Veri-SFL: Privacy-Preserving Verification of Resource Allocation and Data Trustworthiness in Sustainable Federated Learning

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage extends to the users of the system, typically practitioners in fields such as healthcare and finance. Practitioners rely on AI developers to have used relevant, trustworthy data, but may have limited insight and recourse. This article introduces a software architecture and implementation of a system based on design patterns from the field of self‐sovereign identity. Scientists can issue signed credentials attesting to qualities of their data resources. Data contributions to ML models are recorded in a bill of materials (BOM), which is stored with the model as a verifiable credential. The BOM provides a traceable record of the supply chain for an AI system, which facilitates on‐going scrutiny of the qualities of the contributing components. The verified BOM, and its linkage to certified data qualities, is used in the AI scrutineer, a web‐based tool designed to offer practitioners insight into ML model constituents and highlight any problems with adopted datasets, should they be found to have biased data or be otherwise discredited.

Summary This paper presents a systematic proven and effective nine-step data governance framework that helps to enable oil and gas organizations to improve results by treating data as a strategic asset. The world is becoming increasingly data driven and technologically disrupted, and this momentum is expected to continue. According to the World Economic Forum (Kirk Bresniker 2018), "every two years we create more data than we've created in all of history. Our ambitions are growing faster than our computers can improve." Our industry's success will depend on timely development and appropriate application of machine learning, artificial intelligence (AI), and other advanced analytics. However, these tools may be ineffective and even destructive if the data used is compromised. In our study, we identified the factors that contribute to the problem of compromised data. Technical data and business landscapes are highly complex, segmented and largely disjointed. Most organizations are fragmented, working in silos rather than collaboratively. Production data owners in the field are traditionally far removed from data users at the office. Often "raw" data owners in the field have limited appreciation of the importance of "trusted data," while data users at the office spend 80% of their time looking for and cleansing data, and only 20% of their time transforming data into actionable insights to drive informed decisions (Gabernet and Limburn 2017). The disconnect between departments often results in significant "hard-dollar" loss, which contributes to unknown potential value loss. Our data governance framework (Fig. 1) is a nine-step, methodical procedure that helps address these problems and positions the company to be more agile moving forward. Determine organizational priorities and define the scope Invest in organizational change management – key for adoption and sustainability Establish the data governance organization, demonstrating comprehensive leadership support Connect and align teams through a fit-for-purpose data catalog Establish data governance policies to support priorities Define new operating model by transforming policies into new ways of working Design enabling technologies to support the data governance objectives Implement ongoing data quality and availability monitoring Facilitate sustainability via periodic audits based on pre-defined key performance indicators (KPIs) Figure 1 Our tested and effective nine-step data governance framework. Data governance isn't solved in any one business unit of an organization; it is a partnership and collaboration between all functional domains. We developed and applied this systematic procedure to create trusted data in a world-leading natural resources company. Quality production data clearly enabled better informed and more strategic decisions and helped protect the company against potential litigations. Specific benefits were quantified to the following annual recurring economic value (in the context of sub-USD 60/bbl oil price environment) for 1,700 wells in the U.S. shale: Improved information and decision quality (USD 3.7 million) Reduced cost and cycle time (USD 4.4 million) Increased production (USD 3.2 million) Trusted production data converted into robust, actionable insights improved performance in the following functions: Well performance Predictive maintenance Operational intervention Revenue and joint venture accounting Regulatory reporting Reservoir analysis In this paper we detail our tested and effective nine-step data governance framework, developed based on years of experience in the oil and gas industry, ranging from operations, engineering, and production volumes allocations to information technology and continuous improvement. Key to the importance of this work is treating data as a strategic asset and recognizing that, without trustworthy data, planning and performance analysis, whether performed by human colleague or robotics/advanced analytics, is of limited and misleading value.

