Sequential Process Research Articles

An Unhelpful Chain: Antecedents and Consequences of COVID-19 News Avoidance in China and Singapore

ABSTRACT This study used large-scale telephone surveys in China and Singapore in 2022 to examine the antecedents and outcomes of news avoidance amid the COVID-19 “infodemic.” The findings suggested a sequential process: Exposure to COVID-19 misinformation was related to information overload and subsequent news avoidance, which in turn was a correlate of skepticism toward vaccines. Moreover, the government’ pandemic-control policies conditioned the process. Under the “living with COVID” policy, Singaporean respondents exhibited greater exposure to misinformation and showed more pronounced vaccine distrust. In contrast, feeling overloaded, Chinese respondents under “zero-COVID” policy were more likely to avoid news about the pandemic.

The posterior intralaminar thalamic nucleus promotes nose-to-nose contacts leading to prosocial reception in the sequence of mouse social interaction.

Efficient social interaction is essential for an adaptive life and consists of sequential processes of multisensory events with social counterparts. Social touch/contact is a unique component that promotes a sequence of social behaviours initiated by detection and approach to assess a social stimulus and subsequent touch/contact interaction to form prosocial relationships. We hypothesized that the thalamic sensory relay circuit from the posterior intralaminar nucleus of the thalamus (pIL) to the paraventricular nucleus of the hypothalamus (PVN) and the medial amygdala (MeA) plays a key role in the social contact-mediated sequence of events. We found that neurons in the pIL along with the PVN and MeA were activated by social encounters and that pIL activity was more abundant in a direct physical encounter, whereas MeA activity was dominant in an indirect through grid encounter. Chemogenetic inhibition of pIL neurons selectively decreased the investigatory approach and sniffing of a same-sex, but not an opposite-sex, stimulus mouse in an indirect encounter situation and decreased the facial/snout contact ratio in a direct encounter setting. Furthermore, chemogenetic pIL inhibition had no impact on anxiety-like behaviours or body coordinative motor behaviours, but it impaired whisker-related and plantar touch tactile sense. We propose that the pIL circuit can relay social tactile sensations and mediate the sequence of nonsexual prosocial interactions through an investigatory approach to tactile contact and thus plays a significant role in establishing prosocial relationships in mouse models.

Electroplating nickel patterns onto polyol-mediated reduced graphene oxide printed textile surfaces

This report explores advancements in multi-step printing/electroplating technique for creating conductive patterns on flexible substrates, which serve as potential structures in wearable electronic devices. The study introduces a novel procedure that employs a sequential process involving polyol screen printing and electroplating, enabling the development of conductive nickel-coated patterns with a conductivity value of about 1123 S/cm on textiles. A significant innovation in this study is the use of a graphene oxide/ethylene glycol (GO/EG) ink, which can be converted into reduced graphene oxide (rGO) through in-situ polyol treatment on the surface of PET fabrics. The effectiveness of this treatment is evidenced by various analytical and microscopic techniques, which confirm the successful printing of conductive rGO patterns onto PET fabric at 220 ˚C. The printed rGO patterns on the fabric surface are then subjected to electroplating as a target substrate and cathode for varying durations, ranging from 5 to 30 minutes. The study thoroughly examines the influence of electroplating time on the microstructures and electrical conductivity of the resulting patterns, with nickel deposition reaching up to 50.8 mg/cm².

Testing the sequence of successional processes in miniature ecosystems.

Hypotheses regarding the underlying processes of ecological successions have primarily emerged from and have been tested in observational studies, lacking substantial support through controlled experiments. The design of such experiments should focus on testing contemporary ecological theories at the intersection of community assembly and successional research. To achieve this, we developed and employed 3D-printed "Ecosystems on a Plate" (EsoaP) within controlled laboratory settings. EsoaPs surmount several limitations of nanoscale instruments that had hindered their application in ecologically meaningful research. By sharing 3D printing designs, experimental protocols, and data openly, we facilitate reproducibility of our experiments by researchers across diverse ecological disciplines. Moreover, our approach facilitates cost-effective replication of experiments, democratizing access to tools for ecological research, and thus holds the potential to serve as a model for future studies and educational purposes.

