Insider threats pose a significant security risk, yet personality traits are rarely incorporated into detection and prevention efforts. This study offers the first systematic examination of how Dark Tetrad (i.e. Machiavellianism, narcissism, psychopathy, and sadism) and Big Five personality traits, together with situational motivators, predict the propensity to engage in three types of insider attacks. Participants (N = 470; 71% female; age M = 35.94, SD = 12.70) completed measures of the Dark Tetrad (SD3, SSIS) and Big Five domains and facets (BFI-2), then responded to three counterbalanced vignettes depicting National Security Espionage, Information Technology Sabotage, and Fraud. For each vignette, participants rated their likelihood of engaging in the behaviour under baseline conditions and when (a) aggrieved, (b) facing termination, or (c) able to secure a financial gain. Participants showed greater willingness to engage in fraud and information technology sabotage if they felt aggrieved or anticipated termination. Machiavellianism (r = .49), psychopathy (r = .50), and sadism (r = .53) showed strong positive associations with insider-attack propensity. Agreeableness (r = -.50) and conscientiousness (r = -.37) were the strongest Big Five predictors. The findings indicate that antagonistic and exploitative personality traits, combined with situational pressures, meaningfully shape intentions to engage in insider misconduct. What is already known about this topic: Insider threats can cause significant financial, operational, and security harm, yet most detection and prevention efforts focus on technical or organizational controls rather than individual differences.Personality traits, particularly low agreeableness and low conscientiousness, are known predictors of workplace deviance more broadly.Situational pressures such as perceived injustice, termination, and opportunities for financial gain can increase the likelihood of misconduct. What this study adds: This study provides the first systematic assessment of how Big Five traits and Dark Tetrad traits jointly relate to insider-attack intentions across multiple attack types.Antagonistic and exploitative traits (psychopathy, Machiavellianism, and sadism) were the strongest dispositional predictors, while low agreeableness and low conscientiousness also meaningfully contributed.Situational motivators, particularly feeling aggrieved or facing termination, substantially increased willingness to engage in insider misconduct, highlighting the value of integrating trait and context information in insider-risk programs.
Climate regulatory risk is becoming increasingly important, yet its implications for firms' information environments and insider behavior remain unclear. We examine how climate regulation affects insiders' trading profitability using the staggered finalization of state climate adaptation plans (SCAPs) in the United States over the period 1996 to 2021. Climate regulation may influence insider trading profitability through competing channels. Increased regulatory uncertainty can enhance insiders' informational advantages, while heightened scrutiny, improved disclosure, and stronger internal monitoring can constrain them. We find that SCAP finalization is associated with a significant decline in insider trading profitability, with stronger effects for firms facing greater climate-related uncertainty and risk exposure. Overall, the evidence indicates that transparency and monitoring effects dominate, reducing information asymmetry and insider gains. These findings highlight how climate policy shapes firms' information environments and limits insider advantages, with implications for market transparency and regulatory effectiveness.
This study aimed to investigate the mediating role of perceived insider status and organisational identification between authentic leadership and job embeddedness among Chinese nurses. Previous studies have explored the influence of authentic leadership on job embeddedness in the nursing profession. However, the chain-mediating effect of perceived insider status and organisational identification between authentic leadership and job embeddedness has not been clarified among nurses. A cross-sectional study. A structural equation model was utilised to examine the proposed hypothesis regarding job embeddedness in Chinese clinical nurses and to investigate potential mediating factors influencing nurses' job embeddedness. There were positive correlations among authentic leadership, perceived insider status, organisational identification, and job embeddedness. Moreover, authentic leadership exerted a noteworthy influence on job embeddedness through three significant indirect pathways: the separate mediating effect of perceived insider status and organisational identification, and the chain mediating effect of perceived insider status and organisational identification. Nursing managers aiming to enhance nurses' job embeddedness. Recognising the crucial role of perceived insider status and organisational identification as mediators between authentic leadership and job embeddedness, our findings suggest actionable strategies. Elevating authentic leadership creates a supportive environment, positively impacting nurses' commitment and reducing turnover. We would like to thank the clinical nurses from three hospitals in Wuhan who participated in the study and the hospital managers who supported this study. This study distributed survey links in the WeChat groups of three hospitals, and collected research data under the principle of ensuring anonymity and informed consent.