The widespread adoption of Industry 4.0 technologies is fundamentally transforming manufacturing operations management. Emerging technologies facilitate resource coordination and data sharing among manufacturing assets, effectively mitigating traditional data silos. However, this transformation imposes heightened demands on system security and operational efficiency during production processes. To address these challenges, this study proposes a blockchain-centric Cyber-Physical System (CPS) architecture that integrates multiple key Industry 4.0 enabling technologies. This framework aims to establish a secure, trustworthy, and decentralised operational environment for the smart factory. Building on this architecture, we systematically design a comprehensive blockchain solution encompassing data processing workflows, smart contract, consensus mechanism, and multi-node coordination mechanism. Furthermore, to improve communication efficiency, response latency, and data trustworthiness among nodes during data processing, we formulate an integer programming model based on multi-dimensional cost evaluation. An auction-based distributed algorithm is then introduced to solve this model, thereby reinforcing the system's decentralised decision-making capabilities. Finally, simulation experiments validate the proposed methodology. The results demonstrate that the blockchain-enabled solution exhibits significant performance advantages over traditional centralised frameworks, particularly in large-scale industrial systems comprising numerous devices.

Machine-type communications (MTC) are gaining significant research attention as one of the most promising technologies for the fifth generation (5G) mobile networks. A critical issue handled by MTC is support for massive numbers of connections, which is a growing problem that will become increasingly challenging as MTC share spectrum resources with cellular communication. Here, not only the number of connections but also the data rate requirements of cellular users (CUEs) need to be considered. Given these issues, in this paper, we formulate a group-basedjoint signaling and data resource optimization model constrained by network resource and data rate requirements in order to maximize the number of connections. We also note that this problem is nonconvex and that obtaining an optimal solution is computationally complex for MTC with massive numbers of users (UEs). Therefore, we decompose the problem into group-based data aggregation and resource allocation subproblems.To solve these two subproblems, we develop an adaptive group head selection algorithm and a joint signaling and data resource allocation algorithm that satisfy both the data rate requirement and resource constraints, respectively. Our simulation results show that our proposed algorithms significantly improve the number of connections when compared with other classic methods. Furthermore, our results reveal that thelimiting factor on the number of connections changes with the ratio of the number of MTC UEs to that of CUEs and the ratio ofdata requirement of MTC UEs to that of CUEs. Finally, we note that our proposed group-based resource allocation algorithm can effectivelyimprove the number of connections, especially when more MTC UEs and a small amount of MTC data are present.

The rise of online recruitment and data collection in qualitative research has led to an increase in imposter participants. Imposter participants are dishonest individuals who fake or exaggerate their identities to participate in studies, usually for financial gain. This issue poses significant challenges to research integrity, ethics, and resource allocation. To raise awareness and address the ethical, diversity and inclusivity considerations related to imposter participation in qualitative research. Our cross-organisational steering group, comprising 17 individuals with diverse experiences in qualitative health studies, employed an educational action research approach to address this issue. Online recruitment methods, while efficient, increase vulnerability to imposter participation. Monetary compensation, while necessary for some populations such as underserved communities, may attract imposter participation. Screening processes must balance verification methods with participant autonomy and anonymity. Data collection methods need adaptation to deter imposters while remaining inclusive to genuine participants. Imposter participation significantly impacts researcher well-being and data trustworthiness. Recommendations include a critical evaluation of recruitment and data collection methods, involving patient and public involvement and engagement (PPIE) groups; increased researcher support and training on handling imposter participants; and greater awareness and guidance from Research Ethics Committees on appropriate mitigation strategies. Our findings highlight the need for a proactive, collaborative approach to enhance the trustworthiness of qualitative health research while maintaining inclusivity. Future work should focus on the effectiveness of mitigating techniques and their impact on participation from underserved communities.

