Information Exchange Research Articles

Speech Acts and Hidden Meaning: A Journey into Pragmatics

This article examines the core principles of pragmatics, focusing on how speech acts and hidden meanings guide communication across diverse contexts. It explores John Austin and John Searle’s foundational work on speech acts—locutionary, illocutionary, and perlocutionary—demonstrating how utterances perform actions beyond mere information exchange. The discussion then turns to implicature, introduced by H. P. Grice, highlighting the ways in which inferred meanings depend on shared knowledge and context. By integrating cultural considerations, the article underscores how pragmatic competence shapes both cross-cultural understanding and effective discourse. Practical applications include challenges in translation, advances in artificial intelligence, and strategies in media and political communication. Ultimately, the study affirms that pragmatics offers crucial insights into the power of language to form relationships, shape societies, and influence global interaction.

"He was not listening to hear me": Parent experiences with communication, inclusion, and marginalization in the pediatric emergency department.

Inequities in pediatric emergency department (ED) care may be influenced by disparities in clinician communication. We sought to examine, from the perspective of parents from marginalized racial and ethnic backgrounds, how clinician-parent communication is characterized during pediatric ED visits. We conducted and analyzed in-depth semistructured individual interviews with parents of pediatric ED patients from marginalized racial and ethnic backgrounds. We applied a constant comparative method approach to conduct a thematic analysis informed by grounded theory. To ensure rigor, we collected and analyzed data concurrently. We used the patient-centered communication (PCC) framework and the 10 principles of Public Health Critical Race Praxis as sensitizing constructs during analysis. Two coders followed several analytical steps: (1) open coding for concept discovery, (2) grouping concepts into themes, (3) axial coding to identify thematic properties, and (4) identification of exemplar excerpts for rich description. Thematic saturation was based on repetition, recurrence, and forcefulness. Nineteen parents participated. Parents described three clinician communicative behaviors that facilitated their sense of inclusion or marginalization: information exchange, empathic communication, and partnership-building. Parents also stressed the importance of their own proactive communication in facilitating their inclusion. Few participants described experiencing racism during their child's ED visit, yet many did during prior health care encounters, which they connected to their current experience. In particular, parents described how their use of proactive communication was motivated by their past experiences of racism in medical encounters. These narratives demonstrate ways in which experienced racism, both past and present, may inform how parents receive and respond to gaps in PCC. Communication focused interventions that adapt a race-conscious perspective may have a role in promoting health equity.

Transformer-based fusion model for mild depression recognition with EEG and pupil area signals.

Early detection and treatment are crucial for the prevention and treatment of depression; compared with major depression, current researches pay less attention to mild depression. Meanwhile, analysis of multimodal biosignals such as EEG, eye movement data, and magnetic resonance imaging provides reliable technical means for the quantitative analysis of depression. However, how to effectively capture relevant and complementary information between multimodal data so as to achieve efficient and accurate depression recognition remains a challenge. This paper proposes a novel Transformer-based fusion model using EEG and pupil area signals for mild depression recognition. We first introduce CSP into the Transformer to construct single-modal models of EEG and pupil data and then utilize attention bottleneck to construct a mid-fusion model to facilitate information exchange between the two modalities; this strategy enables the model to learn the most relevant and complementary information for each modality and only share the necessary information, which improves the model accuracy while reducing the computational cost. Experimental results show that the accuracy of the EEG and pupil area signals of single-modal models we constructed is 89.75% and 84.17%, the precision is 92.04% and 95.21%, the recall is 89.5% and 71%, the specificity is 90% and 97.33%, the F1 score is 89.41% and 78.44%, respectively, and the accuracy of mid-fusion model can reach 93.25%. Our study demonstrates that the Transformer model can learn the long-term time-dependent relationship between EEG and pupil area signals, providing an idea for designing a reliable multimodal fusion model for mild depression recognition based on EEG and pupil area signals.

More than an extra set of ears: A video-based analysis exploring the role of companions in routine oncology visits.

Patients with advanced cancer commonly bring a companion to medical appointments, often a family member. Despite increasing focus on family involvement, few studies are based on observations of real-life consultations. This study aimed to explore how companions contribute to the information exchange process during advanced cancer outpatient visits. Data were ten videos of routine oncology visits. We identified when and how companions participated using microanalysis of clinical interaction. Companion participation varied greatly. They tended to speak up spontaneously, contributing medical, personal, and contextual information that might otherwise have remained unsaid. They both brought information into the discussion and facilitated information exchanged between the physician and patient. Companions displayed few of their own concerns. The findings suggest that companions may be an important resource during oncology visits, not only for the patient, but also for healthcare providers aiming for person-centered care to the individual patient.