Insider research refers to research conducted by individuals who are members of the group being studied. This reflexive article explores the personal and methodological journey of being an insider researcher investigating firefighters' experiences of suicide exposure, while being both an active firefighter and someone with lived experience of suicide bereavement. Through first-person narrative, the article traces evolution from positivist certainty to embracing the metaphor of 'sitting in the soup' of uncertainty as essential to skilled insider research. Drawing on Consoli's life capital framework and affect theory scholarship, the article demonstrates how healing from personal trauma became a methodological prerequisite, enabling conscious engagement with the affective entanglement inherent in insider positioning. Central to this approach is recognition that reflexivity must operate as core practice prior to and throughout the entire research process. The article proposes that insider positioning is paradoxical and dynamic rather than fixed, and that thorough self-knowledge transforms embedded subjectivity from methodological liability into epistemological strength. When conducted with sophisticated reflexive competence, insider research enables deeper, more nuanced understanding of human experience while maintaining academic rigour.
This study examines how South Korean lesbian feminists negotiate safety and threat within Twitter (X)-based safe spaces, drawing on digital ethnography and in-depth interviews with eight participants. We develop the concept of insider-invaders to capture how individuals simultaneously constitute these spaces while also being perceived as potential or actual threats. Rather than fixed boundaries between insiders and outsiders, we show that belonging and safety are continuously reconfigured through participants' interpretations of misogyny, their situated understandings of feminism and lesbian identity, and their assessments of others' identities and practices. The insider-invader framework illuminates how suspicion, identification, and conditional acceptance emerge within a community often imagined as homogeneous, rendering safety an ongoing relational achievement rather than a stable condition. By moving beyond the binary between exclusionary and non-exclusionary feminisms, this study reconceptualizes safe spaces as fluid and contested formations, shaped by the shifting interplay of solidarity, difference, and vulnerability in digital queer feminist contexts.
Insider threats remain among the most critical challenges in cybersecurity, as malicious or compromised employees can bypass traditional defences and cause disproportionate damage to organizations. Detecting such threats is difficult because anomalous behaviour is often subtle, context-dependent, and concealed within vast volumes of normal user activity. Conventional anomaly detection techniques suffer from high false positive rates and limited ability to capture both temporal and relational patterns of behavior, which constrains their operational utility in Security Operations Centers (SOCs). This study presents a hybrid User and Entity Behavior Analytics framework that integrates Transformer-based sequence modeling with graph neural networks (GNNs) to simultaneously capture temporal workflows and relational dependencies. Using the CERT Insider Threat Dataset, raw multi-source logs are sessionized and transformed into dense event representations combining categorical actions, resource identifiers, and normalized numerical attributes. A Transformer encoder models long-range event dependencies, while a GNN encodes user-resource interactions, their outputs are fused and evaluated via anomaly scoring, with explainability mechanisms providing interpretable SOC alerts. Experimental evaluation demonstrates that the proposed model achieves 97.9% accuracy, 0.88 F1-score, and 0.99 AUC, reducing false positives to 11 per 1000 sessions and lowering detection latency to 1.9 h. These results establish that fusing sequential and relational perspectives yields a robust, accurate, and interpretable solution for insider threat detection in enterprise environments.
This Questions and Quandaries article focusses on a topic less explored in research conversations, namely that of the tensions that can arise when researchers from distinct professions and disciplines engage in health professions education (HPE) research, each bringing their own set of assumptions and ways of knowing. The article argues that in this potentially conflicting space there are opportunities for productive engagement to improve both the insights and the impact of the academic work being done by HPE scholars within the field.
As organizational competition intensifies, employees have become increasingly responsive to evaluative cues from their work environment. Among these, underdog expectations-employees' perceptions that others view them as unlikely to succeed-can trigger strong psychological reactions that shape interpersonal behavior. Drawing on self-determination theory, this study examines how underdog expectations influence employees' interpersonal counterproductive work behavior (CWB-I). Using a three-wave time-lagged survey design with 221 employees, we found that underdog expectations positively predict CWB-I through two parallel psychological mechanisms: increased moral disengagement and reduced perceived insider status. In addition, organization-based self-esteem (OBSE) strengthens these indirect effects, such that the mediating relationships are stronger among employees with high OBSE. These findings extend research on underdog expectations by revealing both relational and cognitive pathways linking negative evaluative expectations to interpersonal deviance, while also highlighting the complex role of self-evaluative organizational identity in shaping employees' behavioral responses to status-based threats.