Monitoring and evaluation are crucial for healthcare quality assurance. This study systematically aims to explore the unique challenges encountered during the implementation of mandatory hospital accreditation in a centralized healthcare context and develops a set of operationally sound solutions. A qualitative methodology was utilized, with semi-structured interviews conducted with 17 hospital managers, supervisors and accreditation officials in Iranian hospitals in 2024. Participants were selected through purposive sampling with maximum variation, and interviews continued until theoretical saturation. Lincoln and Guba's criteria were applied to ensure the data's trustworthiness. The data were analyzed using the content analysis technique with MAXQDA 10 software. Twenty-two challenges were identified in the themes of leadership and strategic management; human capital development and engagement and contextual coordination and adaptation. In total, 26 practical solutions were also identified for implementing the accreditation program in hospitals. This study provides a comprehensive examination of the experiences of specialists and experts regarding the challenges of hospital accreditation and practical solutions for addressing these challenges. In fact, this research offers multi-dimensional, actionable recommendations for policymakers, accreditation management bodies and service providers, moving beyond mere compliance to leverage accreditation for genuine competitive advantage and public accountability. While this research provides essential data for administrative decision-makers overseeing the accreditation process, we recognize that a purely administrative focus is inherently one-sided. Therefore, to fully realize the social and political value of our findings, this section translates the results into multi-dimensional, actionable recommendations for various key groups: For policymakers/governmental bodies (establish a cross-sectoral review board composed of representatives from patient advocacy groups, insurance providers and regulatory agencies to periodically review and validate accreditation criteria against current societal expectations, ensuring relevance and public accountability), accreditation management bodies and industry/service providers (service providers must move beyond mere compliance by utilizing the findings of this study to proactively map internal processes against the high-implementability factors identified). This involves viewing accreditation not as a hurdle, but as a roadmap for competitive advantage through demonstrable quality assurance that directly influences patient trust and market share. This study yields significant implications across several domains, capitalizing on the crucial role of accreditation in enhancing quality, ensuring patient safety and promoting stakeholder satisfaction. Despite the implementation of accreditation requirements in hospitals and continuous monitoring, we still observe shortcomings and deficiencies in some cases, which are addressed in hospital accreditation standards. By identifying the challenges faced by hospitals in this area locally and providing practical solutions, as well as implementing corrective interventions, positive steps can be taken towards the effective implementation of accreditation in hospitals. Previous studies largely focus on challenges within voluntary accreditation models (e.g. JCI and ACS), where commitment is market-driven. This study, conversely, investigates these challenges within the nationally mandated, highly centralized and culturally hierarchical Iranian healthcare context. This shift in mandatory governance creates unique friction points, such as the intense impact of frequent management changes and political influence on resource allocation, which are absent or minimized in voluntary systems. The identification of challenges directly linked to international sanctions (e.g. resource shortages and outdated technology) is a novel contribution that connects quality improvement literature with geopolitical realities. This link is vital for understanding accreditation barriers in sanctioned or developing economies and has not been adequately addressed in the existing global literature. While generalizability across all countries is limited, the findings serve as a crucial benchmark for the wider set of Middle Eastern, highly centralized, and developing economies facing similar governance and resource constraints. The 22 challenges and 26 solutions identified offer a localized framework that is highly applicable to systems operating under similar resource pressures.

Traditional technical solutions face inherent limitations in ensuring data ownership transparency, tamper-proofing, and traceability in data factor markets, particularly regarding core challenges like unclear ownership rights, high transaction costs, and trust deficits. This paper proposes and designs a blockchain-based data trust registration system. The system is theoretically grounded in the “three rights separation” framework of “rights bundles” and architecturally divided into six layers: data resource layer, storage layer, core layer, service layer, consensus and security layer, and application layer. It enables systematic processing and hierarchical interaction of complex data through progressive data trust certification, ownership transfer and traceability, and integrity verification. This establishes a collaborative, efficient, and trustworthy data registration infrastructure. By integrating on-chain and off-chain storage coordination, the system achieves efficient certification and secure balance of data rights. Additionally, the paper explores extended technologies like cross-chain interoperability and zero-knowledge proofs to address future needs for multi-chain coexistence and sensitive data registration. Ultimately, this solution aims to provide core technological support for building a trustworthy, efficient, and open data factor circulation environment, thereby reducing transaction trust costs and unlocking the latent economic value of data.

