Search Results (4,627)

The automatic inspection of airport infrastructure, specifically horizontal surface markings and Airfield Ground Lighting (AGL), is a critical task for maintaining aviation safety and operational efficiency. As the aviation industry shifts from manual surveys to automated visual inspections utilizing unmanned aerial vehicles (UAVs) and smart service vehicles, the demand for robust, real-time computer vision systems has surged. However, deploying computationally intensive deep learning models in the field introduces severe Size, Weight, and Power (SWaP) constraints. This paper presents a comprehensive framework for the semantic segmentation of runway/taxiway markings and the point-localization of AGL lamps, specifically focusing on the deployment paradigm shift from the expensive, GPU-accelerated heterogeneous system on chip (SoC) to highly efficient, dedicated Neural Processing Units (NPUs). We evaluate the performance of U-Net, LinkNet, U-Net-Point and HRNet-Lite-Point architectures trained on a custom dataset from the Poznań-Ławica Airport. Crucially, this study conducts a rigorous comparative hardware analysis between the flagship NVIDIA Jetson AGX Orin and a highly cost-effective heterogeneous setup comprising a Raspberry Pi 5 augmented with an NPU Hailo-8 AI accelerator. Experimental results demonstrate that while both platforms achieve real-time inference, the Hailo-8 integration fundamentally disrupts the traditional cost-to-performance ratio. Furthermore, the Hailo-8 configuration consumed less electrical power and memory footprint required by the Jetson, proving that dedicated NPUs are vastly superior for continuous, battery-operated edge deployment in autonomous airport maintenance systems. Specifically, the Raspberry Pi setup with the Hailo-8 accelerator demonstrated superior energy efficiency, requiring a significantly lower energy consumption per processed video frame compared to the Jetson platform. Full article

The growing problem of air pollution and its direct impact on human health have created an urgent need for reliable, intelligent, and machine learning (ML)-enabled gas-sensing technologies. Among various sensing platforms, metal oxide gas sensors (MO-GSs) have emerged as promising candidates owing to their low cost, high sensitivity, and scalability. However, their practical application is limited by poor selectivity, cross-sensitivity, sensor drift, and high operating temperatures. Recent advances in ML have provided effective strategies to overcome these limitations through data-driven optimization of sensing performance. This review summarizes recent progress in ML-assisted MO-GSs, covering sensor array design, feature engineering, and classification algorithms, including support vector machines (SVMs), random forests (RFs), and deep neural networks (DNNs). In addition, key data-processing techniques such as preprocessing, dimensionality reduction, and hybrid learning approaches are critically discussed. The application of ML-enabled MO-GSs in medical diagnostics, environmental monitoring, industrial safety, and food quality assessment is also reviewed. Despite significant progress, challenges including limited dataset availability, sensor drift, and poor model generalization remain. Future research should focus on developing adaptive, energy-efficient, and IoT-enabled smart sensing systems. The integration of machine learning with metal oxide gas sensors represents a significant step toward intelligent, next-generation, high-performance gas-sensing technologies. Full article

Facing the dual challenges of energy shortage and environmental degradation, photocatalysis and electrocatalysis have emerged as key technologies for converting small molecules into value-added chemicals, yet their conflicting requirements on the electronic structure of catalysts prevent a single material from freely switching between the two modes. Here, we demonstrate a feasible strategy for achieving on-demand switching between these catalytic functions in a single ferroelectric heterojunction, Pd-C₃N₄@In₂Se₃, through polarization engineering. Using first-principles density functional theory calculations, we show that reversing the polarization direction of the α-In2Se3 layer induces a nonvolatile electronic phase transition. The downward polarization (P↓) configuration exhibits metallic behavior, whereas the upward polarization (P↑) state becomes semiconducting with a type-II band alignment. This transition arises from polarization-dependent interfacial built-in electric fields and charge transfer differences. Notably, the metallicity of the P↓ configuration is localized predominantly within the In₂Se₃ layer rather than delocalized over the entire heterostructure. This arises because the enhanced interfacial charge transfer, driven by the larger work-function difference, selectively populates the conduction band of In₂Se₃, pushing its band edge across the Fermi level, while the Pd-C₃N₄ layer remains semiconducting due to charge depletion and the absence of gap-closing hybridization at the interface. In the P↑ state, the heterojunction acts as an efficient photocatalyst for overall water splitting, with band edges straddling the redox potentials. Under illumination, photogenerated electrons and holes make the hydrogen evolution reaction and oxygen evolution reaction thermodynamically spontaneous. In contrast, the metallic P↓ state serves as an excellent electrocatalyst for hydrogen evolution, delivering a limiting potential as low as −0.11 V, attributed to strengthened N 2p and H 1s orbital hybridization. These findings resolve the conflicting electronic requirements of photocatalysis and electrocatalysis and offer a new paradigm for designing smart, dual-functional catalysts adaptable to varying energy inputs, providing valuable theoretical guidance for future experimental realization of switchable catalytic systems. Full article

