North Carolina Agricultural and Technical State University

Abstract Estimating the midpoints of interval-valued data poses significant challenges, particularly when uncertainty and variability are inherent. This paper advances midpoint estimation by leveraging both Maximum Likelihood (ML) and Bayesian methodologies, incorporating various forms of additional information through parameter constraints. In practical applications, such information often naturally arises, enabling the imposition of constraints such as order, rectangular, spherical, and bounded-order conditions on the parameter space. By integrating these constraints, the estimators can exploit structural relationships among parameters to achieve improved precision and accuracy. Through comprehensive simulation studies, we assess the performance of ML and Bayesian estimators under these constrained settings, demonstrating that Bayesian estimators consistently outperform ML estimators, particularly when employing noninformative priors. The constrained Bayesian approach not only results in lower mean squared errors (MSEs) but also yields more precise credible intervals compared to the confidence intervals obtained via ML, showcasing its robustness and efficiency. Furthermore, a real-world climatological case study validates our theoretical and simulation findings, demonstrating the practical utility of the proposed methods for interval-valued temperature data. This example illustrates how integrating additional information–often readily available–enhances the accuracy and reliability of midpoint estimation in multivariate contexts. Our work underscores the potential of structural constraints in symbolic data analysis, providing actionable insights across diverse scientific domains.

This study addresses the limitations of current Smart TV interfaces, which emphasize single-user personalization and insufficiently support co-viewing. We designed, developed, and evaluated a novel Smart TV interface informed by a mental model of co-viewing behaviors derived from qualitative observations and interviews. Four design strategies guided the interface: embedding social cues to support group goal- setting, accommodating browsing strategies during content selection, reducing psychological barriers through social involvement, and mitigating fatigue to sustain consensus-building. In a between-subjects experiment with 60 participants, the co-viewing interface significantly increased user engagement and choice satisfaction while reducing perceived choice difficulty compared to that of a standard personalized interface. Qualitative feedback further revealed richer group interactions. These findings demonstrate the effectiveness of a mental model–driven design in facilitating collaborative decision-making and enhancing the co-viewing experience, offering practical implications for developing adaptive, user-centered Smart TV interfaces.

Fine-grained power estimation is fundamental to modern MPSoC design and operation, enabling early power and thermal budgeting before fabrication, predictive thermal control at runtime, post-silicon microarchitectural energy debugging, and hardware security analysis. Existing System-on-Chip (SoC) designs often rely solely on total power consumption measurements because of the cost, size, and complexity of power sensors. However, the lack of precision and granularity in power measurements leads to inefficient power and thermal management and limits the ability to detect failed or compromised sensors. To tackle these challenges, we propose the Improved Clustering Blind Power Identification (ICBPI) approach, an innovative approach for fine-grained power estimation in SoCs. ICBPI leverages total power measurements and per-unit temperature data, even with potentially unreliable sensors, to deliver accurate per-unit power estimates. This dual focus not only enhances power estimation, leading to better power management decisions, but also improves the detection of malicious sensors—such as those compromised by hardware Trojan attacks—that manipulate sensor data to induce potential system failures. Extensive evaluations across four SoC architectures demonstrate ICBPI’s superior performance in reducing per-core power estimation errors by \({77.56\%} \) compared to the baseline Blind Power Identification (BPI) and by \({68.44\%} \) compared to the state-of-the-art approaches. Furthermore, ICBPI achieves \({100\%} \) detection of malicious thermal sensor attacks and enhances the localization process compared to the Blind Identification Countermeasure (BIC) approach. In the comparative analysis between ICBPI and the most recent model, Alternating Blind Power Identification (ABPI), ICBPI shows error rates as low as \({0.5\%} \) compared to \({2.8\%} \) in a 2 × 2 mesh benchmark and outperforms ABPI on heterogeneous architectures. The NVIDIA Jetson Xavier AGX development board serves as a platform for validating ICBPI. Consequently, ICBPI demonstrates an approximate reduction in average error of \(79.71\% \) on the CPU and \(79.59\% \) on the GPU relative to three existing state-of-the-art approaches.

This paper investigates the degree of oil prices pass-through to import, producer, and consumer prices in Canada and the United States from 1980 to 2017 using a Structural Vector Auto-Regression (SVAR) model. The results indicate a positive long-run correlation between oil prices and aggregate price levels. The impulse response function reveals a persistent and incomplete pass-through for oil prices, i.e., 0.04 for Canada and 0.25 for the U.S. Greater pass-through exists in an economy with more oil import share. Consistent with the impulse response function, variance decomposition reveals that oil price shocks in the United States are the primary cause of the variation in import and producer prices. However, in Canada, oil price shocks explain the variation in producer and consumer prices.

Purpose The purpose of this paper is to examine how colonization experience, length and recency in emerging market multinational enterprise (EMNE) home countries may influence the relationship between formal/informal institutional distance and EMNE equity participation in target firms across border. Design/methodology/approach The study uses panel data of combined measures of formal and informal institutional distance, as well as colonization experience and a variety of firm-level and country-level controls to conduct regression models. The data sample consists of 1,725 mergers and acquisitions (M&As) deals between 2001–2015 across different emerging markets. Findings The findings show that EMNEs are generally likely to choose higher equity participation when there is greater formal institutional distance between the home country and the target firm country, while equity participation is lesser when there is greater informal institutional distance. However, for EMNEs from countries that were colonized, the authors found that colonization experience, colonization duration and years since colonization play a significant role in altering how formal and informal institutional distance affects equity participation choices. Originality/value This study shows that EMNEs from formerly colonized countries approach cross-border M&As differently, interpreting institutional distance and institution-based view in unique ways. This study shows how postcolonialism shapes decision-making. The authors also extend existing frameworks, such as the liability of foreignness, by introducing colonization experience as an additional theoretical lens to understand institutional distance in acquisition decisions.