Search Results (191)

To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an adaptive-clustering mechanism. The primary goals of this topology are, first, to determine a dynamic sequence of cluster heads (CHs) for each data transmission round, aiming to prolong network lifetime by implementing an adaptive-clustering mechanism resilient to network dynamics, where CH selection relies on residual energy and minimal communication distance; second, to enhance packet delivery ratio (PDR) through the application of a data-compression technique; and third, to mitigate the hot-spot issue, wherein sensor nodes nearest to the base station endure higher relay burdens, consequently influencing network longevity. To address this issue, mobility models provide a straightforward solution; specifically, a Random Positioning of Grid Mobility (RPGM) model is employed to alleviate the hot-spot problem. The simulation results show that the network topology incorporating the proposed MEDF algorithm effectively enhances network longevity, optimizes average energy consumption, and improves PDR. Compared to the Energy-Efficient Multiple Data Fusion (EEMDF) algorithm, the proposed algorithm demonstrates enhancements in PDR and energy efficiency, with gains of 5.2% and 7.7%, respectively. Additionally, it has the potential to extend network lifetime by 13.9%. However, the MEDF algorithm increases delay by 0.01% compared to EEMDF. The proposed algorithm is also evaluated against other algorithms, such as the tracking-anchor-based clustering method (TACM) and Energy-Efficient Dynamic Clustering (EEDC), the obtained results emphasize the MEDF algorithm’s ability to conserve energy more effectively than the other algorithms. Full article

This review article investigates the current status and advances in Ku-band gallium nitride (GaN) high-electron mobility transistor (HEMT) high-power amplifiers (HPAs), which are critical for satellite communications, unmanned aerial vehicle (UAV) systems, and military radar applications. The demand for high-frequency, high-power amplifiers is growing, driven by the global expansion of high-speed data communication and enhanced national security requirements. First, we compare the main GaN HEMT process technologies employed in Ku-band HPA development, categorizing the HPAs into monolithic microwave integrated circuits (MMICs) and internally matched power amplifier modules (IM-PAMs) and examining their respective characteristics. Then, by reviewing the literature, we explore design topologies, major issues like oscillation prevention and bias circuits, and heat sink technologies for thermal management. Our findings indicate that silicon carbide (SiC) substrates with gate lengths of 0.25 μm and 0.15 μm are predominantly used, with ongoing developments enabling MMICs and IM-PAMs to achieve up to 100 W output power and 30% power-added efficiency. Notably, the performance of MMIC power amplifiers is advancing more rapidly than that of IM-PAMs, highlighting MMICs as a promising direction for achieving higher efficiency and integration in future Ku-band applications. This paper can provide insights into the overall key technologies for Ku-band GaN HPA design and future development directions. Full article

Regional integration strategically reorganizes spatially heterogeneous resources to maximize the overall benefits. Ecosystem services (ESs) are promising targets for regional integration due to their inherent heterogeneity and mobility, yet research in this area remains limited. This study quantifies crop production (CP), water yield (WY), carbon storage (CS), and habitat quality (HQ) for the years 2000, 2010, and 2020 using the InVEST model and identifies four ES bundles through a K-means cluster analysis. A conceptual ecosystem service flow (ESF) network at the service cluster scale is constructed based on county-level ESF data. The results reveal the following: (1) there is an upward trend in the ES budget for all services from 2000 to 2020, coupled with spatial mismatches between supply and demand; (2) deficit nodes for CP and CS services are concentrated in densely populated districts, while deficits in WY and HQ services are mainly in western Jilin Province; (3) Bundles I and II act as “sources” of ES, Bundle IV serves as a “sink”, and Bundle III is the only cluster with a CP surplus, balancing CP services across the province. In addition, this study provides ecological perspectives for understanding regional integration by suggesting differentiated integrated management for different ecosystem bundles. Full article

Drought and heat stresses represent the primary agricultural challenges in arid and semiarid regions globally. In wheat, among the most vulnerable stages to these stresses is the grain-filling process. This critical phase relies heavily on photosynthesis during the late growth stage and the mobilization of stem reserves. This study evaluated 60 spring wheat lines from the CIMMYT-Mexico Core Germplasm (CIMCOG) panel alongside four Iranian wheat cultivars under normal, drought, heat, and combined drought and heat stress conditions in two growing seasons. Several agronomic traits, including those associated with stem reserve mobilization, were assessed during the study. The combined analysis of variance revealed significant impacts of both independent and combined drought and heat stresses on the measured traits. Moreover, these stresses influenced the inter-relationships among the traits. High-yielding genotypes were identified through a combination of ranking and genotype and genotype by environment (GGE) biplot analysis. Among the top 40 genotypes, 21 were identified as environment-specific, while 19 remained common across at least two environments. Environmental dependence of grain yield responses to the sinks including stem reserve mobilization and spike reserve mobilization was found. Utilizing a machine learning algorithm, a regression tree analysis unveiled specific traits—including grain filling and canopy temperature—that contributed significantly to the high-yielding features of the identified genotypes under the various environmental conditions. These traits can serve as indirect selection criteria for enhancing yield under stressful conditions and can also be targeted for manipulation to improve wheat stress tolerance. Full article

