Spectrum Research (United States)

Men with presentations consistent with borderline personality disorder (BPD) and antisocial personality disorder (ASPD) are highly visible in community and forensic services. However, mis/underdiagnosis may be a consequence of their lower than expected engagement with mental health services, and when they do engage, systematised diagnostic biases may inaccurately differentiate these two subtypes. Given the substantial harms experienced by men who have personality disorder (PD), it is important to understand how personality disorder can be better identified and supported in clinical and forensic settings. Ten nationally and internationally recognised clinician participants who have expertise in the diagnosis and treatment of men with PD (80% male; 60% psychologists, 40% psychiatrists) participated in individual, semi-structured qualitative interviews. Participants shared their observations regarding PD presentations in men and recommendations for maximising treatment engagement in community and forensic settings. Transcripts from the 60-90-min interviews were analysed using inductive and deductive thematic analysis. The analysis identified four themes: Getting men through the door, Unmasking Complexity, Holding Steady in the Face of Risk, and Addressing Shame through Connection. Men with BPD tend to under-report their symptoms and distrust mental health services. Clinicians may struggle to differentiate BPD from ASPD, as men's emotional dysregulation often manifests as anger. Participants emphasised appealing to the person's self-interest, building a trusting therapeutic alliance, and carefully addressing shame to facilitate treatment engagement. Half of the expert clinicians were employed in forensic settings where men with BPD are disproportionately prevalent, rendering their perspectives especially valuable. Assessment of empathetic functioning can help counterbalance clinicians' overemphasis on aggressive behaviours when differentiating BPD from ASPD in men. Incorporation of gendered attributes and targeting associated defensive barriers may improve treatment engagement and effectiveness for men with personality disorder.

To determine whether alcohol consumption in England had returned to pre-pandemic levels by December 2024, after the initial rise in 2020 across the total population and subgroups. Monthly representative surveys were conducted through face-to-face interviews until February 2020, and then by telephone. England, March 2014 to December 2024. 208 010 adults aged 18+ living in private households. Mean weekly alcohol consumption (in UK units), prevalence of risky drinking (Alcohol Use Disorders Identification Test for Consumption [AUDIT-C] score≥5), and possible dependence (AUDIT-C ≥ 11). Further measures included age, gender, and social grade. All outcomes increased in April 2020: prevalence of risky drinking by 30.3% (95% confidence interval [CI]: 26.8, 33.8, from 26.2% in February 2020 to 34.0% in April 2020), prevalence of possible dependence by 90.2% (95% CI: 62.2, 122.9, from 0.9% to 1.7%) and mean weekly alcohol consumption by 34.5% (95% CI: 31.0, 38.0, from 5.0units to 6.6units). When adjusting for the survey mode change from face-to-face to telephone interviews, the step changes between February and April 2020 remained but were substantially attenuated. The post-pandemic trend declined more quickly than the pre-pandemic trend for the prevalence of risky drinking (difference: -1.5%/year, 95% CI: -2.4, -0.6) and mean weekly alcohol consumption (difference: -2.4%/year, 95% CI: -3.3, -1.6), indicating a slow but incomplete return to pre-pandemic levels. The trend in prevalence of possible dependence was similarly stable before and after the pandemic (difference: -1.3%/year, 95% CI: -6.2, 3.8). Alcohol consumption declined more slowly among people from less advantaged than from more advantaged social grades. The prevalence of risky drinking and mean weekly alcohol consumption in England appear to be trending towards pre-pandemic levels but the prevalence of dependent drinking in England appears to have increased since the start of the pandemic and remains elevated compared with pre-pandemic levels. Alcohol-related inequalities may be worsening due to slower declines in consumption following the pandemic among less advantaged drinkers.

