Search Results (762)

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

Vehicle emissions, as a source of air pollution and greenhouse gases, have a significant impact on the environment and climate change. Battery electric vehicles (BEVs) have the potential to reduce air pollution and GHGs. However, BEVs often attract the criticism that their benefits are minimal as the power plant emissions compensate for emissions from the tailpipes of vehicles. This study compared two scenarios: scenario A considers all vehicles as internal combustion engine vehicles (ICEVs), and scenario B considers all vehicles as BEVs. The study used the City of San Antonio, Texas, as the study area. The study also focused on the seasonal and spatial variation in ICEV emissions. The results indicate that scenario A has a considerably higher volume of emissions than scenario B. For ICEVs, PM2.5 emissions were up to 50% higher in rural areas than urban areas, but 45% lower for unrestricted versus restricted conditions. CO₂ emissions were highly affected by seasonal variations, with a 51% decrease from winter to summer. The full adoption of BEVs could reduce CO₂ and N₂O emissions by 99% and 58% per km, especially for natural gas power resources. Therefore, BEVs play a significant role in reducing emissions from the transportation sector. Full article

The ongoing rise in global electricity demand highlights the need for advanced, efficient, and environmentally responsible energy conversion technologies. This research presents a comprehensive design, modeling, and experimental validation of a tubular permanent magnet linear alternator (PMLA) integrated with a free piston engine system. Linear alternators offer a direct conversion of linear motion to electricity, eliminating the complexity and losses associated with rotary generators and enabling higher efficiency and simplified system architecture. The study combines analytical modeling, finite element simulations, and a sensitivity-based design optimization to guide alternator and engine integration. Two prototype systems, designated as alpha and beta, were developed, modeled, and tested. The beta prototype achieved a maximum electrical output of 550 W at 57% efficiency using natural gas fuel, demonstrating reliable performance at elevated reciprocating frequencies. The design and optimization of specialized flexure springs were essential in achieving stable, high-frequency operation and improved power density. These results validate the effectiveness of the proposed design approach and highlight the scalability and adaptability of PMLA technology for sustainable power generation. Ultimately, this study demonstrates the potential of free piston linear generator systems as efficient, robust, and environmentally friendly alternatives to traditional rotary generators, with applications spanning hybrid electric vehicles, distributed energy systems, and combined heat and power. Full article

This research is part of a broader investigation into innovative simulation-based approaches for improving traffic efficiency and road safety in roundabout corridors. These corridors, composed of successive roundabouts along arterials, present systemic challenges due to the dynamic interactions between adjacent intersections. While previous studies have addressed localized inefficiencies or proposed isolated interventions, this paper introduces possible replicable methodology based on a microsimulation and surrogate safety analysis to evaluate roundabout corridors as integrated systems. In this context, efficiency refers to the ability of a road corridor to maintain stable traffic conditions under a given demand scenario, with low delay times corresponding to acceptable levels of service. Safety is interpreted as the minimization of vehicle conflicts and critical interactions, evaluated through surrogate measures derived from simulated vehicle trajectories. The proposed approach—implemented through Aimsun Next and the SSAM tool—is tested on two real-world corridors: Via Aurelia Nord in Pisa (Italy) and Route de Marseille in Avignon (France), assessing multiple intersection configurations that combine roundabouts and signal-controlled junctions. Results show how certain layouts can produce unexpected performance outcomes, underlining the importance of system-wide evaluations. The proposed framework aims to support engineers and planners in identifying optimal corridor configurations under realistic operating conditions. Full article

The urgent demand for sustainable energy solutions in the face of climate change and resource depletion has catalyzed a global shift toward cleaner energy production and more efficient storage technologies. Lithium-ion batteries (LIBs), as the cornerstone of modern portable electronics, electric vehicles, and grid-scale storage systems, are continually evolving to meet the growing performance requirements. In this dynamic context, two-dimensional (2D) materials have emerged as highly promising candidates for use in electrodes due to their layered structure, tunable electronic properties, and high theoretical capacity. Among 2D materials, molybdenum disulfide (MoS₂) has gained increasing attention as a promising low-dimensional candidate for LIB anode applications. This review provides a comprehensive yet concise overview of recent advances in the application of MoS₂ in LIB electrodes, with particular attention to its unique electrochemical behavior at the nanoscale. We critically examine the interplay between structural features, charge-storage mechanisms, and performance metrics—chiefly the specific capacity, rate capability, and cycling stability. Furthermore, we discuss current challenges, primarily poor intrinsic conductivity and volume fluctuations, and highlight innovative strategies aimed at overcoming these limitations, such as through nanostructuring, composite formation, and surface engineering. By shedding light on the opportunities and hurdles in this rapidly progressing field, this work offers a forward-looking perspective on the role of MoS₂ in the next generation of high-performance LIBs. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

