Search Results (99)

Urbanization affects bird communities by reducing habitat and fragmenting ecosystems. Urban parks can help counteract these effects. However, anthropogenic noise can further alter bird composition. We examined the distribution and abundance of bird species in nine urban parks in Mexico City. We used a ten minute fixed-radius (25 m) point-counting technique to count birds along their annual cycle, with ten minutes allocated for bird counts. The quality of green areas was analyzed in terms of vegetation (Normalized Difference Vegetation Index), park size, and mean noise level dB(A) (based on MIN and MAX values), and species were grouped into trophic guilds. A total of 108 bird species were recorded, 5 of which are under special protection; we grouped all species into 14 trophic guilds, showing different responses to environmental gradients. Redundancy analysis (RDA) explained 89.98% of the variance, with noise and park size being the most influential variables. Granivores and omnivores were more tolerant to noise, while insectivores and frugivores preferred quieter areas with more vegetation. A positive association was observed between the presence of winter resident species and the park size. On the other hand, mean noise level dB(A) was negatively related to permanent resident species, winter resident species, and those with protected status. Conservation efforts should focus on maintaining ample green spaces and reducing noise pollution, as recorded high mean noise levels (>53 dB(A)) exceed the recommended thresholds for avifauna conservation. Full article

The cislunar space navigation satellite system is essential infrastructure for lunar exploration in the next phase. It relies on high-precision orbit determination to provide the reference of time and space. This paper focuses on constructing a navigation constellation using special orbital locations such as Earth–Moon libration points and distant retrograde orbits (DRO), and it discusses the simplification of planetary perturbation models for their autonomous orbit determination on board. The gravitational perturbations exerted by major solar system bodies on spacecraft are first analyzed. The minimum perturbation required to maintain a precision of 10 m during a 30-day orbit extrapolation is calculated, followed by a simulation analysis. The results indicate that considering only gravitational perturbations from the Moon, Sun, Venus, Saturn, and Jupiter is sufficient to maintain orbital prediction accuracy within 10 m over 30 days. Based on these findings, a method for simplifying the ephemeris is proposed, which employs Hermite interpolation for the positions of the Sun and Moon at fixed time intervals, replacing the traditional Chebyshev polynomial fitting used in the JPL DE ephemeris. Several simplified schemes with varying time intervals and orders are designed. The simulation results of the inter-satellite links show that, with a 6-day orbit arc length, a 1-day lunar interpolation interval, and a 5-day solar interpolation interval, the accuracy loss for cislunar space navigation satellites remains within the meter level, while memory usage is reduced by approximately 60%. Full article