A universal molecular oxygen-mediated photocatalysis strategy to boost visible-light induced hydrogen evolution through partial water splitting

A universal oxygen-mediated, stepwise strategy is proposed for efficiently inducing visible-light photocatalytic partial water decomposition into hydrogen over various semiconductor photocatalysts with conduction band bottoms below the single-electron oxygen reduction potential. In this scenario, molecular O2 can be transformed into reactive oxygen species, serving as both an oxidant and a homogeneous catalyst for producing hydrogen from alkaline aqueous solution containing various organic substrates. Further enhancement the performance is achieved by doping with phosphorous and oxygen, which constructs a local internal electric field and introduces sulfur vacancies, thereby facilitating the transport of photogenerated charge carriers, particularly on a representative CdS photocatalyst. The optimal hydrogen evolution performance reaches 2321.4 and 8521.4 μmol·gcatatlyst−1·h−1 in methanol and formaldehyde solution systems, respectively, with an apparent quantum efficiency exceeding 59.4 % under 450 nm visible light irradiation. Mechanistic studies demonstrate that the oxygen-mediated, sequential single-electron transfer process can occur with virtually zero activation energy.

Beta Distribution Function for Cooperative Spectrum Sensing against Byzantine Attack in Cognitive Wireless Sensor Networks

In order to explore more spectrum resources to support sensors and their related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PU). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensor paradigms is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attacks from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attacks, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on the sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. The numerical simulation results demonstrate that, compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability and average number of samples under various attack ratios and probabilities.

Identification of candidate exposure biomarkers for acetyl tributyl citrate and acetyl triethyl citrate using suspect screening in human liver microsomes

Acetyl tributyl citrate (ATBC) and acetyl triethyl citrate (ATEC) are increasingly used as alternatives to phthalates in various products, including food packaging, medical devices, and personal care items, raising concerns about their potential health impacts. This study aimed to investigate the in vitro human metabolism of ATBC and ATEC and identify potential exposure biomarkers applicable in human biomonitoring. Pooled human liver microsomes were utilized to conduct in vitro metabolism assays of deuterium labeled ATBC (ATBC-d3) and ATEC, and ultra performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC-qToF/MS) was employed for analysis. Suspect screening workflow and confidence level assignment were applied for metabolite identification. Time-course analysis revealed rapid metabolism of both compounds, with estimated apparent half-lives of approximately 5 min for ATBC-d3 and less than 15 min for ATEC. Eleven metabolites were identified for ATBC-d3 and six for ATEC. The predominant chemical reactions observed were carboxylic ester hydrolysis, deacetylation, and hydroxylation. Based on their abundance and specificity, MB1 (hydroxylated) and MB11 (hydrolyzed and hydroxylated) were proposed as candidate exposure biomarkers for ATBC, and ME1 (hydrolyzed and deacetylated) for ATEC. The identified metabolites and proposed sequences of kinetic process enhance our understanding of the fate of these compounds in the human body, potentially informing future toxicological assessments and guiding the development of more comprehensive human biomonitoring strategies.

Influence of educational composition on firm innovation and performance

ABSTRACT Despite a strong focus on innovation from both politicians and managers, our current understanding of the relationship between managers’ education level and the educational composition of the workforce in terms of innovation and their joint association with performance remains limited. For example, while most studies include workforce quality as a control, and recognize its importance, there is still little knowledge about how it influences the innovation process. The data and estimation framework used in this study enable for a detailed analysis of the importance of both managers’ and workers’ skill intensity and workers’ skill diversity throughout the sequential innovation process that transforms ideas into valuable innovation in three stages. In addition, the availability of panel data allows us to account for various econometric issues, such as sample selection bias, reverse causality, simultaneity, and measurement errors. We conclude that educated managers, together with a balanced workforce across education length and field enhance both innovation and performance. Even more interestingly, we show that the educational level of managers influences the innovation process primarily through the recruitment of more educated workers with diverse educations. And the importance of including the indirect effect of educational composition through R&D decisions on the likelihood of innovation.

