Search Results (197)

This paper examines the association between external verification of sustainability reports and ESG performance of Saudi-listed firms from the years 2014–2021. With regard to the Saudi stock exchange (Tadawul) dataset consisting of 188 firm-year observations, it is concluded that external sustainability report verification and ESG performance are positively associated. This study constructs the premise that the enhancement of credibility and transparency of sustainability reports in turn fosters stakeholder confidence. This paper documents a positive association between voluntary assurance and ESG performance from an emerging market perspective, which broadens the scope of the ESG literature. This observation particularly justifies the need to endorse more assurance services in support of sustainable development and to strengthen the reporting frameworks and policies. The study results support the objectives of Vision 2030, specifically the pillars of promoting environmental sustainability, corporate transparency, and governance. The evidence aligning national goals to encourage transparency in corporate systems and sustainability in assurance services is the positive relationship between ESG and sustainability reporting assurance. Moreover, the results highlight Saudi Arabia’s dedication to the United Nations Sustainable Development Goals, specifically SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), as they underscore the role of assurance and disclosure practices in fostering sustainable business practices in Saudi Arabia. Full article

Using an international sample of firms from 47 countries/regions over the years 2010–2020, we examine whether third-party verification of carbon emissions information affects the speed at which firms adjust their capital structure toward the trade-off theory’s optimal leverage target. Using alternative estimation techniques and robustness checks, we find that third-party carbon assurance significantly accelerates firms’ leverage adjustment speed. Firms that engage in independent carbon verification adjust more rapidly toward their target capital structure than non-assured firms. We extended our investigation and confirmed that this effect persists across both developed and developing markets. These results support the notion that carbon assurance is associated with lower information asymmetry between firms and lenders, thereby lowering the cost of external debt and facilitating faster capital structure rebalancing. We further investigate whether the relationship differs by assurance provider type by distinguishing between Big Four and non-Big Four assurance providers. The results remain robust when distinguishing between Big Four and non-Big Four assurance providers regardless of the assurer quality, confirming that assured firms adjust their capital structures faster than non-assured firms. The outcomes of this study demonstrate that firms’ sustainability reporting can shape the speed of capital structure adjustment. Full article

This systematic narrative review synthesizes 42 distinct sources including peer-reviewed journal articles, selected conference papers, and policy documents to examine the role of knowledge management (KM) processes in Saudi higher education accreditation, with specific focus on the National Center for Academic Accreditation and Evaluation (NCAAA) standards. Drawing on literature published between 2005 and 2025, the review investigates how KM frameworks, including Nonaka and Takeuchi’s SECI model (socialization, externalization, combination, and internalization), may be associated with accreditation outcomes in Saudi universities. The reviewed literature suggests an association between systematic KM approaches and more effective accreditation processes; causal conclusions are not warranted given the observational and case study nature of the evidence base. Certainty of the overall evidence body is rated as low to moderate. The study reveals significant challenges, including information decentralization, inadequate training, resistance to change, and the absence of dedicated governance structures that appear to impede effective knowledge transfer during accreditation processes. A secondary sustainability coding pass identified associations between KM-driven accreditation practices and institutional sustainability, environmental sustainability, and alignment with SDG 4 (Quality Education) and SDG 16 (Strong Institutions); these findings are hypothesis-generating rather than confirmatory. It should be noted that all screening and data extraction were conducted by a sole reviewer; a post hoc validation exercise achieved Cohen’s kappa = 0.81 (95% CI: 0.72–0.90) for inclusion/exclusion decisions, providing retrospective assurance of acceptable consistency. This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Full article

