Search Results (25,202)

We investigated the spatial structure and colony itinerancy of Audouin’s gull (Ichthyaetus audouinii) adult breeders across multiple breeding sites in the central Mediterranean Sea during 25 years of fieldwork. Using cluster analysis of marked individuals from different years and sites, we identified five spatial breeding units of increasing hierarchical scale—Breeding Sites, Colonies, Districts, Regions and Marine Sectors—which reflect biologically meaningful boundaries beyond simple geographic proximity. To determine the most appropriate scale for monitoring local populations, we applied multievent capture–recapture models and examined variation in survival and site fidelity across these units. Audouin’s gulls frequently change their location at the Breeding Site and Colony levels from one year to another, without apparent survival costs. In contrast, dispersal beyond Districts boundaries was found to be rare and associated with reduced survival rates, indicating that breeding Districts represent the most relevant biological unit for identifying local populations. The survival disadvantage observed in individuals leaving their District likely reflects increased extrinsic mortality in unfamiliar environments and the selective dispersal of lower-quality individuals. Within breeding Districts, birds may benefit from local knowledge and social information, supporting demographic stability and higher fitness. Our findings highlight the value of adopting a District-based framework for long-term monitoring and conservation of this endangered species. At this scale, demographic trends such as population growth or decline emerge more clearly than when assessed at the level of singular colonies. This approach can enhance our understanding of population dynamics in other mobile species and support more effective conservation strategies aligned with natural population structure. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person’s plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints using a topological graph and regions by semantic labels. This is incorporated into an MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario and a waypoint planner that generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the-art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to new and unseen environments. Full article

Background/Objectives: Campylobacteriosis in human populations is an ongoing issue in both developed and developing countries. Poultry production is recognized as a reservoir for antimicrobial resistance and main source of human Campylobacter infection. Methods: In this study, sixty-five Campylobacter isolates were cultured from fecal samples collected from 17 flocks of broiler chickens in Alberta, Canada over two years (2015–2016). Susceptibility assays and PCR assays were performed to characterize resistance phenotypes and resistance genes. Conjugation assays were used to examine the mobility of AMR phenotypes. Results: Campylobacter jejuni was the predominant species recovered during both years of sampling. There were no Campylobacter coli isolates found in 2015; however, approximately 33% (8/24) of isolates collected in 2016 were Campylobacter coli. The two most frequent antimicrobial resistance patterns in C. jejuni collected in 2015 were tetracycline (39%) and azithromycin/clindamycin/erythromycin/telithromycin resistance (29%). One isolate collected in 2015 has resistance pattern ciprofloxacin/nalidixic acid/tetracycline. The tetO gene was detected in all tetracycline resistant isolates from 2015. The cmeB gene was detected in all species isolates with resistance to azithromycin/clindamycin/erythromycin/telithromycin, and from two isolates with tetracycline resistance. Alignment of the nucleotide sequences of the cmeB gene from C. jejuni isolates with different resistance patterns revealed several single nucleotide polymorphisms. A variety of multi-drug resistance patterns were observed through conjugation experiments. Conclusions: These data suggest that poultry production may serve as a potential reservoir for and source of transmission of multi-drug resistant Campylobacter jejuni and supports the need for continued surveillance. Full article

Artificial intelligence (AI) has become ubiquitous in modern computing systems, from high-performance data centers to resource-constrained edge devices. As AI applications continue to expand into mobile and IoT domains, the need for energy-efficient neural network implementations has become increasingly critical. To meet this requirement of energy-efficient computing, this work presents a BWNN (binary-weighted neural network) architecture implemented using FeRAM (Ferroelectric RAM)-based synaptic arrays. By leveraging the non-volatile nature and low-power computing of FeRAM-based CIM (computing in memory), the proposed CIM architecture indicates significant reductions in both dynamic and standby power consumption. Simulation results in this paper demonstrate that scaling the ferroelectric capacitor size can reduce dynamic power by up to 6.5%, while eliminating DRAM-like refresh cycles allows standby power to drop by over 258× under typical conditions. Furthermore, the combination of binary weight quantization and in-memory computing enables energy-efficient inference without significant loss in recognition accuracy, as validated using MNIST datasets. Compared to prior CIM architectures of SRAM-CIM, DRAM-CIM, and STT-MRAM-CIM, the proposed FeRAM-CIM exhibits superior energy efficiency, achieving 230–580 TOPS/W in a 45 nm process. These results highlight the potential of FeRAM-based BWNNs as a compelling solution for edge-AI and IoT applications where energy constraints are critical. Full article