The core structure of modern power systems reflects a fundamental symmetry between electricity supply and demand, and accurate load forecasting is essential for maintaining this dynamic balance. To improve the accuracy of short-term load forecasting in power systems, this paper proposes a novel model that combines a Multi-Strategy Improved Sand Cat Swarm Optimization algorithm (MSCSO) with a Self-Attention Temporal Convolutional Network (SA TCN). The model constructs efficient input features through data denoising, correlation filtering, and dimensionality reduction using UMAP. MSCSO integrates Uniform Tent Chaos Mapping, a sensitivity enhancement mechanism, and Lévy flight to optimize key parameters of the SA TCN, ensuring symmetrical exploration and stable convergence in the solution space. The self-attention mechanism exhibits structural symmetry when processing each position in the input sequence and does not rely on fixed positional order, enabling the model to more effectively capture long-term dependencies and preserve the symmetry of the sequence structure—demonstrating its advantage in symmetry-based modeling. Experimental results on historical load data from Panama show that the proposed model achieves excellent forecasting accuracy (RMSE = 24.7072, MAE = 17.5225, R² = 0.9830), highlighting its innovation and applicability in symmetrical system environments. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Background/Objective: The Rs1 exon-1-del rat (Rs1KO) XLRS model shows normal retinal development until postnatal day 12 (P12) when small cystic spaces start to form in the inner nuclear layer. These enlarge rapidly, peak at P15, and then collapse by P19. These events overlap with eye opening at P12–P15. We investigated whether new light-driven retinal activity could contribute to the appearance and progression of schisis cavities in this rat model of XLRS disease. Methods: For dark rearing (D/D), mating pairs of Rs1KO strain were raised in total darkness in a special vivarium at UC Davis. When pups were born, they were maintained in total darkness, and eyes were collected at P12, P15, and P30 (n = 3/group) for each of the D/D and cyclic light-reared 12 h light–12 h dark (L/D) Rs1KO and wild-type (WT) littermates. Eyes were fixed, paraffin-embedded, and sectioned. Tissue morphology was examined by H&E and marker expression of retinoschisin1 (Rs1), rhodopsin (Rho), and postsynaptic protein 95 (Psd95) by fluorescent immunohistochemistry. H&E-stained images were analyzed with ImageJ version 1.54h to quantify cavity size using the “Analyze Particles” function. Results: Small intra-retinal schisis cavities begin to form by P12 in the inner retina of both D/D and L/D animals. Cavity formation was equivalent or more pronounced in D/D animals than in L/D animals. We compared Iba1 (activation marker of immune cells) distribution and found that by P12, when schisis appeared, Iba1⁺ cells had accumulated in regions of schisis. Iba1⁺ cells were more abundant in Rs1KO animals than WT animals and appeared slightly more prevalent in D/D- than L/D-reared Rs1KO animals. We compared photoreceptor development using Rho, Rs1, and Psd95 expression, and these were similar; however, the outer segments (OSs) of D/D animals with Rho labeling at P12 were longer than L/D animals. Conclusions: The results showed that cavities formed at the same time in D/D and L/D XLRS rat pups, indicating that the timing of schisis formation is not light stimulus-driven but rather appears to be a result of developmental events. Cavity size tended to be larger under dark-rearing conditions in D/D animals, which could be due to the decreased rate of phagocytosis by the RPE in the dark, allowing for continued growth of the OSs without the usual shedding of the distal tip, a key mechanism behind dark adaptation in the retina. These results highlight the complexity of XLRS pathology; however, we found no evidence that light-driven metabolic activity accounted for schisis cavity formation. Full article

This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article

In this work, we aim to adjust vehicle speeds in real time by predicting the surrounding population density based on the spacing of customer locations. We comprehensively consider fixed costs, cargo loss costs, fuel costs, penalty costs, and environmental costs; build a cold chain distribution vehicle path optimization model with the goal of minimizing the total cost and maximizing customer satisfaction; and design a hybrid genetic algorithm solution optimization model. The algorithm dynamically adjusts the tournament scale through the standard deviation of the fitness value, uses the OX cross operator, determines the position of variation based on the customer information matrix, and performs local search optimization with the removal and insertion operators. Through comparison to other algorithms in the literature, the results show that the hybrid genetic algorithm not only improves customer satisfaction, but also maintains a lower total cost, which is obviously superior when solving the complex cold chain distribution path optimization problem; further comparison and analysis of the mathematical model in this paper with the single-dimension satisfaction model reveals that under the same satisfaction constraint threshold, the model in this paper can significantly reduce the system operating cost; we also deeply discuss the influence mechanism of vehicle traveling mode and customer point sparsity radius on distribution path planning. Full article

Ancient fairs in China were designated as commercial zones with fixed stalls that had emerged from commodity exchange demands and socio-cultural factors such as clan systems and gentry intervention, exhibiting dual commercial–communal characteristics. Several ancient fairs in Shenzhen have been retained, including Gongming Ancient Fair, which maintains its original spatial configuration adjacent to industrial zones and urban villages, attracting a high concentration of migrant workers. Survey results show that 85% of Gongming residents demand public space renovations, citing inadequacy of the spaces to support public activities. Given the intrinsic link between public spaces and public activities, fostering their positive interaction is crucial for enhancing urban vitality. However, existing studies predominantly focus on the physical environment and neglect activity-driven optimization perspectives. This study first employed social network analysis (SNA) to construct two networks of Gongming Ancient Fair, using the software Ucinet 6.755, including a public space network based on spatial connectivity and a public activity network based on pedestrian flow. Second, the networks’ structural characteristics were analyzed using seven metrics: node degree, density, betweenness centrality, betweenness centralization, clustering coefficient, average path length, and small-world property. Discrepancies between the networks were quantified through betweenness centrality comparisons, with field surveys and interviews identifying causal factors including seasonal product limitations, spatial constraints, inadequate supporting facilities, and substandard management. Based on the survey data and analytical results, the key renovation nodes were categorized into three types: high-control-capacity nodes, high-expectation nodes, and culturally distinctive nodes. Finally, three optimization strategies are proposed. This study integrates sociological perspectives into ancient fair revitalization, addressing gaps in activity-driven spatial research. Full article