Performing a complete and accurate desktop analysis of a Test Program Set (TPS) with all the supporting data can be an extremely exhaustive experience. True TPS transparency has plagued the world of test and diagnosis for decades. Programs managers and users have a need to know exactly how the TPS works and how the Automatic Test Equipment (ATE) resources are allocated. It is fundamental to automatically make available a total envelope of TPS instrument usage and determine or make suggestions about TPS resource allocation considerations or facts. Exposing TPS facts which are somewhat hidden and providing guidance to aid in the determination of planning and support is important for process improvement. The evaluation of ATE resource allocation for a group of TPSs will aid in ATE design engineering. TPS resource transparency needs to be made available to all high level users and managers. Those who use a TPS and those who manage or oversee TPSs should have the resource data readily available to evaluate the TPS to know things like resource allocation usage and how the resources are used to expose TPS instrument requirements for future development and support. There are many pertinent and critical aspects which pertain to instrument settings and usage. Instrument or resource evaluation for a TPS is a much needed notion to judge test program performance and long term support. ATE resource utilization, selection, and recurrent problems of specific instruments, programming techniques or instrument settings can be revealed. There is a potential to refine the way a unit is tested, how resources are allocated and if resources can be optimized. Optimal resource allocation can potentially lower test time, solve TPS weaknesses, and keep current with technology to reduce long term support costs. An emulator can reveal run-time inefficiencies, range settings, limit levels, check program flow, allow assigning values to TPS variables, etc. The comprehensive information contained in the TPS and supporting data can serve to expose under and over utilized test equipment, proper resource selection, and many other issues which determine the quality of the TPS and ATE resources. Software programmable algorithms could expose facts automatically. A TPS developed by different engineers can and probably will utilize different instruments and/or instrument settings to perform some tests. The optimal use of instrumentation can be seen by RTOK rates, diagnostics, optimal measurements and glitches. There will always be some similarities in a TPS developed by different engineers but optimizing resource allocation is vital. To do an automated analysis of TPS resource usage data does provide valuable information but there can be questions about whether or not the TPS developer allocated the ATE resources properly or optimally. It is a fact, TPS developers vary in skill level and there can be profound differences in how resources are allocated. Relying on improperly allocated resources can produce superfluous results. This paper will cover the practical aspects of TPS Resource data. Also discussed is the availability of resource data and how to derive this data.

Data trading platforms play a crucial role in facilitating data circulation and promoting the sustainable allocation of data resources. Establishing a transparent, fair, and efficient pricing mechanism is key to ensuring the long-term stability and development of such platforms. However, these platforms face challenges in pricing due to the small sample problem, as traditional machine learning methods typically rely on large amounts of data. To address this issue, this paper proposes a data resource pricing model that combines WGAN-GP data augmentation and the Reptile algorithm. Data augmentation generates related datasets to increase sample size, enhancing the renewability of data resources, while meta-learning transfers knowledge across tasks, improving the model’s ability to quickly adapt to new tasks and efficiently utilize resources. Validation using actual trading data from the data trading platform shows that the proposed model accurately predicts data resource prices under small-sample conditions, outperforming other models. This study addresses the limitations of existing pricing methods in small-sample scenarios, providing a sustainable pricing solution for small-sample data resources and improving the accuracy and long-term stability of data pricing in the market.

This chapter introduces a criticality-aware data segmentation and resource allocation framework for real-time machine perception pipelines at the edge, for running DNN-based perception models in real time on resource-constraint edge platforms to process the sensing data stream (i.e., sequence of image frames). Mainstream machine inference frameworks commonly adopt a simple First-in-First-out (FIFO) policy to process the perceived images in a holistic manner without differentiating the data criticality, which results in a significant form of algorithmic priority inversion issue. Priority inversion happens when data of lower priority are processed ahead of or together with data of higher priority. The proposed framework first segments the input data into fine-grained subframe regions with different criticality, and processes them in a priority-based manner with differentiated deadlines and computation resource allocation. We design the general architecture in a modularized way and implement multiple alternative algorithms for data segmentation, prioritization, and resource allocation respectively for different edge scenarios. Experimental results on autonomous driving applications show that the framework is able to provide more timely responses to critical regions with only negligible degradation in overall perception quality. We also extend the idea into two generalized edge AI scenarios: collaborative multi-camera surveillance and edge-assisted live video analytics.

Open science, especially open data and services, is critical for achieving global goals on sustainable development and disaster risk reduction. Sharing data and collaborating openly can help put science into action. Through selected reports, this paper summarizes the 2023 International Data Week session on 'Open Data and Open Services for Disaster Risk Reduction'. Key highlights identified the importance of advancing global resilience in data preparedness throughout the full cycle of a crisis, robust digital technologies adoption, trustworthy and interoperable data infrastructure implementation, and others. The session also underscored the necessity for community-centered approaches to address data challenges and mitigate the impacts of disasters. Open and coordinated efforts are called for at the local, national, regional, and international levels to depict the crisis data landscape, explore cutting-edge technologies, and co-build robust open science infrastructures that prioritize data interconnectivity, interoperability and intelligibility for open data and open service delivery in times of crisis.

