use and phenology of movements for seven species of fish (cownose rays, dusky sharks, smooth dogfish, alewife, striped bass, common carp, and blue catfish) and one invertebrate (horseshoe crabs). A total of 288 acoustically tagged individuals were detected >3.2 million times (6,743 to 2,095,717 detections per species) on receivers across ~20.5 degrees of latitude spanning the North American Atlantic seaboard from Florida, USA, to New Brunswick, Canada. Common metrics of movement and phenology grouped these species as resident (common carp, blue catfish, horseshoe crabs), primarily resident in estuaries (juvenile striped bass), and coastal migrant (cownose rays, dusky sharks, smooth dogfish, alewife); maximum distance traveled varied by three orders of magnitude among these species. Further analysis of phenology for coastal migrants elucidated the timing and duration of these species' use of Chesapeake Bay. Collectively, movements linked habitats within Chesapeake Bay and connected the estuary to coastal ecosystems both to the north (e.g., alewife) and south (e.g., cownose rays), creating networks of fisheries management jurisdictions that varied in complexity and identified opportunities for enhancement to current management or co-management of some species. Our results elucidate the importance of estuaries to species with diverse movement behaviors, identify scales and pathways of habitat connectivity via animal movements, and highlight the utility of collaborative acoustic telemetry networks for quantifying movements relevant to both ecological research and fisheries management.

IoT Sensor Network Electricity Consumption Behaviour Using ClusterAnalysis Algorithm for Network Environment

The Internet of Things (IoT) sensor network plays a crucial role in monitoring electricity consumption behavior by collecting real-time data from various connected devices. Utilizing cluster analysis algorithms, researchers can effectively segment and analyze consumption patterns within a network environment. By grouping similar usage behaviors, these algorithms reveal insights into energy efficiency, peak usage times, and anomalies in consumption. The paper presents a novel methodology for analyzing and classifying users' electricity consumption behavior, utilizing the Clustering Behavior Analysis Weighted Classification (CBAWC) algorithm. By leveraging cluster analysis techniques and weighted classification, the proposed approach allows for the segmentation of consumers into distinct clusters based on their consumption patterns. Through the application of CBAWC, the study provides insights into the diverse behaviors exhibited by consumers, ranging from moderate to high consumption levels, varied peak hours, and peak days. The classification results demonstrate the effectiveness of the algorithm in accurately assigning users to their respective clusters, enabling stakeholders to better understand consumption trends and tailor energy management strategies accordingly. Through the application of CBAWC, consumers are segmented into distinct clusters based on their consumption patterns. For example, clusters exhibit varying average daily consumption levels, with Cluster 1 consuming around 300 kWh, Cluster 2 consuming approximately 450 kWh, and Cluster 3 consuming about 280 kWh on average. Additionally, clusters display different peak hours per day and peak days per month. The classification results demonstrate the algorithm's effectiveness, with high accuracy scores ranging from 0.86 to 1.00 across different user groups.

The effects of earthworm species on organic matter transformations and soil microbial communities are only partially related to their bioturbation activity

Earthworms are pivotal in shaping soil ecological processes through their bioturbation activity and organic matter consumption. Earthworm species are known to have different impacts on soil structure, but only a small number of species have been studied so far, and few studies have examined how earthworms simultaneously affect soil functions. Here, we measured the impact of different earthworm species on soil structure (bioturbation function), carbon (C) and nitrogen (N) dynamics and the microbial community (organic matter transformation function), while exploring the links between these functions, across distinct soil compartments (surface casts, below-ground drilosphere, and bulk soil). Six earthworm species (Lumbricus terrestris, Allolobophora chlorotica, Octolasion cyaneum, Octodrilus complanatus, Aporrectodea caliginosa meridionalis and Microscolex dubius) of different ecological categories and functional groups were incubated in soil cores with soil and alfalfa litter for 6 weeks. Our results on the bioturbation function illustrated a great diversity of behaviors and confirmed the relevance of a functional classification based on bioturbation metrics. The main microbial hotspots were surface casts, whose characteristics allowed to distinguish two groups of species. Octod. complanatus, L. terrestris and M. dubius induced high humidity (respectively, +57, +48, +74%), high C (respectively, 19.9, 24.8, 33.2 g kg⁻1 dry soil) and N (respectively, 2, 2.3, 3.2 g kg⁻1 dry soil) content and microbial community selection, promoting C and N mineralization. The three other species had a lower impact. The below-ground drilosphere only showed specific characteristics in the case of L. terrestris. The effects of the studied species on the organic matter transformation function did not align with their bioturbation activities nor with their ecological category. These findings show that the contribution of earthworms to C and N turnover is only partially dependent on their bioturbation effects and suggest the usefulness of developing distinct functional groups based on the specific soil functions under consideration.

