Abstract: As urban areas face increasing challenges, integrating smart infrastructure, particularly IoT and AI technologies, has become vital for enhancing resilience. This study focuses on Baltimore as a case study to explore how scalable and adaptable smart infrastructure solutions can address diverse urban needs within a mid-sized U.S. city. Through a comprehensive review of Baltimore’s socioeconomic indicators and the development of a composite resilience score, this paper identifies key factors that facilitate or hinder the scalability and adaptability of smart infrastructure in economically and demographically varied urban contexts. The resilience score provides a quantitative measure of urban resilience, enabling the analysis of trends and dependencies among socioeconomic indicators over time. Findings reveal critical roles for both community engagement and policy support in adapting technologies to local needs, while economic and technical factors influence the scalability of IoT and AI projects. Based on these insights, the study proposes a framework that offers practical guidance for expanding Baltimore’s smart infrastructure in ways that are economically feasible, technically viable, and socially inclusive. This framework aims to assist Baltimore’s policymakers, urban planners, and technologists in advancing resilient, scalable solutions that align with the city's unique infrastructure needs and resource constraints.

Abstract: This paper presents a nonlinear velocity observer of the tether deployment using the Immersion and Invariance technique, and the velocity observer design problem is recast as a problem of designing an attractive and invariant manifold inside the Hamiltonian framework. The passivity-based control theory is used to define an expected Hamiltonian function, and the stability of the designed velocity observer is addressed by using the passivity-based methodology. Finally, a simple tension control law with measurable and unmeasurable states is employed for controlling the tether deployment, where the unmeasurable states use the proposed velocity observer. Numerical simulations demonstrate that the proposed velocity observer is working successfully. Sensitivity analyses are conducted to test the effectiveness and robustness of the proposed velocity observer.

Abstract: The corrosion resistance of a Cr3C2-25(Ni20Cr) cermet coating applied to an Al7075 substrate (Cr3C2-25(Ni20Cr)/Al7075) was investigated. The coating was produced by cold spraying (CS). To improve its properties, the coatings were annealed at 100°C, 300°C, and 500°C for 24 hours in air. The mechanical properties of the Cr3C2-25(Ni20Cr)/Al7075 composite were assessed through microhardness (HV) meas-urements. The surface morphology and microstructure of the specimens were examined using a scanning electron microscope (SEM). Electrochemical testing in an acidic chloride solution was employed to evalu-ate the corrosion behavior of the materials. The cermet coating effectively protected the Al7075 substrate from the aggressive corrosive environment. Heat treatment homogenized the structure of the cermet coat-ing, eliminating microcracks and pores on the Cr3C2-25(Ni20Cr)/Al7075 surface. Notably, annealing at 300°C in air significantly enhanced the corrosion resistance of the cermet coating. The corrosion rate was reduced by more than five times compared to the non-heat-treated Cr3C2-25(Ni20Cr)/Al7075 coating.

Abstract: Milling machines remain of great importance in modern manufacturing, with tool optimization playing a significant role in cost reduction. Insert-compound cutting tools can reduce the cost of operations by optimizing their lifespan. Subsequently, an accurate prediction of insert wearing is essential. This article analyses the wear of cutting tools in milling machines, focusing on accu-rately predicting their lifespan using artificial intelligence algorithms. The study highlights how artificial intelligence, through various models, has greatly improved the precision of wear pre-dictions, surpassing the results of prior studies. Results obtained from the dataset of an insert tool, which are widely used today, can be extrapolated to other milling inserts. The results demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. By analyzing this wear, the system can alert the machine operator when it is necessary to replace the cutting insert before failure occurs, thereby preventing suboptimal workpiece quality. This proactive approach not only enhances operational efficiency and reduces the overall cost of production but also minimizes the need for continuous supervision, contributing to more streamlined and autonomous machining processes.

Abstract: Electric connected automated vehicle (CAV) shuttles as a part of sustainable microtransit system have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centers. There is a need for knowledge on treating risk in such applications. This paper covers planning and economic feasibility of advanced technology level 4 automated vehicle-based microtransit system considering uncertain service and economic feasibility factors. Methods are advanced for addressing uncertainties in travel demand, service factors, and economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic and is adapted for risk analysis in financial decision making. The effects of uncertain life cycle costs on fares and rate of return are described. Results are favorable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on feasibility of implementing CAV-based first/last mile and other microtransit services under uncertainty.