Aiming at the problem of low response speed and unbalanced distribution of data resources of production process (DRPP) for the distributed workshop production environment, an optimization scheduling method of DRPP based on a multicommunity cooperative search algorithm is proposed. A heuristic data resource service scheduling framework including a load manager and dynamic scheduling engine is first built to deal with the uncertainty of data resource service response and the imbalance of resource allocation; a core scheduling optimization mathematical model with the objectives: resource service efficiency, reduced response time, and load balancing, is established. Then, a multicommunity cooperative search algorithm for the scheduling model is presented, and the mapping relationship between the particle position vector and resource allocation is established via binary coding. Thus, the optimization algorithm is mapped to discrete data space, and the multicommunity bidirectional driving evolutionary mechanism is used to realize the cooperative and interactive search between common and model community, which enhances the adaptability of the algorithm to dynamic random scheduling tasks. Finally, the effectiveness of the proposed method is verified by an example of multiprocess quality prediction service scheduling in silk production process, which provides an effective means for solving the complex scheduling problem of production process data.

Due to the uncertainty of renewable energy generation, frequency fluctuations and supply-demand imbalance issues of the power grid are becoming increasingly prominent. Since computing jobs can be allocated varying amounts of resources within service level agreement constraints, the data centers possess temporal flexibility in energy consumption. By leveraging this flexibility, data centers can support the grid by participating in ancillary service markets while earning additional revenue. However, the multi-time-scale requirements of grid regulation demand create challenges for joint market bidding and resource allocation strategies. In this paper, we propose a multi-time-scale decision-making approach for data center bidding and resource allocation to provide both frequency regulation and reserve services. First, we model the hour-ahead market bid, real-time power decision, and real-time resource allocation processes as three-layer Markov Decision Processes. Next, we develop a multi-time-scale optimization method based on hierarchical reinforcement learning to solve the proposed model. At the upper layer, the market bid decision is optimized using the Deep Deterministic Policy Gradient algorithm. Based on the bidding, the second layer determines the target operation power. Based on the target power, the third layer optimizes job resource allocation. Finally, we validate the effectiveness of the proposed method for data center market bidding and resource allocation through numerical experiments and analysis.

Purpose: This study aims to identify critical factors that enhance health service delivery in post-conflict Afghanistan, focusing on effective resource allocation, comprehensive training programs for healthcare workers, community engagement, and the utilization of Health Management Information Systems (HMIS) data. The novelty of this research lies in its integrative approach to addressing health service delivery in post-conflict settings. Unlike previous studies that have often examined these factors in isolation, this research uniquely combines them to provide a comprehensive framework for improving healthcare outcomes. Method: Using a narrative literature review methodology, this study synthesizes findings from peer-reviewed journals, government reports, and international health organization publications. Results and Discussion: The findings reveal that targeted resource allocation is essential for reducing health inequalities and improving service delivery. Comprehensive training programs enhance the skills and knowledge of healthcare providers, leading to better service quality and management efficiency. Community engagement fosters a sense of ownership and accountability, improving health practices and outcomes. Effective utilization of HMIS data enhances decision-making, monitoring, and resource allocation. The study concludes that a holistic approach, integrating these elements, is necessary to build resilient health systems in post-conflict settings. Research Implications: The implications for policymakers and health managers include prioritizing resource allocation, training, community engagement, and data utilization to improve health outcomes. Future research should focus on developing and implementing strategies that incorporate these factors to strengthen health systems in similar environments. Originality/Value: The novelty or urgency of this study is underscored by the profound disruptions in Afghanistan's healthcare system following the Taliban's takeover in August 2021. Decades of conflict have severely impacted healthcare delivery, exacerbating existing challenges and creating new barriers to accessing essential services. The withdrawal of international aid and restrictive policies have further strained the healthcare infrastructure, particularly affecting vulnerable populations such as women and children. This urgent backdrop justifies the need for a comprehensive analysis to identify strategies that can effectively address these challenges.