The patterns of biomass allocation are crucial for understanding the growth, reproduction, and community functions of plant individuals. We investigated the allometric growth characteristics and biomass allocation patterns of Salix gordejevii fascicular branches in various habitats of the Hunshandake Sandy Land to delve into their adaptability to environmental changes and role in the carbon cycle. We discovered the following: (1) The base diameter-to-branch length of S. gordejevii fascicular branches exhibited allometric growth relationships in mobile dunes and interdune lowlands, whereas it showed isometric growth relationships in semifixed and fixed dunes. As the soil moisture gradient increased, the length growth rate of S. gordejevii fascicular branches became faster than the base diameter growth rate in mobile dunes, demonstrated isometric growth in semifixed and fixed dunes, and was slow in interdune lowlands. (2) The biomasses of S. gordejevii fascicular branches significantly varied across different habitats, with the biomass of each component showing an increasing trend as habitat conditions improved. This study revealed the resource utilization strategies and adaptability of S. gordejevii fascicular branches in different habitats, providing new insights into the carbon sink function of desert ecosystems in semiarid regions. Full article

For the first time, data on the emission of climate-active gases from soils of different types of use of the south taiga sub-zone were obtained. Soils of the boreal belt are key elements of the global carbon cycle. They determine the sink and emission of climate-active gases. Soils near large cities are a major carbon sink, in the face of climate change, soils from sinks can become a source of carbon and contribute significantly to climate change on the planet. Studies of F_CO2 and F_CH4 fluxes were carried out on the territory of the monitoring site “Ladoga” located in the southern taiga subzone in soils of land not used in agriculture, former agriculture lands, and wetlands. During the chamber measurements, a portable gas analyzer GLA131-GGA (ABB, Canada) was used. The chamber was placed on the soil, after which the concentration of CO₂, CH₄ and H₂O in the mobile chamber was recorded. As a result of the study it was found that the lowest emission of carbon dioxide is characteristic of soils developing on the soils of wetland and is 0.64 gCO₂/(m²*year). Which is associated with a high degree of hydrophobicity of the territory and changes in the redox regime. The highest emission of carbon dioxide is registered in soils on the land not used in agriculture and is 4.16 gCO₂/(m²*year). This is due to the formation of predominantly labile forms of carbon in the soil, which can be relatively rapidly involved in the carbon cycle and affect the active emission of carbon from the soil. According to the data obtained on F_CH4 emission from soils, it was found that soils of land not used in agriculture and former agriculture lands were net sinks, while soils of wetlands were characterized by CH₄ source, the emission was from 0.05 to 0.83 gCH₄/(m²*year). The results obtained indicate spatial heterogeneity and changes in the carbon cycle within the monitoring site “Ladoga”, which are due to the change of plant communities and habitat type. Monitoring the release of important greenhouse gases in close proximity to major urban areas is an important task in the face of predicted climate change and increasing rates of urbanization. Full article

The land-use, land-use change and forestry (LULUCF) sector is receiving increasing attention in climate change mitigation and greenhouse gas (GHG) emission offsetting. The sector itself and measures applied to mobilize this sector in order to tackle climate change are dominant in nationally determined contributions under the Paris Agreement as well as in national strategies, as in the case of Lithuania. Lithuania has set the goal of becoming a carbon-neutral country in 2050, reducing GHGs by 80% compared to 1990 and offsetting the remaining 20% through the LULUCF sector. Therefore, this paper aims at analyzing historical land-use changes in 1990–2021, as reported for the United Nations Framework Convention on Climate Change (UNFCCC) secretariat, and LULUCF’s potential to achieve climate change mitigation goals, taking into account different land-use change scenarios (business as usual, forest development, forest development + additional measures and forest land 40% + additional measures) for 2030 and 2050 in Lithuania. The scenarios are based on historical and potential future policy-based land-use changes. Projections of GHG emissions/removals for different scenarios are prepared according to the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2006) by the Intergovernmental Panel on Climate Change (IPCC). The results indicate that land-use changes over the period 1990–2021 remained rather stable, with some increases in forest area and grassland at the expense of cropland. The whole LULUCF sector acted as a carbon sink in most cases, forests being a key category for removal. However, reaching climate neutrality in 2050 might be challenging, as the goal to offset 20% of remaining GHG emission compared to 1990 through LULUCF would not be met in any of the scenarios analyzed, even the scenario of maximal forest-area development and additional measures. Considering the high historical GHG-removal fluctuations and the uncertainties of the sector itself, caution should be taken when relying on LULUCF’s potential to reach the set goals. Full article