The coordinated operation of interconnected multi-carrier energy hubs is a key enabler of cost-efficient and flexible energy management in modern smart cities. This paper develops a comprehensive optimization framework for the day-ahead scheduling of interconnected energy hubs in residential and commercial sectors. The problem is formulated as a mixed-integer linear programming (MILP) model that jointly manages electricity, natural gas, and thermal energy flows. To enhance operational flexibility, the proposed model incorporates demand response programs for both electrical and thermal loads, multiple energy storage technologies, and electric vehicles with vehicle-to-grid (V2G) capability. Six operating scenarios are defined to assess the impact of different resources and coordination levels, ranging from independent hub operation to fully integrated interconnected scheduling. Simulation results show that coordinated operation of the energy hubs, supported by flexible loads, storage systems, and electric vehicles, can significantly reduce total daily operating costs compared with conventional standalone configurations. The findings confirm that energy exchange among hubs, combined with demand-side flexibility and EV participation, improves both economic performance and system efficiency. The proposed framework offers a scalable scheduling approach for future integrated multi-energy systems. Full article

The transition towards smart megacities has deeply integrated Artificial Intelligence (AI) with power–transport networks. While AI empowers complex operations like multi-network coordinated dispatch and emergency rescue, current algorithm-centric perspectives largely ignore its massive physical energy costs. Accordingly, this Perspective examines the dual role of AI, considering it not only as an intelligent decision-support tool but also as a potential source of additional stress on physical infrastructure. First, through a structured synthesis of the representative literature, we deconstruct the functional dependencies between algorithms and physical infrastructures, identifying how AI reshapes the operational paradigms of power, ground transport, and aerial networks under routine and emergency scenarios. We then introduce the concept of the “Computation–Energy Paradox.” Integrating conceptual analysis with a quantitative case study of a typical community, we illustrate a plausible failure mechanism: during extreme disasters, intensified AI invocation for emergency management generates surging computational loads, which paradoxically exacerbate power shortages and reduce the operating margin of already weakened systems. In addition, we analyze core engineering bottlenecks, including spatiotemporal computation–energy mismatches and physical constraints in extreme edge environments. To address these challenges, we outline a prospective roadmap encompassing lightweight emergency AI and computation–power-coordinated offloading mechanisms. Finally, the sustainable development of such systems suggests a paradigm shift: AI must evolve from a purely virtual algorithm into a physical component of an integrated compute–power–transport system. Full article

A transition towards sustainable transportation requires efficient integration of electric vehicles (EVs) with renewable energy sources. This work proposes a two-stage Parking Lot Energy Management Scheme (PLEMS) to minimize charging costs while maximizing solar photovoltaic utilization in commercial parking facilities. In the first stage, the optimization phase is formulated using a mixed-integer linear programming (MILP) that minimizes the overall cost of EV charging while ensuring maximum utilization of locally available PV energy. In the second stage, a gated recurrent unit (GRU)-based deep learning model performs state of charge (SOC) forecasting for EVs parked in the parking lot. Using the predicted SOC for the next time step, the system decides whether each EV will be charged or discharged, ensuring consistency with the cost-optimal MILP strategy from the first stage. The proposed PLEMS achieves up to 62% daily cost savings in charging compared to uncoordinated direct grid charging. However, this cost saving is the outcome of proposed optimization as well as the integration of PV panels in power grid. When compared with nine similar vehicles to grid (V2G)-enabled approaches from the literature, which report cost savings ranging from 9.73% to 52%, the proposed framework shows an improvement of 10% to 52% over these methods. This hybrid MILP-GRU framework offers practical V2G operation and high scalability for large EV fleets in solar-powered smart parking lots. Full article