How Come They Know? The Effects of Social Comparison on Value Consciousness and Price Mavenism

ABSTRACTSocial interactions and the exchange of interpersonal information are important to consumers' shopping experiences. Social interactions can trigger social comparisons that can influence consumer's value and price perceptions. This research investigates the effects of social comparison orientation (SCO) on consumers' value consciousness (VC) and price mavenism (PM). It also examines the moderating role of general self‐efficacy (GSE) and identification. We employed a mixed‐method approach and investigated the conceptual framework using a survey (N = 229) and an experimental (N = 516) method. The findings revealed the positive effects of SCO on VC and PM. GSE negatively moderates the impact of SCO on VC. Further, when identification with the social referent is high (vs. low) and assimilation (vs. contrast) effect is observed then upward (vs. downward) comparison leads to greater levels of VC and PM. The paper concludes with implications for marketing value‐focused brands and retailers.

Magnon-magnon coupling in noncollinear synthetic antiferromagnets

Abstract We report a theoretical analysis of magnon-magnon coupling in a noncollinear magnetic sandwiched structure with interlayer exchange interaction, which consists of two ferromagnetic layers with perpendicular and in-plane magnetic anisotropy, respectively. Based on the Landau-Lifshitz equation, the spin wave dispersion is derived, and then the frequency gap is observed due to the magnon-magnon coupling effect induced by symmetry-breaking. The influence of saturation magnetization, exchange coupling interaction, perpendicular magnetic anisotropy, and wave vector on the coupling strength is studied in detail. We find that the coupling strength is strongly dependent on the saturation magnetization and a small saturation magnetization can lead to strong coupling strength. By selecting the appropriate magnetic materials, ultra-strong coupling regime can be achieved. The precession information in time-domain is solved and the alternate change of the precession components in two ferromagnetic layers implies the exchange of energy and information.

Enhancing cognitive metrics in supply chain management through information and knowledge exchange

PurposeThis research primarily aims to investigate the impact of organizational implants on knowledge transmission, process innovation and security integration in intricate supply chains.Design/methodology/approachThe research utilizes a mixed-method approach, employing a stratified sampling strategy to get a representative sample of 1,284 enterprises from various sectors within the logistics industry within the European Union. Data were gathered by computer-assisted web interviewing (CAWI) and analysed utilizing structural equation modelling (SEM) to evaluate hypotheses concerning cognitive congruence, process diffusion and security integration.FindingsThe results indicate that while task interdependence clearly improves face-to-face communication, excessive cognitive congruence can hinder process innovation, resulting in what the article terms “cognitive rigidity.” The study suggests that achieving a balance between cognitive congruence and cognitive flexibility is crucial to improving the safety diffusion and integration process.Originality/valueThis study presents an innovative conceptual framework that synthesizes cognitive congruence, cognitive flexibility and cognitive rigidity to examine their combined influence on knowledge transfer and process dissemination throughout supply chains. It presents cognitive stiffness as a boundary condition, contesting the conventional belief that more cognitive congruence is invariably advantageous.

Efficient feature fusion network for small objects detection of traffic signs based on cross-dimensional and dual-domain information

Abstract The objectives in traffic sign detection and recognition(TSDR) scenario are predominantly small, which frequently result in missed and erroneous detection due to their limited information content and complex environment. To address these problems, this paper proposes a new network architecture CDFF-YOLO(Cross-dimensional and Dual-domain Feature Fusion-You Only Look Once) which is integrated of various modules. For the purpose of overcoming the difficulty in extracting information from small objects, the MSF(Multi-dimension Spatial information Fusion) module in the network are used to extract feature sequences at different dimensions by superposition. So as to address the issue of the loss of detail information of small objects, embed the MPA(Multi-branch Perceptual Attention) module into the C2f module to capture feature information and enhance the global-local feature information exchange. In order to solve the issue of uneven illumination and occlusion in the detection scene. The DFF(Dual-domain Feature Fusion) module is employed to transform and fuse the extracted small object feature information from the SPDConv(Space-to-depth convolution) at the frequency and spatial domains, thereby enhancing the network's capability to reconstruct and fuse feature information in dual-domains. The experimental data on the TT100K dataset demonstrate that the enhanced algorithm exhibits an increase of 3.7\% in mAP@50, 4.8\% in mAP@50:95, and a 4.5\% and 3.7\% rise in the AP(average precision) and AR(average recall) for small objects, respectively. Additionally, the FPS(Frames Per Second) remains 157. The improved algorithm also performs well on the CCTSDB dataset. It is evident that the CDFF-YOLO algorithm has the capacity to markedly enhance the detection efficacy of traffic signs, while maintaining optimal detection speed.