Complement is increasingly recognised as a driver and modulator of antitumour immunity, with context-dependent effects across T cells, myeloid subsets, stromal elements and tumour cells. Although best known for pathogen clearance and membrane attack complex (MAC) formation, complement also acts intracellularly via the 'complosome' to regulate cellular homeostasis and gene expression. Complosome activity may dampen antitumour responses by rewiring single-cell metabolism and transcription, altering nutrient flux and fostering an immunosuppressive microenvironment. Here, we synthesise advances in intracellular and extracellular complement, with emphasis on complement component 3 (C3) and receptors (C3aR1, C5aR1/CD88, C5aR2/C5L2), highlighting how these pathways shape T-cell metabolism, exhaustion programmes and inflammatory tone within tumours. Evidence indicates that tonic C3/C5 signalling restrains cytotoxicity via C5aR1-driven myeloid recruitment and cytokine cascades, while complosome signalling tunes T-cell activation thresholds and bioenergetics. We outline considerations for selectively modulating intracellular versus extracellular complement, propose cell-type-resolved biomarker strategies and identify opportunities for complosome-directed therapies in cancer, integrating roles across T cells, macrophages, B cells, neutrophils, NK cells, regulatory T cells, dendritic cells, myeloid-derived suppressor cells and cancer-associated fibroblasts. KEY POINTS: Intracellular complement (complosome) shapes the tumor immune microenvironment. Complosome's role in cancer is underrecognized yet central to tumor immunity. C3/C5-driven complosome signals rewire T cell activation, fate, and metabolism. Complosome activity can promote pro-tumor immune cell function. Blocking the complosome, alone or with checkpoint inhibitors, unveils a new tumor target.
With the growing global interest in health protection and traditional medicine, acupuncture practitioners are receiving widespread international recognition. However, research on burnout among this professional group remains limited-particularly in the context of public hospitals. This study adopts a novel perspective that bridges acupuncture practitioners and occupational health, aiming to assess both burnout level and its determinants among acupuncture practitioners in public hospitals. It helps reveal the unique occupational stressors and health risks faced by this group within institutional medical settings. Drawing on the person-environment fit theory, a survey was conducted among 614 acupuncture practitioners employed in China's public hospitals, by using the Chinese version of the Maslach Burnout Inventory-Human Services Survey (MBI-HSS). The study used SPSS for statistical analyses, including one-way ANOVA, Pearson correlation, and multiple linear regression, to examine the main factors contributing to acupuncture practitioners' burnout. The findings indicated a moderate level of burnout among acupuncture practitioners (mean = 2.63 ± 0.89) in China's public hospitals, with emotional exhaustion (2.69 ± 0.90), depersonalization (2.56 ± 0.97), and reduced personal accomplishment (2.61 ± 0.93) as the primary dimensions. Key determinants were categorized into four levels: individual factors (gender, marital status, age, years of work experience, professional title); organizational factors (institutional support, organizational management systems, remuneration, career advancement, departmental competition, interpersonal relationships); occupational factors (work intensity, work specialization); and societal factors (patient negativity, public misunderstanding). Burnout is prevalent among acupuncture practitioners in China's public hospitals and is influenced by multiple factors. Alleviating burnout in this context requires a multi-level strategy: developing a human-centered occupational support system, aligning organizational management with the specific needs of acupuncture practice, implementing tailored and sustainable work mechanisms, and fostering a socially supportive environment. This study broadens the scope of burnout research by applying it to acupuncture practitioners and provides actionable recommendations to improve working conditions and promote the well-being of acupuncture practitioners in public healthcare systems.
Advanced Persistent Threats (APTs) represent sophisticated cyberattacks characterized by stealth, persistence, and evasion of traditional detection mechanisms. We observed that APT behaviors during lateral movement and data exfiltration share notable similarities with insider threat activities, leading us to explore cross-domain learning opportunities. This paper introduces a novel machine learning approach leveraging the CERT Insider Threat Dataset to simulate and detect APT behaviors through AI-augmented analytics. Our methodology integrates multi-modal data analysis, language model-driven behavioral understanding, and advanced machine learning to create realistic APT simulations from insider threat data. We developed three key technical components: a multi-agent language model architecture for log analysis, temporal sequence modeling for behavioral pattern recognition, and deep evidential clustering for uncertainty-aware threat detection that reduces false positives. Our research contributes four advances: a novel methodology for simulating APT patterns using insider threat data, an AI-enhanced multi-modal approach processing structured logs and communications, superior performance compared to existing methods, and practical deployment guidelines for enterprise environments. Experimental results achieved 96.3% detection accuracy while reducing false positives by 42% compared to state-of-the-art methods. Our system successfully simulates realistic APT scenarios across attack stages while providing interpretable explanations through natural language generation. The integration of large language models enables sophisticated analysis of unstructured data sources, offering contextual understanding beyond traditional approaches. This research addresses a critical gap for organizations seeking enhanced APT detection without extensive APT-specific training data. Our approach's ability to learn from insider threat patterns while maintaining high accuracy makes it valuable for enterprise security operations and threat hunting teams facing resource constraints.
