Systems, Applications & Products in Data Processing (United Kingdom)

Angiotensin-converting enzyme (ACE) inhibitors are widely used antihypertensive agents with proven cardioprotective effects. Previous mechanistic and clinical studies have suggested that ACE inhibitor therapy may slow disease progression and reduce mortality in idiopathic pulmonary fibrosis (IPF). Does ACE inhibitor use associate with reduced all-cause mortality in a real-world population of patients with IPF, and if so, is the association also present in patients with COPD? A retrospective analysis was conducted by using electronic health records from the Clinical Practice Research Datalink GP Online Database (2019), linked with Hospital Episode Statistics Admitted Patient Care and Office for National Statistics death registration data. Patients with IPF and COPD were stratified based on ACE inhibitor use (defined as ≥ 3 prescriptions within the 5 years preceding diagnosis) and matched by age, sex, and smoking history using propensity score matching. Multivariable Cox regression analyses were performed, adjusting for age, sex, BMI, smoking status, indices of multiple deprivation, diabetes mellitus, chronic kidney disease, and common cardiovascular comorbidities. In the IPF cohort, competing risk analysis was used to account for cause-specific mortality. The study included 3,579 patients with IPF and matched COPD control participants (mean age, 74 years; 36% female). Among the IPF cohort, 1,328 (37%) were ACE inhibitor users, compared with 1,061 (30%) among patients with COPD. ACE inhibitor use was associated with improved survival, independent of coexisting comorbidities, in patients with IPF (hazard ratio, 0.82; 95% CI, 0.75-0.91; P ≤ .001), but a similar association was not found in patients with COPD (hazard ratio, 1.09; 95% CI, 0.96-1.23; P = .180). In this study, ACE inhibitor therapy was independently associated with reduced all-cause mortality in IPF but not in COPD. Prospective trials are warranted to confirm these findings in IPF populations.

Machine learning as a service (MLaaS) enables scalable model deployment and inference on cloud servers. However, MLaaS exposes user queries and model parameters to servers. To guarantee confidentiality of queries and model parameters, multi-party computation (MPC) enables secure inference by distributing data and computations across multiple service providers. MPC eliminates single points of failure, mitigates provider breaches and ensures confidentiality beyond legal agreements. Beyond confidentiality of queries and parameters, the model itself can memorize and leak training data during inference. To mitigate privacy concerns, differential privacy (DP) provides a formal privacy guarantee for training data, which can be satisfied by injecting carefully calibrated noise into gradients during training. However, naive combinations of DP and MPC amplify accuracy loss due to DP noise and MPC approximations, and incur high computational and communication overhead due to cryptographic operations. We present SPRINT, the first scalable solution for efficient MPC inference on DP fine-tuned models with high accuracy. SPRINT fine-tunes public pre-trained models on private data using DP. It integrates DP-specific optimizations, e.g., parameter-efficient fine-tuning and noise-aware optimizers, with MPC optimizations, e.g., cleartext public parameters and efficient approximations of non-linear functions. We evaluate SPRINT on GLUE benchmark with RoBERTa, achieving up to 1.6x faster MPC inference than the state-of-the-art non-DP solution SHAFT, reducing communication by 1.6x. Notably, SPRINT maintains high accuracy during MPC inference, with <1 percentage point gap compared to cleartext accuracy.

RationaleThe precise nature of small airway obstructions in chronic obstructive pulmonary disease (COPD) remains poorly understood, especially at early disease stages.ObjectivesThis study aimed to characterize small airway obstructions and numbers up to the terminal bronchioles (TBs) in smokers with limited emphysema and end-stage COPD. We hypothesized that obstruction subtypes would differ in morphology, nature, and number from early to end-stage COPD.MethodsWhole lungs were inflated and processed from seven control donors (control: declined for extrapulmonary reasons); from eight donors with a history of smoking, of whom three had <5% emphysema (smokers with no emphysema) and five had >5% emphysema (smokers with emphysema); and from eight patients with end-stage COPD. Micro–computed tomography of tissue was used to assess number of TBs, aerated TBs, and number and type of obstructions and was cross-correlated with histopathology.Measurements and Main ResultsObstructions were mainly present in smokers with emphysema and patients with COPD, resulting in less aerated TBs. On the basis of emphysema extent, more nonaerated TBs were present in regions with no emphysema than in regions with mild emphysema; however, destruction was more prominent in mild emphysema. Multiple types of obstructions were identified, comprising occlusions, webs, and collapses. In smokers with emphysema, obstructions primarily comprised webs and occlusions, whereas all obstruction types were present in COPD. On histopathology, obstructions were identified as mucus plugs.ConclusionsMultiple types of obstruction characterized as mucus plugs were identified in smokers with emphysema and patients with end-stage COPD. Their morphology, nature, and number evolved from smokers with emphysema to end-stage COPD. A shift from obstruction-dominant dysfunction to destruction-dominant pathology was found in smokers on the basis of emphysema presence.

The exponential growth of heterogeneous enterprise data has exposed the limitations of traditional Big Data architectures, which prioritize storage scalability over semantic understanding. This paper presents a Semantic Data Lake (SDL) framework that integrates Big Data technologies with knowledge graphs and ontology-driven reasoning to enhance enterprise decision support. The SDL architecture introduces a multi-layered system encompassing data ingestion, semantic annotation, ontology alignment, and graph-based reasoning for contextual query processing. Implemented using Hadoop, Spark, and GraphDB, the framework demonstrates superior performance in query efficiency, scalability, and semantic accuracy compared to conventional data lakes. Experimental evaluations show up to a 40% reduction in query latency and a 19% improvement in semantic precision, achieved through ontology mapping and reasoning-based query optimization. The results validate that semantic enrichment transforms data lakes into intelligent ecosystems capable of delivering explainable, context-aware analytics. The paper concludes by outlining future research directions, including AI-driven ontology learning, federated semantic integration, and hybrid reasoning for real-time knowledge discovery.