Power beacon-assisted energy harvesting symbiotic radio networks: Outage performance.

The evolution of next-generation Internet-of-Things (IoT) in recent years exhibits a unique segment that wireless communication paradigms are oriented towards not only improved spectral efficiency transmission but also energy efficiency. This paper addresses these critical issues by proposing a novel communication model, namely power beacon-assisted energy-harvesting symbiotic radio. In particular, the limited energy primary IoT source communicates with its destination by first harvesting energy from a dedicated power beacon and then performing information exchange, while the backscatter device communicates by exploiting the available radio frequency emitted by the primary IoT source. The destination uses successive interference cancellation mechanisms to decode both its received signals. To assess the performance quality of the proposed communication model, we theoretically derive the coexistence outage probability (COP) in terms of highly accurate expressions and upper-bound and lower-bound approximations. Subsequently, we carry out a series of numerical results to verify the developed theory frameworks on the one hand, and on the other hand, analyze the COP performance against the variations of system key parameters (transmit signal-to-noise ratio, the time-splitting coefficient, the energy conversion efficiency factor, the reflection coefficient, and the coexistent decoding threshold). Our numerical results demonstrate that the proposed communication model can potentially work well in practices with reliable communication over 90% (COP is less than 0.1). Additionally, it also demonstrates that optimizing the reflection coefficient at the backscatter device can facilitate achieving minimal COP performance.

Role of primary and secondary care data in atrial fibrillation ascertainment: impact on risk factor associations, patient management, and mortality in UK Biobank.

Electronic healthcare records (EHR) are at the forefront of advances in epidemiological research emerging from large-scale population biobanks and clinical studies. Hospital admissions, diagnoses, and procedures (HADP) data are often used to identify disease cases. However, this may result in incomplete ascertainment of chronic conditions such as atrial fibrillation (AF), which are principally managed in primary care (PC). We examined the relevance of EHR sources for AF ascertainment, and the implications for risk factor associations, patient management, and outcomes in UK Biobank. UK Biobank is a prospective study, with HADP and PC records available for 230 000 participants (to 2016). AF cases were ascertained in three groups: from PC records only (PC-only), HADP only (HADP-only), or both (PC + HADP). Conventional statistical methods were used to describe differences between groups in terms of characteristics, risk factor associations, ascertainment timing, rates of anticoagulation, and post-AF stroke and death. A total of 7136 incident AF cases were identified during 7 years median follow-up (PC-only: 22%, PC + HADP: 49%, HADP-only: 29%). There was a median lag of 1.3 years between cases ascertained in PC and subsequently in HADP. AF cases in each of the ascertainment groups had comparable baseline demographic characteristics. However, AF cases identified in hospital data alone had a higher prevalence of cardiometabolic comorbidities and lower rates of subsequent anticoagulation (PC-only: 44%, PC + HADP: 48%, HADP-only: 10%, P < 0.0001) than other groups. HADP-only cases also had higher rates of death [PC-only: 9.3 (6.8, 12.7), PC + HADP: 23.4 (20.5, 26.6), HADP-only: 81.2 (73.8, 89.2) events per 1000 person-years, P < 0.0001] compared to other groups. Integration of data from primary care with that from hospital records has a substantial impact on AF ascertainment, identifying a third more cases than hospital records alone. However, about a third of AF cases recorded in hospital were not present in the primary care records, and these cases had lower rates of anticoagulation, as well as higher mortality from both cardiovascular and non-cardiovascular causes. Initiatives aimed at enhancing information exchange of clinically confirmed AF between healthcare settings have the potential to benefit patient management and AF-related outcomes at an individual and population level. This research underscores the importance of access and integration of de-identified comprehensive EHR data for a definitive understanding of patient trajectories, and for robust epidemiological and translational research into AF.