Maritime routes ensure connectivity between nations, carrying a vast flow of goods across borders, while ports serve as the critical junctions within this network, managing a wide spectrum of commodities from raw materials to finished goods. Ports also generate employment across numerous sectors and underpin a broad range of allied industries. A seaport is a maritime facility equipped with docks, cranes, and storage infrastructure for international trade, where ships load and unload cargo, containers, and passengers. Key functions of seaports include customs processing, warehousing, and vessel services, with major global hubs such as Shanghai, PSA Singapore, DP World, and Rotterdam handling immense volumes of cargo each year. In contrast, Bangladesh’s ports, Chittagong, Mongla, and Payra, play a vital role in sustaining regional commerce. Today, ports are widely recognized as essential capital infrastructure and prime movers of economic activity. Smart ports are automated facilities that leverage advanced digital technologies, including sensors, big data analytics, artificial intelligence (AI), machine learning (ML), deep learning (DL), augmented reality (AR), digital twins, the Internet of Things (IoT), and various automation systems, to optimize overall operational efficiency. These tools streamline cargo movement while embedding sustainable practices to protect the environment. Beyond operational gains, smart ports deliver faster, more advanced services to all stakeholders involved in port operations, including shipping companies, customs agencies, local communities, and other relevant parties. Renewable energy sources, electric vehicle charging stations, onshore power supply, and smart logistics infrastructure are among the defining sustainability features of smart ports in the present-day context. This study examines the current status and future development trajectory of Bangladesh’s sea ports in relation to the broader global imperative toward smart port transformation. Full article

As data centers become major and schedulable loads of the new power system, connecting them to multiple microgrids offers a promising route to absorb local renewable energy through cross-domain coordination. However, when the microgrids belong to competing operators, coordinated scheduling forces each party to disclose its data-center load curve, storage state, and pricing strategy, which constitutes a core operational secret that no microgrid is willing to reveal. This paper develops a secure and privacy-preserving distributed scheduling scheme for data-center-integrated microgrids built on blockchain. A “data-stays-local, energy-crosses-centers” model is established that elevates privacy from an add-on feature to a first-order architectural constraint, defining a “three-no” principle and a two-layer architecture in which each microgrid optimizes its interior in plaintext and exposes only encrypted matchable factors. On this basis, a decentralized ciphertext scheduling-negotiation algorithm is designed on blockchain smart contracts, performing cross-microgrid matching under secure multi-party computation entirely in the encrypted domain, committing auditable encrypted digests on-chain, and dynamically allocating scheduling priority through an on-chain reputation mechanism. Case studies on a cluster of interconnected microgrids show that the proposed scheme attains cost and renewable accommodation within about three-tenths of a percent of the centralized optimum while reducing operational data-leakage risk from 96.7 percent to 3.8 percent, at the manageable expense of a few seconds of negotiation latency. Benchmarking against an exact mixed-integer solver on small-scale systems bounds the mean optimality gap of the decomposed scheme at 0.74 percent, with a worst case of 2.54 percent over sixty instances. Full article

Advanced wireless sensor networks powered by artificial intelligence are increasingly required for applications demanding continuous monitoring, autonomous operation, reliable communication, and fast decision support. This systematic review examines recent work from 2023 to 2026 on energy-efficient, AI-enabled wireless sensor networks (WSNs) in mission-critical environments, with particular focus on power electronics and urban infrastructure systems. The authors synthesize a corpus of 50 DOI-indexed studies satisfying inclusion criteria that received qualitative thematic coding and comparative analysis. Other references were only cited to provide historical, methodological, or technical context and were not included in the systematic review corpus. As such, our results show that AI can improve WSN energy behaviour through routing and clustering, edge AI, reinforcement learning, fuzzy logic, metaheuristic optimization, and AI-based security. At the same time, energy efficiency cannot be treated as an isolated performance target. In mission-critical systems, security, latency, and reliability are closely interlinked requirements. The review concludes that future work should move away from optimizing protocols in isolation, and instead focus on building lightweight, explainable, secure, and field-tested AI-driven WSN architectures suited to real operational environments. Full article