From unknown to familiar: An exploratory longitudinal field study on occupational exoskeletons adoption

Occupational exoskeletons hold promise in preventing musculoskeletal disorders, but their effectiveness relies on their long-term use by workers.This study aims to characterize the adoption process of occupational exoskeletons by analyzing the experiences of 25 operators. Using a mixed-methods approach, both quantitative and qualitative data were collected before and during a four-week familiarization period. We primarily focused on users' expectations, subjective assessments over time, and initial experiences.Findings elucidate shifts in operators' perceptions of the devices over time. Through their narratives, we highlight how exoskeleton use impact operators' movements and the subsequent adaptations. Operators demonstrated diverse exploratory behaviors, indicating their efforts to get to grips with the effects of exoskeletons in their own ways.This study offers insights into the initial stages of occupational exoskeleton adoption, thus enriching our comprehension of rejection patterns and pathways toward their widespread acceptance.

Climate change distress and impairment in Germany.

Climate change has been widely recognized as one of the most challenging problems facing humanity and it imposes serious mental health threats. It is important, however, to differentiate between the affective experience of distress over climate change and the functional impairments associated with climate change. Such a distinction is crucial because not all negative affective states are pathological, and they might even motivate pro-environmental behavior. Functional impairments, like not being able to work or maintaining social relationships, however, might require immediate treatment. This study assesses climate change distress and climate change impairment within the population of Germany using a population-representative sample. The results identify vulnerable subgroups and thereby can help to facilitate the development of target group specific intervention programs. Furthermore, this study explores whether climate change distress and climate change impairment are associated with general health, physical health, mental health, and diverse health behaviors. Study participants were drawn from a panel which is representative of the German-speaking population in Germany with Internet access. Participants answered a series of questionnaires regarding their climate change distress, climate change impairment, general health, physical health, mental health, and diverse health behaviors. To evaluate differences between subgroups, Bayesian independent samples t-tests were calculated. To evaluate associations between constructs, Bayesian correlations were calculated. Especially women, younger people, people from West Germany, and people with a high level of formal education seem to experience higher levels of climate change distress. Regarding climate change impairment, the results suggest that especially women, older people, people from West Germany, people with a low level of formal education, people with a low or middle social status, and people with an inadequate/problematic health literacy seem to experience higher levels of climate change impairment. Furthermore, climate change distress and climate change impairment were weakly and differently associated with general health, physical health, mental health, and diverse health behaviors. Climate change distress and impairment are not evenly distributed within German society. The results of this study provide a starting point for the development of target group specific intervention programs.

CHIMERA_NA: A Customizable Mutagenesis Tool for Structural Manipulations in Nucleic Acids and Their Complexes.

Studying the structure and dynamics of nucleic acids and their complexes is crucial for understanding fundamental biological processes and developing therapeutic interventions. However, the limited availability of experimentally characterized nucleic acid structures poses a challenge for exploring their properties comprehensively. To address this, we developed a customizable mutagenesis tool, CHIMERA_NA, to manipulate nucleic acid structures and their complexes. Utilizing the user-friendly CHIMERA_NA, researchers can perform mutations in nucleic acid structures, enabling the exploration of diverse structural configurations and dynamic behaviors. The tool offers the flexibility to generate all possible combinations of mutations or specific user-defined mutations based on research requirements. CHIMERA_NA leverages the capabilities of UCSF Chimera software, a widely used platform for molecular structure analysis, to facilitate the generation of mutations in nucleic acids. Our tool modifies the reference structure of nucleic acids or their complexes to generate initial coordinates of mutated structures/complexes within seconds for further computational exploration. This capability allows users to extend their investigations beyond structural repositories, enabling the study of DNA/RNA drug recognition, nucleic acid-protein interactions, and the intrinsic structural and dynamic properties of nucleic acids. By providing a user-friendly and customizable approach to nucleic acid mutagenesis, CHIMERA_NA contributes to advancing our understanding of nucleic acid biology and facilitating drug discovery efforts targeting nucleic acid-based mechanisms. CHIMERA_NA is freely available in the Supporting Information of this article.

Research on Employee Green Behavior from the Perspective of Sustainable Human Resource Management: The Role of Diversity, Inclusion, and Well-being

Enterprises are increasingly prioritizing social and ecological issues, cultivating sustainable Human Resource Management (HRM), and focusing on the sustainability of both people and the environment to achieve overall Sustainable Development Goals (SDGs). This study primarily draws on green HRM, selecting four key variables, including organizational diversity and inclusion, employee well-being (EWB), work stress, and employee green behavior (EGB), focusing on Chinese enterprises. This study employs quantitative research and Structural Equation Modelling (SEM) analysis to explore the relationships among these variables, aiming to promote internal green behavior within organizations and achieve SDGs. Data were collected from a randomly selected sample, resulting in a valid sample size of 453 questionnaire responses after data cleaning. The research found that organizational diversity and inclusion have a positive impact on EWB and EGB. There is also a positive correlation between EWB and EGB. However, organizational diversity and inclusion are negatively related to work stress, and work stress is negatively related to EGB. EWB and work stress mediate the relationship between organizational diversity, inclusion, and EGB. This study deepens the understanding of the relationship between diversity, inclusion, well-being, and green behavior, providing new directions for future research, and offering pathways for organizations to improve their environment and EWB to promote social sustainability.

