HCL Technologies Inc.

This study examines how social media influencers affect pricing dynamics in electronic marketplaces. Drawing on platform ecosystem theory and differentiated demand models as a theoretical lens, we conceptualise influencers as attention brokers who reallocate consumer attention within algorithmic marketplace environments, thereby reducing effective price sensitivity among exposed consumers. Using a panel dataset combining YouTube influencer campaign data and Amazon product-level data (price, sales rank, reviews, and ratings), we employ a two-stage least squares (2SLS) framework to assess how influencer exposure alters the observed price-demand relationship. Our findings indicate that products featured in influencer campaigns exhibit meaningfully lower observed price responsiveness in sales rank-based demand proxies compared to non-featured products. These results are consistent with the theoretical prediction that influencer-driven attention shifts reduce the slope of the effective demand curve, enabling sellers to maintain higher price positions with limited sales rank deterioration. We interpret these results as reduced-form evidence of altered competitive price pressure rather than structurally identified causal elasticity estimates, and we acknowledge the limitations of the identification strategy. The study contributes to platform pricing theory and influencer marketing research by providing empirical evidence that social influence processes can reshape pricing power in highly transparent electronic marketplaces.

Decomposing long-context inference across cooperating agents bounds the active KV cache per call rather than total evidence, which matters when KV-cache memory binds. Many such systems add a persistent tier storing and recalling reasoning traces, usually validated by an ablation reporting an accuracy gain. We measure both on one three-tier agent architecture. Decomposition delivers: peak KV working set of 14.3 MiB per query against 35.5 and 35.3 MiB for single-pass and retrieval-augmented baselines. The persistent tier does not: across eight controlled dataset pairs at n=100 per arm it costs +0.368 MiB [+0.167, +0.590] of peak cache and produces no detectable accuracy change (+0.015, 95

The recent surge in the number of Internet of Things (IoT) devices has led to a substantial increase in the number of botnet-driven attacks, which in turn has increased their potential severity. To counter this, existing research has investigated a wide range of machine learning and deep learning solutions for intrusion detection in IoT networks. However, the effect of feature set dimensionality and redundancy on intrusion detection performance, especially in the context of class imbalance, has not been adequately explored. This study presents a comparative analysis of statistically identified feature subsets for IoT intrusion detection on a reduced form of the Bot-IoT dataset. The features are scored using a hybrid statistical ranking approach and tested using seven supervised learning classifiers: Gaussian Naive Bayes (GNB), Linear Discriminant Analysis (LDA), Logistic Regression (LR), linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). Classifier performance is measured using macroaveraged F1-score, balanced accuracy, and Matthews Correlation Coefficient (MCC). The empirical results show that tree-based and instance-based classifiers have excellent and competitive performance on reduced feature sets. Detection accuracy reaches saturation quickly with the addition of more features. These observations offer empirical evidence on feature sufficiency and redundancy in IoT intrusion detection systems.

Enterprise investment in generative artificial intelligence (AI) tripled in a single year to roughly US$37 billion, yet independent field research finds that about 95% of enterprise generative-AI pilots deliver no measurable profit-and-loss impact. We argue that the dominant explanation--that models are not yet capable enough--is mistaken, and that enterprise AI has entered a Deployment Era in which advantage derives not from model intelligence but from the removal of the organizational and architectural friction that prevents a capable model from reaching production. Building on the software-engineering literature on technical debt and machine-learning deployment, and on a structured synthesis of independent field studies, we make the diagnosis operational. We introduce three linked constructs and one measurement instrument: the Deployment Wall, a six-stage value-leak model that mechanically reproduces observed survival rates; the Seam Index, a reproducible 0-12 diagnostic that scores any platform by how many of six recurring friction "seams" it removes natively rather than leaving to the adopter; and Deployment Debt, a construct that reframes unresolved friction as a compounding, quantifiable liability. We specify a scoring protocol with evidence anchors so the instrument can be applied consistently, illustrate it on a worked platform-selection example, and derive six falsifiable propositions with a research agenda for validation. The framework converts an eight-figure platform decision from a benchmark comparison into an architecture comparison.

In modern engineering, compressors play a vital role across numerous industries by enabling the delivery of fluids at elevated pressures for a variety of applications, including HVAC systems, aircraft engines, and process industries. The performance of centrifugal compressors is characterized by parameters such as flowrate, efficiency, and pressure rise. Traditional methods of evaluating compressor performance, such as physical testing, are often time-consuming and costly, making them less practical for iterative design or optimization. Advancements in Computational Fluid Dynamics (CFD) have provided a faster and more cost-effective means of assessing compressor behavior. This study presents a comprehensive CFD-based analysis of a two-stage centrifugal compressor utilized in HVAC applications aimed at predicting its performance, that is, flow factor vs head factor and flow factor vs efficiency for given rotational speeds and inlet guide vane (IGV) angle positions. Focus is on predicting surge flow points, choke flow points, mapping the compressor performance curve and mapping surge line for IGV partial opening cases at various rotational speeds of the impeller. Simulations were conducted using the ANSYS CFX software. The results illustrate the effectiveness of CFD in accurately predicting critical performance metrics and operational limits for centrifugal compressors. Additionally, the study can potentially explore the impact of different geometric modifications on compressor stability and surge margin, providing valuable insights for future design improvements.