Environmental sustainability and fairness in auction systems are becoming increasingly important as systems evolve with the integration of digital technologies. This paper introduces a novel demand-supply matchmaking (DSM) framework designed to improve fairness and sustainability in auction environments, aligning with the principles of the circular economy. The framework addresses key challenges in supply chain management, such as equitable resource distribution and the reduction of environmental footprints. The framework integrates key aspects of environmental impact assessments, fairness assessments, and behavioral analytics. This enables the simulation of bidder behavior and assessment of auction scenarios. Our simulation results demonstrate that the platform can promote sustainable, fair, and informed auction practices. By comparing our approach with existing tools, we highlight the advantages of using the DSM framework to improve sustainability and fairness in digital marketplaces. This work supports the development of platforms that integrate economic efficiency with environmental responsibility and social equity.

Kubernetes clusters now underpin the bulk of modern production workloads, recent 2024 Cloud Native Computing Foundation surveys report >96% enterprise adoption, stretching from 5G edge nodes and AI/ML pipelines to heavily-regulated fintech and healthcare back-ends. Every action in those environments funnels through the API server, so a single access-control slip can jeopardise an entire fleet. Yet most deployments still rely on a patchwork of Role-Based Access Control (RBAC) rules and policy-as-code admission controllers such as OPA Gatekeeper or Kyverno. In practice these controls are brittle: minor syntactic oversights, wildcard privileges, or conflicting rules can silently create privilege-escalation paths that elude linters and manual review. This paper presents a framework that models both RBAC and admission policies as first-order logic and uses an SMT solver to exhaustively search for counter-examples to stated security invariants before policies reach the cluster. The approach detects policy conflicts, unreachable denies, and unintended permissions. Three real-world case studies are presented to illustrate how the framework reveals latent misconfigurations and validates the soundness of the corrected rules. These case studies include a supply-chain image bypass, an RBAC “shadow-admi” escalation, and a multi-tenant namespace breach. To aid replication and further study, we release a fully scripted GitHub testbed: a Minikube cluster, AuthzForce PDP, admission-webhook adapter, and Z3-backed CLI that recreates each scenario and verifies policies end-to-end. While the framework does not address runtime threats, it closes a critical verification gap and substantially raises the bar for attackers targeting the most widely deployed orchestration platform. 

Access control in modern digital ecosystems is challenging due to dynamic resources and diverse stakeholders. Traditional mechanisms struggle to adapt, causing inefficiencies and inequities. We propose a novel algorithm that automates access control policy negotiation via objective optimization and utility-based methods. It enables stakeholders to jointly select policies aligned with their preferences, provided a suitable policy exists. Suggested criteria guide the evaluation of predefined policies, and a mathematical formulation quantifies stakeholder preferences with utility functions, using optimization to achieve consensus. The algorithm’s multilinear scalability is demonstrated through time and space complexity analysis. An evaluation tool supports practical testing, and the approach enhances efficiency and trust by ensuring equitable data access within digital ecosystems.

In contemporary research, the digital transformation of industries and societies has increased the importance of interdisciplinary exploration, particularly when addressing the complex challenges faced by modern organizations and social systems. From the perspective of digitalization, this literature review examines the intricate interactions between three key research domains: behavior dynamics, environmental impact, and fairness. By reviewing a wide range of studies and methodologies, it reveals new insights, challenges, and opportunities that arise at the intersection and through the interdependencies of these areas within digital ecosystems. Through a structured approach covering preliminary background, state-of-the-art methods, and comprehensive analysis, this document seeks to reveal the synergies and divergences among these domains. Special emphasis is placed on their implications in the digitalization of modern circular economy, business models, and supply chain management contexts where these domains converge in meaningful ways. Additionally, through an extensive review of the existing literature, this document highlights the current state of research and identifies notable gaps. These include issues such as ensuring fairness in digitalized sustainable strategies, understanding the role of digital behavior dynamics in promoting environmental management, and managing environmental impacts in new digitally driven business models. By weaving together these diverse elements, this work offers a novel perspective, emphasizing the importance of collaborative and integrative research in shaping a sustainable and equitable digital future.

Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.

The efficiency of an assembly line depends on how the work is distributed along the line. This is known as the Assembly Line Balancing Problem, an NP-hard optimization problem. Automatic solvers for this problem have been studied for decades but have not been widely adopted in the industry, resulting in a theory-practice gap. The typical automation approach assumes that all constraints and objectives are known and can be statically defined ahead of time such that solvers with a precisely defined objective function can take a fully specified problem instance as input and produce a (near) optimal solution as output. In some industries, meeting these assumptions is particularly challenging because of properties such as mixed-model production with high model variance, multi-manned stations, large task graphs, etc. This paper explains why, in certain industries, such as automotive end assembly, complete automation is likely infeasible in practice due to challenges in modeling the problem, collecting data, and specifying the objective function. Manual intervention by an engineer as a decision-maker is therefore unavoidable. We argue that maximizing automation, by helping the decision-maker be as effective as possible, requires a decision support system (DSS) that supports an interactive and iterative workflow, thereby enabling assisted planning. Furthermore, we identify solver features that become relevant in the DSS context, thus making the case that focusing on standalone solvers, and treating the integration into a DSS as an implementation detail, is not a viable option. We conclude that decision support systems play a central role in closing the theory-practice gap.

The short-loading cycle is a construction task where a wheel loader scoops material from a nearby pile in order to move that material to the tipping body of a dump truck. The short-loading cycle is a vital task performed in high quantities and is often part of a more extensive never-ending process to move material for further refinement. This, together with the highly repetitive nature of the short-loading cycle, makes it a suitable candidate for automation. However, the short-loading cycle is a complex task where the mechanics of the wheel loader together with the interaction between the wheel loader and the environment needs to be considered. This must be achieved while maintaining some productivity goal and, concurrently, minimizing the used energy. The main objective of this work is to analyze the short-loading cycle, assess the current state of research in this field, and discuss the steps required to progress towards a minimal viable product consisting of individual automation solutions that can perform the short-loading cycle well enough to be used by early adopters. This is achieved through a comprehensive literature study and consequent analysis of the review results. From this analysis, the requirements of an MVP are defined and some gaps which are currently hindering the realization of the MVP are presented.

Open-cry electronic auctions have revolutionized the landscape of high-value transactions for buying and selling goods. Online platforms such as eBay and Tradera have popularized these auctions due to their global accessibility and convenience. However, these centralized auctioning platforms rely on trust in a central entity to manage and control the processing of bids, e.g., the submission time and validity. The use of blockchain technologies for constructing decentralized systems has gained popularity for their versatility and useful properties toward decentralization. However, blockchain-based open-cry auctions, are sensitive to the order of transactions and deadlines which, in the absence of a governing party, need to be provided in the system design. In this paper, we identify the key properties for the development of decentralized open-cry auctioning systems, including verifiability, transaction immutability, ordering, and time synchronization. Three prominent blockchain platforms, namely, Ethereum, Hyperledger Fabric, and R3 Corda were analyzed in terms of their capabilities to ensure these properties for gap identification. We propose a solution design that addresses these key properties and presents a proof-of-concept (PoC) implementation of such design. Our PoC uses Hyperledger Fabric and mitigates the identified gaps related to the time synchronization of this system by utilizing an external component. During the chaincode execution, the creation and submission of bids initiate requests to the time service API. This API service retrieves trusted timestamps from NTP services to obtain accurate bid times. We then analyzed the system design and implementation in the context of the identified key properties. Lastly, we conducted a performance evaluation of the time service and the PoC system implementation in time-sensitive scenarios and assessed its overall performance.