Research on Cross-Layer Design in MANETs (Mobile Ad-hoc Networks)

Cross-layer design in Mobile Ad-hoc Networks (MANETs) refers to the intercommunication and exchange of information across different protocol layers to improve overall network performance. In a typical networking protocol stack, each layer operates independently, but in MANETs, the traditional layer-by-layer communication model is often not enough due to the dynamic nature of wireless communication, limited resources, and fluctuating network conditions. Therefore, cross-layer design has become a vital research area to optimize the efficiency and performance of MANETs

Strengthening women’s economic empowerment: The post-COVID re-opening of the weekly market through participatory action research and deliberative forums in western Nepal

The haat, an open-air traditional marketplace, provides small farmers with access to markets where they can sell their products. These economic spaces are particularly important for women farmers, whose access to distant markets is limited amidst persisting gender-based constraints including mobility restrictions and security concerns. Beyond direct economic benefits, haats also serve as social spaces for women to connect with other farmers and exchange information. However, one such market (haat), operating twice a week in Sandhikharka, Arghakhanchi district for the past four decades, was closed during the COVID-19 pandemic and remained closed even after other restrictions were lifted. This prompted our participatory action research team to investigate the causes and consequences of its prolonged closure and to facilitate efforts to reopen it. In collaboration with the local government, the research institution organised deliberative forums where relevant stakeholders collectively identified the key constraints to reopening the haat and developed strategies for its revival. Ongoing efforts are focused on institutionalising the haat and ensuring its sustainable operation. In this paper, we outline the process followed and share insights generated from the research and engagement conducted during the reopening effort. Women smallholder farmers have expressed their appreciation for the reopening, noting that the haat is crucial not only for securing better prices for their produce but also for significantly reducing the challenges of selling their products elsewhere, while encouraging further economic activities and contributing to their economic empowerment.

Perioperative Anticholinergic Medication Use and Incident Dementia among Older Surgical Patients: a Retrospective Cohort Study using Real-World Data.

Inpatient anticholinergic medications have been associated with a higher likelihood of postoperative delirium in older adults. However, it remains unclear whether administering anticholinergic medications after surgery adversely affects long-term cognitive function. We aimed to evaluate the relationship between in-hospital anticholinergic medications and time to incident dementia in a cohort of older surgical patients. We also sought to determine whether the association between in-hospital anticholinergic drugs and dementia differed by sex and prehospital anticholinergic exposure. This was a retrospective analysis of electronic health record data from a regional health information exchange. The study population included patients aged 50 years and older who underwent major surgery requiring an inpatient stay between 2014 and 2021. Orders for anticholinergic medications were identified using the anticholinergic cognitive burden (ACB) scale. A Cox proportional hazards model was used to estimate the association between inpatient orders for strong anticholinergics and incident dementia after hospital discharge. Cause-specific hazards were modeled. Stratification and relative excess risk due to interaction (RERI) were used to investigate multiplicative and additive interaction, respectively. In total, 66,420 surgical encounters were analyzed. Approximately 90% of patients received one or more strong anticholinergics during hospitalization, and 3806 patients developed dementia during a median follow-up of 3.4years. The median time to dementia was 2.2years. Each one-order increase in inpatient anticholinergic medications was associated with a 0.60% increase in dementia risk (HR 1.006; 95% CI 1.003-1.008). This association was stronger among patients who were prescribed anticholinergics before hospitalization (RERI 0.10; 95% CI 0.08-1.12; p=0.0122). Perioperative anticholinergics may increase the risk of dementia after major surgery. Avoiding these medications in hospitalized older adults may improve long-term cognitive outcomes.

Challenges and Opportunities for Data Sharing Related to Artificial Intelligence Tools in Health Care in Low- and Middle-Income Countries: Systematic Review and Case Study From Thailand.