This article introduces two enhanced techniques: the Natural Transform Iterative Method (NTIM) and the Optimal Auxiliary Function Method (OAFM). These approaches provide a close approximation for solving fractional-order Navier–Stokes equations, which are widely employed in domains such as biology, ecology, and applied sciences. By comparing the solutions derived from these methods to exact solutions, it is clear that they provide accurate and efficient outcomes. These findings highlight the straightforward yet effective use of these methodologies in modeling engineering systems. Navier–Stokes equations have numerous practical uses, including analyzing fluid flow in pipelines and channels, predicting weather patterns, and constructing aircraft and vehicles. Full article

The transportation sector in South Asia largely depends on internal combustion engine (ICE) vehicles, which are responsible for a large share of greenhouse gas (GHG) emissions, air pollution, and the increase in fuel prices. Although hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fully electric vehicles (EVs) constitute promising alternatives, the rate of their implementation is low due to factors such as the high initial investment, the absence of the required infrastructure, and the reliance on fossil fuel-based electricity. This study is the first of its kind to examine Bangladesh’s drivetrain options in a comprehensive way, with in-depth real-world emission testing and economic analysis as the main tools of investigation into the environmental and economic feasibility of different technologies used in the vehicles available in Bangladesh, including lifecycle costs and infrastructure constraints. The study findings have shown that hybrid and plug-in hybrid vehicles are the best options, since they have moderate emissions and cost efficiency, respectively. Fully electric vehicles, however, face two main challenges: the overall lack of charging infrastructure and the overall high purchase prices. Among the evaluated technologies, PHEVs exhibited the lowest environmental and economic burden. The Toyota Prius PHEV emitted 98% less NOx compared to the diesel-powered Pajero Sport and maintained the lowest per-kilometer cost at BDT 6.39. In contrast, diesel SUVs emitted 178 ppm NOx and cost 22.62 BDT/km, reinforcing the transitional advantage of plug-in hybrid technology in Bangladesh’s context. Full article

This paper investigates the robust tracking control of unmanned aerial vehicles (UAVs) against external time-varying disturbances. First, by introducing a virtual position controller, we innovatively decouple the UAV dynamics into independent position and attitude error subsystems, transforming the robust tracking problem into two zero-sum differential games. This approach contrasts with conventional methods by treating disturbances as strategic “players”, enabling a systematic framework to address both external disturbances and model uncertainties. Second, we develop an integral reinforcement learning (IRL) framework that approximates the optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equations without relying on precise system models. This model-free strategy overcomes the limitation of traditional robust control methods that require known disturbance bounds or accurate dynamics, offering superior adaptability to complex environments. Third, the proposed recursive Ridge regression with a forgetting factor (${\mathrm{R}}^3{\mathrm{F}}^2$ ) algorithm updates actor-critic-disturbance neural network (NN) weights in real time, ensuring both computational efficiency and convergence stability. Theoretical analyses rigorously prove the closed-loop system stability and algorithm convergence, which fills a gap in existing data-driven control studies lacking rigorous stability guarantees. Finally, numerical results validate that the method outperforms state-of-the-art model-based and model-free approaches in tracking accuracy and disturbance rejection, demonstrating its practical utility for engineering applications. Full article

In structural and bridge engineering, the axle weights and interaxle spacings of heavy trucks are useful for assessing the capacity of existing bridges, developing live load models, and other issues. Weigh-in-motion data have become the most common source for recording axle weights and interaxle spacings; however, information is not as direct and may not be as precise as that from static surveys. Surveying vehicles by stopping them beside the highway is not common nowadays; nevertheless, surveys provide very reliable information on truck axle weights and interaxle spacing. In this study, data from three surveys on two Mexican highways recorded in 2016 and 2018 are provided. The data contain the gross vehicular weights, axle weights, and interaxle spacings of heavy trucks. The discussion is given as to how the provided information can be useful for the bridge and transportation engineering community and for reliability and code calibration tasks for Mexican bridges and a future design code for bridges in Mexico City. It is concluded that statistical values are consistent with WIM data, with differences due to different methods used, recording time, samples size and others, and that trucks heavier than the legal weight circulate in Mexican highways; static surveys are useful to strongly support this important issue. Further research to compare samples from different surveying techniques, as well as the use of the information to investigate load effects on bridges, is recommended. Full article