Maintaining a spacecraft’s center of mass at the origin of a body-fixed coordinate system is often key to precision trajectory tracking. Typically, the inertia matrix is estimated and verified with preliminary ground testing. This article presents groundbreaking preliminary results and significant findings from on-orbit space experiments validating recently proposed methods as part of a larger study over multiple years. Time-varying estimates of inertia moments and products are used to reveal time-varying estimates of the location of spacecraft center of mass using geosynchronous orbiting test satellites proposing a novel two-norm optimal projection learning method. Using the parallel axis theorem, the location of the mass center is parameterized using the cross products of inertia, and that information is extracted from spaceflight maneuver data validating modeling and simulation. Mass inertia properties are discerned, and the mass center is experimentally revealed to be over thirty centimeters away from the assumed locations in two of the three axes. Rotation about one axis is found to be very well balanced, with the center of gravity lying on that axis. Two-to-three orders of magnitude corrections to inertia identification are experimentally demonstrated. Combined-axis three-dimensional maneuvers are found to obscure identification compared with single-axis maneuvering as predicted by the sequel analytic study. Mass center location migrates 36–95% and subsequent validating experiments duplicate the results to within 0.1%. Full article

The dissipation of excessive heat in high-power light-emitting diodes (LEDs) is essential for maintaining luminous efficiency, color stability, and device lifetime. While the incorporation of thermal vias in substrates is commonly used to improve heat dissipation, increasing their number is difficult in the limited area due to fabrication constraints. In this study, we use finite element analysis to investigate the effects of thermal via configurations on LED performance, including variations in the number of vias, spacing between vias, and their misalignment relative to the LED, arising from manufacturing tolerances. We found that the reduction in LED temperature saturated beyond a certain number of vias. Moreover, heat reduction can be further enhanced by optimizing the spacing between vias under a fixed number of vias. Based on these findings, the design of via configurations can achieve both fabrication feasibility and effective heat dissipation in high-power LEDs. Full article

Weather radar, as a crucial component of remote sensing data, plays a vital role in convective weather forecasting through radar echo extrapolation techniques. To address the limitations of existing deep learning methods in radar echo extrapolation, this paper proposes a radar echo extrapolation model based on video vision transformer and spatiotemporal sparse attention (ViViT-Prob). The model takes historical sequences as input and initially maps them into a fixed-dimensional vector space through 3D convolutional patch encoding. Subsequently, a multi-head spatiotemporal fusion module with sparse attention encodes these vectors, effectively capturing spatiotemporal relationships between different regions in the sequences. The sparse constraint enables better utilization of data structural information, enhanced focus on critical regions, and reduced computational complexity. Finally, a parallel output decoder generates all time step predictions simultaneously, then maps back to the prediction space through a deconvolution module to reconstruct high-resolution images. Our experimental results on the Moving MNIST and real radar echo dataset demonstrate that the proposed model achieves superior performance in spatiotemporal sequence prediction and improves the prediction accuracy while maintaining structural consistency in radar echo extrapolation tasks, providing an effective solution for short-term precipitation forecasting. Full article

This work presents a high-speed hybrid communication system integrating Underwater Optical Wireless Communication (UOWC), Multimode Fiber (MMF), and Free-Space Optics (FSO) channels, leveraging Orbital Angular Momentum (OAM) beams for enhanced data transmission. A Photodetector, Remodulate, and Forward Relay (PRFR) is employed to enable wavelength conversion from 532 nm for UOWC to 1550 nm for MMF and FSO links. Four distinct OAM beams, each supporting a 5 Gbps data rate, are utilized to evaluate the system’s performance under two scenarios. The first scenario investigates the effects of absorption and scattering in five water types on underwater transmission range, while maintaining fixed MMF length and FSO link. The second scenario examines varying FSO propagation distances under different fog conditions, with a consistent underwater link length. Results demonstrate that water and atmospheric attenuation significantly impact transmission range and received optical power. The proposed hybrid system ensures reliable data transmission with a maximum overall transmission distance of 1125 m (comprising a 25 m UOWC link in Pure Sea (PS) water, a 100 m MMF span, and a 1000 m FSO range in clear weather) in the first scenario. In the second scenario, under Light Fog (LF) conditions, the system achieves a longer reach of up to 2020 m (20 m UOWC link + 100 m MMF span + 1900 m FSO range), maintaining a BER ≤ 10⁻⁴ and a Q-factor around 4. This hybrid design is well suited for applications such as oceanographic research, offshore monitoring, and the Internet of Underwater Things (IoUT), enabling efficient data transfer between underwater nodes and surface stations. Full article