Health care systems in low- and middle-income countries (LMICs) can greatly benefit from artificial intelligence (AI) interventions in various use cases such as diagnostics, treatment, and public health monitoring but face significant challenges in sharing data for developing and deploying AI in health care. This study aimed to identify barriers and enablers to data sharing for AI in health care in LMICs and to test the relevance of these in a local context. First, we conducted a systematic literature search using PubMed, SCOPUS, Embase, Web of Science, and ACM using controlled vocabulary. Primary research studies, perspectives, policy landscape analyses, and commentaries performed in or involving an LMIC context were included. Studies that lacked a clear connection to health information exchange systems or were not reported in English were excluded from the review. Two reviewers independently screened titles and abstracts of the included articles and critically appraised each study. All identified barriers and enablers were classified according to 7 categories as per the predefined framework-technical, motivational, economic, political, legal and policy, ethical, social, organisational, and managerial. Second, we tested the local relevance of barriers and enablers in Thailand through stakeholder interviews with 15 academic experts, technology developers, regulators, policy makers, and health care providers. The interviewers took notes and analyzed data using framework analysis. Coding procedures were standardized to enhance the reliability of our approach. Coded data were reverified and themes were readjusted where necessary to avoid researcher bias. We identified 22 studies, the majority of which were conducted across Africa (n=12, 55%) and Asia (n=6, 27%). The most important data-sharing challenges were unreliable internet connectivity, lack of equipment, poor staff and management motivation, uneven resource distribution, and ethical concerns. Possible solutions included improving IT infrastructure, enhancing funding, introducing user-friendly software, and incentivizing health care organizations and personnel to share data for AI-related tools. In Thailand, inconsistent data systems, limited staff time, low health data literacy, complex and unclear policies, and cybersecurity issues were important data-sharing challenges. Key solutions included building a conducive digital ecosystem-having shared data input platforms for health facilities to ensure data uniformity and to develop easy-to-understand consent forms, having standardized guidelines for data sharing, and having compensation policies for data breach victims. Although AI in LMICs has the potential to overcome health inequalities, these countries face technical, political, legal, policy, and organizational barriers to sharing data, which impede effective AI development and deployment. When tested in a local context, most of these barriers were relevant. Although our findings might not be generalizable to other contexts, this study can be used by LMICs as a framework to identify barriers and strengths within their health care systems and devise localized solutions for enhanced data sharing. PROSPERO CRD42022360644; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=360644.

A genetically confirmed hybrid between Natrix astreptophora and N. maura, two deeply divergent and ancient snake species

Using two mitochondrial DNA fragments and six nuclear genes, we confirmed that a morphologically intermediate snake, captured in Andalusia, southwestern Spain (municipality of Huelva), was an F1 hybrid between a female Natrix astreptophora and a male N. maura. The two parental species diverged 21.5 million years ago, which is why their capability to hybridize is remarkable. The interspecific mating could have been facilitated by the rarity of N. astreptophora and the abundance of N. maura in southwestern Spain, making it difficult for N. astreptophora females to find conspecific males. In the face of previously published genomic signatures for ancient gene flow between N. astreptophora and N. maura, our finding raises the question of whether occasional hybridization still contributes to a continued exchange of genetic information between these two deeply divergent and old taxa.

Non-academic writing on popular music in the Czech lands after 1945

Abstract This article examines writing about popular music which took place outside the academic sphere in the Czech nation after 1945. This is not just the usual magazine and book journalism. Owing to the censorship of all publishing entities by the state and the Communist Party of Czechoslovakia 1948–1989, alternative publishing platforms played a very important role in mediating the professional exchange of opinions and information on topics that were not preferred or even forbidden elsewhere. These included periodicals of various social organisations and interest institutions as well as privately produced and distributed printed matter and manuscripts. The article compares how these activities have evolved over time in the context of political change and reflects on the functions they have fulfilled in society.

Towards the interoperability of scholarly repository registries

The enactment of Open Science relies on scholarly repositories that make research products findable and accessible, while scholarly repository registries maintain authoritative metadata and persistent identifiers (PIDs) to help researchers and infrastructure providers discover and access needed repositories. However, the proliferation of repositories targeting different research products (e.g., publications, data, and software) or serving specific disciplines has led to the creation of multiple registries whose scope is not mutually exclusive. Such a fragmented landscape poses significant concerns regarding authoritativeness, disambiguation, and coverage for scholarly communication service and infrastructure providers who consume content from these registries. These providers must either limit their focus to a single registry or manage complex data fusion strategies to integrate diverse repository profiles from various sources. While favouring the existence of a plurality of registries, this paper advocates for their interoperability, which is essential to eliminate the aforementioned barriers and enable their full, unambiguous utilisation. We analyse the data models of four prominent registries—FAIRsharing, re3data, OpenDOAR, and ROAR—and classify their properties and overlap. We provide a crosswalk between their data models and suggest a common data model shared across the examined registries to pave the way toward interoperability. As a means of validation, we include a coverage evaluation of the proposed data model.The paper adopts a pragmatic approach towards scholarly registry interoperability and suggests a common metadata model to foster the exchange of information across these platforms. The purpose of the paper is to serve as a cornerstone, initiating and engaging the community in discussions surrounding the interoperability of scholarly repository registries.

