Search Results (1,109)

Recently, diffractive optics systems have garnered increasing attention due to their myriad benefits in various applications, such as creating vortex beams, Bessel beams, or optical traps, while refractive optics systems still exhibit some disadvantages related to materials, substrates, and intensity shapes. The manufacturing of diffractive optical elements has become easier due to the development of lithography techniques such as direct laser writing, photo lithography, and electron beam lithography. In this paper, we improve the results from previous research and propose a new methodology to design and fabricate advanced binary diffractive optical elements that achieve a square focal spot independently, reducing reliance on additional components. By integrating a binary square zone plate with an axicon zone plate of the same scale, we employ machine learning for laser path optimization and direct laser lithography for manufacturing. This streamlined approach enhances simplicity, accuracy, efficiency, and cost effectiveness. Our upgraded binary diffractive optical elements are ready for real-world applications, marking a significant improvement in optical capabilities. Full article

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

This study presents a comprehensive analysis of CO₂ trapping mechanisms in subsurface reservoirs by integrating numerical reservoir simulations, geochemical modeling, and machine learning techniques to enhance the design and evaluation of carbon capture and storage (CCS) strategies. A two-dimensional reservoir model was developed to simulate CO₂ injection dynamics under realistic geomechanical and geochemical conditions, incorporating four primary trapping mechanisms: residual, solubility, mineralization, and structural trapping. To improve computational efficiency without compromising accuracy, advanced machine learning models, including random forest, gradient boosting, and decision trees, were deployed as smart proxy models for rapid prediction of trapping behavior across multiple scenarios. Simulation outcomes highlight the critical role of hysteresis, aquifer dynamics, and producer well placement in enhancing CO₂ trapping efficiency and maintaining long-term storage stability. To support the credibility of the model, a qualitative validation framework was implemented by comparing simulation results with benchmarked field studies and peer-reviewed numerical models. These comparisons confirm that the modeled mechanisms and trends align with established CCS behavior in real-world systems. Overall, the study demonstrates the value of combining traditional reservoir engineering with data-driven approaches to optimize CCS performance, offering scalable, reliable, and secure solutions for long-term carbon sequestration. Full article

Grain boundaries are among the most influential structural features that control the charge transport in polycrystalline organic semiconductors. Acting as both charge trapping sites and electrostatic barriers, they disrupt molecular packing and introduce energetic disorder, thereby limiting carrier mobility, increasing threshold voltage, and degrading the stability of organic thin-film transistors (OTFTs). This review presents a detailed discussion of grain boundary formation, their impact on charge transport, and experimental strategies for engineering their structure and distribution across several high-mobility small-molecule semiconductors, including pentacene, TIPS pentacene, diF-TES-ADT, and rubrene. We explore grain boundary engineering approaches through solvent design, polymer additives, and external alignment methods that modulate crystallization dynamics and domain morphology. Then various case studies are discussed to demonstrate that optimized processing can yield larger, well-aligned grains with reduced boundary effects, leading to great mobility enhancements and improved device stability. By offering insights from structural characterization, device physics, and materials processing, this review outlines key directions for grain boundary control, which is essential for advancing the performance and stability of organic electronic devices. Full article

To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540–1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable output, based on high irradiance and spectral uniformity. A compound beam homogenization structure—combining a multimode fiber and an apodizator—achieves 85.8% far-field uniformity over a 200 mm aperture. A power–spectrum co-optimization strategy is introduced for filter design, achieving a spectral matching degree of 78%. The system supports a tunable output from 2.5 to 130 mW with a 50× dynamic range and maintains power control accuracy within ±0.9%. To suppress internal background interference, a BRDF-based optical scattering model is established to trace primary and secondary stray light paths. Simulation results show that by maintaining the surface roughness of key mirrors below 2 nm and incorporating a U-shaped reflective light trap, stray light levels can be reduced to 5.13 × 10⁻¹² W, ensuring stable detection of a 10⁻¹⁰ W signal at a 10:1 signal-to-background ratio. Experimental validation confirms that the system can faithfully reproduce solar outage conditions within a ±3° field of view, achieving consistent performance in spectrum shaping, irradiance uniformity, and background suppression. The proposed platform provides a standardized and practical testbed for ground-based anti-interference assessment of optical communication terminals. Full article

Helium (He) accumulation in tungsten—widely used as a plasma-facing material in fusion reactors—can lead to clustering, trap mutation, and eventual formation of helium bubbles, critically impacting material performance. To clarify the atomic-scale mechanisms governing this process, we conducted systematic molecular statics and molecular dynamics simulations across a wide range of vacancy cluster sizes (n = 1–27) and temperatures (500–2000 K). We identified the onset of trap mutation through abrupt increases in tungsten atomic displacement. At 0 K, the critical helium-to-vacancy (He/V) ratio required to trigger mutation was found to scale inversely with cluster size, converging to ~5.6 for large clusters. At elevated temperatures, thermal activation lowered the mutation threshold and introduced a distinct He/V stability window. Below this window, clusters tend to dissociate; above it, trap mutation occurs with near certainty. This critical He/V ratio exhibits a linear dependence on temperature and can be described by a size- and temperature-dependent empirical relation. Our results provide a quantitative framework for predicting trap mutation behavior in tungsten, offering key input for multiscale models and informing the design of radiation-resistant materials for fusion applications. Full article

