Pace University

Abstract Context Climate change is increasing the frequency and severity of droughts worldwide, with profound consequences for forest ecosystems. Although previous studies suggest that taller forests are more vulnerable during drought, their capacity to recover following drought remains poorly understood, creating substantial uncertainty for forest management. Objectives This study aimed to quantify how canopy height influences post-drought forest recovery at a regional scale. Methods We investigated evergreen broad-leaved forests in southwest China following a severe drought in 2010. We used the MODIS Enhanced Vegetation Index (EVI) to derive forest recovery metrics, the Standardized Precipitation Evapotranspiration Index (SPEI) to classify drought severity, and a LiDAR-derived canopy height map. We then used linear regression and ANOVA to evaluate relationships between forest recovery and canopy height. Results Short-term (1 year) recovery increased with the intensity of the preceding drought. Under severe and extreme drought conditions, recovery increased significantly with canopy height ( p < 0.001). In contrast, long-term (3 years) recovery did not differ significantly among canopy height classes. Conclusions Our results indicated that although tall forests may experience greater drought impacts, they exhibit stronger short-term recovery, resulting in comparable long-term resilience across canopy-height classes. This size-dependent recovery dynamic is a previously underappreciated mechanism that should be incorporated into forest dynamics models and climate-adaptive management strategies.

The rapid deployment of fifth-generation (5G) networks and the adoption of network slicing have significantly enhanced connectivity and service customization, but they have also expanded the cyber-attack surface of telecommunications infrastructure. At the same time, emerging quantum computing capabilities threaten the security of widely used classical cryptographic schemes such as RSA and Elliptic Curve Cryptography. Despite growing concerns, existing security frameworks for 5G slicing rarely integrate post-quantum cryptography with intelligent threat detection mechanisms. This study addresses this gap by developing a quantum-resistant security architecture for 5G network slicing that combines post-quantum cryptography and artificial intelligence–based anomaly detection. The framework incorporates NIST-standardized algorithms including CRYSTALS‑Kyber for secure key exchange and CRYSTALS‑Dilithium and FALCON for authentication. A hybrid post-quantum/classical TLS scheme was implemented to support secure key establishment with minimal latency overhead of 1.65 ms. Additionally, an AI-driven anomaly detection model using a stacked ensemble of Isolation Forest and XGBoost monitored slice orchestration behavior to detect cyber threats. Simulation results demonstrated high detection performance with a macro F1-score of 0.962 and AUC-ROC of 0.985. The proposed framework offers a practical and scalable approach for securing next-generation telecommunications infrastructure against both current cyber threats and future quantum attacks.

Collective health crises, such as the COVID-19 pandemic, pose significant psychosocial challenges that may increase vulnerability to posttraumatic stress (PTS). The present longitudinal study examined the associations between COVID-19-related PTS and two psychosocial vulnerability factors: loneliness and COVID-19-related worries. Data were collected from a sample of 747 adults in the United States (U.S.), recruited via Amazon Mechanical Turk (MTurk), with follow-ups conducted at approximately 6-month intervals beginning in March 2020. Retention rates across the four waves were as follows: Time 2 (T2)=458, Time 3 (T3)=445, and Time 4 (T4)=432. Cross-lagged panel analyses indicated that loneliness consistently predicted subsequent increases in PTS symptoms across waves, whereas the reverse pathways from PTS to loneliness were not significant. Both loneliness and PTS measured at Time 1 (T1) significantly predicted COVID-19-related worries at T2; however, these effects did not persist in subsequent waves. Additionally, a delayed effect was observed in which COVID-19-related worries at T2 significantly predicted increased loneliness at T3, although this association was not observed at later time points. These findings highlight the enduring role of loneliness in the development of pandemic-related PTS and suggest that both loneliness and PTS contribute to elevated worry during the early stages of widespread infectious disease outbreaks. Early interventions targeting loneliness may be critical for reducing long-term psychological distress and enhancing resilience in the context of pandemics and similar public health emergencies.

ABSTRACT As firms increasingly incorporate environmental, social, and governance (ESG) concerns into their strategic agendas, stakeholder legitimacy—an audience‐conferred judgment of organizational appropriateness—has become pivotal. We theorize legitimacy as expanding a hybrid response portfolio in which firms may pursue substantive change (business model innovation [BMI]) alongside symbolic communication that can become ESG washing when disproportionate to operations. Integrating legitimacy, institutional, and signaling perspectives, we argue that institutional pressures allocate effort across these responses rather than forcing an either–or choice. Using a two‐wave survey of South Korean firms ( N = 478), we find that legitimacy generally promotes BMI and deters ESG washing. Regulatory pressure strengthens the legitimacy—BMI link and amplifies the deterrent effect of legitimacy on washing, while normative pressure attenuates both relationships, consistent with a shift toward symbolic compliance. These findings highlight the ethical ambivalence of legitimacy and demonstrate how institutional context shapes ESG behavior. The study contributes to corporate responsibility literature by clarifying when legitimacy drives innovation versus deception and by bridging signaling, legitimacy, and institutional perspectives. We also offer implications for managers balancing ethical responsibility with signaling and for policymakers designing regulations to curb ESG washing.

Purpose Artificial intelligence (AI) is a transformative technology reshaping many industries globally. The aim of this article is to focus on AI in three industries, notably healthcare, education and technology sectors in the United States and India. We explore the distinctive applications, opportunities and challenges of AI in these countries and sectors, by comparing them. Design/methodology/approach This is an editorial insight piece that analyzes and outlines some of the key themes related to AI application in the United States and India. Findings This article analyzes differences in how AI is utilized in the United States and India across the three sectors, highlighting benefits and challenges. Originality/value The editorial summarizes key academic and practitioner and offers new perspectives.