Search Results (36)

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

The main purpose of this paper is to explore the benefits of combining two radio interfaces onboard an unmanned aerial vehicle (UAV) to communicate with a ground control station (GCS) and other UAVs inside a swarm. The goals are to use the IEEE 802.11ah standard (Wi-Fi HaLow) combined with the IEEE 802.11ax specification (Wi-Fi 6/6E) to enable real-time video transmission from UAVs to the GCS. While airport runway inspection serves as the proof-of-concept use case, the proposed multi-hop architectures apply to other medium-range UAV operations (i.e., a few kilometers) requiring real-time video transmission, such as natural disaster relief and agricultural monitoring. Several scenarios in which a UAV swarm performs infrastructure inspection are emulated. During the missions, UAVs have to send real-time video to the GCS through a multi-hop network when some damage in the infrastructure is found. The different scenarios are studied by means of emulation. Emulated scenarios are defined using different network architectures and radio technologies. Once the emulations finish, different performance metrics related to time, energy and the multi-hop video transmission network are analyzed. The capacity of a multi-hop network is a limiting factor for the transmission of high-quality video. As a first contribution, an expression to find this capacity from distances between UAVs in the emulated scenario is found using the NS-3 simulator. Then, this expression is applied in the algorithms in charge of composing the multi-hop network to offer on-demand quality video. However, the main contribution of this work lies in the development of efficient mechanisms for exchanging control information between UAVs and the GCS, and for forming a multi-hop network to transmit video. Full article

Background: A precise drug delivery system enables the optimization of treatments with minimal side effects if it can deliver medication only when activated by a specific light source. This study presents a controlled drug delivery system based on poly(lactic-co-glycolic acid) (PLGA) microparticles (MPs) designed for the sustained release of vancomycin hydrochloride. Methods: The MPs were co-loaded with indocyanine green (ICG), a near-infrared (NIR) responsive agent, and fabricated via the double emulsion method.They were characterized for stability, surface modification, biocompatibility, and antibacterial efficacy. Results: Dynamic light scattering and zeta potential analyses confirmed significant increases in particle size and surface charge reversal following chitosan coating. Scanning electron microscopy revealed uniform morphology in uncoated MPs (1–10 $\mathsf{\mu }$m) and irregular surfaces post-coating. Stability tests demonstrated drug retention for up to 180 days. Among formulations, PVI1 exhibited the highest yield (76.67 ± 1.3%) and encapsulation efficiency (56.2 ± 1.95%). NIR irradiation (808 nm) enhanced drug release kinetics, with formulation PVI4 achieving over 48.9% release, resulting in improved antibacterial activity. Chitosan-coated MPs (e.g., PVI4-C) effectively suppressed drug release without NIR light for up to 8 h, with cumulative release reaching only 10.89%. Without NIR light, bacterial colonies exceeded 1000 CFU; NIR-triggered release reduced them below 120 CFU. Drug release data fitted best with the zero-order and Korsmeyer–Peppas models, suggesting a combination of diffusion-controlled and constant-rate release behavior. Conclusions: These results demonstrate the promise of chitosan-coated NIR-responsive PLGA MPs for precise, on-demand antibiotic delivery and improved antibacterial performance. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article

Although wireless sensor networks (WSNs) have a wide range of applications, their efficient utilization is still limited by the sensor node battery life. To overcome this issue, wireless power transfer technology (WPT) has recently been used to wirelessly charge sensor nodes and extend their lifespan. This technique paved the way to develop a wireless rechargeable sensor network (WRSN) in which a mobile charger (MC) is employed to recharge the sensor nodes. Several wireless charging technologies have been proposed in this field, but they are all tied up in two classes: periodic and on-demand strategies. This paper proposes a proactive charging method as a new charging strategy that anticipates the node’s need for energy in advance based on factors such as the node’s remaining energy, energy consumption rate, and the distance to the MC. The goal is to prevent sensor nodes from depleting their energy before the arrival of the MC. Unlike conventional methods where nodes have to request energy, the proactive charging strategy identifies the nodes that need energy before they reach a critical state. Simulation results have demonstrated that the proactive charging approach using a single MC can significantly improve the network lifespan by 500% and outperform the Nearest Job Next with Preemption (NJNP) and First Come First Serve (FCFS) techniques in terms of the number of survival nodes by 300% and 650%, respectively. Full article

