Truven Health Analytics (United States)

The high growth rate in digital businesses has heightened issues around the world about the environmental footprint of information technology (IT) infrastructure, especially as the data centres, cloud services, and high-performance computing workloads are becoming a large contributor to increasing energy use and carbon emissions. Although the topic of sustainability has gained increased regulatory and corporate attention, there remains no set, data-based approaches to measure the ecological performance of the organizations with regard to the digital operations. The paper presents a holistic analytical model that combines sustainable IT infrastructure indicators, cloud resource optimization policy, and corporate sustainability key performance indicators (KPIs). Based on a mixed-methodology, which integrates empirical data related to industry benchmarking, cloud provider sustainability reporting, and already existing environmental reporting criteria, the framework provides a systematic approach to connect micro-level IT energy telemetry (including power usage effectiveness (PUE), server utilization rates, virtualization efficiency, and carbon intensity of workloads) to macro-level sustainability results, including reductions in greenhouse gas (GHG) emissions, integration of renewable energy, energy cost reductions, and improvement in ESG performance. Quantitative modeling based on real world data demonstrates how green data analytics can be used to aid in carbon-conscious scheduling, predict IT energy demand and optimizing allocation of cloud resources. The outcomes indicate that the incorporation of IT operating data with high-end analytics can improve greatly the level of transparency, the measurement precision, and the environmental responsibility of digital enterprises. The originality of the study is the cross-layered mapping of technical IT metrics on organizational sustainability KPIs, which can be replicated to achieve the net-zero digital transformation. The suggested framework offers practical information to businesses, policymakers, and cloud service providers interested in realizing sustainability goals in fast-changing digital realms.

Insider threats are a continuous and dynamic issue in the field of organizational security that may take various forms, such as an abuse of authorized access to critical information systems and physical infrastructure. The traditional methods of isolating system log analysis, behavioural monitoring, or physical access control often do not have the ability to identify multi-layered and subtle patterns of threats. Multimodal data integration predictive analytics is a holistic approach since it integrates heterogeneous data volumes into coherent threat models that are able to pre-empt any possible threats before they escalate. The paper discusses the effectiveness of predictive analytics in insider threat detection through analysis of the connection between log records, behavioural indicators and physical security data. Instead, the focus is made on designing integrative frameworks, using advanced machine learning algorithms, and the applied implications on operational resilience. Issues like scalability, transparency of algorithms, and even ethical considerations are also critical issues that are considered to provide a sound deployment in the modern security environment. The paper highlights the need to embrace multimodal predictive approaches as a tactical defence mechanism and developing theoretical arguments and practical interventions in cybersecurity risk management.

Linking patient records across disparate healthcare systems is essential to create comprehensive views of patient health, yet this task is complicated by inconsistent identifiers, data quality issues, and privacy constraints. Although traditional deterministic and probabilistic methods have been widely used for record linkage, their performance is often limited in the presence of noisy or incomplete personally identifiable information (PII), and privacy-preserving variants commonly restrict matching to exact token equality. This work presents a hybrid record linkage approach, which integrates a deep embedding model with deterministic rules to leverage both the flexibility and noise-robustness of soft embeddings and reliably and predictable baseline performance from deterministic rules. Using a large-scale real-world dataset, a BERT-based embedding model is fine-tuned using a siamese network with contrastive loss to encode PII fields as numeric vectors. This system is implemented and evaluated on a commercial database consisting of more than 250 million PII records, showing the successful use of the system in a real-world healthcare setting.

As the US rapidly moves towards cleaner energy sources, solar energy is fast becoming the pillar of its renewable energy mix. Even while solar energy is increasingly being used, its variability is a key hindrance to grid stability, storage efficiency, and system stability overall. Solar energy has emerged as one of the fastest-growing renewable energy sources in the United States, adding noticeably to the country's energy mix. Retrospectively, the necessity of inserting the sun's energy into the grid without disrupting reliability and cost efficiencies highlights the necessity of good forecasting software and smart control systems. The dataset utilized for this research project comprised both hourly and daily solar energy production records collected from multiple utility-scale solar farms across diverse U.S. regions, including California, Texas, and Arizona. Training and evaluation of all models were performed with a time-based cross-validation scheme, namely, sliding window validation. Both the Random Forest and the XG-Boost models demonstrated noticeably greater and the same performance across each of the measures considered, with relatively high accuracy. The almost perfect and equal performance by the Random Forest and XG-Boost models also shows both models to have learned the patterns in the data very comprehensively, with high reliability in their predictions. By incorporating AI-powered time-series models like XG-Boost in grid management software, utility companies can dynamically modify storage cycles in real-time as well as dispatch and peak load planning, based on their predictions. AI-powered solar forecasting also has profound implications for renewable energy policy and planning, particularly as U.S. federal and state governments accelerate toward ambitious decarbonization goals. To bridge existing limitations and achieve improved forecast accuracy, future research should investigate hybrid deep models, specifically designs including Long Short-Term Memory (LSTM) and Convolutional Neural Networks with LSTM (CNN-LSTM) architectures. Moreover, future innovation needs to include solar forecasting integration with energy storage modeling and direct real-time deployment in microgrids.

An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience

This study investigates how artificial intelligence enabled, data driven decision making supports global market integration among agribusiness firms in structurally different systems in the United States and South Asia. The problem addressed is the lack of quantitative, comparative evidence on whether and how AI capabilities translate into higher export intensity, market diversification and participation in global value chains for agribusiness enterprises. The purpose is to test a TOE based model linking AI adoption, organizational and environmental conditions and data driven decision practices to firm level global market integration. A quantitative, cross sectional, case-based design was applied using a structured five-point Likert survey of 320 agribusiness enterprise cases, comprising 162 U.S. firms and 158 South Asian firms. Key variables included AI Adoption, Data Driven Decision Making, Supply Chain Connectivity, Organizational Readiness, Environmental Support and Global Market Integration indices. Reliability was high (α = 0.89 for AI adoption, 0.87 for data driven decisions, 0.88 for market integration), and analysis combined descriptive statistics, correlation and multiple regression. AI adoption (β = 0.34, p < 0.001) and data driven decision making (β = 0.28, p < 0.001) together raised explained variance in global market integration to 41.5 percent, with a strong bivariate correlation between AI adoption and integration (r = 0.56, p < 0.001). U.S. firms showed higher mean AI adoption (M = 3.84 vs 3.21) and a stronger AI integration linkage than South Asian firms. The findings imply that AI should be treated as a strategic, enterprise level capability for agribusiness internationalization, while investments in organizational readiness, secure data infrastructure and supply chain connectivity are especially critical in South Asia for converting AI use into durable global market gains.