Search Results (75)

As a reliable and high-potential wireless communication technology for the Internet of Things (IoT), LoRa excels in long-distance and low-power transmission. The star topology adopted by traditional LoRaWAN suffers from poor deployment flexibility and insufficient scalability in scenarios with complex terrain or harsh environments. LoRa-Mesh networks can effectively solve coverage challenges through characteristics such as multi-hop and self-organization; however, the relay and forwarding requirements of nodes also introduce new challenges in energy consumption management. To address the energy consumption management challenges of LoRa-Mesh, this paper proposes a Data-Driven Energy Saving (DDES) protocol. It flexibly sets and dynamically fine-tunes node sleep durations based on data changes, constructs an efficient energy-saving framework through uplink data streams, and implements precise control over nodes via downlink post-analysis messages to achieve on-demand energy saving. Simulation results in the smart agriculture scenario of soil moisture monitoring and irrigation show that compared with protocols without a sleep mechanism, the battery life of the LoRa-Mesh network using the DDES protocol is extended by approximately 20 times. The proposed protocol breaks through the limitations of fixed sleep schemes, realizes refined and flexible division of sleep regions, and exhibits significant advantages in LoRa network energy saving. Full article

Unmanned aerial vehicles (UAVs) acting as mobile aerial edge platforms can deliver on-demand communication and computing for the Internet of Vehicles (IoV) via flexible deployment and line-of-sight (LoS) links, improving reliability and reducing latency. However, high vehicle mobility, time-varying channels, and limited onboard energy make task offloading and resource coordination challenging. This paper studies joint task offloading and resource allocation in a UAV-assisted IoV system, where the UAV selects its hovering position from discrete candidate sites each time slot and splits vehicular tasks between the UAV and a roadside unit (RSU) to relieve backhaul congestion and enhance edge resource utilization. Considering vehicle mobility, multi-stage queue dynamics, and UAV energy consumption for communication, computation, and movement, the online optimization of position selection, task splitting, and bandwidth allocation is formulated as a constrained Markov decision process (CMDP). The goal is to maximize the number of tasks completed within the latency deadlines while satisfying the UAV energy budget. To solve this CMDP, we propose a graph-attention-based constrained twin delayed deep deterministic policy gradient (GAT-CTD3) algorithm. A graph attention network captures spatial correlations and resource competition among active vehicles, while a Lagrangian TD3 framework enforces long-term energy constraints and improves learning stability via twin critics, delayed policy updates, and target smoothing. The simulation results demonstrate that it outperforms the comparative scheme in terms of task completion rate, delay, and energy consumption per completed task, and exhibits strong robustness in situations with dense traffic. Full article

This study evaluates the On-Demand All-Red Interval (ODAR) at signalized intersections to address red-light running (RLR) issues. Traditional fixed all-red intervals fail to adapt to dynamic traffic conditions, leading to potential safety risks and unnecessary delays. This study introduces a novel approach for dynamically extending the all-red interval on demand to enhance intersection efficiency while maintaining safety by eliminating unnecessary clearance intervals when no risk exists. Utilizing software-in-the-loop simulation, the study assesses the effectiveness of the ODAR method compared to conventional fixed-duration and Dynamic All-Red Extension (DARE) methods, allowing realistic controller testing without field deployment. The ODAR method adapts to real-time traffic conditions by incorporating vehicle speed and signal timing, ensuring vehicles with high collision risk clear the intersection safely. The study is conducted using a microsimulation model based on the Washington Street arterial network in Lake County, Illinois, validated against real traffic conditions. The results demonstrate that ODAR increases throughput and, in specific scenarios, reduces delays and stop occurrences compared to FAR and DARE strategies, based on a field-calibrated microsimulation dataset of a real-world arterial corridor. Importantly, these efficiency improvements are achieved while maintaining comparable intersection safety outcomes, as measured by red-light-running events, conflict frequency, and conflict severity. Full article