Background: Gait impairments are common in individuals with knee osteoarthritis awaiting total knee arthroplasty, affecting their mobility and quality of life. This study aimed to assess and compare biomechanical gait features between individuals awaiting total knee arthroplasty and healthy, non-arthritic controls, focusing on less-explored variables using sensor-based measurements. Methods: A cross-sectional observational study was conducted with 60 participants: 21 individuals awaiting total knee arthroplasty and 39 nonarthritic controls aged 64–85 years. Participants completed a standardized 14 m walk, and 17 biomechanical gait parameters were measured using the BTS G-Walk inertial sensor. Key variables, such as stride duration, cadence, symmetry indices, and pelvic angles, were analyzed for group differences. Results: The pre-total knee arthroplasty group exhibited significantly longer gait cycles and stride durations (p < 0.001), reduced cadence (p < 0.001), and lower gait cycle symmetry index (p < 0.001) than the control group. The pelvic angle symmetry indices for tilt (p = 0.014), rotation (p = 0.002), and obliquity (p < 0.001) were also lower. Additionally, the pre-total knee arthroplasty group had lower propulsion indices for both legs (p < 0.001) and a lower walking quality index on the right leg (p = 0.005). The number of elaborated steps was significantly greater in the pre-total knee arthroplasty group (left, p < 0.001, right: p < 0.001). No significant differences were observed in any other gait parameters. Conclusions: This study revealed significant gait impairment in individuals awaiting total knee arthroplasty. Although direct evidence for prehabilitation is lacking, future research should explore whether targeted approaches, such as strengthening exercises or gait retraining, can improve gait and functional outcomes before surgery. Full article

With the rise of the low-altitude urban traffic system, urban air mobility (UAM) has developed rapidly. As a critical component of the UAM system, the strategic layout of vertiports helps divert ground traffic pressure. To satisfy various demand patterns, different vertiport levels are needed, so we focus on the hierarchical vertiport location problem. Considering the capacity limitation, a median location model is established to minimize vertiport construction cost, passenger commuting cost, and penalty cost. For the nonlinear term in the objective function, the Big-M method is employed. Based on the reformulated model, we improve the branch-and-bound algorithm (LVBB) to solve it, where the Lagrange relaxation method is used to decompose the large-scale problem into parallel subproblems and compute the lower bound, and the variable neighborhood search algorithm is used to obtain the upper bound. Numerical experiments are performed in the 11 administrative districts of Nanjing, China. The results demonstrate that the proposed location scheme effectively balances vertiport construction cost and passenger commuting cost while satisfying capacity limitations. It also significantly reduces commuting time to improve passenger satisfaction. This scheme can offer strategic guidance for infrastructure planning in UAM. Full article

The heavy metal contamination of soils is a global environmental challenge threatening water quality, food safety, and human health. Using a systematic literature review approach, this study aimed to assess the potential of bacterial strains to immobilize cadmium (Cd²⁺), lead (Pb²⁺), and copper (Cu²⁺) in contaminated soils. A total of 45 articles were analyzed, focusing on studies that reported heavy metal concentrations before and after bacterial treatment. The analysis revealed that bacterial genera such as Bacillus, Pseudomonas, and Enterobacter were most commonly used for the immobilization of these metals. Immobilization efficiencies ranged from 25% to over 98%, with higher efficiencies generally observed when microbial consortia or amendments (e.g., phosphate compounds and biochar) were applied. The main immobilization mechanisms included biosorption, bioprecipitation (such as carbonate-induced precipitation), bioaccumulation, and biomineralization, which convert mobile metal ions into more stable, less bioavailable forms. These findings highlight the promising role of microbial-assisted immobilization in mitigating heavy metal pollution and reducing ecological risks. Further laboratory and field studies are needed to optimize the use of these microbial strains under site-specific conditions to ensure effective and sustainable soil remediation practices. Full article