Background/Objectives: Multi-leaf collimator (MLC) positioning accuracy critically affects delivered dose fidelity in volumetric modulated arc therapy (VMAT), yet conventional gamma-based quality assurance (QA) provides only plan-level pass/fail outcomes without leaf-specific error localization. This study developed and validated a convolutional neural network (CNN) framework that classifies the magnitude and direction of individual MLC leaf positioning errors directly from fluence map data. Methods: Three patient cohorts were analyzed: 20 prostate cancer patients for model development under an 8:1:1 train/validation/test split and 20 additional prostate and 10 head and neck (H&N) patients reserved for external validation. For inner MLC leaves 21–40, systematic offsets from −5 mm to +5 mm in 1.0 mm increments were independently applied to the two leaf banks, yielding 121 error combinations per leaf. A CNN was trained as a 121-class classifier on two-channel inputs pairing the reference and error-induced fluence map regions and was compared against three tree-based baselines using five-fold cross-validation. Results: The CNN achieved 97.00% accuracy on the internal test set and 96.54 ± 0.43% accuracy across the five patient-level cross-validation folds. Across all test samples, 99.88% and 99.83% of predictions were within 1 mm of the true offset for Bank A and Bank B, respectively, well within the AAPM TG-142 1 mm MLC positioning tolerance. External validation yielded 96.19% accuracy on the additional prostate cohort and 93.72% on the H&N cohort, suggesting reproducibility within the same anatomical site and potential robustness across anatomically distinct treatment sites within a single-institution dataset. Conclusions: The proposed CNN framework demonstrates the feasibility of leaf-specific identification of MLC positioning errors in both magnitude and direction from simulated fluence maps. These findings support further investigation using physically measured fluence data for future clinical translation. Full article

This article discusses the influence of carbon emissions, both direct and indirect, on firm value. It also takes into account the moderating variable of green investment and whether governance mechanisms—like external assurance of greenhouse gas (GHG) emissions and CSR/sustainability committees—affect these relationships. The hypotheses of the study were developed using the lens of the natural-resource-based view, legitimacy theory, and agency theory. This paper leverages panel data spanning 2017 to 2024 on firms in the UK FTSE 350 to examine the moderating role of green investment on the linkage between GHG emissions and firm value. We then conduct sub-sample analyses for firms with and without externally verified GHG disclosures and CSR/sustainability committees, respectively. Firm value is captured using enterprise value, shareholder value, and the price-to-book ratio as alternative proxies for robustness. The results reveal that GHG emissions have a significant negative impact on firm value, while green investment mitigates this adverse effect. This impact is driven by both Scope 1 and Scope 2 emissions. However, green investments are more likely to be interpreted as genuine, durable, and value-creating when (a) the firm’s emissions data are externally verified and (b) an active CSR/sustainability committee guides and monitors implementation. This study adds to the environmental accounting and corporate governance literature by providing empirical evidence that external assurance and internal sustainability oversight strengthen the relationship between environmental responsibility and firm value creation. Full article

Antimicrobial resistance and persistent biofilm-associated infections have renewed interest in bacteriophages as alternatives or complements to conventional antibiotics. However, broader therapeutic adoption remains constrained by slow phage discovery, incomplete genome characterization, narrow host range, complex therapeutic matching, and manufacturing variability. Artificial intelligence (AI) offers computational approaches that may help address several of these limitations. This comprehensive narrative review discusses current AI applications across the bacteriophage pipeline, including metagenomic phage discovery, genome annotation, phage–host interaction prediction, personalized phage selection, cocktail optimization, and phage–antibiotic combination design. The review also examines AI-assisted synthetic biology approaches, including receptor-binding protein redesign, CRISPR-enabled engineering, generative genome design, and biosafety screening, as well as emerging applications in bioprocess optimization, yield prediction, purification analytics, quality assurance, and supply-chain management. Current evidence suggests that AI may accelerate phage identification, improve host-range prediction, support therapeutic optimization, and strengthen manufacturing consistency, potentially facilitating the transition of phage therapy from individualized rescue interventions toward more scalable antimicrobial platforms. Nevertheless, major limitations remain, including fragmented, taxonomically biased datasets; limited external validation; restricted interpretability; privacy concerns; biosafety oversight; and evolving regulatory frameworks. Future progress will depend on standardized datasets, multimodal validation, scalable manufacturing systems, experimental and clinical verification, and coordinated regulatory development. Full article

