Articles

Decentralized control loops are widely credited with improving system resilience, on the reasoning that removing a central point of coordination also removes a central point of vulnerability. This article separates two properties that applied literature on decentralized architecture routinely conflates: bounded fault tolerance, the capacity of a control architecture to remain stable when an individual loop or node is switched off or corrupted in isolation, and network-level resilience, the capacity to recover from correlated disturbances such as communication loss, targeted compromise, or cascading propagation across dependency chains. Reviewing control-theoretic stability results alongside consensus and infrastructure-resilience research shows that the first property follows from specific, testable structural conditions: a steady-state gain condition in one case, a minimum-neighbor redundancy count in another. These conditions come in different strengths depending on whether the disturbance is benign disconnection or active compromise, a distinction the second property depends on and that decentralization leaves for the designer to resolve. The analysis concludes that a resilience claim attached to a decentralized control loop is a claim about a specific structural parameter matched to a stated disturbance model, a parameter whose verification requires evidence beyond the topology itself.

Conventional cyclone risk assessments in Small Island Developing States (SIDS) systematically fail to predict food system collapse locations, leading to misallocated adaptation investments. This study quantifies a fundamental exposure-impact disconnect across Vanuatu's 71 area councils: cyclone exposure explains only 14% of food security impact variance (R² = 0.14, r = 0.38), with topographic, agro-ecological, and infrastructure mediators determining the remaining 86%. Using data from five Category 4–5 cyclones (2015–2023), we constructed a Hazard Exposure Index (HEI; proximity 40%, intensity 35%, frequency 25%) and an Impact Severity Index (ISI; four equally weighted dimensions: agriculture, infrastructure, food insecurity, recovery). Regression with interaction terms reveals three mediating mechanisms. First, topographic elevation reduces ISI by 40–60% at highland area councils (>400 m) relative to coastal equivalents at comparable HEI (r = −0.54, p < 0.001). Second, crop diversity buffers medium-exposure zones (HEI 0.3–0.6; β = −0.082, p = 0.018) but provides no significant protection under extreme cyclone loading (HEI > 0.7), consistent with resilience theory stability thresholds. Third, infrastructure capacity generates "hidden hotspots": Torba province (low exposure, ISI = 0.34) required a median 18 days to reach 50% of affected households versus 6 days in Shefa (high exposure, ISI = 0.61) — a threefold differential driven solely by maritime connectivity deficits. High-exposure provinces with strong infrastructure systematically outperform low-exposure provinces with distribution network weaknesses. A Food Security Resilience Index (FSRe; range: 46–61 on a 0–100 scale) reveals stark provincial heterogeneity — Sanma as buffered regional hub (FSRe = 61) versus Tafea and Shefa as critical/fragile (FSRe 46–47) — enabling a four-category spatial typology for targeted adaptation investments under SIDS fiscal constraints. Findings demonstrate that spatially explicit integration of topography, agro-ecology, and infrastructure is essential for efficient climate adaptation planning in archipelago SIDS, with direct implications for Sendai Framework Target E national DRR strategy development and Green Climate Fund adaptation investment prioritization.

Smallholder farmers play a pivotal role in global food security; however, they remain exceptionally vulnerable to the impacts of climate change due to their reliance on natural resources and limited adaptive capacities. This narrative review synthesizes a wide range of global sources to explore the intersection of smallholder agriculture and climate adaptation strategies. The review examines adaptation practices, agroecological methods, and the adoption of climate-resilient crop varieties. It reveals that the implementation of these strategies is frequently hindered by systemic barriers such as financial constraints, limited technological access, and institutional inefficiencies. Recognizing that previous studies have addressed isolated aspects of adaptation or relied on secondary data, this review highlights research gaps and offers a comprehensive synthesis of relevant literature. This review uses a narrative synthesis model suitable for integrating evidence from agronomy, economics, and social science to capture the complex challenges faced by smallholder farmers. The review emphasizes the importance of policy frameworks and participatory approaches that empower smallholder communities. This review synthesizes current evidence to inform potential directions for targeted interventions and future field-based studies, while recognizing the limitations of relying on secondary data. These recommendations aim to facilitate integrated policy reforms and drive research initiatives, ultimately strengthening the resilience and adaptability of smallholder agriculture in the face of ongoing climate change.