The subtle pragmatic language differences that characterize the broad autism phenotype have also been observed among female FMR1 premutation carriers, suggesting a potential role of the FMR1 gene in autism-related pragmatic language differences. This study evaluated the use of discourse markers (language units that support conversational discourse) in a direct comparison of these groups to examine how this key pragmatic skill may be used differently and potentially contribute to pragmatic language profiles in each group. Participants included 83 mothers of autistic individuals, 61 female FMR1 premutation carriers, and 41 female controls. Discourse marker use was evaluated in the context of a semi-structured conversation. Patterns were compared across groups and evaluated in relationship to global pragmatics and variability in the FMR1 gene across the CGG range. Mothers of autistic individuals demonstrated clear differences in discourse marker patterns compared with FMR1 premutation carriers and controls. Relationships to pragmatics were observed in the use of backchannels only. Links to FMR1were observed within the premutation CGG range. Findings suggest that differences in discourse marker use could be an important component of the broader pragmatic differences noted in first degree relatives of autistic individuals, although evidence of a direct relationship with FMR1 was limited.

The article is dedicated to the conceptual analysis of customer interaction models in digitally mediated service ecosystems and their relationship with sustainable growth in sales and service indicators. The relevance of the study is determined by the growing mismatch between linear management models and the non-linear dynamics of contemporary customer interactions shaped by digital platforms, algorithms, and omnichannel environments. The novelty of the work lies in treating customer interaction as a systemic configuration of resource integration rather than as a sequence of isolated transactions. The article describes how value emerges through the interaction of human, digital, and organizational resources and how this process affects long-term performance indicators. Special attention is paid to algorithmic mediation, omnichannel coherence, and the conditions under which value co-creation transforms into value co-destruction. The article sets itself the goal of developing a multi-layered conceptual framework linking interaction configurations to sustainable economic outcomes. To achieve this goal, analytical synthesis, comparative analysis, and conceptual modeling are employed. The study draws on contemporary research in service management, consumer behavior, and AI-enabled organizations. The conclusions demonstrate that sustainable growth depends on managing interaction density, employee agency, and technological governance. The article will be useful for researchers, managers, and practitioners working in service design, customer experience management, and digital strategy.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative solution for next-generation wireless networks, offering a low-power and cost-efficient means of controlling electromagnetic wave propagation. Through the integration of tunable elements within engineered metasurfaces, RIS can dynamically adjust the phase, amplitude, or polarization of incoming signals, enabling flexible and programmable wireless environments suitable for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 G}, \mathbf{6 G}$</tex>, and beyond. In this work, an active 1-bit RIS unit cell is designed and analyzed using full-wave simulation, featuring a PIN diode as the rapid switching element. The active configuration supports binary phase modulation, providing two discrete reflection states determined by the diode bias condition. The unit cell is further optimized to yield an approximately 180° phase shift between the ON and OFF states, with a tolerance of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 20^{\circ}$</tex> at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4. 6 ~ G H z}$</tex>, ensuring reliable and consistent operation at the intended frequency. Simulation results verify that the use of the PIN diode enables fast and efficient reconfigurability without compromising compactness or practical implementability. These outcomes advance ongoing RIS research and establish a solid platform for future hardware fabrication, prototyping, and experimental validation.

This paper critically reviews the strategic position of environmental, social and Governance (ESG) in corporate strategy focusing on differences across industry sectors. Using simulated data and global investment data, this research de-constructs ESG into its constituents and assesses their differential impact on firm performance within-industry sectors. The studies show that environmental factors have a significant effect on the level of energy, manufacturing companies' cost reduction and productivity improvement and good governance structure reduces valuation uncertainty and strategic value enhancement in financial, technology industry. Measures for the social sustainability, especially in sectors with a high share of manual labour, increase the trustworthiness of stakeholders and effectiveness of employees. This paper takes the debate forward by framing ESG integration in governance and risk management views, leaving behind compliance‐based approaches towards an understanding of ESG as a strategic tool for value creation and long run sustainability. Importantly the research demonstrates that sector weightings in the ESG space are contributing to performance.