In this paper, we explore the Hyers–Ulam stability of a generalized Drygas functional equation, which extends the classical Drygas equation by incorporating additional parameters and conditions. Our investigation focuses on mappings from a real vector space into a Banach space and employs the fixed-point method to establish stability criteria. Our findings provide new insights into the conditions under which the generalized Drygas equation maintains stability, contributing to the broader understanding of functional equations in mathematical analysis. The results have implications for the study of functional equations and their applications in various mathematical contexts. Full article

The adoption of kiosks in public spaces is steadily increasing, with a trend toward providing more natural user experiences through embodied conversational agents (ECAs). To achieve human-like interactions, ECAs should be able to appropriately gaze at the speaker. However, kiosks in public spaces often face challenges, such as ambient noise and overlapping speech from multiple people, making it difficult to accurately identify the speaker and direct the ECA’s gaze accordingly. In this paper, we propose a lightweight gaze control system that is designed to operate effectively within the resource constraints of kiosks and the noisy conditions common in public spaces. We first developed a speaker detection model that identifies the active speaker in challenging noise conditions using only a single camera and microphone. The proposed model achieved a 91.6% mean Average Precision (mAP) in active speaker detection and a 0.6% improvement over the state-of-the-art lightweight model (Light ASD) (as evaluated on the noise-augmented AVA-Speaker Detection dataset), while maintaining real-time performance. Building on this, we developed a gaze control system for ECAs that detects the dominant speaker in a group and directs the ECA’s gaze toward them using an algorithm inspired by real human turn-taking behavior. To evaluate the system’s performance, we conducted a user study with 30 participants, comparing the system to a baseline condition (i.e., a fixed forward gaze) and a human-controlled gaze. The results showed statistically significant improvements in social/co-presence and gaze naturalness compared to the baseline, with no significant difference between the system and human-controlled gazes. This suggests that our system achieves a level of social presence and gaze naturalness comparable to a human-controlled gaze. The participants’ feedback, which indicated no clear distinction between human- and model-controlled conditions, further supports the effectiveness of our approach. Full article

Bioenergetic membranes of mitochondria, thylakoids, and chromatophores are primary sites of ATP production in living cells. These membranes contain an electron transport chain (ETC) in which electrons are shuttled between a series of redox proteins during the generation of ATP via oxidative phosphorylation. The phospholipid composition of these membranes, which often include negative lipids, plays a role in determining the electrostatics of their surface owing to the spatial distribution of their charged head groups. Cardiolipin (CDL) is a phospholipid commonly associated with bioenergetic membranes and is also a significant contributor to the negative surface charge. Interactions between cytochromes and phospholipid head groups in the membrane can in principle affect the rate of its travel between ETC components, hence influencing the rate of ATP turnover. Here, we use molecular dynamic (MD) simulations that feature an accelerated membrane model, termed highly mobile membrane mimetic (HMMM), to study protein–lipid interactions during the diffusion of cytochrome c₂ between redox partners in a bioenergetic membrane. We observe a “skipping” mode of diffusion for cytochromes along with a bias for binding to anionic lipids, particularly with a strong preference for CDL. During diffusion, cytochrome c₂ maintains a relatively fixed tilt with respect to the membrane normal with wider fluctuations in its angle with respect to the plane of the membrane. The obtained results describing the behavior of cytochrome c₂ on a representative bioenergetic membrane have direct ramifications in shuttling motions of other similar electron-carrying elements in other bioenergetic membranes, which are composed of a significant amount of anionic lipids. The mode of surface-restricted diffusion reported here would modulate rapid electron transfer between the ETC complexes anchored in bioenergetic membranes by reducing the search space between them. Full article