Generative AI is transforming industries worldwide and SAP is no exception. In the rapidly evolving digital landscape, SAP has strategically integrated Artificial Intelligence (AI) across its product suite to enhance operational efficiency, decision-making, and user experiences. From the last couple of years, SAP has been investing aggressively in AI, embedding generative AI capabilities into various SAP solutions as well as SAP BTP. This paper explores SAP's strategic adoption of Artificial Intelligence (AI) across its product suite, detailing the specific AI-driven features integrated into various offerings. It also examines SAP's overarching AI strategy aimed at delivering enhanced value to its customers. Furthermore, the paper presents case studies from diverse industries to illustrate the tangible benefits businesses have realized by leveraging AI capabilities within SAP products.

Modern enterprises increasingly leverage public cloud services to host application workloads with underlying cloud components, service tiers, and associated costs. These usage-based charging models introduce complexity. This necessitates the analysis of substantial data volumes to derive valuable insights. In this paper, we aim to address the challenge of enabling business and financial stakeholders to efficiently interpret cloud cost data and its trends without SQL expertise. We present an AI-powered system built on the DSPy framework that translates natural language questions into SQL queries using an enterprise-grade Large Language Model (LLM), delivering context-aware and desired responses. Existing dashboards and NLP approaches rely on rule-based parsing, requiring predefined data schemas and manual configuration. DSPy also uses these schemas but enhances flexibility by adapting them through structured pipelines and multi-step reasoning to improve accuracy. To train and evaluate our model, we augment a small dataset of question-SQL pairs through LLM-based generation and manual refinement. Additionally, we develop a custom recommendation engine using search-based techniques in Python to identify and disambiguate field names and values for both users and the LLM. Our system achieves 90% execution accuracy on our dataset, which indicates that the generated SQL queries return correct results when executed on the target database. These results demonstrate the effectiveness of prompt-tuned LLMs and in-context learning in providing a scalable, accessible solution for enterprise data analysis, ultimately enhancing decision-making in financial management.

Abstract Background The efficient allocation of resources is a core challenge in health services management. In pharmacy services, accurate forecasting is fundamental to ensuring the availability of essential medicines, managing operational costs, and improving service quality. However, simple forecasts are often insufficient for robust operational planning. This study demonstrates a multi-level analytical framework to provide deeper, more actionable insights for the management of pharmaceutical resources. Methods A retrospective analysis was conducted using a public dataset of daily and weekly pharmaceutical sales from a single pharmacy (2014–2019). We analyzed three drug classes with distinct demand patterns: M01AE (anti-inflammatories), N02BE (analgesics), and R06 (antihistamines). A comparative analysis of SARIMA and Prophet forecasting models was performed on weekly data to guide strategic inventory planning. To provide deeper operational insights, we integrated three additional analyses: Change Point Detection to identify structural shifts in demand, Volatility Analysis to quantify inventory risk, and Market Basket Analysis to uncover product purchasing associations from daily transaction data. Volatility metrics were translated into a Safety Stock Index to inform inventory policy. Results The analysis confirmed that the optimal forecasting model depends on the characteristics of the service demand data. The SARIMA model performed best for the N02BE (RMSE: 57.46) and R06 (RMSE: 9.24) classes, while the Prophet model was more accurate for the M01AE class (RMSE: 9.52). Change Point Detection identified a significant structural break in demand for N02BE, which also exhibited the highest volatility (Std. Dev: 76.07) and thus the greatest inventory risk. The Market Basket Analysis revealed no strong purchasing associations between the selected drug classes (highest Lift: 1.01), suggesting that co-promotion strategies would be ineffective. Conclusions A multi-level analytical framework provides a more comprehensive evidence base for the management of pharmacy services than forecasting alone. By integrating analyses of demand volatility, structural breaks, and purchasing behavior, health service managers can develop more resilient and efficient resource allocation strategies, ultimately improving service delivery and operational performance.

EVIDENCE-BASED EDUCATION. ADVANCES AND POTENTIALS FOR EDUCATIONAL PRACTICE AND RESEARCH Abstract This work aims to critically examine the contributions of evidence-based education (EBE), a topic that has garnered increasing attention in Italy over recent years. The potential of the EBE perspective in Italy is analyzed according to two key criteria: (i) the practical application of the most reliable existing knowledge, guiding training through established models of effective education; (ii) a paradigm shift from predominantly descriptive approaches or those based on action research toward experimental studies. Several quasi-experimental programs that are both sustainable and effective are presented and discussed within a framework focused on continuous improvement.

In the context of digital transformation, copyright protection has become a major challenge due to the widespread prevalence of online copyright infringement. The United States has been an early mover in establishing a comprehensive legal framework with the Digital Millennium Copyright Act (DMCA), which laid the foundation for digital work protection and the widely applied “notice-and-takedown” mechanism. In recent years, Vietnam has also made significant progress by amending the Intellectual Property Law, issuing Decree No. 17/2023/ND-CP, and incorporating international standards to strengthen copyright protection on the Internet. A comparison between the Vietnamese and U.S. legal systems reveals both similarities and differences, thereby offering valuable lessons for Vietnam to further improve its legal framework, enhance enforcement effectiveness, and foster a healthy creative environment in the digital era.