Background: Oncology and palliative care staff frequently encounter death yet often lack structured opportunities for reflection following these experiences.Objectives: This study asked: How can we develop and implement a reflective practice program for oncology and palliative care staff to support them with patient death? Reflective practice is recognized as a valuable tool to support staff well-being; however, there is limited literature describing how such interventions are implemented in the hospital setting.Methods: A participatory insider-action research approach was undertaken to codevelop a reflective practice program. A multidisciplinary Action Research Group collaborated through a series of meetings to codesign the program. The data consisted of contemporaneous notes capturing group discussions on what was needed to develop the program. Thematic analysis was used to identify key ideas and patterns that emerged from these discussions, enabling the group to identify factors required to develop a format to support guided reflection.Results: Key themes included the importance of language, timing and location, facilitation and structure, and case selection. The final outcome was a practical, semistructured format for reflective practice sessions for staff to follow as a framework to support reflection and emotional processing in response to patient death.Conclusions: Using an insider action research approach, this staff-led project offers an adaptable framework for structured reflection in acute hospital settings and represents an important initial step towards embedding reflective practice into the culture of health care.
An unwritten expectation in our everyday social interactions is that intimate personal information about someone-"insider knowledge"-is usually confined within close relationships. For example, it would be odd, or even unsettling, if a stranger knew about your favorite movie. Such expectations about who knows what about whom constitute a cornerstone of complex social behavior, but much remains unknown about their cognitive underpinnings and developmental origins. Drawing on parental report (Study 1) as well as a novel experimental approach using controlled but naturalistic videochat conversations (Study 2 & 3), we find that 4- to 5-y-old children have an abstract, theory-like understanding of how social connections give rise to interpersonal knowledge. Self-report, facial expressions, and memory errors provide converging evidence that children were surprised when someone possessed insider knowledge that is misaligned with their relationships, such as a stranger knowing their favorite food (Study 2a) or their own parent knowing a stranger's favorite movie (Study 2b). Children also generated coherent ad-hoc explanations about how someone might have acquired that knowledge, appealing to either first-hand observations or second-hand sources (Study 3). These findings demonstrate an early-emerging understanding of how individual minds are shaped in the context of their social networks, supporting a precocious ability to detect and explain anomalies in what people know about each other in real-time conversations. The current work also opens possibilities for leveraging open-ended online interactions to study social cognition without compromising experimental control.
This study addresses a core managerial dilemma: should leaders equalize relationships to preserve harmony, or differentiate them to stimulate initiative? We examine how employees' comparative evaluations of their own leader-member exchange (LMX) relative to coworkers' LMX function as identity signals that channel them into distinct innovation forms. We conducted a three-wave, multi-source field study of 432 employees nested in 42 high-tech SMEs in Taiwan. At Time 1, employees reported self-referenced and other-referenced LMX; at Time 2, they reported constructive felt responsibility and perceived insider status; at Time 3, supervisors rated employees' expedient and proactive innovation. We define expedient innovation as short horizon, non accumulative innovation for immediate stabilization, rather than compliance, reactive problem fixing, or incremental or exploitative improvement. Using multilevel polynomial response surface analysis and Bayesian mediation, we show that LMX congruence is associated with expedient innovation through constructive felt responsibility, whereas directional incongruence in which self-referenced LMX exceeds other-referenced LMX is associated with proactive innovation through perceived insider status. By distinguishing congruence effects from directional incongruence, including self-high other low and self-low other high configurations, we help reconcile mixed findings in the LMX differentiation literature. Theoretically, these findings reframe LMX differentiation as an identity signaling process that explains variation in innovation form, and managerially, they suggest that calibrating relationship equalization versus differentiation can help leaders balance short-term operational stabilization with longer-term change initiation.