The rise of hybrid electric vehicles (HEVs) marks a shift away from traditional engines driven by environmental and economic concerns. With the rapid growth of HEVs worldwide, their reliability becomes of utmost concern; thus, guaranteeing the proper operation of HEVs is a crucial quest. Condition-based monitoring (CBM), which intends to observe different kinds of parameters in the system to detect defects and reduce any unwanted breakdowns and equipment failure, plays an efficient role in enhancing HEVs’ reliability and ensuring their healthy operation. The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. Hence, the condition monitoring of this machine is of great importance. The magnet crack is one of the most severe faults that may arise in this machine. Artificial intelligence (AI) is showing high capability in the field of CBM, fault detection, and fault identification and prevention. Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. Applying and evaluating various CBM methods is essential to identifying the most effective approach to maximizing reliability, minimizing downtime, and optimizing maintenance strategies. A strategy to specify the remaining useful life (RUL) of the defected element is proposed. Full article

Few studies have examined Hybrid Aquatic–Aerial Vehicles (HAAVs), autonomous vehicles designed to operate in both air and water, especially those that are aircraft-launched and recovered, with a variable-sweep design to free dive into a body of water and breach under buoyant and propulsive force to re-achieve flight. The novel design research examines the viability of a recoverable sonar-search child aircraft for maritime patrol, one which can overcome the prohibitive sea state limitations of all current HAAV designs in the research literature. This paper reports on the analysis from computational fluid dynamic (CFD) simulations of such an HAAV diving into static seawater at low speeds due to the reverse thrust of two retractable electric-ducted fans (EDFs) and its subsequent breach back into flight initially using a fast buoyancy engine developed for deep-sea research vessels. The HAAV model entered the water column at speeds around 10 ms⁻¹ and exited at 5 ms⁻¹ under various buoyancy cases, normal to the surface. Results revealed that impact force magnitudes varied with entry speed and were more acute according to vehicle mass, while a sufficient portion of the fuselage was able to clear typical wave heights during its breach for its EDF propulsors and wings to protract unhindered. Examining the medium transition dynamics of such a novel HAAV has provided insight into the structural, propulsive, buoyancy, and control requirements for future conceptual design iterations. Research is now focused on validating these unperturbed CFD dive and breach cases with pool experiments before then parametrically and numerically examining the effects of realistic ocean sea states. Full article

In order to address the problem of error accumulation in long-duration autonomous navigation using Strapdown Inertial Navigation Systems (SINS), this paper proposes an error prediction and correction method based on Deep Neural Networks (DNN). A 12-dimensional feature vector is constructed using angular increments, velocity increments, and real-time attitude and velocity states from the inertial navigation system, while a 9-dimensional response vector is composed of attitude, velocity, and position errors. The proposed DNN adopts a feedforward architecture with two hidden layers containing 10 and 5 neurons, respectively, using ReLU activation functions and trained with the Levenberg–Marquardt algorithm. The model is trained and validated on a comprehensive dataset comprising 5 × 10³ seconds of real vehicle motion data collected at 100 Hz sampling frequency, totaling 5 × 10⁵ sample points with a 7:3 train-test split. Experimental results demonstrate that the DNN effectively captures the nonlinear propagation characteristics of inertial errors and significantly outperforms traditional SINS and LSTM-based methods across all dimensions. Compared to pure SINS calculations, the proposed method achieves substantial error reductions: yaw angle errors decrease from 2.42 × 10⁻² to 1.10 × 10⁻⁴ radians, eastward velocity errors reduce from 455 to 4.71 m/s, northward velocity errors decrease from 26.8 to 4.16 m/s, latitude errors reduce from 3.83 × 10⁻³ to 7.45 × 10⁻⁴ radians, and longitude errors reduce dramatically from 3.82 × 10⁻² to 1.5 × 10⁻⁴ radians. The method also demonstrates superior performance over LSTM-based approaches, with yaw errors being an order of magnitude smaller and having significantly better trajectory tracking accuracy. The proposed method exhibits strong robustness even in the absence of external signals, showing high potential for engineering applications in complex or GPS-denied environments. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article