With their superior switching speed, GaN high-electron-mobility transistors (HEMTs) enable high power density, reduce energy losses, and increase power efficiency in a wide range of applications, such as power electronics, due to their high breakdown voltage. GaN-HEMT devices are subject to long-term reliability due to the self-heating effect and lattice mismatch between the SiC substrate and the GaN. Depletion-mode GaN HEMTs are utilized for radio frequency applications, and this work investigates three wide-bandgap (WBG) GaN HEMT fixed-frequency oscillators with output buffers. The first GaN-on-SiC HEMT oscillator consists of an HEMT amplifier with an LC feedback network. With the supply voltage of 0.8 V, the single-ended GaN oscillator can generate a signal at 8.85 GHz, and it also supplies output power of 2.4 dBm with a buffer supply of 3.0 V. At 1 MHz frequency offset from the carrier, the phase noise is −124.8 dBc/Hz, and the figure of merit (FOM) of the oscillator is −199.8 dBc/Hz. After the previous study, the hot-carrier stressed RF performance of the GaN oscillator is studied, and the oscillator was subject to a drain supply of 8 V for a stressing step time equal to 30 min and measured at the supply voltage of 0.8 V after the step operation for performance benchmark. Stress study indicates the power oscillator with buffer is a good structure for a reliable structure by operating the oscillator core at low supply and the buffer at high supply. The second balanced oscillator can generate a differential signal. The feedback filter consists of a left-handed transmission-line LC network by cascading three unit cells. At a 1 MHz frequency offset from the carrier of 3.818 GHz, the phase noise is −131.73 dBc/Hz, and the FOM of the 2nd oscillator is −188.4 dBc/Hz. High supply voltage operation shows phase noise degradation. The third GaN cross-coupled VCO uses 8-shaped inductors. The VCO uses a pair of drain inductors to improve the Q-factor of the LC tank, and it uses 8-shaped inductors for magnetic coupling noise suppression. At the VCO-core supply of 1.3 V and high buffer supply, the FOM at 6.397 GHz is −190.09 dBc/Hz. This work enhances the design techniques for reliable GaN HEMT oscillators and knowledge to design high-performance circuits. Full article

A cable-driven hybrid mobile robot is a kind of robot consisting of two modules connected in series, which uses multiple parallel cables to drive the moving platforms. Cable-driven robots benefit from a large workspace, low inertia, excellent dynamic performance due to the lightweight and high extensibility of cables, making them ideal for a wide range of applications, such as sports cameras, large radio telescopes, and planetary exploration. Considering the fundamental dynamic constraint imposed by the unilateral constraint of cables, the workspace and dynamic modeling for cable-driven robots require specialized study. In this paper, a novel cable-driven hybrid robot, which has two motion patterns, is designed, and an arc intersection method for analyzing workspace is applied to solve the robot workspace of two motion patterns. Based on the workspace analysis, a dynamic model for the cable-driven hybrid robot is established, laying the foundation for subsequent trajectory planning. Simulation results in MATLAB R2021a demonstrate that the cable-driven hybrid robot has a large workspace in both motion patterns and is capable of meeting various motion requirements, indicating promising application potential. Full article

Biostimulants, particularly single amino acids, can increase plant growth and crop quality, gaining significant attention. This study investigates the effects of 10 amino acids via root/foliar application on the growth, quality, taste, and volatile flavor of mini-watermelons and compares the differences between the application methods. Here, we employed electronic noses, electronic tongues, and gas chromatography–ion mobility spectrometry to investigate these effects. Root application excels in fruit growth and pectin accumulation, while foliar application boosts soluble protein and specific nutrients. Specifically, root application (except for Val) significantly increases fruit weight, with Gly being most effective for longitudinal diameter, while most amino acids (except Val/Lys) promote transverse diameter. Pectin content shows bidirectional regulation: root application of Glu/Gly/Lys/Pro/Trp/Val enhances pectin, whereas foliar application inhibits it. For taste indices, most treatments improve soluble solids (except Glu root/Arg-Leu foliar), and Ala/Asp/Glu/Gly reduce titratable acids, optimizing the sugar–acid ratio. Foliar application is more efficient for soluble protein accumulation (Ala/Glu/Gly/Pro/Leu). For nutritional quality, except for Lys, all treatments increase vitamin C and widely promote total phenolics and lycopene, with only minor exceptions, and only Arg foliar application enhances ORAC. Additionally, the results revealed that root-applied lysine and valine greatly raised the levels of hexanal and 2-nonenal, whereas foliar-applied valine significantly increased n-nonanal and (Z)-6-nonenal. Overall, we found that amino acids can considerably improve mini-watermelon production, quality, taste, and antioxidant capacity, providing theoretical and practical references for their widespread use in agriculture. Full article