In the age of artificial intelligence (AI), architectural practice faces a paradox of immense potential and creeping standardization. As humans are increasingly relying on AI-generated outputs, architecture risks becoming a spectacle of repetition—a shuffling of data that neither truly innovates nor progresses vertically in creative depth. This paper explores the critical role of data in AI systems, scrutinizing the training datasets that form the basis of AI’s generative capabilities and the implications for architectural practice. We argue that when architects approach AI passively, without actively engaging their own creative and critical faculties, they risk becoming passive users locked in an endless loop of horizontal expansion without meaningful vertical growth. By examining the epistemology of architecture in the AI age, this paper calls for a paradigm where AI serves as a tool for vertical and horizontal growth, contingent on human creativity and agency. Only by mastering this dynamic relationship can architects avoid the trap of passive, standardized design and unlock the true potential of AI. Full article

In the forest ecosystems of Central European Russia, the influence of forest edges on the spatial distribution of Drosophilidae was studied for the first time. The research was conducted during the period of 2021–2022 in the Republic of Mordovia. Beer traps baited with fermented beer and sugar were used to collect Drosophilidae. Two study plots were selected, differing in their forest edges, tree stands, and adjacent open ecosystems. In both cases, the forest directly bordered an open ecosystem. Edges serve as transitional biotopes, where both forest and meadow (open area) faunas coexist. Knowing that many drosophilid species prefer forest habitats, we designated forest interior sites as control points. Traps were set at heights of 1.5 m (lower) and 7.5 m (upper) on trees. A total of 936 specimens representing 27 species were collected. Nine species were common across all traps, while ten species were recorded only once. At the forest edges, 23 species were captured across both heights, compared to 19 species in the forest interiors. However, the total abundance at the forest edges was 370 specimens, while it was 1.5 times higher in the forest interiors. Both abundance and species richness varied between plots. Margalef’s index was higher at the forest edges than in the forest interiors, particularly at 1.5 m height at the edge and at 7.5 m height in the forest interior. Shannon and Simpson indices showed minimal variation across traps at different horizontal and vertical positions. The highest species diversity was observed among xylosaprobionts (9 species) and mycetophages (8 species). All ecological groups were represented at the forest edges, whereas only four groups were recorded in the forest interiors, with the phytosaprophagous species Scaptomyza pallida being absent. In general, both species richness and drosophilid abundance increased in the lower strata, both at the forest edge and within the interior. Using the R package Indicspecies, we identified Gitona distigma as an indicator species for the forest edge and Scaptodrosophila rufifrons as an indicator for the forest interior in the lower tier for both plots. In addition, Drosophila testacea, D. phalerata, and Phortica semivirgo were found to be indicator species for the lower tier in both plots, while Leucophenga quinquemaculata was identified as an indicator species for the upper tier at the second plot. Full article

Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and selectively capture nocturnal insect pests under real agricultural conditions. The proposed trap integrates light and rain sensors, servo-controlled mechanical gates, and a single-layer perceptron neural network deployed on an ATmega-2560 microcontroller by Microchip Technology Inc. (Chandler, AZ, USA). The perceptron processes normalized sensor inputs to autonomously decide, in real time, whether to open or close the gate, thereby enhancing the selectivity of insect capture. The system features a removable tray containing a food-based attractant and yellow and green LEDs designed to lure target species such as moths and flies from the orders Lepidoptera and Diptera. Field trials were conducted between June and August 2023 in La Barca, Jalisco, Mexico, under diverse environmental conditions. Captured insects were analyzed and classified using the iNaturalist platform, with the successful identification of key pest species including Tetanolita floridiana, Synchlora spp., Estigmene acrea, Sphingomorpha chlorea, Gymnoscelis rufifasciata, and Musca domestica, while minimizing the capture of non-target organisms such as Carpophilus spp., Hexagenia limbata, and Chrysoperla spp. Statistical analysis using the Kruskal–Wallis test confirmed significant differences in capture rates across environmental conditions. The results highlight the potential of this low-cost device to improve pest monitoring accuracy, and lay the groundwork for the future integration of more advanced AI-based classification and species recognition systems targeting nocturnal Lepidoptera and other pest insects. Full article