This work investigates nanostructured Ho_0.5Ca_0.5MnO₃, considered a model system of the Ln_0.5Ca_0.5MnO₃ series of manganites with perovskite structures featuring small lanthanide (Ln) ions half-substituted by Ca ions. Here, we propose a modified hybrid sol–gel–solid-state approach to produce multiple samples with a single batch, obtaining very high crystalline quality and ensuring the same chemical composition, with an average particle size in the range 39–135 nm modulated on-demand by a controlled calcination process. Our findings evidence that, provided the crystalline structure is preserved, the charge-ordering transition can be observed even at the nanoscale. Additionally, this research explores the presence of glassy phenomena, which are commonly seen in this class of materials, to enhance our understanding beyond simplistic qualitative observations. Comprehensive characterization using DC and AC magnetometry, along with relaxation and aging measurements, reveals that the complex dynamics typical of glassy phenomena emerge only at the nanoscale and are not visible in the bulk counterpart. Nevertheless, the analysis confirms that even the sample with the smallest nanoparticles cannot be intrinsically classified as canonical spin glass. Full article

Wireless rechargeable sensor networks (WRSNs) have emerged as a critical infrastructure for monitoring and collecting data in large-scale and dynamic environments. The energy autonomy of sensor nodes is crucial for the sustained operation of WRSNs. This paper presents a comprehensive survey on the state-of-the-art approaches and technologies in on-demand energy provisioning in large-scale WRSNs. We explore various energy harvesting techniques, storage solutions, and energy management strategies tailored to the unique challenges posed by the dynamic and resource-constrained nature of WRSNs. This survey categorizes existing literature based on energy harvesting sources, including solar, kinetic, and ambient energy, and discusses advancements in energy storage technologies such as supercapacitors and rechargeable batteries. Furthermore, we investigate energy management techniques that adaptively balance energy consumption and harvesting, optimizing the overall network performance. In addition to providing a thorough overview of existing solutions, this paper identifies opportunities and challenges in the field of on-demand energy provisioning for large-scale WRSNs. By synthesizing current research efforts, this survey aims to provide insight to researchers and policymakers in understanding the landscape of on-demand energy provisioning in large-scale WRSNs. The insights gained from this study pave the way for future innovations and contribute to the development of sustainable and self-sufficient wireless sensor networks, critical for the advancement of applications such as environmental monitoring, precision agriculture, and smart cities. Full article

Liposome-based drug delivery technologies have showed potential in enhancing medication safety and efficacy. Innovative drug loading and release mechanisms highlighted in this review of next-generation liposomal formulations. Due to poor drug release kinetics and loading capacity, conventional liposomes have limited clinical use. Scientists have developed new liposomal carrier medication release control and encapsulation methods to address these limits. Drug encapsulation can be optimized by creating lipid compositions that match a drug’s charge and hydrophobicity. By selecting lipids and adding co-solvents or surfactants, scientists have increased drug loading in liposomal formulations while maintaining stability. Nanotechnology has also created multifunctional liposomes with triggered release and personalized drug delivery. Surface modification methods like PEGylation and ligand conjugation can direct liposomes to disease regions, improving therapeutic efficacy and reducing off-target effects. In addition to drug loading, researchers have focused on spatiotemporal modulation of liposomal carrier medication release. Stimuli-responsive liposomes release drugs in response to bodily signals. Liposomes can be pH- or temperature-sensitive. To improve therapeutic efficacy and reduce systemic toxicity, researchers added stimuli-responsive components to liposomal membranes to precisely control drug release kinetics. Advanced drug delivery technologies like magnetic targeting and ultrasound. Pro Drug, RNA Liposomes approach may improve liposomal medication administration. Magnetic targeting helps liposomes aggregate at illness sites and improves drug delivery, whereas ultrasound-mediated drug release facilitates on-demand release of encapsulated medicines. This review also covers recent preclinical and clinical research showing the therapeutic promise of next-generation liposomal formulations for cancer, infectious diseases, neurological disorders and inflammatory disorders. The transfer of these innovative liposomal formulations from lab to clinical practice involves key difficulties such scalability, manufacturing difficulty, and regulatory limits. Full article