The evolution of agriculture towards a modern, intelligent system is crucial for achieving sustainable development and ensuring food security. In this context, leveraging the Internet of Things (IoT) stands as a pivotal strategy to enhance both crop quantity and quality while effectively managing natural resources such as water and fertilizer. Wireless sensor networks, the backbone of IoT-based smart agricultural infrastructure, gather ecosystem data and transmit them to sinks and drones. However, challenges persist, notably in network connectivity, energy consumption, and network lifetime, particularly when facing supernode and relay node failures. This paper introduces an innovative approach to address these challenges within heterogeneous wireless sensor network-based smart agriculture. The proposed solution comprises a novel connectivity management scheme and a dynamic clustering method facilitated by five distributed algorithms. The first and second algorithms focus on path collection, establishing connections between each node and m-supernodes via k-disjoint paths to ensure network robustness. The third and fourth algorithms provide sustained network connectivity during node and supernode failures by adjusting transmission powers and dynamically clustering agriculture sensors based on residual energy. In the fifth algorithm, an optimization algorithm is implemented on the dominating set problem to strategically position a subset of relay nodes as migration points for mobile supernodes to balance the network’s energy depletion. The suggested solution demonstrates superior performance in addressing connectivity, failure tolerance, load balancing, and network lifetime, ensuring optimal agricultural outcomes. Full article

Microbial respiration determines the accumulation of reduced solutes and negative redox potential in organic sediments, favoring the mobilization of dissolved inorganic phosphorus (DIP), generally coprecipitated with Fe oxyhydroxides. Macrophytes releasing oxygen from the roots can contrast DIP mobility via the oxidation of anaerobic metabolism end-products. In this work, the submerged macrophyte Vallisneria spiralis was transplanted into laboratory microcosms containing sieved and homogenized organic sediments collected from a contaminated wetland. Sediments with and without plants were incubated under light and dark conditions for oxygen and DIP fluxes measurements and pore water characterization (pH, oxidation-reduction potential, DIP, dissolved Mn, and Fe). Bare sediments were net DIP sources whereas sediments with V. spiralis were weak DIP sources in the dark and large sinks in light. V. spiralis radial oxygen loss led to less negative redox potential and lower Fe, Mn, and DIP concentrations in pore water. Roots were coated by reddish plaques with large amounts of Fe, Mn, and P, exceeding internal content. The results demonstrated that at laboratory scale, the transplant of V. spiralis into polluted organic sediments, mitigates the mobility of DIP and metals through both direct and indirect effects. This, in turn, may favor sediment colonization by less-tolerant aquatic plants. Further in situ investigations, coupled with economic analyses, can evaluate this potential application as a nature-based solution to contrast eutrophication. Full article

Aiming at the problem of being unable to meet some high computing power, high-precision applications due to the limited capacity of underwater sensor nodes, and the difficulty of low computation power, in this paper, we introduce the edge servers, known as base stations for underwater sensor nodes, and propose a scheme to process the computational tasks based on coalition game theory. This scheme provides functions such as cooperation among different base stations within the coalition, the smart division of tasks, and efficient computational offloading. In order to reduce the complexity of the algorithm, the artificial neural network model is introduced into the method. Each task is divided into sub-parts and fed to an artificial neural network for training, testing, and validation. In addition, the scheme delivers the computed task from base stations back to sink nodes via a shortened path to enhance the service reliability. Due to the mobility of the base station in the ocean, our proposed scheme takes into account the dynamic environment at the same time. The simulation results show that, compared with the existing state-of-the-art methods, the success rate of our proposed approach improves by 30% compared with the Greedy method. The total service time of our proposed approach decreases by 12.6% compared with the Greedy method and 31.2% compared with the Always-Migrate method. Full article

With the increasing concerns for the environment, the amount of the data monitored by wireless sensor networks (WSNs) is becoming larger and the energy required for data transmission is greater. However, sensor nodes have limited storage capacity and battery power. The WSNs are faced with the challenge of handling larger data volumes while minimizing energy consumption for transmission. To address this issue, this paper employs data compression technology to eliminate redundant information in the environmental data, thereby reducing energy consumption of sensor nodes. Additionally, an unmanned aerial vehicle (UAV)-assisted compressed data acquisition algorithm is put forward. In this algorithm, compressive sensing (CS) is introduced to decrease the amount of data in the network and the UAV serves as a mobile aerial base station for efficient data gathering. Based on CS theory, the UAV selectively collects measurements from a subset of sensor nodes along a route planned using the optimized greedy algorithm with variation and insertion strategies. Once the UAV returns, the sink node reconstructs sensory data from these measurements using the reconstruction algorithms. Extensive experiments are conducted to verify the performance of this algorithm. Experimental results show that the proposed algorithm has lower energy consumption compared to other approaches. Furthermore, we employ different data reconstruction algorithms to recover data and discover that the data can be better reconstructed in a shorter time. Full article

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures. Full article

Purpose: Develop a treatment planning framework for neurosurgeons treating high-grade gliomas with LITT to minimize the learning curve and improve tumor thermal dose coverage. Methods: Deidentified patient images were segmented using the image segmentation software Materialize MIMICS©. Segmented images were imported into the commercial finite element analysis (FEA) software COMSOL Multiphysics© to perform bioheat transfer simulations. The laser probe was modeled as a cylindrical object with radius 0.7 mm and length 100 mm, with a constant beam diameter. A modeled laser probe was placed in the tumor in accordance with patient specific patient magnetic resonance temperature imaging (MRTi) data. The laser energy was modeled as a deposited beam heat source in the FEA software. Penne’s bioheat equation was used to model heat transfer in brain tissue. The cerebrospinal fluid (CSF) was modeled as a solid with convectively enhanced conductivity to capture heat sink effects. In this study, thermal damage-dependent blood perfusion was assessed. Pulsed laser heating was modeled based on patient treatment logs. The stationary heat source and pullback heat source techniques were modeled to compare the calculated tissue damage. The developed bioheat transfer model was compared to MRTi data obtained from a laser log during LITT procedures. The application builder module in COMSOL Multiphysics© was utilized to create a Graphical User Interface (GUI) for the treatment planning framework. Results: Simulations predicted increased thermal damage (10–15%) in the tumor for the pullback heat source approach compared with the stationary heat source. The model-predicted temperature profiles followed trends similar to those of the MRTi data. Simulations predicted partial tissue ablation in tumors proximal to the CSF ventricle. Conclusion: A mobile platform-based GUI for bioheat transfer simulation was developed to aid neurosurgeons in conveniently varying the simulation parameters according to a patient-specific treatment plan. The convective effects of the CSF should be modeled with heat sink effects for accurate LITT treatment planning. Full article

This is the first study of dissolved organic matter (DOM) at the Lake Baikal water-bottom interface. High-resolution profiles of dissolved organic carbon (DOC) were obtained simultaneously with dissolved inorganic carbon (DIC), total dissolved carbon, cations (Na⁺, K⁺, Ca²⁺, Mg²⁺, Fe²⁺, and Mn²⁺), and anions (HCO₃⁻, Cl⁻, NO₃⁻, and SO₄²⁻) in the pore water of Lake Baikal deepwater oxidized sediments. We evaluated the DOC fluxes quantitatively and qualitatively. They changed their direction twice under different redox conditions in the sediments (at the redox interfaces). The study revealed that the mobilization of DOC in anoxic sediments was closely related to the reductive dissolution of Fe(III) minerals, and the oxidized surface lake sediments represented an effective DOC trap binding DOC to ferric minerals. Redox conditions appeared to be the main regulator of the DOC exchange. Oxygen conditions led to the uptake of DOC by sediments (31–78 mmol C m⁻² yr⁻¹), i.e., the Lake Baikal sediments are a sink of DOC. The DOC flux was approximately 25–35% of the carbon flux at the sediment–water interface. The results of this study allow for a better understanding of the nature and properties of DOC in freshwater ecosystems and compensate for the underestimation of DOC in the internal carbon cycle of the lake. Full article

Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the routing protocol design to consider the network lifetime extension problem. Based on this, we designed a new routing protocol that takes into account both low energy consumption and balanced energy consumption, and achieves effective extension of the network lifetime, called adaptive power-controlled depth-based routing protocol for underwater wireless sensor networks (APCDBRP). The protocol consists of two phases: (1) the route establishment phase and (2) the data transmission phase. In the route establishment phase, the initial path is established by the sink node broadcasting beacon packets at the maximum transmission power. The receiving nodes update their routing tables based on the beacon information and forward the beacon packets. In the data transmission phase, APCDBRP introduces a novel forwarding factor that considers both energy efficiency and energy balance. It selects the optimal next hop based on high energy efficiency and relatively abundant energy, thus extending the network’s lifetime. Additionally, APCDBRP proposes a new data protection and route reconstruction mechanism to address issues such as network topology changes due to node mobility and data transmission failures. Our simulation is based on AquaSim–Next Generation, which is a specialized tool built on the NS3 platform for researching underwater networks. Simulation results demonstrate that, compared to other typical routing protocols, APCDBRP exhibits superior performance in reducing network energy consumption and extending the network’s lifetime. It also achieves a high packet delivery rate with lower energy consumption. Full article