“Impacts of neo-banks on North African migrants’ remittances and financial inclusion”

This study examined remittance trends and financial institution preferences among North African migrants in Europe and assessed neobanks' potential to foster financial inclusion and cost reductions. The main objetive was to explore how neobanks can alleviate economic pressures on North African migrants and help achieve the World Bank Group's goal of reducing remittance costs to 5 % and the UN-SDG target of 3 %. Exploratory in nature, both surveys and interviews were used for data collection. The sample consisted of 64 North African migrants from Morocco, Tunisia, and Algeria (North Africa) residing in France, Spain, and Italy (EU). Key findings revealed that North African migrants had diverse remittance behaviors, with varying frequencies and amounts sent home. Trust in traditional banks remained high due to familiarity, cultural norms, and accessibility issues in rural areas. However, neobanks offered significant cost savings, with potential annual savings ranging from €30 to €134 depending on the monthly remitted amount that ranges between a €100 and a €1000 . Adoption of neobanks was higher among younger, and digitally literate migrants with better access to technology. Barriers to wider adoption of neobanks included limited digital and financial literacy, lack of necessary technology in home countries, and cultural resistance to change. High regulatory and compliance costs for traditional banks also contributed to inflated remittance fees. To address these issues, the paper recommends enhancing market competition, improving access to new payment technologies, encouraging larger remittances through incentives, learning from successful public bank models like France's La Banque Postale, and fostering financial literacy and digital inclusion. Additionally, it suggests building trust and cultural sensitivity, investing in robust cybersecurity measures, and developing offline transaction capabilities to increase accessibility for migrants in remote areas.

SBD-Net: Incorporating Multi-Level Features for an Efficient Detection Network of Student Behavior in Smart Classrooms

Detecting student behavior in smart classrooms is a critical area of research in educational technology that significantly enhances teaching quality and student engagement. This paper introduces an innovative approach using advanced computer vision and artificial intelligence technologies to monitor and analyze student behavior in real time. Such monitoring assists educators in adjusting their teaching strategies effectively, thereby optimizing classroom instruction. However, the application of this technology faces substantial challenges, including the variability in student sizes, the diversity of behaviors, and occlusions among students in complex classroom settings. Additionally, the uneven distribution of student behaviors presents a significant hurdle. To overcome these challenges, we propose Student Behavior Detection Network (SBD-Net), a lightweight target detection model enhanced by the Focal Modulation module for robust multi-level feature fusion, which augments feature extraction capabilities. Furthermore, the model incorporates the ESLoss function to address the imbalance in behavior sample detection effectively. The innovation continues with the Dyhead detection head, which integrates three-dimensional attention mechanisms, enhancing behavioral representation without escalating computational demands. This balance achieves both a high detection accuracy and manageable computational complexity. Empirical results from our bespoke student behavior dataset, Student Classroom Behavior (SCBehavior), demonstrate that SBD-Net achieves a mean Average Precision (mAP) of 0.824 with a low computational complexity of just 9.8 G. These figures represent a 4.3% improvement in accuracy and a 3.8% increase in recall compared to the baseline model. These advancements underscore the capability of SBD-Net to handle the skewed distribution of student behaviors and to perform high-precision detection in dynamically challenging classroom environments.

Towards solving the origin of circular polarisation in FRB 20180301A

Abstract Fast Radio Bursts (FRBs) are short-timescale transients of extragalactic origin. The number of detected FRBs has grown dramatically since their serendipitous discovery from archival data. Some FRBs have also been seen to repeat. The polarimetric properties of repeating FRBs show diverse behaviour and, at times, extreme polarimetric morphology, suggesting a complex magneto-ionic circumburst environment for this class of FRB. The polarimetric properties such as circular polarisation behaviour of FRBs are crucial for understanding their surrounding magnetic-ionic environment. The circular polarisation previously observed in some of the repeating FRB sources has been attributed to propagation effects such as generalised Faraday rotation (GFR), where conversion from linear to circular polarisation occurs due to the non-circular modes of transmission in relativistic plasma. The discovery burst from the repeating FRB 20180301A showed significant frequency-dependent circular polarisation behaviour, which was initially speculated to be instrumental due to a sidelobe detection. Here we revisit the properties given the subsequent interferometric localisation of the burst, which indicates that the burst was detected in the primary beam of the Parkes/Murriyang 20-cm multibeam receiver. We develop a Bayesian Stokes-Q, U, and V fit method to model the GFR effect, which is independent of the total polarised flux parameter. Using the GFR model we show that the rotation measure (RM) estimated is two orders of magnitude smaller and opposite sign (∼28 rad m−2) than the previously reported value. We interpret the implication of the circular polarisation on its local magnetic environment and reinterpret its long-term temporal evolution in RM.