In many large enterprises, scaling agile has become a central part of broader digital transformation efforts. While the benefits of agile at the team level are well documented, the role of managers, particularly those in middle management, remains conceptually underdeveloped and practically ambiguous. This paper presents a structured review of 15 recent studies on large scale agile transformations published between 2019 and 2025. The synthesis identifies four recurring domains of managerial tension. These domains are leadership and authority, decision making and governance, coordination and integration, and performance and sustainability. The analysis interprets these tensions as paradoxes that require managers to reconcile autonomy with control, short term delivery with long term capability building, and local experimentation with enterprise stability. Drawing on paradox theory and dynamic capabilities, the paper develops a conceptual framework that positions middle managers as brokers of paradox across organizational levels. The framework contributes to scholarship by integrating fragmented evidence on managerial work in agile transformations and by extending paradox theory into the context of large scale agile. For practitioners, it offers a lens for redesigning leadership development, governance arrangements, and performance systems so that managers are better equipped to sustain agility over time.

In this article, the inhibition efficiency of the corrosion inhibitor obtained on the basis of monoethanolamine, methyl methacrylate and phosphoric acid was studied by electrochemical methods, Electrochemical impedance spectroscopy (EIS) measurements and potentiodynamic polarization measurements.

Electric buses use regenerative braking a lot to make them more energy-efficient and lower the amount of particles they release into the air. This means that friction braking parts are used less often. This change makes brake parts last longer, but it also changes the thermal cycles and wear patterns, which can cause corrosion, uneven wear, and problems with braking performance that regular maintenance schedules based on time or mileage can't fix. This paper suggests a way to use high-resolution telematics and machine-learning techniques to predict when friction brakes in electric buses will need maintenance. We process operational data like regenerative and hydraulic braking signals, deceleration behavior, thermal cycles, state-of-charge limits, and passenger load estimates to create a Brake Wear Index and train hybrid models that use both Random Forest and LSTM architectures with Weibull reliability estimation. Results show that wear prediction accuracy has improved and that there are up to 40% fewer unplanned maintenance events than with scheduled maintenance methods. The results show how important it is to use regenerative-aware diagnostic analytics to make sure that electric buses run safely, cheaply, and reliably in urban transport networks.

Background: Modern supply chains—especially in healthcare and agriculture—face mounting pressures from sustainability imperatives, technological disruption, public-health requirements, and complex social responsibilities (Duque-Uribe et al., 2019; Haji et al., 2022). Emerging technologies such as blockchain and artificial intelligence (AI) offer transformative potential, yet their adoption raises novel operational, ethical, and governance questions (Dutta et al., 2020; Elufioye et al., 2024).

 Aim: This article develops a publication-ready, theory-informed, and practice-oriented integrative framework that synthesizes sustainable supply chain management (SSCM) principles with blockchain-enabled transparency and AI-driven predictive analytics to improve environmental, social, and operational performance in healthcare and agricultural supply chains (Mani et al., 2016; Elabed et al., 2019).

 Methods: Using a rigorous, narrative-synthesis approach anchored in the provided literature, the study constructs conceptual linkages across sustainability dimensions, technology affordances, stakeholder partnerships, and measurement approaches. The method entails deep theory elaboration, cross-referencing of empirical and conceptual studies, and development of testable propositions and managerial pathways (Hsu et al., 2013; Govindan et al., 2015).

 Results: The resulting framework identifies four integrative pillars—Governance & Partnerships; Technological Transparency (Blockchain); Predictive & Prescriptive Analytics (AI); and Social-Environmental Performance Management—each mapped to specific mechanisms, barriers, and evaluation metrics. The framework explicates how blockchain can reduce waste and increase traceability (Chowdhury, 2025; Dutta et al., 2020), how AI can optimize demand forecasting and reduce resource inefficiencies (Elufioye et al., 2024), and how combined solutions support resilience against disruption while advancing social sustainability and patient safety (Grumiller et al., 2022; Kanokphanvanich et al., 2023).

This article describes the main design features, technical characteristics and scope of application of the FSM-230 polycrystalline solar photovoltaic module (230 W). The module consists of an anodized aluminum frame and tempered glass, and has high mechanical strength. Silicon photocells are protected from external environmental influences and mechanical damage due to the fact that they are placed between protective layers based on lamination technology. The FSM-230 module is used in autonomous and backup power supply systems, and together with a charge regulator, battery and inverter, it forms an independent power supply complex. The voltage at the open contacts of the module is 36.6 V, the operating current is 7.8 A, the short-circuit current is 8.42 A, and the efficiency is 16–18%. A plastic waterproof contact box (IP65) and bypass diodes ensure reliable operation of the module. The manufacturer guarantees a 25-year service life of the module and long-term maintenance of power at 80–90%.

The rapid digital transformation of financial institutions has increased the demand for secure, scalable, and compliant platforms that can support advanced analytics and artificial intelligence (AI). Salesforce Hyperforce, a cloud-native re-architecture of the Salesforce platform, enables enterprises to leverage public cloud infrastructure while meeting regulatory and performance requirements. This paper examines how Hyperforce facilitates AI and analytics adoption in financial services by enabling elastic compute, real-time data integration, and seamless connections with data warehouses such as Snowflake and BigQuery. It evaluates architectural patterns, compliance considerations, and case studies where Hyperforce drives financial insights, fraud detection, and personalized banking experiences.

The increasing reliance on data-driven decision-making in financial services has intensified the demand for platforms that can seamlessly support advanced analytics and artificial intelligence (AI) while maintaining regulatory compliance and operational resilience. Salesforce Hyperforce, a cloud-native re-architecture of the Salesforce platform, addresses these needs by deploying Salesforce services on hyperscaler infrastructures such as AWS, Azure, and Google Cloud. By enabling data residency controls, elastic compute capabilities, and enhanced integration options, Hyperforce creates a robust foundation for financial institutions to harness AI and analytics at scale.

This paper explores how Hyperforce empowers banks and financial organizations to unlock real-time insights by integrating seamlessly with modern data ecosystems, including Snowflake, BigQuery, and AI/ML frameworks. It highlights use cases such as fraud detection, customer personalization, risk assessment, and regulatory reporting—areas where the convergence of Hyperforce infrastructure and AI-driven analytics generates measurable business value. Furthermore, the study evaluates architectural patterns, data governance models, and compliance strategies critical for adopting Hyperforce in highly regulated financial environments.

Through a combination of technical analysis and real-world case studies, this work demonstrates that Salesforce Hyperforce is not only an enabler of cloud-scale CRM but also a strategic platform for financial analytics innovation. By leveraging cloud-native infrastructure, institutions can achieve faster time-to-insight, enhanced scalability, and improved resilience while maintaining customer trust and adherence to stringent regulatory frameworks. The findings suggest that Hyperforce, when aligned with AI and analytics strategies, represents a pivotal step in shaping the future of intelligent, customer-centric financial ecosystems.