ABSTRACT This paper presents the implications of integrating deep reinforcement learning (DRL) and the Internet of Things (IoT) in optimizing energy management, specifically in smart buildings for sustainable urban development. It further explores how DRL, along with real‐time IoT sensor‐based data, helps improve energy performance in responding to actual HVAC, lighting and renewable energy conditions. Key techniques like genetic algorithms, particle swarm optimization and hybrid techniques are critically examined in maintaining an equilibrium between energy consumption versus renewable sourcing in smart building models. Boundary‐preserving strategies and federated learning appear as techniques addressing expansibility and information protection difficulties, notably over IOT systems. Further research would include technology in local processing and situation‐responsive DRL to enhance more independent, user‐focused and ecologically responsive buildings. This review provides a roadmap for implementing robust, privacy‐conscious AI frameworks in smart buildings, underlining their potential to cut energy use and contribute to broader environmental goals.

The reliability of embedded processors in safety- and mission-critical domains is increasingly threatened by radiation-induced soft errors, particularly multiple-cell upsets (MCUs) that simultaneously corrupt adjacent cells in external SDRAM. While prior studies on the LEON3 processor have largely focused on single-event upsets (SEUs) in internal SRAM structures, they overlook MCU effects in off-chip SDRAM, a critical gap that limits fault coverage and compromises system-level reliability assessment in modern high-density embedded systems. This paper presents an SDRAM-based fault injection framework using FPGA emulation to evaluate the impact of MCUs on the LEON3 soft-core processor, with faults directly injected into the external memory subsystem where data corruptions can rapidly propagate into system-level failures. The methodology injects spatially correlated two-bit MCUs directly into SDRAM during realistic workload execution. Three architecturally diverse benchmarks were analyzed, each representing a distinct computational workload: a numerical (matrix multiplication), signal-processing (FFT), and a cryptographic (AES-128 encryption) application, chosen to capture arithmetic-intensive, iterative, and control-intensive execution profiles, respectively. The results reveal a distinct workload-dependent vulnerability profile. Matrix multiplication exhibited &gt;99.99% fault activation, with outcomes overwhelmingly dominated by data store errors. FFT showed &gt;97% activation in steady-state execution, following an initial phase sensitive to alignment and data access exceptions. AES displayed 88.12% non-propagating faults, primarily due to injections in inactive memory regions, but remained exposed to critical memory access violations and control-flow exceptions that enable fault-based cryptanalysis. These findings demonstrate that SEU-only models severely underestimate real-world MCU risks and underscore the necessity of selective, workload-aware fault-tolerance strategies: lightweight ECC for cryptographic data structures, alignment monitoring for signal processing, and algorithm-based fault tolerance (ABFT) for numerical kernels. This work provides actionable insights for hardening LEON3-based systems against emerging multi-bit threats in radiation-rich and adversarial environments.

This paper presents an advanced frequency control solution for multi-microgrid systems (MMGS) with high renewable energy penetration, where conventional control methods struggle with scalability and disturbance resilience. We propose a Distributed Consensus Control Strategy (DCS) using a Fractional Order PID (FOPID) controller adaptively tuned by a Fuzzy-Recurrent Neural Network (FRNN). Our method delivers significant improvements in both transient and steady-state performance compared to established approaches. Extensive real-time hardware-in-the-loop (HIL) testing on a three-microgrid platform demonstrates the superiority of the proposed controller. In MG1, it reduces settling time by 29% (4.19 s vs. 5.93 s for PID), lowers peak overshoot by over 90% (0.0007 vs. 0.0076 for PID), and cuts absolute error by more than 80% (1.017 vs. 6.32 for PID). Similar improvements are validated across MG2 and MG3, and comprehensive scenario testing confirms the method’s robustness under diverse disturbances. The proposed FRNN-tuned FOPID-based DCS stands out as the first adaptive, distributed framework offering real-time self-optimization and exceptional resilience for frequency regulation in MMGS. Its demonstrated performance and adaptability make it a leading candidate for future smart grids and cyber-physical energy systems.