Background: Rapid pathogen detection is crucial for the timely containment of outbreaks, particularly for respiratory infectious diseases which are highly transmissible and possess high epidemic potential. Methods: We developed a sensitive reverse transcription recombinase-aided amplification (RT-RAA) assay for the rapid detection of six common respiratory viruses: respiratory syncytial virus type A (RSV A), influenza A virus (Flu A), influenza B virus (Flu B), human parainfluenza virus (HPIV), SARS-CoV-2 and adenovirus (ADV). The assay employs a single, standardized protocol for the on-demand detection of any one of the six targets. Its performance was validated using nucleic acid standards and clinical pharyngeal swab specimens. Results: The assay enables rapid detection within 20 min at 39 °C using a portable, self-powered device. It demonstrated high sensitivity, with detection limits below 10³ copies/mL for all targets and as low as 10¹ copies/mL for ADV. Cross-reactivity testing with 21 other pathogens confirmed excellent specificity. Validation with 85 clinical samples showed 100% concordance with RT-PCR, while offering significantly faster results and enhanced portability compared to RT-PCR. Conclusions: This sensitive, specific, and user-friendly RT-RAA assay provides a robust tool for rapid detection of respiratory viruses, particularly suitable for deployment in resource-limited settings and point-of-care testing during outbreaks. Full article

Time division duplexing (TDD) technology holds great promise for future satellite communication systems. To address the interference and low resource utilization encountered in satellite TDD scenarios, this paper proposes a flexible and on-demand frame structure, where the interference can be mitigated by scheduling the UE transmissions instead of configuring a long guard period (GP). Based on the frame structure, the interference between downlink broadcasting signals and preambles is analyzed, followed by formulating a random access channel (RACH) occasion (RO) configuration optimization problem that aims to maximize the RO utilization, and a structured global candidate exploration algorithm (SGCEA) is proposed to solve it. Some simulation experiments are carried out based on the practical configurations from the third-generation partnership project (3GPP)standards. Simulation results show that the proposed algorithm consistently identifies the optimal RO configuration from the predefined configurations, and the utilization remains above 80% as the satellite coverage area increases, which demonstrates the superior performance of the proposed approach and highlights its potential for practical deployment in future TDD-based satellite communication systems. Full article

Three-Dimensional Printing (3DP) is rapidly emerging as a pivotal technology for advancing Facilities Management (FM) toward smart and sustainable buildings. This systematic review, following PRISMA 2020 guidelines, critically evaluates 3DP applications, benefits, and challenges across core FM domains—construction, maintenance and repair, supply chain management, and specialized applications—through analysis of 179 studies. To our knowledge, this represents the first comprehensive, FM-specific systematic review of 3DP implementation frameworks. Evidence synthesis reveals that 3DP enables on-demand, localized manufacturing of bespoke components, with documented inventory cost reductions in maintenance applications, substantial production cost decreases for complex geometries, and significant lead time improvements from traditional procurement cycles to rapid on-demand fulfillment for spare parts applications. However, quantitative evidence remains limited and context-dependent, particularly regarding economic feasibility and scalability. 3DP adoption in FM faces significant barriers: quality assurance protocols, workforce readiness, BIM/IoT integration challenges, and regulatory uncertainty. This review identifies the absence of validated decision-making frameworks to guide FM professionals on 3DP implementation versus traditional alternatives, a fundamental research and practice gap. Through structured quality assessment and stakeholder analysis, we propose strategic recommendations emphasizing cross-sector collaboration, standardization development, and workforce upskilling. A novel conceptual decision framework supports practical implementation decisions. These findings position 3DP as potentially transformative for sustainable building operations while highlighting critical research priorities for systematic FM sector deployment. Full article