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article

Microcalcification clusters (MCCs) are key indicators of breast cancer, with studies showing that approximately 50% of mammograms with MCCs confirm a cancer diagnosis. Early detection is critical, as it ensures a five-year survival rate of up to 99%. However, MCC detection remains challenging due to their features, such as small size, texture, shape, and impalpability. Convolutional neural networks (CNNs) offer a solution for MCC detection. Nevertheless, CNNs are typically trained on single-resolution images, limiting their generalizability across different image resolutions. We propose a CNN trained on digital mammograms with three common resolutions: 50, 70, and 100 $\mathsf{\mu }$m. The architecture processes individual 1 cm² patches extracted from the mammograms as input samples and includes a MobileNetV2 backbone, followed by a flattening layer, a dense layer, and a sigmoid activation function. This architecture was trained to detect MCCs using patches extracted from the INbreast database, which has a resolution of 70 $\mathsf{\mu }$m, and achieved an accuracy of 99.84%. We applied transfer learning (TL) and trained on 50, 70, and 100 $\mathsf{\mu }$m resolution patches from the MEXBreast database, achieving accuracies of 98.32%, 99.27%, and 89.17%, respectively. For comparison purposes, models trained from scratch, without leveraging knowledge from the pretrained model, achieved 96.07%, 99.20%, and 83.59% accuracy for 50, 70, and 100 $\mathsf{\mu }$m, respectively. Results demonstrate that TL improves MCC detection across resolutions by reusing pretrained knowledge. Full article

The integration of unmanned aerial vehicles (UAVs) into next-generation wireless networks greatly enhances the flexibility and efficiency of communication and distributed computation for ground mobile devices. Federated learning (FL) provides a privacy-preserving paradigm for device collaboration but remains highly vulnerable to poisoning attacks and is further challenged by the resource constraints and heterogeneous data common to UAV-assisted systems. Existing robust aggregation and anomaly detection methods often degrade in efficiency and reliability under these realistic adversarial and non-IID settings. To bridge these gaps, we propose FedULite, a lightweight and robust federated learning framework specifically designed for UAV-assisted environments. FedULite features unsupervised local representation learning optimized for unlabeled, non-IID data. Moreover, FedULite leverages a robust, adaptive server-side aggregation strategy that uses cosine similarity-based update filtering and dimension-wise adaptive learning rates to neutralize sophisticated data and model poisoning attacks. Extensive experiments across diverse datasets and adversarial scenarios demonstrate that FedULite reduces the attack success rate (ASR) from over 90% in undefended scenarios to below 5%, while maintaining the main task accuracy loss within 2%. Moreover, it introduces negligible computational overhead compared to standard FedAvg, with approximately 7% additional training time. Full article

Background: Cerebral aneurysm (CA) is a focal or diffuse pathological dilation of the cerebral arterial wall that arises due to various etiological factors. It represents a serious vascular condition, particularly affecting the elderly, and carries a high risk of rupture and neurological morbidity. Clonidine (CL), an α2-adrenergic receptor agonist, has been reported to suppress aneurysm progression; however, its underlying molecular mechanisms, especially in relation to cerebral endothelial dysfunction, remain unclear. This study aimed to investigate the potential of CL to mitigate CA development by modulating apoptosis, inflammation, and oxidative stress in an Angiotensin II (Ang II)-induced endothelial injury model. Methods: Human brain microvascular endothelial cells (HBMECs) were used to establish an in vitro model of endothelial dysfunction by treating cells with 1 µM Ang II for 48 h. CL was administered 2 h prior to Ang II exposure at concentrations of 0.1, 1, and 10 µM. Cell viability was assessed using the MTT assay. Oxidative stress markers, including reactive oxygen species (ROS) and Nitric Oxide (NO), were measured using 2′,7′–dichlorofluorescin diacetate (DCFDA). Gene expression levels of vascular endothelial growth factor (VEGF), matrix metalloproteinases (MMP-2 and MMP-9), high mobility group box 1 (HMGB1), and nuclear factor kappa B (NF-κB) were quantified using RT-qPCR. Levels of proinflammatory cytokines; tumor necrosis factor-alpha (TNF-α), Interleukin-6 (IL-6), and interferon-gamma (IFN-γ); were measured using commercial ELISA kits. Results: Ang II significantly increased ROS production and reduced NO levels, accompanied by heightened proinflammatory cytokine release and endothelial dysfunction. MTT assay revealed a marked decrease in cell viability following Ang II treatment (34.18%), whereas CL preserved cell viability in a concentration-dependent manner: 44.24% at 0.1 µM, 66.56% at 1 µM, and 81.74% at 10 µM. CL treatment also significantly attenuated ROS generation and inflammatory cytokine levels (p < 0.05). Furthermore, the expression of VEGF, HMGB1, NF-κB, MMP-2, and MMP-9 was significantly downregulated in response to CL. Conclusions: CL exerts a protective effect on endothelial cells by reducing oxidative stress and suppressing proinflammatory signaling pathways in Ang II-induced injury. These results support the potential of CL to mitigate endothelial injury in vitro, though further in vivo studies are required to confirm its translational relevance. Full article