Automated biological laboratories are a growing accelerator of scientific research, enabling remote execution through advanced robotics and computational systems. Automation is deployed across a range of settings, including modular automation platforms, centralized biofoundries, and cloud laboratories. For the most highly automated of these, the defining characteristics include remote operation, limited human oversight, and integrated automation. These technologies might enable fully autonomous biological research, also referred to as "closed-loop" discovery or self-driving laboratories. They can also introduce new vulnerabilities that existing biosafety and biosecurity frameworks do not sufficiently address. We first map the current landscape of automated biological laboratories, define what distinguishes them from conventional laboratories, and argue that existing biosafety and biosecurity oversight fails to account for risks from the latent capabilities of these laboratories. We then analyze multiple threat vectors, including malicious orders, insider threats, and cyberattacks. Through these vectors, an actor may be able to compromise laboratory infrastructure and orchestrate pathogen synthesis or protein engineering, without the knowledge of laboratory operators. We propose the Automated Laboratory Security Tier (AST) framework to categorize facilities into three tiers based on the biosecurity risks they could pose if fully compromised. We use "fully compromised" to mean an attacker can control protocol execution and relevant information technology and operational technology (IT/OT) controls sufficiently to bypass all oversight and safeguards. We outline methods to improve customer verification, order screening, cybersecurity measures, insider threat protections, and independent security assessments. Although limited information regarding automated laboratory capabilities is publicly available, we argue that most current automated laboratories do not possess latent capacity for serious harms. These risks appear low for most current facilities, but laboratories outside the scope of our review may possess greater latent capability, and capabilities may advance faster than anticipated. Our analysis draws on expert interviews with automated laboratory operators and biosecurity experts. We recommend that researchers and industry stakeholders engage with standard-setting bodies to establish security standards for automated biological laboratories. With ongoing monitoring of capability levels and adoption of proportionate safeguards, these laboratories can be safely operated.
The intersection of smart home Internet of Things (IoT) devices, enterprise Information Technology (IT) infrastructures and Operational Technology (OT) systems has greatly expanded the cyberattack surface, opening up smart environments to advanced persistent threats (APTs), ransomware, insider attacks and OT protocol exploits. Current cyber security solutions are largely domain specific, and do not have a unified approach for cross domain threat correlation, adaptive response orchestration and proactive cyber resilience. In this regard, this study introduces a novel framework called Cross-Domain Cyber Resilience Framework (CD-CRF) that combines multi-domain telemetry fusion, behavioral anomaly detection, intelligent threat deduction, probabilistic risk scoring, and adaptive response orchestration under a single architecture. The framework utilizes signature independent behavioral analytics and cross domain threat correlation with identification of complex multi-stage attacks in heterogeneous Home, IT and OT environments. Experimental assessment was performed with a set of heterogenous cybersecurity data sets, including intrusion detection logs, traces of ransomware activities, IoT botnet traffic, SCADA/ICS telemetry, authentication data and communication data for malware. The proposed CD-CRF successfully reduced the false positive rate to 1.8%, and attained a detection accuracy of 98.6%, threat prediction rate of 94.8% and a cross-domain correlation efficiency of 97.1%. Moreover, it had a response time of 120ms, about 50% faster mitigation than traditional methods, and statistically significantly outperformed GCSM and CAT and TinyML models (p < 0.05). The results show that CD-CRF improves cyber situational awareness, it increases the ability to detect threats early, it decreases operational risks, and it offers scalable, adaptive cyber resilience solution to converged IT-OT environments.
This study analyzes how U.S. healthcare organizations view regulatory changes to the accessibility of patient health information as part of the 21st Century Cures Act. Rulemaking for the Cures Act recommended a technical change that would enable vendors in the consumer marketplace outside the institutional context and special data protections of health care to gain access to private patient data. We examine organizational stakeholders' comments during the Notice of Public Rulemaking to show how organizational actors both inside and outside of health care use the institutional values and relationships of health care versus the market to evaluate the impact of the technical change. Healthcare insiders use professional ethics and doctor-patient relationships to defend the status quo of data protections in health care. Outsiders, such as consumer health apps, use the logic and relationships of the marketplace to challenge clinical control of patient information as well as data protections and property rights over patient health data. Technical change alone does not alter the information order of health care, but it creates an opening to challenge the existing meaning and management of information and thereby potentially disrupt established institutions in health care in the United States.