Abstract: Cellulose microgel beads fabricated using the dropping technique suffer from structural irregularity and mechanical variability. This limits their translation to biomedical applications that are sensitive to variations in material properties. Ionic salts are often uncontrolled by-products of this technique, despite the known effects of ionic salts on cellulose assembly. In this study, the coagulation behavior of cellulose/salt solutions was explored as a way to combat these challenges. An ionic salt (NaCl) was added to a cellulose solution (cellulose/NaOH/urea) prior to coagulation in a hydrochloric acid bath. Quantification of the bead geometry and characterization of the pore architecture revealed that balancing the introduction of salt with the resultant solution viscosity is more effective at reducing structural variability and diffusion limitations than other pre-gelling techniques like thermal gelation. 3-D visualization of the internal pore structure of neat cellulose, thermo-gel, and salt-gel beads revealed that adding salt to the solution is the most effective way to achieve three-dimensional structural uniformity throughout the bead. Coupled with nanoindentation, we confirmed that the salt produced during coagulation plays a critical role in mechanical variability, and that adding salt to the solution before dropping into the coagulation bath completely screens this effect – leading to uniform microgel beads with reproducible mechanical properties.

Abstract: The concept of digital twins (DT)s enhances the traditional structural health monitoring (SHM) by integrating real-time data with digital models for predictive maintenance and decision-making whilst combined with finite element modelling (FEM). However, the computational demand of FE modelling necessitates surrogate models for real-time performance, alongside the requirement of inverse structural analysis to infer overall behaviour via measured structural response of a structure. A FEM-based machine learning (ML) model is an ideal option in this context as it can be trained to perform those calculations instantly based on FE-based training data. However, the performance of the surrogate model depends on the ML model architecture. In this light, current study investigates three distinct ML models to surrogate FE modelling for DTs. It was identified that all models demonstrated a strong performance, with the tree-based models outperforming the performance of the neural network (NN) model. The highest accuracy of the surrogate model was identified in the random forest (RF) model with an error of 0.000350 whilst the lowest inference time was observed with the trained XGBoost algorithm which was at approximately 1 millisecond. By leveraging the capabilities of ML, FEM and DTs, this study presents an ideal solution for implementing real-time DTs to advance the functionalities of current SHM systems.

Abstract: The paper consists on the exact wave results of the (1+1)-dimensional nonlinear compound Korteweg-De Vries, and Burgers (KdVB) equation with a truncated M- fractional derivative. This model represents the generalization of Korteweg-De Vries- modified Korteweg-De Vries and Burgers equations. We obtained the periodic, combo- singular,dark-bright, and other wave results with the use of the extended sinh-Gordon equation expansion (EShGEE), and the modified (G′/G2)-expansion techniques. The use of effective fractional derivative makes our results much better than the existing results. The gained solutions are useful as well as applicable in the various fields including the mathematical physics, plasma physics, ocean engineering, optics, etc. The obtained solutions are demonstrated by 2-D, 3-D, and Contour plots. The achieved results are fruitful in future research concerned equation. Stability analysis is used to check that the results are precise as well as exact. The modulation instability (MI) is performed to find stable steady-state solutions of the concerned model. At the end, it is suggested that the methods used are easy and reliable.

Abstract:

In today's modern era, with the development of 5G and 6G technologies, free frequency domains for communication have become a valuable resource. As the frequencies increase, atmospheric conditions produce different challenges and effects that affect the propagation of the signal. These effects require a deep understanding in order to enable Point-to-Point communication. Point-to-Point communication can occur over short distances, such as a few meters, or over long distances, such as in deep space. This article presents a solution based on an integrative analytical model for calculating atmospheric inferior values of an electromagnetic signal at any desired distance transmitted from the Earth. The model encompasses dispersion, refraction, and absorption effects caused by various atmospheric phenomena. The mathematical analysis adds that the placement of the signal along its height is not linear, and the transmission angle also affects the values. The results presented will influence the selection of working frequencies in different areas and distances, such as communication between ground and space and inter-satellite communication link (ISL).

Abstract: Risk management in power systems has become an essential component of ensuring the reliability, stability, and economic efficiency of modern electrical grids. As power systems become increasingly complex and vulnerable to both natural and man-made disruptions, accurate and effective risk measurement techniques are crucial for mitigating potential threats and minimizing system failures. This paper provides a comprehensive review of risk measurement methodologies used in the context of power system risk management. It explores various quantitative techniques, including probabilistic risk assessment, fault tree analysis, and Monte Carlo simulations, and examines their application in evaluating system vulnerabilities, identifying potential hazards, and optimizing risk mitigation strategies. Furthermore, we discuss the integration of these methods with modern power system planning, operation, and control strategies, particularly in light of evolving renewable energy sources and distributed generation technologies. By focusing on both theoretical and practical aspects, this paper offers valuable insights for enhancing the robustness of power systems in the face of growing risks and uncertainties.