Middle Tennessee State University

Purpose This special issue aims to bridge the gap between theoretical understanding and empirical advancement in neurophysiological research within retail and service contexts. While the retail and service sectors dominate global economies, the adoption of neurophysiological tools to study consumer behavior has been lagging. This editorial presents nine empirical studies demonstrating how neurophysiological measures capture dynamic, moment-by-moment consumer experiences across retail and service encounters. These contributions capture process-oriented dynamics and advance consumer neuroscience theory while offering actionable managerial and policy implications. The issue prioritizes empirical perspectives, methodological rigor and ethical practices to establish a foundation for future neuromarketing research. Design/methodology/approach This editorial encompasses nine empirical studies that utilize multimodal neurophysiological measures. Papers were selected through a rigorous peer review process, requiring authors to move beyond mere tool application to demonstrate how their findings advance existing theory. Studies employ diverse methodologies, including electroencephalography, event-related potentials, eye-tracking, electrodermal activity and facial electromyography. All papers combine neurophysiological measures with self-report scales and behavioral data. The editorial organizes contributions around three thematic clusters: technology-mediated interactions, sensory marketing and cognitive-moral constraints, reflecting contemporary retail and service research priorities and advancing what is termed “Neuromarketing 2.0.” Findings Neurophysiological measures reveal consumer processes undetectable with traditional methods. Technology-mediated research shows that AI credibility influences satisfaction through emotional engagement, while chatbots enhance arousal during purchase decisions. Empathy transfers customer emotions to employees, impacting service recovery outcomes. Sensory marketing studies show that multisensory imagery reduces cognitive load and enhances brand recall; stylized packaging designs engage consumers more powerfully than realistic depictions. Information overload degrades attention strategies, yet brand familiarity buffers against overload. Moral decision-making shows heightened cognitive processing during ethically conflicted choices. Collectively, these findings demonstrate that multimodal neurophysiological integration captures real-time mechanisms shaping consumer experiences in retail and service. Originality/value This special issue integrates multiple themes: technology-mediated consumer interactions, sensory marketing and cognitive and moral constraints in consumer decision-making. It illustrates how retail and service research is moving toward a more integrated measurement and modeling architecture toward a new phase named “Neuromarketing 2.0.” The editorial outlines future research directions and synthesizes the emerging consensus on methodological harmonization and theoretical rigor. By bridging lab-based precision with field-based applicability, establishing ethical guardrails and proposing longitudinal, cross-cultural validation pathways, this issue presents a comprehensive research agenda that advances transparency, inclusivity and rigorous innovation in retail and service.

The Revised Universal Soil Loss Equation, Version 2 (RUSLE2), is the primary water erosion prediction tool used by the USDA Natural Resources Conservation Service (NRCS) for land management planning across the United States. Despite its widespread adoption, RUSLE2’s reliance on a personal computer-based model limits its capacity for large-scale, dynamic applications. This research addresses these constraints by developing a novel cloud-based platform to host and enhance RUSLE2, enabling server-based computation, geospatial data integration, and scalable modeling capabilities. Built on Amazon Web Services (AWS), the platform integrates web-based user interfaces, spatial databases, and geoprocessing tools to streamline soil erosion modeling. It incorporates historical data on soil properties, weather patterns, and land use practices to support precise assessments of rill and interrill erosion. A redesigned database architecture ensures computational efficiency, data security, and collaborative development. Scientific advancements in RUSLE2 include quantifying the effects of precipitation variability and land use on the spatiotemporal dynamics of key soil properties. Leveraging advanced field and laboratory methods, remote sensing, and machine learning, the platform improves the measurement and mapping of soil erodibility and soil loss across diverse U.S. agricultural landscapes. These enhancements enable more accurate forecasts of erosion risk under evolving environmental scenarios and support flexible land management strategies. This transformative, cloud-based platform delivers innovative tools to guide land use practices and improve long-term agricultural productivity. By integrating cutting-edge technologies and data-driven modeling, this work addresses longstanding challenges in erosion science and enhances regional and national resilience in soil resource management.

We consider the impact of network connectivity on the dynamics of a population in a stream environment. The population is modeled using a graph theoretical framework, with habitats represented by isolated patches. We introduce a change in connectivity into the model through the addition of a bi-directional or one-directional edge between two patches and examine the impact of this edge modification on the metapopulation growth rate and the network biomass. Our main results indicate that adding a bi-directional edge often decreases both measures, while the effect of adding one-directional edge is more intricate and dependent on the model parameters. We establish complete analytical results for stream networks of three patches, and provide some generalizations and conjectures for more general stream networks of n patches. These conjectures are supported with numerical simulations.