Quality assurance in higher education increasingly depends on the capacity of institutions to transform stakeholder engagement into usable evidence for decision-making and continuous improvement. Among external stakeholders, alumni represent a potentially strategic but underutilized source of information on the relevance of training processes and their alignment with professional trajectories. However, the existence of alumni engagement does not guarantee its integration into formal quality assurance systems. This study analyzes how an institutional alumni management model is designed to articulate graduate engagement with internal quality assurance processes. Adopting a qualitative case study approach based on documentary analysis, the research examines the organizational architecture of a Chilean university, focusing on the mechanisms through which alumni participation is expected to be translated into evidence for academic decision-making. The findings show that the model combines strong relational infrastructures with emerging mechanisms for data capture and circulation. However, the institutionalization of processes for interpreting and using evidence remains less developed, revealing an asymmetry between participation, data production, and decision-making. Based on these results, the study conceptualizes alumni integration into quality assurance as a multi-stage process involving participation, data capture, circulation, and use, highlighting the organizational conditions required for each stage. The study contributes by proposing a process model of institutional translation that identifies the organizational breakdowns through which alumni engagement may remain disconnected from formal quality assurance processes. In doing so, it shows that the effectiveness of quality assurance systems depends not on the availability of data alone, but on the governance arrangements that enable evidence to be interpreted, circulated, and used. Full article

Cold-chain supply systems depend on a sequence of linked production and logistics decisions. In prepared-food cold chains, quality may deteriorate not because one visible failure occurs, but because testing, traceability records, temperature monitoring, and abnormal-condition reporting are gradually weakened under cost pressure. Once such hidden effort reduction accumulates, external disturbances may push the system from strict assurance to weakened governance. To explain this nonlinear process, an asymmetric evolutionary game is built between prepared-food producers and cold-chain logistics providers, each choosing between strict and weakened quality assurance. White Gaussian noise is introduced to represent random operating shocks, and the two-population strategy system is projected onto a system-level quality-governance coordinate, $q$. This projection is used as a transparent baseline coordinate rather than as an assumption of linear system evolution. The reduced system is then transformed into a stochastic cusp catastrophe model, with a resilience indicator used to measure the distance from critical transition conditions. Numerical simulations show that quality assurance costs and short-term cost-saving benefits move the system toward a weakened-governance basin, whereas external incentives, coordination degree, and credible accountability mechanisms support recovery toward strict collaboration. The framework offers a scenario-based resilience diagnosis approach for identifying threshold effects in cold-chain quality governance. Digital traceability, temperature-data sharing, incentive alignment, and accountability rules are further interpreted as operational innovations that improve resilience and reduce avoidable quality losses in sustainable cold-chain operations. Full article

AI-enabled cyber–physical systems (CPSs) are increasingly deployed in public governance contexts where they sense human populations, infer classifications or risks, and trigger interventions that can shape liberty, equality, and access to essential services. In these deployments, governance failures often arise not only from model error but from systems-level interactions across data generation, model updates, organizational practices, and downstream actuation. This paper introduces a Risk–Rights–Rules (3R) architecture that treats fundamental rights and legal rules as enforceable constraints on the sensing–inference–actuation loop, rather than as external ethical aspirations. Building on established risk-management baselines and safety engineering practice, we specify a testable assurance object, a structured 3R assurance case, that links rights claims to explicit assumptions, measurable evidence, and accountable control points across the lifecycle. The approach is designed to reduce “legitimacy drift” in stochastic decision pipelines by making uncertainty, demographic error, contestability, and procurement leverage auditable at the system level. The result is a governance blueprint for high-consequence public-sector AI deployments for governance failures, which is both technically robust and institutionally defensible. Full article