With the arrival of sixth-generation (6G) wireless systems comes radical potential for the deployment of autonomous aerial vehicle (AAV) swarms in mission-critical applications, ranging from disaster rescue to intelligent transportation. However, 6G-supporting AAV environments present challenges such as dynamic three-dimensional topologies, highly restrictive energy constraints, and extremely low latency demands, which substantially degrade the efficiency of conventional routing protocols. To this end, this work presents a Q-learning-enhanced ad hoc on-demand distance vector (QL-AODV). This intelligent routing protocol uses reinforcement learning within the AODV protocol to support adaptive, data-driven route selection in highly dynamic aerial networks. QL-AODV offers four novelties, including a multipath route set collection methodology that retains up to ten candidate routes for each destination using an extended route reply (RREP) waiting mechanism, a more detailed RREP message format with cumulative node buffer usage, enabling informed decision-making, a normalized 3D state space model recording hop count, average buffer occupancy, and peak buffer saturation, optimized to adhere to aerial network dynamics, and a light-weighted distributed Q-learning approach at the source node that uses an ε-greedy policy to balance exploration and exploitation. Large-scale simulations conducted with NS-3.34 for various node densities and mobility conditions confirm the better performance of QL-AODV compared to conventional AODV. In high-mobility environments, QL-AODV offers up to 9.8% improvement in packet delivery ratio and up to 12.1% increase in throughput, while remaining persistently scalable for various network sizes. The results prove that QL-AODV is a reliable, scalable, and intelligent routing method for next-generation AAV networks that will operate in intensive environments that are expected for 6G. Full article

On-demand urban delivery increasingly relies on electric delivery bicycles (EDBs), yet their limited battery capacity creates coupled challenges of routing efficiency and energy replenishment. We study a novel battery swapping cabinet location-routing problem (BSC-LRP) with multiple depots, which jointly optimizes routing and modular energy infrastructure deployment under time-window and battery constraints. To address the problem’s complexity, we design an improved branch-and-price algorithm enhanced with adaptive heuristic-exact labeling (IBP-HL) and a robust arc-based branching scheme. This hybrid framework accelerates column generation while preserving exactness, representing a methodological advancement over standard B&P approaches. Computational experiments on modified Solomon instances show that IBP-HL consistently outperforms Gurobi in both runtime and solution quality on small cases, and achieves substantial speedups and improved bounds over baseline B&P on medium and large cases. These results demonstrate not only the scalability of IBP-HL but also its practical relevance: the framework provides decision support for operators and planners in designing cost-efficient, reliable, and sustainable last-mile delivery systems with battery-swapping infrastructure. Full article

Floating photovoltaic (FPV) systems are promising for coastal aquaculture where reliable electricity is essential for pumping, oxygenation, sensing, and control. A sustainable FPV–storage hybrid tailored to monsoon-prone sites is developed, with emphasis on energy efficiency and structural resilience. The prototype combines dual-axis solar tracking with a spray-cooling and cleaning subsystem and an active wind-protection strategy that automatically flattens the array when wind speed exceeds 8.0 m/s. Temperature, wind speed, and irradiance sensors are coordinated by an Arduino-based supervisor to optimize tracking, thermal management, and tilt control. A 10 W floating module and a fixed-tilt reference were fabricated and tested outdoors in Penghu, Taiwan. The FPV achieved a 25.17% energy gain on a sunny day and a 40.29% gain under overcast and windy conditions, while module temperature remained below 45 °C through on-demand spraying, reducing thermal losses. In addition, a hybrid energy storage system (HESS), integrating a 12 V/10 Ah lithium-ion battery and a 12 V/24 Ah lead-acid battery, was validated using a priority charging strategy. During testing, the lithium-ion unit was first charged to stabilize the control circuits, after which excess solar energy was redirected to the lead-acid battery for long-term storage. This hierarchical design ensured both immediate power stability and extended endurance under cloudy or low-irradiance conditions. The results demonstrate a practical, low-cost, and modular pathway to couple FPV with hybrid storage for coastal energy resilience, improving yield and maintaining safe operation during adverse weather, and enabling scalable deployment across cage-aquaculture facilities. Full article