ABSTRACT Categorical perception (CP) reflects the human auditory system’s ability to map continuous acoustic signals onto discrete categories. Understanding the relationship between neural dynamics and perceptual decisions is central to speech–language processing. Here, we implemented a data-driven approach using Bayesian nonparametrics and machine learning to characterize the relationship between auditory cortical responses and speech categorization behaviors. By applying a hierarchical Dirichlet process hidden Markov Model (HDP-HMM) to source-reconstructed event-related potential (ERP) data, we identified temporally distinct neural microstates that capture the evolving stages of speech categorization without imposing predefined temporal windows. Machine learning classifiers, including extreme gradient boosting (XGBoost), support vector machines, and random forests, were applied to decode prototypical (Tk1/5) versus ambiguous (Tk3) speech sound tokens from the microstate data. Using whole-brain activity, the XGBoost classifier achieved the highest decoding accuracy of 94.1% with an area under the curve (AUC) 94.1% within a specific encoding microstate occurring approximately 200-250 ms after stimulus onset. A reduced set of 15 informative brain regions identified via Shapley additive explanations (SHAP) yielded comparable classification performance (90.3% accuracy; AUC 90.0%), with many regions localized to frontal, temporal, and parietal regions in the left hemisphere. Furthermore, neural activity within these regions robustly predicted listeners’ behavioral identification slopes ( R 2 = 0.92, p < 0.00001), linking microstate-specific cortical dynamics to individual differences in perceptual gradiency of speech perception. These findings demonstrate that speech categorization emerges within temporally discrete neural microstates during early sensory–perceptual encoding and is supported by a selective, distributed cortical network with clear behavioral relevance.

The growing dependence on third-party foundries for integrated circuit (IC) fabrication has created major security concerns because of hardware Trojan (HT) insertion risks. Traditional detection methods, including side-channel analysis and golden reference models, face limitations such as sensitivity to noise, high cost, and impracticality for large-scale deployment. This work introduces a machine learning framework for HT detection that eliminates the need for golden references. The framework automatically extracts statistical features from chip data, groups chips into clusters, and uses an internal filtering process to identify the most reliable patterns. These patterns are then used to guide a learning model that can accurately separate Trojan-infected chips from clean ones. Experimental evaluation demonstrates that the proposed method achieves high detection accuracy with zero false negatives, while remaining resilient against adversarial perturbations. These findings indicate that cluster-filtered pseudo-labeling provides a practical and scalable solution for enhancing hardware security in modern IC supply chains.

IntroductionSocial isolation, reduced social interaction, and social anhedonia are associated with a range of neuropsychiatric conditions. While the search for novel pharmacological agents to treat social symptoms persists, more precise social behavior measures in pre-clinical animal models are needed to make the most accurate predictions of therapeutic outcomes.MethodsIn the current study, we propose two novel behavioral tasks to measure social motivation in mouse models. We define social motivation as the willingness to exert effort to access a social partner. The first social motivation test, the weighted door task, requires a mouse to push open a one-way, weighted door that increases in weight across successive trials to access a social partner behind the door. The second social motivation test, the ladder task, requires a mouse to climb a ladder that increases in steepness across trials to access a social partner on a platform at the top of the ladder. To validate these tasks, we compared behavioral outcomes across three common inbred strains, C57BL/6J, DBA/2J, and BTBR T + Itpr3 tf /J. Social motivation outcomes were then compared to outcomes in two standard social behavior tests: the three-chamber task and the free dyadic social interaction task.ResultsFollowing behavioral testing, we found that each strain displayed distinct behavioral responses in social motivation tasks with BTBR mice demonstrating low social motivation, DBA mice demonstrating high social motivation, and C57 mice demonstrating conditionally high social motivation during low effort trials.DiscussionWhen combined with standard social behavior testing, our measures provide more detailed social behavior phenotypes unique to each strain. In addition to allowing the creation of more complete social behavior ethograms, these tasks offer advantages as compared to existing conditioning-based behavior tasks measuring social motivation and reward such as the social conditioned place preference task and operant conditioning for social reward. The weighted door and ladder tasks leverage innate exploration behaviors that do not require prior learning which allows for more models, including those with memory, attention, and learning deficits, to be used. These pre-clinical measures of social motivation may prove useful in improving predictions of social behavior outcomes of proposed pharmacological interventions for clinical populations.

Accurate reconstruction of Respiratory Virus Transmission Networks (RVTNs) depends heavily on the reliability of RT-PCR results, which are directly influenced by upstream RNA extraction processes. This study evaluates three commonly used extraction methods silica column, magnetic bead–based, and rapid lysis to determine their impact on RT-PCR sensitivity, Ct variability, viral load estimation, and downstream RVTN model stability. Experimental analyses showed that magnetic bead extraction consistently produced the highest RNA yield, lowest Ct variability, and most stable amplification performance, resulting in minimal propagation error within transmission modeling. Silica column extraction demonstrated moderate reliability, while rapid lysis produced substantial Ct variability and high diagnostic uncertainty, significantly weakening network coherence and transmission link detection. By quantifying how extraction-induced errors propagate into epidemiological modeling outputs, the study demonstrates that optimized RNA extraction is critical not only for diagnostic accuracy but also for producing reliable and interpretable transmission networks. These findings underscore the need for standardized extraction practices and improved quality control frameworks to strengthen outbreak response systems and enhance public health decision-making.