The rapid expansion of renewable energy systems has intensified the need for advanced Battery Energy Storage Systems (BESS) capable of supporting grid stability, operational efficiency, and resilient infrastructure development. This paper proposes an AI-driven integrated framework for the construction, operation, and grid optimization of BESS, addressing limitations in existing fragmented approaches that treat design, control, and grid interaction as isolated processes. The proposed next-generation architecture introduces a multi-layered system that unifies construction design, digital monitoring, artificial intelligence optimization, and grid integration into a cohesive framework. The construction layer emphasizes modular design principles and advanced thermal safety systems to enhance scalability, reliability, and lifecycle performance. The digital layer incorporates real-time monitoring and digital twin models, enabling continuous system representation, predictive simulation, and performance tracking. The AI layer leverages machine learning algorithms for predictive dispatch, fault detection, and adaptive control, ensuring efficient energy utilization and proactive system maintenance. The grid layer focuses on frequency regulation and voltage stabilization, enabling seamless integration with renewable energy sources and enhancing overall grid resilience. A key contribution of this study is the development of a holistic BESS architecture that integrates AI into construction-informed design, allowing operational insights to influence structural and system configurations. This bidirectional interaction between design and operation improves system optimization and reduces long-term operational risks. Furthermore, the framework establishes a foundation for smart grid resilience by enabling real-time decision-making, automated control, and adaptive response to grid disturbances. The proposed model advances the field by providing a unified approach to BESS deployment, offering practical implications for energy providers, infrastructure developers, and policymakers seeking to enhance sustainability and reliability in modern power systems.
Enterprise IT organizations increasingly face challenges in delivering scalable, efficient, and governed digital services across complex environments. ServiceNow, as a leading cloud-based IT Service Management (ITSM) platform, offers extensive capabilities for automating workflows, consolidating enterprise processes, and enabling data-driven decision-making. This study proposes a conceptual and applied framework for the design, governance, and scalable delivery of ServiceNow programs across organizations. The framework integrates strategic program planning, modular architecture design, and best-practice governance structures to ensure alignment with organizational objectives, regulatory compliance, and operational efficiency. Key elements include the standardization of process workflows, role-based access controls, and continuous performance monitoring to support iterative improvement and value realization. Applied methodologies focus on phased program deployment, stakeholder engagement, and change management strategies that mitigate operational risks and enhance adoption. The framework further emphasizes scalability through reusable configuration patterns, integration with enterprise systems, and automated orchestration of cross-functional processes. Case-based scenarios illustrate how the framework addresses common challenges such as service delivery bottlenecks, inconsistent process adoption, and governance gaps, while enabling measurable performance improvements. This research contributes to both theory and practice by providing a structured approach to ServiceNow program management, demonstrating how conceptual design principles can be translated into practical, enterprise-scale implementations. Ultimately, the proposed framework supports organizations in achieving resilient, adaptive, and high-performing IT service operations, fostering sustainable growth and digital transformation. Future studies may explore the integration of artificial intelligence, predictive analytics, and real-time operational dashboards within the framework to further optimize service delivery and strategic decision making.
BACKGROUND:Surgical procedures remain the gold standard for treating basal cell carcinoma (BCC), although commonly associated with cosmetic defects. The demand for noninvasive alternatives remains high. OBJECTIVE:To determine efficacy and safety of red light photodynamic therapy (PDT) with 10% 5-aminolevulinic acid (ALA) gel vs vehicle for treatment of superficial BCC. METHODS:This randomized, double-blind, vehicle-controlled, pivotal phase III study was conducted at 21 centers in the US. Eligible participants had ≥1 naïve superficial BCC and received 1-2 PDT cycles (2 PDTs each cycle) followed by clinical and histological assessment 12 weeks after start of the last PDT cycle. RESULTS:Of 187 randomized participants, 145 received PDT with 10% ALA gel and 42 with vehicle. Histological clearance was 75.9% with 10% ALA gel vs 19.0% with vehicle (P < .0001). Clinical clearance was 83.4% with 10% ALA gel vs 21.4% with vehicle (P < .0001). A total of 88.1% of participants treated with 10% ALA gel rated the esthetic outcome as very good or good. No previously unknown adverse events occurred. LIMITATIONS:Few participants with lesions on face/scalp; to date, a 60-month follow-up is still ongoing. CONCLUSION:10% ALA gel showed significantly higher clearance rates than vehicle with unproblematic safety and positive esthetic outcome.
Editors’ note: The Ogawa-Yamanaka Stem Cell Prize recognizes groundbreaking work in translational regenerative medicine using reprogrammed cells. The prize is supported by Gladstone Institutes, in partnership with Cell Press. Winner of the 2024 Ogawa-Yamanaka Stem Cell Prize Rusty Gage made landmark discoveries that fundamentally shifted the field of neuroscience.