Digital Twin (DT) technologies are increasingly transforming maritime and port systems by enabling real-time monitoring, predictive analytics, operational optimisation, and sustainability-oriented decision-making. Despite growing academic and industrial interest, the integration of DTs into sustainable maritime ecosystems remains fragmented, particularly in assessing digital maturity and sustainability performance. This study follows the PRISMA 2020 methodology to systematically review 26 peer-reviewed studies from Scopus and Web of Science. Through thematic synthesis, based on inductive coding and the identification of recurring patterns across the reviewed studies, four Digital Twin dimensions and a four-level maturity framework supported by Key Performance Indicators (KPIs) were derived: smart port development, artificial intelligence integration, energy optimisation, and environmental governance. Based on the findings, the study proposes a DT Maturity Framework for sustainable maritime systems, structured across progressive levels of technological integration, operational intelligence, sustainability alignment, and governance capacity. The framework provides an operational, multidimensional approach to assessing DT maturity across heterogeneous maritime ecosystems. The study contributes to the emerging literature on sustainable maritime digitalisation by offering a systematic conceptual synthesis that can support future research, strategic planning, and policy development in smart and sustainable port ecosystems. The proposed framework constitutes a conceptual assessment model whose empirical validation is identified as a priority for future research. As a literature-derived conceptual assessment model, the proposed framework requires empirical validation before its maturity levels and associated KPIs can be considered empirically established. Full article

As vehicle intelligence and automotive electrification advance, the extensive deployment of distributed sensing nodes for comprehensive monitoring has grown rapidly. This poses severe challenges, such as rising onboard power consumption and the inability of conventional centralized power supply systems to sustain these sensors. Triboelectric nanogenerators (TENGs), an emerging technology for energy harvesting and self-powered sensing, exhibit great potential to address the above challenges. This review systematically summarizes research on TENGs for vehicle energy harvesting and intelligent sensing, covering their fundamental working principles and applications in diverse vehicle scenarios. First, the basic principle and working modes of TENGs are described, and their suitability for complex and variable vehicle environments is evaluated. Subsequently, existing applications are categorized into three domains: vehicle vibration systems, wheel–road systems, and intelligent vehicle systems. Studies on various topics are reviewed, including vibration energy harvesting and sensing, vehicle collision monitoring, tire energy harvesting, road sensing, smart cockpits, human–machine interaction, and vehicle fluid monitoring. Emphasis is placed on their technical approaches and application prospects. Finally, the state-of-the-art research and prevailing technical bottlenecks are summarized, and potential solutions and future research perspectives are discussed. This review aims to support the reliable practical deployment of TENG technology in vehicle engineering and to provide a technical basis for energy-saving strategies and in situ sensing technologies for future intelligent vehicles. Full article

Electrochromic devices (ECDs) in smart buildings have been advanced as a potential solution for improving energy savings and visual and thermal comfort. The current paper is a review of advanced control strategies for ECDs with respect to predictive, environmental, and adaptive strategies for improving building performance. One of the most frequently employed methods is rule-based control (RBC). RBC is being complemented by more sophisticated model predictive control (MPC) and machine learning (ML) procedures. By adjusting ECD behaviour in dynamic response to changing external circumstances, like daylight, glare, temperature, and solar radiation, these improved techniques ensure a major improvement in real-time adaptivity, energy savings, and occupant comfort. The paper systematically examines ECD control techniques available in the literature, detailing performance indicators, energy conservation, and comfort enhancement for various climatic conditions. It also examines hybrid techniques based on MPC and ML models that tackle the obstacles faced by conventional control systems. Furthermore, their compatibility with renewable energy sources such as PV and thermochromic systems is outlined in relation to net-zero energy buildings. The paper ends with a review of future directions that could lead towards standardization in the form of models, sensor networks and AI-based adaptive frameworks to increase the scale as well as the real-world relevance of ECDs in varying building contexts. Full article

