University of North Carolina at Charlotte

Laser-enhanced contact optimization (LECO) has emerged as an important method for simultaneously reducing contact resistivity and metallization-induced recombination in advanced crystalline silicon solar cells, thereby enabling concurrent gains in fill factor and open-circuit voltage, particularly in TOPCon devices. However, broader industrial transferability remains constrained by the need to preserve these gains within a narrow process window and by unresolved, architecture-dependent questions regarding the kinetic stability of some LECO-modified interfaces. LECO is therefore examined in this review as a coupled multiphysics process that links localized electrothermal activation and microstructural evolution to device-level electrical signatures through an instantaneous regime map and a reliability classification based on time-dependent drift. A predictive workflow is outlined that couples transient electrothermal modeling with reduced state metrics, including effective diffusion depth and local areal energy density, and propagates calibrated thresholds across the recipe space. The framework separates stable optimization from marginal activation and latent damage, while explaining why fine-line scaling and copper-containing contact stacks can tighten stability margins through current localization and diffusion-barrier constraints. These insights provide a basis for reliability-aware process-window design and future digital-twin-assisted optimization of LECO for scalable, high-efficiency silicon photovoltaics.

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located.

The Internet of Things (IoT) has rapidly expanded across multiple sectors, exposing significant opportunities but also raising important concerns. This rapid growth has raised concerns about the security of IoT devices and the protection of the large volumes of data they collect, transmit, store, and process. Numerous large-scale attacks on IoT systems underscore the need for security measures, as well as comprehensive security assessments and benchmarking methods to verify and validate these systems. We conduct a Systematic Literature Review (SLR) to analyze previous studies, methodologies, and tools used to assess and benchmark the security of IoT systems, and to identify critical challenges and gaps in the existing literature. As a result, we highlight that, due to their complexity, IoT systems lack a comprehensive security framework that covers all layers and their security concerns. Despite awareness of known vulnerabilities, there is a lack of best practices, tools, and techniques to prevent, detect, and mitigate threats effectively. The absence of standardized security benchmarks complicates the evaluation and comparison of the solutions. There is also limited alignment with emerging standards such as ISO/IEC 27402 and SESIP. Finally, it is noteworthy that IoT gateway security remains unexplored despite its critical role in IoT ecosystems. CCS Concepts: • Computer systems organization → Embedded systems ; Redundancy ; Robotics; • Networks → Network reliability.

BackgroundForecasting influenza outbreaks remains a significant challenge due to the complexity of disease transmission and the influence of environmental and behavioral factors. Traditional models based solely on the basic reproduction number often fall short in capturing the full scope of outbreak dynamics.MethodsIn this study, we employ a seasonally adjusted SEIRT model incorporating stochastic differential equations (SDEs), including Brownian motion and Lévy jump processes, to simulate random and abrupt fluctuations in transmission. A branching process approximation is used to evaluate the probability of an epidemic under the influence of seasonal variability and stochastic perturbations. The model is calibrated using weekly influenza case data from Mexico, with noise components estimated from publicly available CDC [1] and WHO [2] surveillance data.ResultsSimulation results show that the inclusion of stochastic effects and periodic transmission rates significantly enhances the model's accuracy in reflecting real-world epidemic dynamics. Numerical comparisons between deterministic, Brownian-based, and Lévy-based scenarios reveal that both the initial state of the exposed or infectious subpopulation and the seasonal transmission patterns are critical to determining outbreak probabilities. Results indicate that seasonal transmission rates and stochastic effects significantly alter epidemic probabilities, with Lévy processes capturing abrupt outbreak dynamics more accurately than deterministic models.ConclusionsThe findings underscore that deterministic models may underestimate epidemic risk when they overlook random and sudden changes in contact rates or disease introduction. The proposed stochastic modeling framework yields a deeper understanding of influenza transmission dynamics by incorporating uncertainty and seasonal variability, thereby supporting more informed and effective public health decision-making.

Sentiment analysis (SA) has grown considerably in organizational science research over the past two decades, particularly in the last few years. While enthusiasm for integrating advanced natural language processing algorithms is encouraging, authors are not reaping the benefits of such tools fully. Our systematic review of SA application in the organizational sciences suggests that authors struggle to appreciate all of the decisions that are inherent to SA, the choices that are available at each decision point, and the consequences of each choice. To address this gap, we use a working example to illustrate four critical decision points authors confront when conducting SA, and the subsequent impact different choices can have on one's conclusion. Decision points include selecting the SA method, computing a sentiment score, preprocessing the data, and using an appropriate level of analysis. We conclude with a framework outlining five dimensions (e.g., accuracy, interpretability, computational cost) to guide the selection of an SA approach based on study goals and needs, along with seven recommendations to authors wishing to apply SA.

Educational Research Association (AERA) conference held in Denver, Colorado.The SIG TICL focuses on theoretical foundations, fundamental research, and technical advances at the intersection of technology, instruction, cognition, and learning.Drawing from these areas, the eight studies featured in this issue examine how emerging technologies and pedagogical designs are reshaping learning experiences, learner engagement, and instructional practices across K-12 and higher education contexts, highlighting the interplay between technology, knowledge construction, and learning processes within digitally mediated environments.These studies address critical areas, including artificial intelligence (AI) and data-driven personalization, technologysupported pedagogical design, cognition and metacognition, and design and equity-oriented perspectives.Together, they advance our understanding of how thoughtfully designed and implemented digital technologies can support adaptive, inclusive, and meaningful learning, while also raising important questions related to ethics, equity, and instructional responsibility in technology-rich educational environments.Serving as a bridge between conference scholarship and academic publishing, this special issue extends the reach of these contributions by sharing timely research insights with a broader scholarly audience.