Scoring Individual Moral Inclination for the CNI Test

Item response theory (IRT) is a modern psychometric framework for estimating respondents’ latent traits (e.g., ability, attitude, and personality) based on their responses to a set of questions in psychological tests. The current study adopted an item response tree (IRTree) method, which combines the tree model with IRT models for handling the sequential process of responding to a test item, to score individual moral inclination for the CNI test—a broadly adopted model for examining humans’ moral decision-making with three parameters generated: sensitivity to moral norms, sensitivity to consequences, and inaction preference. Compared to previous models for the CNI test, the resulting EIRTree-CNI Model is able to generate individual scores without increasing the number of items (thus, less subject fatigue or compromised response quality) or employing a post hoc approach that is deemed statistically suboptimal. The model fits the data well, and the subsequent test also supported the concurrent validity and the predictive validity of the model. Limitations are discussed further.

TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management

Transit users’ lifecycle behavior pattern transition reflects the continuous and multi-phase changes in how frequently and regularly users utilize public transit over their lifetime. Predicting transit users’ lifecycle behavior pattern transition is vital for enhancing the efficiency and responsiveness of transportation systems. Thus, this study incorporates lifecycle analysis in predicting long-term sequential behavioral pattern transition processes to go beyond just examining user churning at a single point in time. Specifically, this study proposes the TripChain2RecDeepSurv, a novel model that pioneers the individual-level analysis of lifecycle behavior status transitions (LBST) within public transit systems. The TripChain2RecDeepSurv is composed of (1) the TripChain2Vec module for encoding transit users’ trip chains; (2) the self-attention Transformer module for exploring the latent features related to spatiotemporal patterns; (3) the recurrent deep survival analysis module for predicting LBSTs. We demonstrate TripChain2RecDeepSurv’s predictive performance for empirical analysis by employing Shenzhen Bus data. Our model achieves a 74.39% accuracy rate in churn determination and over 80% accuracy in status sequence identification on the churn path. In addition, our findings highlight the segmented nature of Kaplan-Meier curves and identify the optimal intervention time against the user churning process. Meanwhile, the proposed model provides individual-level heterogeneity analysis, which emphasizes the significance of customizing user engagement strategies, advocating for interventions that extend users’ engagement in patterns with high-frequency transit usage to curb the transition to less frequent travel usage.

Ozone/percarbonate-Fe3+ conditioning enhanced pressurized electro-osmotic dewatering (PEOD) of waste activated sludge: Feasibility and mechanisms

Pressurized electro-osmotic dewatering (PEOD) process is an emerging technology for high-efficiency solid-liquid separation of sludge and has garnered extensive interest. This study proposed a novel sequential conditioning process based on ozone (O3)/percarbonate (SPC) treatment followed by Fe3+ coagulation, namely O3/SPC-Fe3+ conditioning, to improve the performance of PEOD. The optimal operating conditions and the related dewatering performance of O3/SPC-Fe3+ conditioning-PEOD process were investigated to verify the feasibility. The mechanisms of the O3/SPC-Fe3+ conditioning on improving PEOD process were also discussed from the enhancement of the water/filtrate flow in pores. The results showed that 53.44 % water content and 0.95 g/g dry solid (DS) of bound water in dewatered sludge cake were attained in the O3/SPC-Fe3+ conditioning-PEOD process with optimal O3 aeration time of 12 min, SPC dosage of 0.0610 g/g DS and Fe3+ dosage of 0.0310 g/g DS for sludge conditioning then 346 kPa mechanical pressure and 40 V voltage for dewatering. During the O3/SPC-Fe3+ conditioning-PEOD process, the extracellular polymeric substance (EPS) matrix was disintegrated, and the inner EPS migrated to the outside along with water. The values of α-helix/(β-sheet + random coil) adjusted by O3/SPC-Fe3+ conditioning process decreased to 7.38 and 5.12 in slime and LB-EPS, respectively, and these changes transformed to expose more hydrophobic sites. After conditioning, the proportion of protein-N decreased drastically from 81.67 % to 62.34 %, while the proportion of inorganic-N increased significantly from 18.33 % to 37.66 %, indicating that partial protein-N in EPS altered to inorganic-N. The enhancement of PEOD by the O3/SPC-Fe3+ process can be attributed to the EPS hydrophobicity transformation and the degradation/mineralization of EPS by oxidation of generated ·OH. The former triggered the reduction of water/filtrate viscosity within the interfacial region of pores. The latter provoked the decrease of bulk viscosity of water/filtrate within the pores. These two effects relieved the viscous resistance to water/filtrate flow within pores and promoted dewatering.