Muju () is a local opera rooted in Chun'an County, Zhejiang Province, China. Since the re-establishment of its professional troupe in 2015, its vocal style has changed substantially, driven by the recruitment of young performers trained in other opera genres (Huangmeixi, Yueju, Wuju). Previous research has described this change through historical narratives and insider accounts, but the extent to which cross-genre training may shape vocal production has remained insufficiently examined with empirical methods. This study examines how performers' prior vocal training in other operatic genres may affect vocal production when performing Muju, and how this process may have contributed to changes in the genre's local vocal characteristics over time. A total of 691 vocal excerpts were analyzed, including 196 Muju excerpts across three historical stages and 495 reference excerpts from Yueju, Huangmeixi, and Wuju. A three-layer Transformer-decoder model was used as part of an AI-assisted, score-based acoustic-proximity analysis. The model-derived scores were treated as exploratory indices of relative acoustic proximity rather than as validated measures of stylistic similarity. In addition, a case-based acoustic comparison of the same aria was conducted among three performances: a historical male Muju/Sanjiaoxi reference, a contemporary Huangmeixi-trained female performer, and a contemporary Chun'an local female reference. Model-derived scores suggest that Contemporary Muju may show lower relative proximity to Traditional Sanjiaoxi than Old Muju does (Cohen's d = -0.66), along with stronger Huangmeixi-related score tendencies across historical stages. Performer-level results further suggest variation in Yueju- and Wuju-related model-derived scores by training background. At the case level, the acoustic comparison suggests recurrent differences in vowel openness and articulatory placement among the three performances, especially in the contemporary same-gender contrast between P1 and P7. Taken together, the findings are consistent with the possibility that the vocal profile of contemporary Muju has been reconfigured rather than replaced. They are also compatible with the possibility that cross-genre training contributed to this process through the transfer of habitual vocal-production patterns. The acoustic case study provides a focused performance-level illustration, whereas the AI-assisted score-based analysis offers exploratory corpus-level context requiring cautious interpretation.
Korean immigrant families face complex barriers to accessing services for their children with autism and developmental disabilities due to intersecting cultural, linguistic, and socioeconomic factors. However, there is limited information in the literature on strategies to close this gap. This tutorial reports on leveraging an academic-community partnership to establish and sustain a parent support group for Korean immigrant families of children with autism and other developmental disorders. Academic and community partners worked together to develop family-centered support. The partnership focused on iteratively tailoring support to a localized context to increase knowledge, self-efficacy, and community support for navigating service systems. The development of the parent support group progressed through three phases: (a) establishing two separate support groups led by a parent and a faculty member, (b) merging the groups and building capacity to empower families, and (c) supporting community partners in formalizing the group and securing grant funding for partner-driven initiatives. In all phases, academic partners contributed university resources, whereas community partners provided insider knowledge to create effective programming. This approach has resulted in a strong, ongoing academic-community partnership that has lasted for more than 2 decades and successfully engaged over 400 families. The lessons learned offer an example of how to reduce barriers to autism-related services by leveraging community strengths and fostering collaboration and by revisiting both the partnership and supports for families to more strongly address community priorities.
Mobile data collectors (MDCs) play a very important role in Internet of Things (IoT) sensing networks. However, ensuring their trustworthiness against insider threats, such as on-off attacks and spatiotemporal fabrication, remains a critical challenge. Existing trust evaluation methods frequently struggle with these threats due to insufficient evidence dimensions and the inability to quantify behavioral stability. To address these limitations, this paper proposes an enhanced proactive trust evaluation system based on stability sequence extraction (E-PTES-S). E-PTES-S improves the evaluation accuracy by integrating five factors of evidence, stability-computation mechanisms, and an adaptive weight allocation scheme to maintain robustness even when proactive verification data is scarce. In addition to the usual interaction and proactive verification indicators, regional consistency (TRC) and task timeliness (TTT) are introduced to mitigate location falsification and transmit-time deviations more rigorously. Then, a sliding window technique is used to obtain an integrated evidence sequence, which includes a new continuous stability sequence (FCSS) and traditional credible, untrustworthy, and uncertain sequences. This continuous stability sequence adds a variance-based incentive scheme to measure behavioral stability. Finally, the normalized trust value is derived from multiple indicators including multidimensional spatiotemporal evidence and stability metrics. Experimental results show that the proposed E-PTES-S achieves a normal node detection rate of 98.7% under complex dynamic conditions, outperforming the baseline PTES and Trust-SIoT algorithms by approximately 9% and 1%, respectively, while also improving the cumulative data collection profit by 4.8%. Furthermore, robustness analysis demonstrates that E-PTES-S exhibits excellent robustness against physical-layer uncertainties, successfully sustaining an 84.4% detection rate even under severe environmental shadowing.