Crochet increases attention through a requiring motor skill learning

In this study, we compared the effects promoted by a brief single session of crochet in a group of skilled knitters (CRO) and a control group (CRT) on the Attentional Network Test (ANT) and the whole brain Functional Connectivity (FC) revealed by Magnetoencephalography (MEG). Data revealed that crochet determined a significant effect (before, T0, vs after, T1, the crochet session) on reaction times (for all cue and stimulus types), improving alertness and orienting networks (but not executive control) only in the CRO group. Data of FC are coherent with the behavioural ones. We observed that the Betweenness Centrality maximum (BCmax) index in the beta band significantly increased, and global FC in the alpha band significantly increased at T1 for the CRO group but not for the CTR group. Increased global BCmax in the beta band after the crochet activity correlated with better performance (reduced reaction times), suggesting that the brain has become more efficiently integrated, thus increasing the information exchange between different brain areas. Decreased global FC in the alpha band may reflect a transition from a quiet, global rest to a condition of increased alertness and readiness to stimuli. Finally, we discuss the hypothesis that these results could be the reinforcement of connections between motor and attentional networks promoted by learning the complex motor skills of crochet.

A review of accident models and incident analysis techniques.

This review article aims to provide an overview of accident models and incident analysis techniques in the context of radiation oncology. Accident models conceptualize the mechanisms through which accidents occur. Chain-of-event models and systemic models are two main categories of accident models and differ in how accident causation is portrayed. Chain-of-event models focus on the linear sequence of events leading up to an accident, whereas systemic models emphasize the nonlinear relationships between the components in a complex system. The article then introduces various incident analysis techniques, including root cause analysis (RCA), London Protocol, AcciMap, and Causal Analysis Based on Systems Theory (CAST), which are based on these accident models. The techniques based on the chain-of-event model can be effective in identifying causal factors, safety interventions, and improving safety. The other techniques based on the systemic models inherently facilitate an examination of how the influence of personal conditions, environmental conditions, and information exchange between different aspects of a system contributed to an accident. To improve incident analysis, it is essential to translate unsafe human behavior into decision-making flaws and the underlying contextual factors. Where resources allow, it is also crucial to systematically link frontline contributions to organizational and societal aspects of the system and incorporate expertise in safety science and human factors into the analysis team. The article also touches on related concepts such as Perrow's Normal Accident Theory (NAT), Functional Resonance Analysis Method (FRAM), and Bowtie Analysis, which are not based on specific accident models but have been used for safety improvement in radiation oncology. Overall, different incident analysis techniques have strengths and weaknesses. Taking a systems approach to incident analysis requires a shift from linear thinking to a more nuanced understanding of complex systems. However, the approach also brings unique value and can help improve safety as radiation oncology further gains complexity.

A Fog Computing and Blockchain-based Anonymous Authentication Scheme to Enhance Security in VANET Environments

Authentication of vehicles and users, integrity of exchanged messages, and privacy preservation are essential features in VANETs. VANETs are used to collect information on road conditions, vehicle location and speed, and traffic congestion data. The open exchange of information within VANETs poses serious security threats. Furthermore, existing schemes have higher communication and computational costs, making them incompatible with resource-constrained VANET applications. This study proposes a multifactor authentication and privacy-preserving security scheme for VANETs based on blockchain and fog computing to meet all these requirements. The proposed scheme uses fingerprints and Quick Response (QR) codes as a multifactor to authenticate vehicle users and fog-cloud computing techniques to reduce the computational burden on RSUs and improve service quality and resilience. Additionally, the scheme synchronizes a consistent ledger across all RSUs using blockchain technology to store and distribute vehicle authentication statuses. Through a thorough comparison with relevant current protocols, the scheme shows a much-reduced computing expense and communication burden in situations with high vehicle density within a timeframe of 6.3846 ms and 544 bytes for communication costs. In addition, the proposed scheme demonstrates a successful balance between efficacy and complexity, protecting confidentiality, anonymous authentication, and ensuring integrity and conditional tracking. Formal and informal security analysis showed that the proposed scheme is more reliable, practical, and secure against many hostile attacks, such as modification attacks, 51% attacks, Sybil attacks, and MITM attacks.