International accreditation has become an important reference point for quality assurance in higher education; however, its relevance depends on how global standards are interpreted and adapted to local institutional contexts. This study analyzes institutional stakeholders’ perceptions of an international accreditation process, with the aim of examining how global standardization interacts with local relevance in quality assurance. A quantitative, descriptive, cross-sectional study was conducted with 408 participants linked to a university degree program, including students, graduates, faculty members, administrative staff, and authorities. Data were collected using a 49-item questionnaire developed from the evaluation criteria of an international accreditation manual and adapted to the institutional context. Descriptive and nonparametric inferential statistics were applied. The results indicate: (a) an overall positive assessment of the quality model implemented; (b) comparatively higher ratings for management- and resource-oriented dimensions; (c) comparatively lower ratings for pedagogical dimensions; (d) no statistically significant differences across stakeholder profiles, suggesting a broadly shared interpretation of the accreditation process; and, (e) statistically significant but small gender differences, which should be interpretated cautiously. The findings suggest that international accreditation is perceived as contributing to transparency, comparability, and external recognition, although its value depends on the extent to which standardized frameworks remain sensitive to pedagogical and contextual realities. Full article

Lysosomal diseases (LDs, or Lysosomal Storage Disorders) have become increasingly visible in the newborn screening community, with the addition of mucopolysaccharidosis type II (MPS-II) into the Recommended Uniform Screening Panel in August 2022 and Infantile Krabbe disease in June 2024. As more LDs are expected to be considered for screening adoption, the ability to multiplex conditions and expand proficiency testing (PT) using quality control materials is essential. This study examines the use of recombinant enzymes to produce first-tier PT materials for mucopolysaccharidosis type I, MPS-II, Gaucher, Fabry, Krabbe, Pompe, and Niemann–Pick A/B (acid sphingomyelinase deficiency)—adding four disorders to the CDC’s Newborn Screening Quality Assurance Program (NSQAP) LD PT panel. Through an iterative process that included two prototype phases, two pilot phases, and external testing by up to 31 external laboratories, a new manufacturing process was developed for producing high-performing dried blood spot-based LD PT specimens. Materials were evaluated using several methods commonly employed by newborn screening laboratories, including tandem mass spectrometry with flow injection and liquid chromatography, digital microfluidics, and fluorometric assays. This novel process for producing LD PT materials offers several advantages over previous manufacturing methods that relied on immortalized cell lines from affected patients. Improved scalability, for example, has enabled NSQAP to expand LD PT enrollment internationally. Furthermore, the new process makes it easier to support future expansions of the LD screening panel. The updated specimens and expanded program were launched in January 2025. Full article

As global markets navigate the dual transition of digitalization and sustainability, the risk of “digital greenwashing” has emerged as a critical corporate governance challenge. Utilizing a comprehensive dataset of Chinese A-share listed firms from 2018 to 2024—an ideal laboratory characterized by rapid regulatory shifts and unique state-market dynamics that provide highly generalizable insights for other emerging economies—this study empirically investigates whether corporate digital transformation acts as a genuine driver for Environmental, Social, and Governance (ESG) enhancement or merely serves as a symbolic disclosure tool. Fortified by rigorous identification strategies, including Propensity Score Matching and Lewbel heteroskedasticity-based instrumental variable estimations, the results confirm that digitalization serves as an incremental yet statistically significant driver for corporate sustainability. Crucially, mechanism analyses reveal a “full moderation” effect: the positive impact of digitalization on ESG performance is completely activated only in the presence of premium external assurance (e.g., Big 4 audits). Without high-quality IT auditing to act as a credibility enforcer and verify the substance of digital signals, technological adoption alone fails to yield significant ESG improvements. Furthermore, a nuanced structural asymmetry is identified: foundational data infrastructures (Cloud Computing and Big Data) directly enhance quantifiable Environmental and Governance metrics, whereas premium audits are strictly required to activate the “soft,” qualitative Social dimension. Finally, the synergy exhibits distinct boundary conditions. It is heavily concentrated within high-pollution industries where digital transition acts as a regulatory survival imperative rather than mere market expansion, and its reliance on external assurance is fundamentally driven by the market-signaling needs of non-State-Owned Enterprises (non-SOEs) rather than the policy-distorted mandates of SOEs. These findings offer critical theoretical extensions and policy implications for standardizing digital-audit infrastructures globally. Full article