Antibodies are indispensable for protection against biological toxins and pathogens, yet their conventional liquid formulations impose severe constraints, including dosing inaccuracy caused by residual fluid remaining in the syringe and limited user convenience such as pain caused by fluid-induced tissue distension and nerve stimulation as well as instability in ambient temperature, and the requirement for low-temperature storage and logistics. These limitations critically impair rapid deployment during golden hour following acute exposure. Here, we report an antibody-integrated solid-to-gel microfilm—demonstrated with a 100 µg anti-BoNT/A dose—jet-printed and low-temperature dried directly onto metal needles for consistent, on-demand use. Upon intradermal insertion, the microfilm fully dissolves within 5 min, driven by hydration-induced swelling of a hyaluronic acid (HA) support layer and rapid release of the antibody. Time-resolved microscopy and UV–vis analysis showed a decrease in residual solid from 2.34 mm³ to 0 over 300 s, with a concomitant rise at 187 nm indicative of complete dissolution. The solid formulation maintained ambient-temperature stability for 3–6 months with pharmacokinetics comparable to conventional subcutaneous liquid injections. In a lethal BoNT/A challenge, treated mice achieved 100% survival for 12 days, whereas controls succumbed within 16 h. Full article

Salinity intrusion is a growing threat to freshwater resources, particularly in low-lying coastal and estuarine regions, necessitating the development of effective early warning systems (EWS) to support timely mitigation. Although various water quality monitoring technologies exist, many face challenges related to long-term sustainability, ongoing maintenance, and accessibility for local users. This study introduces a novel hybrid real-time salinity intrusion early warning system that uniquely integrates fixed and portable monitoring technologies with strong community participation—an approach not yet widely applied in comparable urban-adjacent delta regions. Unlike traditional systems, this model emphasizes local ownership, flexible data collection, and system scalability in resource-constrained environments. This study presents a real-time salinity intrusion early warning system for Bang Kachao, Thailand, combining eight fixed monitoring stations and 20 portable salinity measurement devices. The system was developed in response to community needs, with local input guiding both station placement and the design of mobile measurement tools. By integrating fixed stations for continuous, high-resolution data collection with portable devices for flexible, on-demand monitoring, the system achieves comprehensive spatial coverage and adaptability. A core innovation lies in its emphasis on community participation, enabling villagers to actively engage in monitoring and decision-making. The use of IoT-based sensors, Remote Telemetry Units (RTUs), and cloud-based data platforms further enhances system reliability, efficiency, and accessibility. Automated alerts are issued when salinity thresholds are exceeded, supporting timely interventions. Field deployment and testing over a seven-month period confirmed the system’s effectiveness, with fixed stations achieving 90.5% accuracy and portable devices 88.7% accuracy in detecting salinity intrusions. These results underscore the feasibility and value of a hybrid, community-driven monitoring approach for protecting freshwater resources and building local resilience in vulnerable regions. Full article

With the development of global digital economy and the digital transformation of enterprises, the demand for cross-border cross-domain sharing and circulation of highly sensitive and high-value data assets is becoming more and more obvious. In the process of shared circulation, data assets are faced with some problems, such as unreliable communication network, uncontrollable cloud storage service, untrusted participants and so on, which leads to data tampering, stealing, blocking and tracing back to the source. However, the existing security protection means are difficult to systematically ensure the safe circulation and utilization of data assets in an uncontrolled, high threat and strong confrontation environment. Therefore, this paper establishes a security protection model of data assets in the whole life cycle with cryptography technology as the core, and designs a security technical framework that runs through each link of data asset sharing and circulation. In addition, an architecture design scheme of data asset security sharing and circulation system based on cryptography service technology is proposed, which can systematically solve the security problem of data asset sharing and circulation in uncontrolled environments, and can improve the ability of on-demand deployment, flexible access and dynamic adjustment while maximizing the security of data assets. Full article