Perovskite solar cells (PSCs) leverage the exceptional photoelectric properties of perovskite materials, yet interfacial energy level mismatches limit carrier extraction efficiency. In this work, energy level alignment was exploited to reduce the charge transport barrier, which can be conducive to the transmission of photo-generated carriers and reduce the probability of electron–hole recombination. We designed a dual-transition perovskite solar cell (PSC) with the structure of FTO/TiO₂/Nb₂O₅/CH₃NH₃PbI₃/MoO₃/Spiro-OMeTAD/Au by finite element analysis methods. Compared with the pristine device (FTO/TiO₂/CH₃NH₃PbI₃/Spiro-OMeTAD/Au), the open-circuit voltage of the optimized cell increases from 0.98 V to 1.06 V. Furthermore, the design of a circular platform light-trapping structure makes up for the light loss caused by the transition at the interface. The short-circuit current density of the optimized device increases from 19.81 mA/cm² to 20.36 mA/cm², and the champion device’s power conversion efficiency (PCE) reaches 17.83%, which is an 18.47% improvement over the planar device. This model provides new insight for the optimization of perovskite devices. Full article

To address the local optimality in path planning for logistics robots using APF (artificial potential field) and the stagnation problem when encountering trap obstacles, this paper proposes VDPF (variable-direction potential field) combined with RL (reinforcement learning) to effectively solve these problems. First, based on obstacle distribution, an obstacle classification algorithm is designed, enabling the robot to select appropriate obstacle avoidance strategies according to obstacle types. Second, the attractive force and repulsive force in APF are separated, and the direction of the repulsive force is modified to break the local optimum, allowing the robot to focus on handling current obstacle avoidance tasks. Finally, the improved APF is integrated with the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm, and a weight factor is introduced to adjust the robot’s acting forces. By sacrificing a certain level of safety for a larger exploration space, the robot is guided to escape from local optima and trap regions. Experimental results show that the improved algorithm effectively mitigates the trajectory oscillation of the robot and can efficiently solve the problems of local optimum and trap obstacles in the APF method. Compared with the algorithm APF-TD3 in scenarios with five obstacles, the proposed algorithm reduces the GS (Global Safety) by 8.6% and shortens the length by 8.3%. In 10 obstacle scenarios, the proposed algorithm reduces the GS by 29.8% and shortens the length by 9.7%. Full article

As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science. Full article

Acoustic coupling agents serve as critical interfacial materials connecting piezoelectric transducers with microfluidic chips in acoustofluidic systems. Their performance directly impacts acoustic wave transmission efficiency, device reusability, and reliability in biomedical applications. Considering the rapidly growing body of research in the field of acoustic microfluidics, this review aims to serve as an all-in-one reference on the role of acoustic coupling agents and relevant considerations pertinent to acoustofluidic devices for anyone working in or seeking to enter the field of disposable acoustofluidic devices. To this end, this review seeks to summarize and categorize key aspects of acoustic couplants in the implementation of acoustofluidic devices by examining their underlying physical mechanisms, material classifications, and core applications of coupling agents in acoustofluidics. Gel-based coupling agents are particularly favored for their long-term stability, high coupling efficiency, and ease of preparation, making them integral to acoustic flow control applications. In practice, coupling agents facilitate microparticle trapping, droplet manipulation, and biosample sorting through acoustic impedance matching and wave mode conversion (e.g., Rayleigh-to-Lamb waves). Their thickness and acoustic properties (sound velocity, attenuation coefficient) further modulate sound field distribution to optimize acoustic radiation forces and thermal effects. However, challenges remain regarding stability (evaporation, thermal degradation) and chip compatibility. Further aspects of research into gel-based agents requiring attention include multilayer coupled designs, dynamic thickness control, and enhancing biocompatibility to advance acoustofluidic technologies in point-of-care diagnostics and high-throughput analysis. Full article

The Centers for Diseases Control (CDC) light trap is widely used for malaria vector surveillance, but its acquisition logistics pose challenges in Africa. Evaluating new traps can improve surveillance tools. This study compared the efficiency of the BG-Pro UV and Silver Bullet 2.1 UV (SB 2.1 UV) against the UV LED CDC trap in western Kenya’s rice irrigation area. The traps were tested indoors in eight houses over 64 nights. Light properties and fan speed were analyzed using spectrometry and an anemometer. The BG-Pro UV trap performed better than the UV LED CDC trap for An. gambiae s.l. (RR 2.0, 95% CI 0.9–3.9) and An. funestus s.l. (RR 3.5, 95% CI 1.9–6.4). The SB 2.1 UV trap was more effective in capturing An. gambiae s.l. (RR 4.3, 95% CI 2.5–7.3) and An. funestus s.l. (RR 7.1, 95% CI 3.9–13.1), and also caught three times more Culex spp. (RR 2.7, 95% CI 1.2–6.0). SB 2.1 UV had the highest downstream force, and all traps emitting UV-A light had consistent wavelengths. Overall, the BG-Pro and SB 2.1 traps’ trapping efficiency was three to six times more than the CDC trap, making them promising surveillance tools, particularly in low-density malaria settings. Full article