Duality and the well-posedness of a martingale problem

For two Polish state spaces EX and EY, and an operator GX, we obtain existence and uniqueness of a GX-martingale problem provided there is a bounded continuous duality function H on EX×EY together with a dual process Y on EY which is the unique solution of a GY-martingale problem. For the corresponding solutions (Xt)t≥0 and (Yt)t≥0, duality with respect to a function H in its simplest form means that the relation Ex[H(Xt,y)]=Ey[H(x,Yt)] holds for all (x,y)∈EX×EY and t≥0. While duality is well-known to imply uniqueness of the GX-martingale problem, we give here a set of conditions under which duality also implies existence without using approximating sequences of processes of a different kind (e.g. jump processes to approximate diffusions) which is a widespread strategy for proving existence of solutions of martingale problems. Given the process (Yt)t≥0 and a duality function H, to prove existence of (Xt)t≥0 one has to show that the r.h.s. of the duality relation defines for each y a measure on EX, i.e. there are transition kernels (μt)t≥0 from EX to EX such that Ey[H(x,Yt)]=∫μt(x,dx′)H(x′,y) for all (x,y)∈EX×EY and all t≥0.As examples, we treat resampling and branching models, such as the Fleming–Viot measure-valued diffusion and its spatial counterparts (with both, discrete and continuum space), as well as branching systems, such as Feller’s branching diffusion. While our main result as well as all examples come with (locally) compact state spaces, we discuss the strategy to lift our results to genealogy-valued processes or historical processes, leading to non-compact (discrete and continuum) state spaces. Such applications will be tackled in forthcoming work based on the present article.

NAC guides a ribosomal multienzyme complex for nascent protein processing.

Approximately 40% of the mammalian proteome undergoes N-terminal methionine excision and acetylation, mediated sequentially by methionine aminopeptidase (MetAP) and N-acetyltransferase A (NatA), respectively1. Both modifications are strictly cotranslational and essential in higher eukaryotic organisms1. The interaction, activity and regulation of these enzymes on translating ribosomes are poorly understood. Here we perform biochemical, structural and in vivo studies to demonstrate that the nascent polypeptide-associated complex2,3 (NAC) orchestrates the action of these enzymes. NAC assembles a multienzyme complex with MetAP1 and NatA early during translation and pre-positions the active sites of both enzymes for timely sequential processing of the nascent protein. NAC further releases the inhibitory interactions from the NatA regulatory protein huntingtin yeast two-hybrid protein K4,5 (HYPK) to activate NatA on the ribosome, enforcing cotranslational N-terminal acetylation. Our results provide a mechanistic model for the cotranslational processing of proteins in eukaryotic cells.

SNN-BERT: Training-efficient Spiking Neural Networks for energy-efficient BERT

Spiking Neural Networks (SNNs) are naturally suited to process sequence tasks such as NLP with low power, due to its brain-inspired spatio-temporal dynamics and spike-driven nature. Current SNNs employ ”repeat coding” that re-enter all input tokens at each timestep, which fails to fully exploit temporal relationships between the tokens and introduces memory overhead. In this work, we align the number of input tokens with the timestep and refer to this input coding as ”individual coding”. To cope with the increase in training time for individual encoded SNNs due to the dramatic increase in timesteps, we design a Bidirectional Parallel Spiking Neuron (BPSN) with following features: First, BPSN supports spike parallel computing and effectively avoids the issue of uninterrupted firing; Second, BPSN excels in handling adaptive sequence length tasks, which is a capability that existing work does not have; Third, the fusion of bidirectional information enhances the temporal information modeling capabilities of SNNs; To validate the effectiveness of our BPSN, we present the SNN-BERT, a deep direct training SNN architecture based on the BERT model in NLP. Compared to prior repeat 4-timestep coding baseline, our method achieves a 6.46× reduction in energy consumption and a significant 16.1% improvement, raising the performance upper bound of the SNN domain on the GLUE dataset to 74.4%. Additionally, our method achieves 3.5× training acceleration and 3.8× training memory optimization. Compared with artificial neural networks of similar architecture, we obtain comparable performance but up to 22.5× energy efficiency. We would provide the codes.