The increasing demand for smaller batch sizes and mass customisation in production poses considerable challenges to logistics and manufacturing efficiency. Conventional methodologies are unable to address the need for expeditious, cost-effective distribution of premium-quality products tailored to individual specifications. Additionally, the reliability and resilience of global logistics chains are increasingly under pressure. Additive manufacturing is regarded as a potentially viable solution to these problems, as it enables on-demand, on-site production, with reduced resource usage in production. Nevertheless, there are still significant challenges to be addressed, including the assurance of product quality and the optimisation of production processes with respect to time and resource efficiency. This article examines the potential of integrating digital twin methodologies to establish a fully digital and efficient process chain for on-site additive manufacturing. This study focuses on wire arc additive manufacturing (WAAM), a technology that has been successfully implemented in the on-site production of naval ship propellers and excavator parts. The proposed approach aims to enhance process planning efficiency, reduce material and energy consumption, and minimise the expertise required for operational deployment by leveraging digital twin methodologies. The present paper details the current state of research in this domain and outlines a vision for a fully virtualised process chain, highlighting the transformative potential of digital twin technologies in advancing on-site additive manufacturing. In this context, various aspects and components of a digital twin framework for wire arc additive manufacturing are examined regarding their necessity and applicability. The overarching objective of this paper is to conduct a preliminary investigation for the implementation and further development of a comprehensive DT framework for WAAM. Utilising a real-world sample, current already available process steps are validated and actual missing technical solutions are pointed out. Full article

The integration of Unmanned Aerial Vehicles (UAVs) into Mobile Edge Computing (MEC) systems has emerged as a transformative solution for latency-sensitive applications, leveraging UAVs’ unique advantages in mobility, flexible deployment, and on-demand service provisioning. This paper proposes a novel multi-agent reinforcement learning framework, termed Multi-Agent Twin Delayed Deep Deterministic Policy Gradient for Task Offloading and Resource Allocation (MATD3-TORA), to optimize task offloading and resource allocation in UAV-assisted MEC networks. The framework enables collaborative decision making among multiple UAVs to efficiently serve sparsely distributed ground mobile devices (MDs) and establish an integrated mobility, communication, and computational offloading model, which formulates a joint optimization problem aimed at minimizing the weighted sum of task processing latency and UAV energy consumption. Extensive experiments demonstrate that the algorithm achieves improvements in system latency and energy efficiency compared to conventional approaches. The results highlight MATD3-TORA’s effectiveness in addressing UAV-MEC challenges, including mobility–energy tradeoffs, distributed decision making, and real-time resource allocation. Full article

The fast growth of photovoltaic (PV) power generation requires dependable forecasting methods to support efficient integration of solar energy into power systems. This study conducts an up-to-date, systematized analysis of different models and methods used for photovoltaic power prediction. It begins with a new taxonomy, classifying PV forecasting models according to the time horizon, architecture, and selection criteria matched to certain application areas. An overview of the most popular heterogeneous forecasting techniques, including physical models, statistical methodologies, machine learning algorithms, and hybrid approaches, is provided; their respective advantages and disadvantages are put into perspective based on different forecasting tasks. This paper also explores advanced model optimization methodologies; achieving hyperparameter tuning; feature selection, and the use of evolutionary and swarm intelligence algorithms, which have shown promise in enhancing the accuracy and efficiency of PV power forecasting models. This review includes a detailed examination of performance metrics and frameworks, as well as the consequences of different weather conditions affecting renewable energy generation and the operational and economic implications of forecasting performance. This paper also highlights recent advancements in the field, including the use of deep learning architectures, the incorporation of diverse data sources, and the development of real-time and on-demand forecasting solutions. Finally, this paper identifies key challenges and future research directions, emphasizing the need for improved model adaptability, data quality, and computational efficiency to support the large-scale integration of PV power into future energy systems. By providing a holistic and critical assessment of the PV power forecasting landscape, this review aims to serve as a valuable resource for researchers, practitioners, and decision makers working towards the sustainable and reliable deployment of solar energy worldwide. Full article