Background: Folate receptor alpha (FRα), commonly expressed in epithelial ovarian cancers, is a clinically actionable biomarker following approval of the antibody–drug conjugate mirvetuximab soravtansine (MIRV). Pivotal trials showed that high FRα expression predicts MIRV benefit, creating the need for standardized testing to ensure timely, equitable access. Methods: To address the need for guidance on FRα testing, the Society of Gynecologic Oncology of Canada convened a multidisciplinary Expert Panel to review the evidence and integrate Canadian clinical, pathology, laboratory, and patient perspectives. Consensus recommendations were developed through structured evidence review, expert discussion, iterative revision, and patient partner input. Results: The panel issued recommendations on the clinical role and timing of FRα testing, tissue requirements, assay selection and validation, interpretation and reporting standards, laboratory quality assurance, reimbursement, and equitable access. It is recommended that FRα testing be available to all patients with epithelial ovarian cancer, with results available no later than platinum-resistant disease, using a validated assay, preferably the Ventana FOLR1 RxDx assay or an appropriately validated laboratory-developed test. Standardized synoptic reporting, participation in external quality assurance programs, and clear patient communication were deemed essential. Conclusions: These recommendations aim to promote integrated, equitable, standardized FRα testing across Canada and support timely identification of patients eligible for FRα-directed therapy, clinical trial enrollment, and future biomarker-driven treatment strategies. Full article

Industrial robotic systems increasingly operate as heterogeneous ecosystems in which production, maintenance, quality assurance, safety, and human–machine interaction are coupled through shared data and cross-layer constraints. Existing modeling approaches remain structurally fragmented: hierarchical taxonomies support decomposition, graph-based models primarily encode pairwise relations, and analytical layers are commonly attached as external pipelines. This paper proposes a hypergraph-based framework for the multi-contextual state representation of industrial robotic systems. The framework combines a multi-layer problem taxonomy, a formal definition of context as an active semantic processing unit, and a directed hypergraph model with signed incidence for representing dependency, interpretative, compositional, and cross-layer constraint relations without binary decomposition. The model is instantiated on grasping and maintenance examples and translated into a numerical interface for downstream analytical processing. Quantitative results are also reported. Benchmarking shows near-linear compile-time and star-expansion scaling, while comparison with pairwise encodings confirms lower representational overhead for higher-order relations. In a canonical grasping scenario, one-cycle hypergraph-grounded inference remains in the microsecond range on CPU, with a median latency of 2.264 µs. These results indicate that the proposed framework is computationally tractable as a representational substrate for context-aware analysis. The contribution of the paper is not a new control algorithm, but a formal representation and numerical translation layer for future learning-based and rule-based analytical methods. Full article

This paper presents the Data Quality Assurance Model (DQAM), a formal model and multilayered architecture designed to guarantee data integrity and robustness in Reliable and Quality-Assured Information Systems. Recognising that inaccurate or corrupted sensor data can lead to system collapses and false alarms in critical services, the DQAM provides a standardised and systematic flow of actions to ensure data excellence for Artificial Intelligence (AI). The architecture is structured into three specialised layers (Acquisition, Processing, and AI Adequacy), implementing formal transformation functions that act as a rigorous filter against data degradation. A core contribution is the mapping of these functions to ISO/IEC 25012 and 5259-2 standards, providing a practical framework for reliable information management. It should be noted that quality dimensions regarding timeliness and data volume are outside the scope of this work, as they depend on external data issuers and end-service requirements. The model’s viability is validated through a real-world implementation on a university campus managing millions of data points, demonstrating its capability to optimise performance—achieving a speedup of up to 43%—and prevent service malfunctions. This work bridges the gap between raw IoT streams, and the high-integrity standards required by modern AI-driven applications. Full article