Sieving the weeds from the grains: an R based package for classifying archaeobotanical samples of cereals and pulses according to crop processing stages

AbstractThe R package CropPro is an open-access resource to classify archaeobotanical samples as products and by-products of different stages of the crop processing sequence for large-seeded cereal and pulse crops in south west Asia, Europe and other Mediterranean regions. It builds on ethnographic research and analysis conducted by Jones (Plants and ancient man: studies in palaeoethnobotany. Balkema, Rotterdam, pp 43–61, 1984), (J Archaeol Sci 14:311–323, 1987), (Circaea 6:91–96, 1990) and a modified method by Charles (Environ Archaeol 1:111–122, 1998). CropPro provides functions, which allow users to construct triplots, to conduct discriminant analysis comparing archaeobotanical samples with ethnographic crop processing stages and to plot the discriminant analysis results. This paper provides two worked examples of the use of CropPro: the early medieval site of Stafford in the UK and the Bronze Age site of Tell Brak in Syria. These examples illustrate the use of the package for identifying crop-processing stages, and for assessing the relevance of taphonomic pathways other than crop processing.

Gravity-assisted gradient size exclusion separation of microparticles by gap-modifiable silicon nanowire arrays

The separation and detection of microparticles within complex samples pose substantial challenges due to the intricate variations in size and concentration. A strategy employing gravity-assisted gradient size exclusion principle based on controllable gap sizes on the surface of silicon nanowire arrays (SiNWAs) has been established to achieve the separation of microparticles with diverse sizes. The formation of gradient gap sizes was accomplished by meticulously investigating the impact of oxidation-reduction reactions through metal-assisted chemical etching. Particles of different sizes were initially aggregated at the accumulation base, followed by a sequential size exclusion process within the finely regulated 0.9–12.5 μm gradient-gap-sized separation region facilitated with gravity-assisted, leading to a comprehensive separation of microparticles based on their respective size differences, progressing from small to large. The effective separation of four model-sized microparticles demonstrated a separation degree of ≥2.7, purity of ≥96.1 %, RSDs of ≤4.6 %, and a separation capacity of up to 107 particles. The separation efficacy of this gradient-sized chip was verified by evaluating the more complex atmospheric particulates with varying sizes, which exhibited separation degree ranging between 2 and 10. This method offers a precise separation range, easily adjustable separation sizes, and simple operation, rendering it a versatile tool for separating complex samples.

Optimization of English Teaching Model Based on Deep Learning Algorithm

With the vigorous development of artificial intelligence technology, especially in the field of deep learning, English education and teaching models are facing unprecedented opportunities and challenges. In order to enable students to master English knowledge more efficiently and align with international standards, it is particularly important to study the optimization of English teaching models. This paper uses specific deep learning algorithms and techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model and optimize English teaching modes, aiming to solve many problems in English teaching and improve the quality of English teaching. Through deep learning algorithms, we can analyze students’ learning behavior, habits and grades, thereby providing them with personalized learning resources and teaching strategies. Deep learning technologies such as CNN and RNN are used to recognize keywords and phrases in text, as well as to process sequence data such as speech and text, helping teachers better understand students’ learning needs and interests, thereby adjusting teaching content and methods. In addition, the adaptive nature of deep learning algorithms allows automatic adjustment of teaching content and difficulty according to the actual situation of students, providing them with learning resources and support that better meet their learning needs. This study not only applies deep learning algorithms to optimize English teaching modes but also delves into how these algorithms affect the learning outcomes of students and the teaching efficiency of teachers. Through empirical research and case analysis, we hope to provide new ideas and methods for the future development of English education.