Unfortunately, it is not disaster recovery, high availability, or cloud technologies that are inherently difficult to understand, but rather the action of implementing them for software applications that is difficult. The unique method of implementation for a microservices architecture is explored. Regulatory compliance doesn’t stop just because an effective disaster recovery requirement is tough to satisfy for infrastructure unique to sleek microservices. The high-availability location transparency bliss offered by a cloud solution is appealing to a security engineering department. However, the headache starts when the technology presents a handful of undesirable surprises that leak RESTful microservices to the outside world. These are the challenges that post-SOA cloud-resident robustly scalable applications will need to address and overcome. The goal is to explore several popular methods of accomplishing these tough objectives so that engineers can further research the most practical solution. An innovative implementation that leverages Service Bus relays as an elegant disaster recovery solution while enforcing a strict subnet where RESTful microservices solely live will be discussed. The curiosity lies in the atypical experimentation beyond basic gateways and the facility of using such simplicity while still answering day-to-day software development infrastructure challenges for applications we build. Resilient full-service web proxy service crashes and delivery latency switches by harnessing the microservices pod health will also be discussed [1].

Considering the current climate conjuncture, it is a consensus that green spaces in large contemporary urban areas should be increasingly more numerous and simultaneously more sustainable, being adapted to the edaphoclimatic conditions of the site, and with reduced maintenance costs. In the case of Algarve, where this research is focused, the current and future water availability, assumes a preponderant role in the design of green spaces, where the demands mentioned above can only be achieved if we deviate from conventional landscape practices and develop holistic strategies of management and design of green spaces that integrate different areas of knowledge and not merely aesthetic issues. In this context, this work aims to develop more adapted and resilient landscaping practices to the current and future climatic conditions of the Algarve, thus reinventing the concept of landscaping in the south of Portugal. Thus, it will be of paramount importance to develop more sustainable, resilient and tolerant projects to worsening ecological conditions, particularly limitations associated with water availability. The xeromorphic succulents are a group of plants with mechanisms of tolerance to water stress and with very specific characteristics, being succulence one of the most relevant. Studies on these mechanisms are increasingly frequent, which may prove to be very advantageous in our adaptation to future climatic challenges. In addition, their ornamental potential is enormous, since their bold forms and colours are a veritable sensory explosion, which, combined with their morphological and physiological characteristics, make them the species of choice in the reconversion or creation of xerophytic gardens.

The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.

Heavy-duty powertrain systems resource multiple sensors collecting streams of data during production. Sensor data streams, originating from various production machines, simultaneously encounter the challenging situation of limited resilience to sensor quality issues in terms of sensors being non-linear, outdated, spawning false alarms, or going offline. Alleviating this critical situation requires a reinforcement technology to assure resilience, with both verified lineage-based approaches and a observe-and-repair mechanism of limited quality. Moreover, legislation advances aimed to guarantee higher future operational safety put additional pressure on the overall manufacturing sensor monitoring frame. For heavy-duty powertrain systems harnessed for combustion engines, an integrated technology based on automatic data lineage and quality governance detection of production machine sensor data streams resource the frame using a unified provenance model and ontology supporting the explicit definition of sensor quality rules. Provenance models automatic integration form the basis that enables the monitoring of multiple machine resources. Used quality rules enable the detection of bad-quality sensor segments while, in parallel, additional quality rules based on observed data control supported by human intervention capabilities repair gaps during production. The proposed automated technology provides an additional layer of resilience that improves the reliability of sensor data stream quality and contributes to the adherence of operational safety future rules. The implementation case study focuses on sensors deployed in powertrain systems production that modern industries must follow to ensure next-generation production safety levels.

The evolution of supply chains (SC) from a linear to a network structure created an opportunity for new processes, product/service offerings, and provider-business. Rising customer service expectations have led to the need for innovative SC designs to develop and sustain competitive performance globally. Firms are forced to respond and adapt accordingly, thereby leading to design, network, operational, and performance dynamics. Traditionally, SCs are treated as static structures, focusing solely on design and/or operational optimization. Such perspectives are not viable options for SC domains, as they address only a portion of the dynamic problem space, use a deterministic assumption of dominant design variables, capitalize on past data to predict future decisions, and offer pre-classified forecasting options complemented with a limited comprehension of systemic SC elasticity. Novel self-learning agentic systems are proposed that blend the sciencematics of SC decisions and dynamics. The designs guide firms seeking to build adaptive SCs using operational decision processes. The designs address the agentic nature of SC, embedding computational interaction models of firm SC networks. The designs contrast the stochastic action-taking and thereby the performance outcomes, discovering opportunities for adaptive operational designs of SC tasks. Fine-tuning and meta-learning are new design capabilities that adapt to evolving dynamic environments. Frameworks for behavioral customization and systematic exploration of the design space are provided as user guides. Exemplar designs are also provided to serve as a translation template for users to express operational models of their own contexts. To account for the dynamics of supply chains (SC), agent-based models are increasingly adopted. Such models exhibit SC structure and/or formulation dynamics. Though existing efforts commence adjacent-only structural changes, dynamism with respect to tasks is crucial for SC design and operational strategy development. Proposed is a process modeling library and workflow for discovering intricate designs of adaptive agentic systems. The library revises Dataflow and Structure, concealing sequencing and context designs of processes. Prompted specifications describe and enact designs. Applications in SC formulation discovery are provided.

Key business objectives for digital infrastructure cloud adoption are often framed in terms of reducing cost, improving fault tolerance and resilience, simplifying scale, and enabling innovation. Given the critical nature of the financial sector, however, where timeliness and price can significantly determine an outcome, cloud migration in delivery environments demands greater throughput on the critical path and, in many enterprise-scale settings, forgoes hybrid complexity and multi-cloud risks. Nevertheless, slack in system designs does exist; financial institutions enable market functionality—trading, clearing/best execution—despite potentially being able to meet such sets with lower service levels than other verticals. A cloud multi-account structure for sensitive data, for example, naturally limits exposure when combined with observed risk. Fulfilling predictions of elasticity during periods of high demand usually requires support from a dedicated environment (or environments) located nearer to the operations. Components can consequently be allocated on a per-account basis or maintained as shared sink systems to which the dedicated streams write. The automation code can similarly be targeted for dedicated accounts, avoiding the resource constraints that beset such operations during industry events like emergency triage/contact desking.

The supply chain ecosystem plays a very important role in the success or failure of organizations, markets, and economies. Supply chain ecosystems are broadly defined as supply chain organizations and their collaborators. Today's combined challenges of pandemic shutdowns, rising internet usage, and skyrocketing climate change concerns demand that the supply chain ecosystem better connect with customers, when and how they want, to provide products and services with high levels of availability and zero defects, yet collaboratively do this to reduce transportation and production risks, often at the same time reducing operational costs and carbon footprints. Addressing these challenges, this work explores the cloud delivery capabilities of cloud-native architectures to enable the big data integrations and analytics that are needed to grow smarter supply chain ecosystems. This work describes what smart supply chain ecosystems are and how they are planning to grow their technology and integration capabilities. Discussing the industry-leading advanced and manufacturing technology producer ecosystems, it is explained how their technology collaboration and investment plans are driven by climate change and job creation goals. With these background models, the work examines the new digital reality of customer-driven experiences and economies that are demanding cloud-native and intelligent technology partnerships to deliver climate objectives, operational responsiveness, and compatibility to avoid trading economies of scale for economies of integration. The final objectives of this paper are to share key ideas about the need to balance the growing customer service direct-to-consumer business models with those for collaborative investment by market and industry. In doing this, it hopes to promote an intelligent supply chain ecosystem foundation for helping its different participating countries survive and thrive in the digital economy.