Soil is a vital component of the ecosystem that drives the holistic homeostasis of the environment. Directly, soil quality and health by means of sufficient levels of soil nutrients are required for sustainable agricultural practices for ideal crop yield. Among these groups of nutrients, soil carbon is a factor which has a dominating effect on greenhouse carbon phenomena and thereby the climate change rate and its influence on the planet. It influences the fertility of soil and other conditions like enriched nutrient cycling and water retention that forms the basis for modern ‘regenerative agriculture’. Implementation of soil sensors would be fundamentally beneficial to characterize the soil parameters in a local as well as global environmental impact standpoint, and electrochemistry as a transduction mode is very apt due to its feasibility and ease of applicability. Organic Matter present in soil (SOM) changes the electroanalytical behavior of moieties present that are carbon-derived. Hence, an electrochemical-based ‘bottom-up’ approach is evaluated in this study to track soil organic carbon (SOC). As part of this setup, soil as a solid-phase electrolyte as in a standard electrochemical cell and electrode probes functionalized with correlated ionic species on top of the metalized electrodes are utilized. The surficial interface is biased using a square pulsed charge, thereby studying the effect of the polar current as a function of the SOC profile. The sensor formulation composite used is such that materials have higher capacity to interact with organic carbon pools in soil. The proposed sensor platform is then compared against the standard combustion method for SOC analysis and its merit is evaluated as a potential in situ, on-demand electrochemical soil analysis platform. Full article

Remotely performing real-time distributed generation control and a demand response is a basic aspect of the grid ancillary services provided by grid operators, both the transmission grid operators (TSOs) and distribution grid operators (DNOs), in order to ensure that voltage, frequency and power loads of the grid remain within safe limits. The stochastic production of electrical power to the grid from the distributed generators (DGs) from renewable energy sources (RES) in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the efforts of the DNOs to keep the grid’s operation within safe limits and prevent cascading blackouts while staying in compliance with the SAIDI and SAIFI indices during repair and maintenance operations. Also taking into consideration the aging of the existing grid infrastructure, and making it more prone to failure year by year, it is yet of great significance for the DNOs to have access to real-time feedback from the grid’s infrastructure—which is fast, has low-cost upgrade interventions, is easily deployed on the field and has a fast response potential—in order to be able to perform real-time grid management (RTGM). In this article, we present the development and deployment of a control system for DG units, with the potential to be installed easily to TSO’s and DNO’s substations, RES plants and consumers (i.e., charging stations of electric vehicles). This system supports a hybrid control mechanism, either via ripple signaling or through a network, with the latter providing real-time communication capabilities. The system can be easily installed on the electric components of the grid and can act as a gateway between the different vendors communication protocols of the installed electrical equipment. More specifically, a commercially available, low-cost board (Raspberry Pi) and a ripple control receiver are installed at the substation of a PV plant. The board communicates in real-time with a remote server (decision center) via a 5G modem and with the PV plants inverters via the Modbus protocol, which acquires energy production data and controls the output power of each inverter, while one of its digital inputs can be triggered by the ripple control receiver. The ripple control receiver receives on-demand signals with the HEDNO, triggering the digital input on the board. When the input is triggered, the board performs a predefined control command (i.e., lower the inverter’s power output to 50%). The board can also receive control commands directly from the remote server. The remote server receives real-time feedback of the acquired inverter data, the control signals from the ripple control receiver and the state and outcome of each performed control command. Full article

Wireless rechargeable sensor networks provide an effective solution to the energy limitation problem in wireless sensor networks by introducing chargers to recharge the nodes. On-demand charging algorithms, which schedule the mobile charger to charge the most energy-scarce node based on the node’s energy status, are one of the main types of charging scheduling algorithms for wireless rechargeable sensor networks. However, most existing on-demand charging algorithms require a predefined charging request threshold to prompt energy-starved nodes with energy levels lower than this threshold to submit an explicit charging request to the base station so that the base station can schedule the mobile charger to charge these nodes. These algorithms ignore the difference in importance of nodes in the network, and charging requests sent by nodes independently can interfere with the mobile charger’s globally optimal scheduling. In addition, forwarding charging requests in the network increases the network burden. In this work, aiming to maximize the network revenue and the charging efficiency, we investigate the problem of scheduling the mobile charger on-demand without depending on explicit charging requests from nodes (SWECR). We propose a novel on-demand partial charging algorithm that does not require explicit charging requests from nodes. Our algorithm accounts for the differences in importance between nodes and leverages the deep reinforcement learning technique to determine the target charging node and each node’s charging time. The simulation results demonstrate that the proposed algorithm significantly improves the charging performance and maximizes the network revenue and the charging efficiency. Full article

Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided. Full article

The digitalisation of finance influenced the emergence of new technological concepts for existing user needs. Financial technology, or fintech, provides improved services for customers and new economic value for businesses. As such, fintech services require on-demand availability on a 24/7 basis. For this reason, they are often deployed in cloud environments that allow connectivity with ubiquitous devices. This allows customers to perform online transactions, which are overseen by the respective financial institutions. However, such cloud-based systems introduce new challenges for information security. On one hand, they represent attractive targets for cyberattacks. On the other, financial frauds can still go unnoticed by the financial institutions in charge. This paper contributes to both challenges by introducing the concept for a cloud-based system architecture for fraud detection and client profiling in the banking domain. Therefore, a systematic risk assessment was conducted in this context, and exploitation probabilities were inferred for multiple attack scenarios. In addition, formal verification was accomplished in order to determine the effects of successful vulnerability exploits. The consequences of such security violations are discussed, and considerations are given for improving the resilience of fintech systems. Full article

Multi-fluid micromixing, which has rarely been explored, typically represents a highly sought-after technique in on-chip biochemical and biomedical assays. Herein, we propose a novel micromixing approach utilizing induced-charge electroosmosis (ICEO) to implement multicomplex mixing between parallel streams. The variations of ICEO microvortices above a sinusoidally shaped floating electrode (SSFE) are first investigated to better understand the microvortex development and the resultant mixing process within a confined channel. On this basis, a mathematical model of the vortex index is newly developed to predict the mixing degree along the microchannel. The negative exponential distribution obtained between the vortex index and mixing index demonstrates an efficient model to describe the mixing performance without solving the coupled diffusion and momentum equations. Specifically, sufficient mixing with a mixing index higher than 0.9 can be achieved when the vortex index exceeds 51, and the mixing efficiency reaches a plateau at an AC frequency close to 100 Hz. Further, a rectangle floating electrode (RFE) is deposited before SSFE to enhance the controlled sequence for three-fluid mixing. One side fluid can fully mix with the middle fluid with a mixing index of 0.623 above RFE in the first mixing stage and achieve entire-channel mixing with a mixing index of 0.983 above SSFE in the second mixing stage, thereby enabling on-demand sequential mixing. As a proof of concept, this work can provide a robust alternative technique for multi-objective issues and structural design related to mixers. Full article

In recent decades, expansion in urban areas has faced issues such as management of public waste, noise, mobility, and air quality, among others. In this scenario, Internet of Things (IoT) and Wireless Sensor Network (WSN) scenarios are being considered for Smart Cities solutions based on the deployment of wireless remote sensor nodes to monitor large urban areas. However, as the number of nodes increases, the amount of data to be routed increases significantly as well, meaning that the choice of the data routing process has great importance in terms of the energy consumption and lifetime of the network. In this work, we describe and evaluate the energy consumption of routing protocols for WSN-based Smart Cities applications in LoRa-based mesh networks, then propose a novel energy-saving radio power adjustment (RPA) routing protocol. The Cupcarbon network simulator was used to evaluate the performance of different routing protocols in terms of their data package delivery rate, average end-to-end delay, average jitter, throughput, and load consumption of battery charge. Additionally, a novel tool for determining the range of nodes based on the Egli propagation model was designed and integrated into Cupcarbon. The routing protocols used in this work are Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Distance Vector Routing (DVR). Our simulation results show that AODV presents the best overall performance, DSR achieves the best results for power consumption, and DVR is the best protocol in terms of latency. Finally, the proposed RPA routing protocol presents power savings of 11.32% compared to the original DSR protocol. Full article