The study investigates the use of Recycled Polystyrene (RP) in the production of particleboard. Boards of 6mm thickness with dimension of 350 mm x 350 mm were produced from mixture of Cocos nucifera stem particles and RP. The boards were made at varying mixing ratio of 1:1, 2:1, and 3:1 and board densities of 1000 kg/m3 1100 kg/m3 and 1200 kg/m3. Thickness swelling (TS), Water Absorption (WA), Modulus of Rupture, and Modulus of Elasticity of the boards were evaluated in accordance to ASTM D-1037 standard. Data obtained were subjected to analysis of variance (ANOVA) at 5% probability level. TS and WA decreases as the mixing ratio increases from 1:1 to 3:1 and board density increases from 1000 kg/m3 to 1200 kg/m3. Also, MOR and MOE of boards increase as the board density was increased from 1000 kg/m3 to 1200 kg/m3. However, MOR and MOE of boards initially increase as mixing ratio increases from 1:1 to 2:1 and later decreases with further increase in mixing ratio. The strongest and most dimensionally stable board was produced at board 1200 kg/m3 and mixing ratio 2:1. This study proves that RP is a good substitute for formaldehyde based resin commonly used in particle boards industries.

The technologies for cloud ERP (Enterprise Resource Planning) have revolutionized the field of information technologies. Any kind of business can benefit from their flexibility, affordability, scalability, adaptation, availability, and customizable data. An advancement of classic ERP, cloud enterprise resource planning (C-ERP) provides the benefits of cloud computing (CC), including resource elasticity and ease of use. The rise of cloud computing affects on-premise ERP systems in terms of architecture and cost. Cloud-based ERP systems make the claim to be appropriate for digital corporate settings. System quality, security, vendor lock-in, and data accessibility are recognized as the technological issues. Industry 4.0 refers to the re-engineering and revitalization of modern factories through the integration of cloud-based operations, industrial internet connectivity, additive manufacturing, and cybersecurity platforms. One of the four main pillars of Industry 4.0, cloud-based Enterprise Resource Planning (Cloud ERP), is a component of cloud operations that aids in achieving greater standards of sustainable performance.

The last few years have seen an increased adoption of cloud infrastructure, which has in turn led to a growth in large-scale distributed architectures in data centers to accommodate cloud resource elasticity and resiliency better. Selecting the right approach to build secure, scalable, and reliable cloud infrastructure within a budget is always a challenge. This text focuses on offering practical solutions for designing and building a secure, scalable, and reliable cloud-based infrastructure where auto-scaling and multi-region deployments are the two key approaches to offer high availability. It covers designing secure and scalable microservices using cloud platforms. The content will provide an understanding of public cloud architecture, the design of microservices running on the cloud, and also the design patterns used in the cloud era. With real-world examples, you will learn how microservices can enable scalable distributed systems. Furthermore, you will be walked through multi-region deployments, auto-scaling, and traffic management in cloud environments, using a sample environment setup and useful tips and tricks for monitoring. Finally, you will see a mock implementation of cloud infrastructure on-premise for a private cloud or single-node cloud. By the end of this text, you will be able to build, manage, and deploy a highly scalable and reliable cloud-ready solution [1].

To ensure maximum flexibility, service providers offer a variety of computing options with regard to CPU, memory capacity, and network bandwidth. At the same time, the efficient operation of current cloud applications requires an infrastructure that can adjust its configuration continuously across multiple dimensions, which are generally not statically predefined. Our research shows that these requirements are hardly met with today's typical public cloud and management approaches. To provide such a highly dynamic and flexible execution environment, we propose the application-driven autonomic management of data center resources as the core vision for the development of a future cloud infrastructure. As part of this vision and the required gradual progress toward it, we present the concept of composable infrastructure and its impact on resource allocation for multi-cloud environments. We introduce relevant techniques for optimizing resource allocation strategies and indicate future research opportunities [1]. Many cloud service providers offer computing instances that can be configured with arbitrary capacity, depending on the availability of certain hardware resources. This level of configurability provides customers with the desired flexibility for executing their applications. Because of the large number of such prerequisite instances with often varying characteristics, service consumers must invest considerable effort to set up or reconfigure elaborate resource provisioning systems. Most importantly, they must differentiate the loads to be distributed between jobs that need to be executed versus placeholder jobs, i.e., jobs that trigger the automatic elasticity functionality responsible for resource allocator reconfiguration. Operations research reveals that the optimization of resource allocator reconfiguration strategies is a fundamentally difficult problem due to its NP-hardness. Despite these challenges, dynamic resource allocation in multi-clouds is becoming increasingly important since modern Internet-based service settings are dispersed across multiple providers [2].

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.