Effect of crystallographic orientation on the deformation and mechanical behavior of CoCrFeNi in Berkovich nanoindentation

The hardness and plastic deformation behavior of single-phase concentrated solid solution alloy CoCrFeNi were studied for selected crystallographic orientations (⟨001⟩, ⟨011⟩ and ⟨111⟩) using Berkovich nanoindentation. The integration of transmission electron microscopy (TEM) and molecular dynamics (MD) simulations was carried out to elucidate comprehensively the distinction in the deformation mechanisms, the evolution of dislocation structures, and the pile-up of differently oriented CoCrFeNi alloys subjected to indentation by a Berkovich indenter. In MDs, a Berkovich indenter, meticulously crafted to replicate its real geometry, was effectively employed in investigating the nanoindentation. The ⟨001⟩-oriented CoCrFeNi exhibited the highest hardness and the lowest magnitude of pop-in events, followed by the ⟨011⟩ and then the ⟨111⟩ orientations. The pile-up behavior was strongly dependent on the crystallographic orientation and occurred by three sequential processes. The deformation twinning was activated only in the ⟨001⟩ orientation and inclined toward the center direction of the indent due to the force inclining toward the radial direction of the indent. The clear manifestation of the evolution of dislocations in the three orientations during indentation was elucidated. More SFTs or Stair-rods were observed in the ⟨001⟩ orientation, attributable to the intensified reaction of Shockley partials stemming from its higher density of Shockley partials, followed by the ⟨011⟩ and then the ⟨111⟩ orientations. The anisotropic nanoindentation-hardness was correlated with the microstructures. The highest hardness and lowest magnitude of pop-in events of the ⟨001⟩ orientation could be ascribed to its intense dislocation interactions, high-density SFTs and the formation of twins. In the ⟨111⟩ orientation, the three-fold symmetry emission of prismatic dislocation loops along the three slip directions not parallel to the surface leaded to a weak interaction between dislocations, and then resulted in the lowest hardness. This research may be helpful to enhance our comprehension of the intricate mechanical behaviors exhibited by different oriented FCC high entropy alloys under Berkovich indentation.

Improving the Purity of Extracellular Vesicles by Removal of Lipoproteins from Size Exclusion Chromatography- and Ultracentrifugation-Processed Samples Using Glycosaminoglycan-Functionalized Magnetic Beads.

Extracellular vesicles (EVs) are present in blood at much lower concentrations (5-6 orders of magnitude) compared to lipoprotein particles (LP). Because LP and EV overlap in size and density, isolating high-purity EVs is a significant challenge. While the current two-step sequential EV isolation process using size-expression chromatography (SEC) followed by a density gradient (DG) achieves high purity, the time-consuming ultracentrifugation (UC) step in DG hinders workflow efficiency. This paper introduces an optimized magnetic bead reagent, LipoMin, functionalized with glycosaminoglycans (GAGs), as a rapid alternative for LP removal during the second-step process in about 10 minutes. We evaluated LipoMin's efficacy on two sample types: (a) EV fractions isolated by size exclusion chromatography (SEC + LipoMin) and (b) the pellet obtained from ultracentrifugation (UC + LipoMin). The workflow is remarkably simple, involving a 10 min incubation with LipoMin followed by magnetic separation of the LP-depleted EV-containing supernatant. Results from enzyme-linked immunosorbent assay (ELISA) revealed that LipoMin removes 98.2% ApoB from SEC EV fractions, comparable to the LP removal ability of DG in the SEC + DG two-step process. Importantly, the EV yield (CD81 ELISA) remained at 93.0% and Western blot analysis confirmed that key EV markers, flotillin and CD81, were not compromised. Recombinant EV (rEV), an EV reference standard, was spiked into SEC EV fractions and recovered 89% of CD81 protein. For UC + LipoMin, ApoA1 decreased by 76.5% while retaining 90.7% of CD81. Notably, both colorectal cancer (CRC) and Alzheimer's disease (AD) samples processed by SEC + LipoMin and UC + LipoMin displayed clear expression of relevant EV and clinical markers. With a 10 min workflow (resulting in a 96% time saving compared to the traditional method), the LipoMin reagent offers a rapid and efficient alternative to DG for LP depletion, paving the way for a streamlined SEC + LipoMin two-step EV isolation process.

Enhancing mutual understanding of e-scooter user’s perspective in overtaking maneuver through replaying own driving trajectory

The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers’ viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation.