Search Results (21)

Unmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks. Full article

Background: The human gut microbiome—a diverse ecosystem of trillions of microorganisms—plays an essential role in metabolic, immune, and neurological regulation. However, modern lifestyle factors such as antibiotic overuse, cesarean delivery, reduced breastfeeding, processed and high-sodium diets, alcohol intake, smoking, and exposure to environmental toxins (e.g., glyphosate) significantly reduce microbial diversity. Loss of keystone species like Bifidobacterium infantis (B. infantis) and Lactobacillus reuteri (L. reuteri) contributes to gut dysbiosis, which has been implicated in chronic metabolic, autoimmune, cardiovascular, and neurodegenerative conditions. Materials and Methods: This Qualitative Systematic Review (QualSR) synthesized data from over 547 studies involving human participants and standardized microbiome analysis techniques, including 16S rRNA sequencing and metagenomics. Studies were reviewed for microbial composition, immune and metabolic biomarkers, and clinical outcomes related to microbiome restoration strategies. Results: Multiple cohort studies have consistently reported a 40–60% reduction in microbial diversity among Western populations compared to traditional societies, particularly affecting short-chain fatty acid (SCFA)-producing bacteria. Supplementation with B. infantis is associated with a significant reduction in systemic inflammation—including a 50% decrease in C-reactive protein (CRP) and reduced tumor necrosis factor-alpha (TNF-α) levels—alongside increases in regulatory T cells and anti-inflammatory cytokines interleukin-10 (IL-10) and transforming growth factor-beta 1 (TGF-β1). L. reuteri demonstrates immunomodulatory and neurobehavioral benefits in preclinical models, while both probiotics enhance epithelial barrier integrity in a strain- and context-specific manner. In murine colitis, B. infantis increases ZO-1 expression by ~35%, and L. reuteri improves occludin and claudin-1 localization, suggesting that keystone restoration strengthens barrier function through tight-junction modulation. Conclusions: Together, these findings support keystone species restoration with B. infantis and L. reuteri as a promising adjunctive strategy to reduce systemic inflammation, reinforce gut barrier integrity, and modulate gut–brain axis (GBA) signaling, indicating translational potential in metabolic and neuroimmune disorders. Future research should emphasize personalized microbiome profiling, long-term outcomes, and transgenerational effects of early-life microbial disruption. Full article

The Lauraceae family, a keystone group in subtropical evergreen broad-leaved forest ecosystems, exhibits exceptional diversity in sexual systems (including hermaphroditic flowers, functionally unisexual flowers, and pseudo-dioecy), serving as a natural model for studying plant sexual differentiation mechanisms. This review synthesizes advances in the evolutionary mechanisms and genomic studies of sexual differentiation in Lauraceae, focusing on three key areas: (1) the evolution of taxonomic classification and floral morphology, (2) molecular trajectories of sexual differentiation, and (3) challenges and future directions in sex determination research (e.g., sex-linked marker development and gene-editing-assisted breeding). Morphological and phylogenetic analyses suggest that ancestral Lauraceae species were late Cretaceous hermaphroditic trees, with recent radiation of unisexual lineages (e.g., Cinnamomum and Laurus) linked to pollinator pressure, genome duplication events (WGD), and incipient sex chromosome evolution. Despite progress, critical challenges remain, including unresolved thresholds for sex chromosome origination, unquantified molecular pathways integrating environmental signals (e.g., photoperiod, temperature) with genetic networks, and the lack of efficient sex-specific markers and genetic transformation systems. Future studies should integrate single-cell omics, epigenetic profiling, and cross-species comparative genomics to elucidate spatiotemporal dynamics and evolutionary drivers of sexual differentiation. These efforts will advance genetic improvement and ecological restoration strategies. This review provides a systematic framework for advancing plant sexual evolution theory and promoting sustainable utilization of Lauraceae resources. Full article

The Tibetan Plateau is a critical ecological habitat where the overpopulation of plateau pika (Ochotona curzoniae), a keystone species, accelerates grassland degradation through excessive burrowing and herbivory, threatening ecological balance and human activities. To address the inefficiency and high costs of traditional pika burrow monitoring, this study proposes an intelligent monitoring solution that integrates drone remote sensing with deep learning. By combining the lightweight visual Transformer architecture EfficientViT with the hybrid attention mechanism CBAM, we develop an enhanced YOLOv11-AEIT algorithm: (1) EfficientViT is employed as the backbone network, strengthening micro-burrow feature representation through a multi-scale feature coupling mechanism that alternates between local window attention and global dilated attention; (2) the integration of CBAM (Convolutional Block Attention Module) in the feature fusion neck reduces false detections through dual-channel spatial attention filtering. Evaluations on our custom PPCave2025 dataset show that the enhanced model achieves a 98.6% mAP@0.5, outperforming the baseline YOLOv11 by 3.5 percentage points, with precision and recall improvements of 4.8% and 7.2%, respectively. The algorithm enhances efficiency by a factor of 15 compared to manual inspection, while seamlessly meeting real-time drone detection requirements. This approach provides high-precision yet lightweight technical support for plateau ecological conservation and serves as a valuable methodological reference for similar ecological monitoring tasks. Full article

Incorporation of plant residues in soil affects microbial community structure and ecological function, which can improve soil fertility. It is reported that substrate qualities could regulate microbial keystone taxa and their interactions, wielding an important effect on nutrient cycling in ecosystems, such as soil labile phosphorus (P) transformation. However, there is little understanding of the specific microbial mechanisms governing P’s availability in acidic soils following the incorporation of plant residues of various qualities. In this 210-day incubation experiment, two high-quality residues of pumpkin stover and mango branch and one low-quality residue of rice straw, different in terms of their labile carbon (C) content and carbon/phosphorus ratio (C/P), were separately mixed with an acidic soil. The aim was to investigate how the residues affected the community composition, keystone species, and interaction patterns of soil bacteria and fungi, and how these microbial characteristics altered soil P mineralization and immobilization processes, along with P availability. The results showed that adding high-quality pumpkin stover significantly increased the soil’s available P content (AP), microbial biomass P content (MBP), and acid phosphatase activity (ACP), by 63.7%, 86.7%, and 171.7% compared to the control with no plant residue addition, respectively. This was explained by both the high abundance of dominant bacteria (Kribbella) and the positive interactions among fungal keystone species. Adding mango branch and rice straw induced cooperation within fungal communities while resulting in lower bacterial abundances, thereby increasing AP, MBP, and ACP less than the addition of pumpkin stover. Moreover, the labile C of plant residues played a dominant role in soil P transformation and determined the P availability of the acidic soil. Therefore, it may be suitable to incorporate high-quality plant residues with high labile C and low C/P into acidic soils in order to improve microbial communities and enhance P availability. Full article

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address the aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving computational efficiency. Finally, the simulation and real data experiments demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods. Full article

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence “SpaceFest” 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm² (0.09 m²) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar’s utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar’s ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities. Full article

As Smart Cities development grows, deploying advanced technologies, such as the Internet of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI), becomes imperative for efficiently managing energy resources. These technologies serve to coalesce elements of the energy life cycle. By integrating smart infrastructures, including renewable energy, electric vehicles, and smart grids, AI emerges as a keystone, improving various urban processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Scopus database, this study meticulously reviews the existing literature, focusing on AI technologies in four principal energy domains: generation, transmission, distribution, and consumption. Additionally, this paper shows the technological gaps when AI is implemented in Smart Cities. A total of 122 peer-reviewed articles are analyzed, and the findings indicate that AI technologies have led to remarkable advancements in each domain. For example, AI algorithms have been employed in energy generation to optimize resource allocation and predictive maintenance, especially in renewable energy. The role of AI in anomaly detection and grid stabilization is significant in transmission and distribution. Therefore, the review outlines trends, high-impact articles, and emerging keyword clusters, offering a comprehensive analytical lens through which the multifaceted applications of AI in Smart City energy sectors can be evaluated. The objective is to provide an extensive analytical framework that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy. This synthesis is aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector. Full article

Millimeter wave radar has the advantage of all-day and all-weather capability for detection, speed measurement. It plays an important role in urban traffic flow monitoring and traffic safety monitoring. The conventional 2-dimensional Fast Fourier Transform (2DFFT) algorithm is performed target detection in the range-Doppler domain. However, the target motion will induce the range walk phenomenon, which leads to a decrease in the target energy and the performance of the target detection and speed measurement. To solve the above problems, this paper proposes a moving vehicle detection algorithm based on Keystone-2DFFT for a traffic scene. Firstly, this paper constructs and analyzes the Frequency Modulated ContinuousWave (FMCW) moving target signal model under traffic monitoring scenario’s radar observation geometry. The traditional 2DFFT moving target detection algorithm is briefly introduced. Then, based on mentioned signal model, an improved moving vehicle detection algorithm based on Keystone-2DFFT transform is proposed. The method first input the echo, then the range walk is removed by keystone transformation. the keystone transformation is achieved via Sinc interpolation. Next is transform data into range-Doppler domain to perform detection and speed estimation. The algorithm is verified by simulation data and real data. Full article

Due to the fast scanning speed of the current phased-array radar and the moving characteristics of the target, the moving target usually spans multiple beams during coherent integration time, which results in severe performance loss for target focusing and parameter estimation because of the unknown entry/departure beam time within the coherent period. To solve this issue, a novel focusing and detection method based on the multi-beam phase compensation function (MBPCF), multi-scale sliding windowed phase difference (MSWPD), and spatial projection are proposed in this paper. The proposed method mainly includes the following three steps. First, the geometric and signal models of multiple beam integration with observed moving targets are accurately established where the range migration (RM), Doppler frequency migration (DFM), and beam migration (BM) are analyzed. Based on that, the BM is eliminated by the MBPCF, the second-order keystone transform (SOKT) is utilized to mitigate the RM, and then, a new MSWPD operation is developed to estimate the target’s entry/departure beam time, which realizes well-focusing output within the beam. After that, by dividing the radar detection area, the spatial projection (SP) method is adopted to obtain multiple-beams joint integration, and thus, improved detection performance can be obtained. Numerical experiments are carried out to evaluate the performance of the proposed method. The results show that the proposed method could achieve superior focusing and detection performances. Full article

In order to enhance the detection and parameter estimation capacity to the maneuvering target with complex motions, a low complexity coherent integration and parameter estimation method named SOKT-IAR-LVD is proposed in this paper. In SOKT-IAR-LVD, first, the second-order keystone transform (SOKT) is utilized to eliminate the range curvature induced by target acceleration. Second, improved axis rotation (IAR) is applied to regulate the linear range migration by rotating the fast time axis and the target envelope is aligned along the slow time axis with a quadratic phase characteristic. At last, the target signal is coherently integrated via the Lv’s Distribution (LVD) transform. The target motion parameters, including range, velocity, and acceleration, are estimated by the IAR and LVD results. The integration gain and computational load of SOKT-IAR-LVD are analyzed. Without needing to estimate the Doppler ambiguity number and target acceleration, the computational burden of SOKT-IAR-LVD is three orders of magnitude lower than that of the Radon-Lv’s Distribution (RLVD) method. Simulation results demonstrate that the detection performance of SOKT-IAR-LVD is almost the same as that of RLVD and that the required input SNR of SOKT-IAR-LVD is 17.4 dB lower than that of SOKT–Radon Fourier transform (SOKT-RFT) when the detection threshold is set to 12 dB. Full article

This paper proposes an improved multi-frame coherent integration algorithm to improve the detection performance of weak targets in heterogeneous radar. In the detection of weak targets, integration within a single frame may fail to provide sufficient signal-to-noise ratio (SNR) gain. In this case, multi-frame coherent integration is an effective solution. However, radar parameters may be different across frames (i.e., heterogeneous radar) in some practical situations, leading to a mismatch of Doppler frequencies and the fixed phases, which poses difficulties to multi-frame coherent integration. To calibrate the ranges and Doppler frequencies of heterogenous multi-frame echoes, this paper firstly employs an improved Keystone Transform (KT). Compared to conventional KT, the improved KT aligns inter-frame carrier frequencies by applying varying degrees of slow-time rescaling based on the carrier frequencies of each frame, and aligns inter-frame Pulse Repetition Frequencies (PRF) through a unified global slow-time resampling. Secondly, this paper derives the explicit expressions of the fixed-phase terms and adopts a method based on fractional range bins, thus achieving explicit compensation for mismatched phases. Finally, heterogenous multi-frame coherent integration is achieved through slow-time fast Fourier transform. The effectiveness of the proposed algorithm is validated by simulation analyses. Compared to existing entropy-based methods, the proposed algorithm demonstrates higher robustness and lower computational complexity, making it more effective in detecting weak targets under low SNR conditions. Full article

Due to the limited transmission gain of ubiquitous radar systems, it has become necessary to use a long-time coherent integration method for range-Doppler (RD) analysis. However, when the target exhibits high-speed and high-maneuver capabilities, it introduces challenges, such as range migration (RM), Doppler frequency migration (DFM), and velocity ambiguity (VA) in the RD domain, thus posing significant difficulties in target detection and tracking. Moreover, the presence of VA further complicates the problem. To address these complexities while maintaining integration efficiency, this study proposes a hybrid integration approach. First, methods called Keystone-transform (KT) and matched filtering processing (MFP) are proposed for compensating for range migration (RM) and velocity ambiguity (VA) in Radar Detection (RD) images. The KT approach is employed to compensate for RM, followed by the generation of matched filters with varying ambiguity numbers. Subsequently, MFP enables the production of multiple RD images covering different but contiguous Doppler frequency ranges. These RD images can be compiled into an extended RD (ERD) image that exhibits an expanded Doppler frequency range. Second, an improved particle-filter (IPF) algorithm is raised to perform incoherent integration among ERD images and to achieve track-before-detect (TBD) for a target. In the IPF, the target state vector is augmented with ambiguous numbers, which are estimated via maximum posterior probability estimation. Then, to compensate for the DFM, a line spread model (LSM) is proposed instead of the point spread model (PSM) used in traditional PF. To evaluate the efficacy of the proposed method, a radar simulator is devised, encompassing comprehensive radar signal processing. The findings demonstrate that the proposed approach achieves a harmonious equilibrium between integration efficiency and computational complexity when it comes to detecting and tracking high-speed and high-maneuvering targets with intricate maneuvers. Furthermore, the algorithm’s effectiveness is authenticated by exploiting ubiquitous radar data. Full article

The echoes collected by wideband radar systems provide abundant information on target scatterers, which is beneficial to target detection, classification, and recognition. However, as the radar range resolution increases, range cell migration (RCM) during the coherent integration (CI) period happens much easier, which may cause a degradation of target detection probability. In addition, due to the target’s orientation and structure relative to the radar, the distribution characteristics of the target scatterers in high-resolution range profiles (HRRPs) and the detection window length may vary from pulse to pulse, which may reduce the performance of traditional energy integration (EI) detectors. To solve those problems, moving range-spread target (RST) detection combining the modified keystone transform (MKT) and improved EI (IEI) is proposed in this paper. Firstly, based on waveform entropy minimization, MKT using hunter–prey optimization (HPO) is introduced to reduce the CI gain loss. The target Doppler ambiguity factor is estimated using such an effective optimization technique. Then, the IEI detector optimized by the adaptive threshold and detection window is utilized to achieve target detection, which minimizes the sensitivity of the traditional EI detector to the detection window length. The proposed method significantly improves the performance of moving RSTs in sea clutter without prior knowledge of the target Doppler ambiguity factor. Experiments are conducted by comparing the proposed method with other competing methods on both simulation data and real sea clutter data. The results demonstrate that the proposed method can obtain the CI more efficiently and has a higher detection probability. Full article

Employer Information Requirements (EIR) are the keystone for developing a successful Building Information Modelling (BIM) project. However, clients’ lack of skill and experience in categorising and defining these requirements often undermines the performance of a construction project and, ultimately, the ability of the finished product to meet their needs. By definition, EIR shortcomings include incomplete and inconsistent requirements and specifications, and whilst some work has been performed to try to address these, this area is still underdeveloped. This paper reports on the development of a transformative approach to the categorisation of requirements in a meaningful way, enabling effective filtering so that stakeholders can access just the information they need for the task at hand. The Ontology-based framework for Employer Information Requirements (OntEIR) seeks to provide a step change in categorisation by identifying ‘static’ and ‘dynamic’ requirements, including related types and sub-types. OntEIR has been rigorously validated by a group of BIM experts, and the results have revealed that this approach to categorisation significantly improved the elicitation and understanding of requirements. Full article