Injectable and self-healing fucoidan hydrogel: A natural anti-inflammatory biomaterial

Fucoidan, a natural sulfated polysaccharide, offers immense potential as a natural immune-modulatory bioactive polymer to develop hydrogels for tissue engineering. Yet, systematically tunable hydrogels composed primarily of fucoidan, that can also be injected into target tissues to reduce inflammation are still lacking. To address this, we have developed a highly tunable, hydrazone crosslinked fucoidan (hcFu) hydrogel that is injectable and can be utilized for various tissue engineering applications. We demonstrate that chemically modified fucoidan retains its ability to induce macrophage polarization. The hcFu hydrogel can be tuned to achieve gelation rate from instantaneous to over 10 min. Moreover, due to the dynamic nature of hydrazone covalent bonds, fucoidan hydrogel displays viscoelasticity, self-healing, extrudability, injectability, and excellent stability in physiological environments. In-vitro biocompatibility assays confirm its capacity to support cell growth, while in-vitro and in-vivo studies reveal its inherent anti-inflammatory and antioxidant effects. Importantly, hcFu hydrogel effectively modulates immune responses without incorporating additional cytokines. These findings position fucoidan hydrazone as a promising natural anti-inflammatory biomaterial with significant potential for tissue engineering and therapeutic applications

Molecular docking and density functional theory studies of flavonoids of Holy basil plant against COX-2 enzyme

Holy basil (Ocimum tenuiflorum) is primarily found in Nepal and India. In Ayurveda, it is commonly used as a traditional medicine to reduce pain, swelling, and various diseases. It has gained significant attention for its potential anti-inflammatory properties. One of the key targets associated with inflammation is Cyclooxygenase-2 (COX-2), an enzyme responsible for prostaglandin synthesis during the inflammatory response. In this study, we selected twenty flavonoids in the Holy Basil plant. These compounds were screened through Lipinski\u27s Rule of Five, followed by ADMET prediction. Virtual screening was conducted on the selected compounds against the COX-2 enzyme as a receptor using molecular docking techniques. Molecular docking study provides valuable insights at the molecular level into the interactions between Holy Basil compounds and COX-2. Furthermore, density functional computations were carried out utilizing the B3LYP method with the 6-311G basis, which is set to gain insight into the structural and electronic properties of the compounds. This study showcases the potential of flavonoids such as rhamnetin, Luteolin and kaempferol to act as anti-inflammatory agents, sparking further interest and research in this area

Simulated online typing performance in a cBCI using different language models

Communication Brain-Computer Interfaces (cBCIs) represent a crucial technological advancement for individuals with severe motor disabilities as they offer a direct pathway to express their thoughts and needs without physical movement. These systems commonly leverage the P300 ERP, a distinct neural response approximately 300-500ms after a novel stimulus. Language modeling presents a promising approach to enhancing the performance and usability of cBCIs. However, integrating language models with cBCI systems presents unique challenges, including balancing model complexity with real-time processing requirements and optimizing system performance parameters. This study utilizes simulations of online cBCI data to investigate the impact of different language models on typing rate and accuracy

Applications of multimodal large language models in construction industry

The advancement of transformer-based models, including multimodal large language models (MLLMs), has led to growing interest in their application across diverse industries, including construction. While a few earlier reviews have explored generative artificial intelligence in the construction sector, they are limited in scope— limited coverage of multimodal models, covering a shorter timeline prior to the expansion of MLLMs research, and offering limited emphasis on practical use cases, adaptation strategies, and integration into construction workflows. This study addresses that gap by reviewing 83 peer-reviewed studies published between January 2020 and February 2025, identified using a structured search process guided by the PRISMA framework and focused on academic literature. By synthesizing these studies, this review highlights trends across application domains, model types, adaptation strategies, technical limitations, and performance evaluation practices—offering a comparative analysis across use cases. It concludes with recommendations for future research, underscoring the need for standardized evaluation frameworks, critical limitations related to technical aspects, ethical risks, and regulatory uncertainty, underscoring the need for responsible development and deployment of MLLMs in construction settings