This study directly compares a photovoltaic (PV)-powered vapor-compression chiller with a solar-thermal-driven absorption chiller for localized cooling of the tomato crop zone in a 1536 m² smart greenhouse under a hot-arid climate. The principal contribution is a controlled system-level comparison of two solar-cooling pathways under the same greenhouse load, solar-aperture area, terminal equipment, rated cooling capacity, and crop-zone temperature-control constraints. The previously validated greenhouse model was transitioned from EnergyPlus 8.9 to Version 23.1, after which the two alternative plants were connected to the same base model. Base-case annual simulations produced nearly identical chiller cooling energy (1669.3 and 1668.9 MWh) and was only 4 and 5 h above 28 °C. The PV-powered system required 101.6 MWh of net grid electricity, whereas the absorption system used 202.3 MWh of electricity and 253.2 MWh of natural gas and achieved an 84.23% solar fraction. Static operational primary energy was 331.2 and 912.7 MWhPE, respectively; HSDH28 was 0.50 and 0.81 °C·h; and peak grid import was 113.46 and 63.61 kW. The absorption case additionally required 10,103.6 m³/yr of cooling-tower makeup water. Storage/EMS sensitivity changed the absorption solar fraction from 58.17% to 88.30% and natural-gas use from 187.7 to 674.0 MWh/yr without materially changing cooling service. Matched 50–100 W/m² daytime latent-load sensitivity increased annual cooling by 14.7–28.8%. At the 100 W/m² bound, HSDH28 increased to 49.32 °C·h for PV and 8.06 °C·h for absorption, while the principal energy–infrastructure trade-off remained: static primary energy was 712.4 versus 1354.2 MWhPE and peak grid import was 137.46 versus 63.96 kW. A bounded hourly primary-energy-factor stress test did not reverse the technology ranking, and balanced TOPSIS scores were 0.766 for PV and 0.234 for absorption. The results show that PV vapor compression minimizes operational primary energy and cooling-water use, whereas solar-thermal absorption reduces electrical peak demand and shows greater thermal-control resilience at the highest tested latent-load bound. Full article

The global plastic pollution crisis demands a fundamental re-evaluation of materials systems beyond incremental improvements to fossil fuel-based polymers. Bioplastics, polymers derived from renewable biological feedstocks, biodegradable under defined conditions, or both, offer a chemically diverse and rapidly evolving platform for transitioning toward circular materials economies in which the value of carbon, energy, and material is retained across multiple use cycles. This review provides a comprehensive and critically organized account of the bioplastics field, spanning three generations of feedstock development from food crops through lignocellulosic residues to algae and waste streams; primary production pathways including microbial fermentation, ring-opening polymerization, and biosynthesis; forming processes from extrusion and injection molding to additive manufacturing; and the mechanical, thermal, and barrier properties that determine application fitness. Particular emphasis is placed on life cycle assessment, which reveals that bioplastics’ climate benefits are conditional on feedstock choice, land-use management, energy source at manufacturing, and end-of-life pathway, and that burden-shifting from greenhouse gas emissions to land use, water consumption, and eutrophication is a systematic risk requiring integrated LCA evaluation rather than single-metric optimization. The review further examines end-of-life recycling, composting, and biodegradation pathways; market applications across packaging, agriculture, automotive, biomedical, and electronics sectors; and the growing role of artificial intelligence and machine learning in accelerating materials design, process optimization, and lifecycle data management. Critical barriers to scale, such as cost premiums of 20–75% over conventional plastics, inadequate composting infrastructure, recycling stream contamination, regulatory fragmentation, and consumer labeling confusion, are systematically analyzed alongside mitigation strategies. The review concludes with a forward-looking discussion of emerging feedstocks, smart and functional bioplastics, and the policy and infrastructure investments required to translate the environmental promise of bio-based polymers into realized circular economy impact. Full article

Advancements in rechargeable batteries and environmental awareness campaigns have highlighted the importance of electric vehicles (EVs) in recent times. The influx of EVs has posed challenges in almost all areas of technology, including demand-side management (DSM). Extra generating units are switched ON to meet the resultant higher electricity demand, thus reducing the sustainability of the system. To overcome this challenge, effective DSM techniques integrating renewable energy sources are proposed to efficiently utilize the existing generating capacity. The primary goal is to fairly distribute available resources among smart homes and EV owners using the Shapley value and tau value. In this work, two scenarios are examined. First, a community energy storage (CES) approach is adopted to maximize CES revenue, reduce the grid peak-to-average ratio (PAR), and minimize electricity costs. Second, a coordinated group of EVs is utilized to minimize the impact of charging loads during peak hours while concurrently reducing EV charging costs. Simulation results show a reduction in the grid PAR from 2.468 to 1.799, or 27.1%, together with an average reduction of approximately 3% in the electricity cost of participating smart homes. In the EV scenario, optimal scheduling reduces total charging expenditure by 24.8% and lowers the system peak by 2.65% relative to uncoordinated charging of the same fleet, with the total cost distributed among the vehicles by the Shapley value. Benchmarking against a proportional-to-demand rule shows that the tau-value allocation coincides with proportional sharing, whereas the Shapley allocation shifts 4.2% of the allocation away from the household contributing most to the system peak. The framework provides a fair and individually rational cost allocation layer for community-scale peer-to-peer energy markets. Full article