Examining equity in fuel treatments for wildfire risk mitigation in the United States Forest Service

This paper used a mixed methods approach to examine whether socially vulnerable populations near U.S. National Forest lands received fuel treatments to reduce wildfire risk. We tested whether the location of recent treatments was related to neighborhood demographics using logistic regression with a National Forest level random intercept and regional fixed effects. Findings showed differential outcomes by race/ethnicity and tribal governance. Tribal lands were about half as likely to be treated, after controlling for biophysical risk, urbanity, land area, National Forest, and region. Neighborhoods with relatively high shares of Hispanic and Black populations were also associated with lower likelihoods of fuel treatment, compared to blocks with lower concentrations of these populations. Qualitative findings from interviews with forest managers, field work, and coding relevant government documents suggested several potential explanations. Resources for doing fuel treatments were limited, and decisions about where to do them were complex, balancing multiple priorities. Forest land management plans, environmental conditions, and environmental regulations guided decision-making about where to do fuel treatments, yet managers had discretion in prioritizing treatment locations. We found no consistent process for integrating social vulnerability– whether and how managers considered vulnerability depended on their personalities, background, and relationships. Some managers dismissed or diminished the importance of considering vulnerability, while others felt there was too much uncertainty and not enough information available to be able to consider populations that might face special risks. Decisions were often made in cooperation with already-invested partners who were knowledgeable about fire risk and could share resources, which may have directed federal resources towards relatively privileged neighborhoods

Phasor-based secondary arc extinction detection method for shunt compensated transmission lines

Secondary arc extinction detection (SAED) is essential for adaptive single-phase auto-reclosing (ASPAR) success. Many SAED/ASPAR methods have been proposed in the literature. However, most of them do not present a practical approach for field implementation. A new methodology is proposed aiming to provide an effective and practical phasor-based SAED/ASPAR scheme for shunt compensated transmission lines. That scheme consists in analyzing the line side voltage phasors in the modal domain to safely and rapidly identify the secondary arc extinction, requiring only the voltage phasors at one line terminal. Furthermore, it can be easily implemented in readily available IED (Intelligent Electronic Devices), such that no additional hardware or equipment is required, being quite suitable for real-world applications. Therefore, a new mathematical formulation is developed considering the shunt and neutral reactors effects. Then, the SAED/ASPAR applicability and limitations for shunt compensated lines are clearly defined. Data from the Brazilian Power Grid and field oscillographic recordings are used for case studies. The results attest the efficiency and reliability of the proposed methodology

Data-driven Identification of Bandgaps in Flexural Metastructures using Component Mode Synthesis and FRF Based Substructuring

Metastructures, characterized by their periodic unit cells, are known for their ability to block the propagation of elastic waves within specific frequency ranges, known as “bandgaps”. To estimate the wave propagation characteristics of these systems, two primary approaches are employed: physics-based methods and data-driven techniques. Physics-based methods depend on the material properties and geometry of the unit cells, while data-driven approaches utilize experimental data, such as steady-state dynamic response data. This study assesses the effectiveness of data-driven techniques, particularly Component Mode Synthesis (CMS) and Frequency Response Function-Based Substructuring (FBS), in identifying bandgaps in metastructures composed of multiple unit cells. The focus is on metastructures consisting of 1D beams that exhibit flexural wave behavior. Within these structures, two significant challenges arise when using frequency response functions based on out-of-plane response data: the absence of rotational degrees of freedom (dofs) and the presence of rigid-body modes. Both factors critically impact the dispersion relationship and, by extension, the bandgap estimation. Traditionally, capturing rotational dynamics has been difficult due to limitations in direct experimental measurement, necessitating the inference of rotational dofs from translational measurements. Furthermore, rigid-body modes are estimated from experimental data. To overcome these challenges, we propose the estimation of rotational dofs by curve-fitting of translational dofs. In addition, this study explores a novel approach to the estimation of rigid body modes from the modal parameters acquired using the well-known Polymax algorithm. The discussed methodologies are also applied to derive dispersion relations for infinite metastructures

On-Road Investigation of Energy Saving Opportunity for Autonomous Light-Duty Vehicles through Automated Vehicle-Following in Safe Distance Scenarios

Reducing aerodynamic drag through Vehicle-Following is one of the energy reduction methods for connected and automated vehicles with advanced perception systems. This paper presents the results of an investigation aimed at assessing energy reduction in light-duty vehicles through on-road tests of reducing the aerodynamic drag by Vehicle-Following. This study provides insights into the effects of lateral positioning in addition to intervehicle distance and vehicle speed, and the profile of the lead vehicle. A series of tests were conducted to analyze the impact of these factors, conducted under realistic driving conditions. The research encompasses various light-duty vehicle models and configurations, with advanced instrumentation and data collection techniques employed to quantify energy-saving potential. The study featured two sets of L4 capable light duty vehicles, including the Stellantis Pacifica PHEV minivan and Stellantis RAM Truck, examined in various lead and following vehicle configurations at different speeds with cruise control enabled. Energy savings per km in the range of 9-17% were observed in Pacifica and savings up to 25% were obtained in RAM within 1-2 seconds following gap for speeds of 55-75 mph. It was also observed that the lateral positioning has a significant impact on energy saving overall. The results are also compared to the previous studies on drag reduction in two-vehicle platoons. This investigation contributes valuable knowledge to the vehicle-following to reduce the aerodynamic drag and thus to reduce the overall energy consumption in the highway driving scenarios. This can also be used in advanced vehicle positioning controls in autonomous vehicles where advanced sensing of the relative positions can be estimated accurately. The results give insights into optimizing energy efficiency, with a focus on the role of Vehicle-Following, aerodynamic drag reduction, and lateral positioning strategies for sustainable and environmentally conscious road transportation

Leveraging Product and Process Characteristics Across the Concrete Pavement Life Cycle to Integrate Global Warming Potential into Project Procurement Processes

The objective of this research is to support sustainable procurement of concrete pavements by linking materials-level global warming potential (GWP) to the project-level. Infrastructure owners require reliable environmental product declarations (EPDs) and methodologies for integrating GWP into the procurement process to ensure equitable decision-making. This work provides insights into current EPD reliability by assessing the sensitivity of concrete GWP to materials-level contributors to recommend a level of supply chain specificity needed to effectively communicate GWP. A benchmarking methodology was developed and implemented to establish reference values for procuring sustainable products. Having provided evidence towards EPD reliability, this work presents a framework that integrates GWP with pay items in project specifications. Linking incentives within infrastructure owner specifications to desired performance characteristics encourages all involved stakeholders to prioritize achievement of those performance characteristics. This same concept can be applied using GWP as the desired performance characteristics. A data collection protocol and life cycle information model (LCIM) for concrete pavement construction were developed to facilitate GWP integration into current project procurement practices. The LCIM methodology was developed and implemented to estimate the production and construction environmental impacts of six real-world concrete pavement construction projects. Applying the LCIM methodology allowed this work to map GWP to pay items and incentives in specifications and provide a pathway to extend a LCIM across the life cycle. The LCIM was further demonstrated on a real-world joint repair project, as well as for a concrete pavement reconstruction, demolition, and waste hauling. The culmination of this research demonstrated that the LCIM can be used to estimate the embodied environmental impacts of a concrete pavement across its life cycle and provided a framework for integrating environmental impacts into the procurement process, facilitating sustainable project procurement for infrastructure owners

Aggregate-Superpose-Project: A Cognitive Model for Quantum Problem Solving

This work proposes a novel cognitive model, called Aggregate-Superpose-Project (ASP), to facilitate problem solving and the analysis of algorithms in Quantum Computing (QC). Our model contains three simple abstractions that help students use classic computing concepts towards specifying quantum states and transformations. Simplicity is a major advantage of ASP along with reinforcing the use of classical concepts in learning QC abstractions. Preliminary evaluations indicate that ASP can provide students with the means to describe quantum algorithms at appropriate levels of abstraction