Advancing Open Science

With the increasing application of unmanned aerial vehicle (UAV) in multiple fields, the path planning problem has become a key challenge in the optimization domain. This paper proposes an Improved Artificial Lemming Algorithm (IALA), which incorporates three strategies: the optimal information retention strategy based on individual historical memory, the hybrid search strategy based on differential evolution operators, and the local refined search strategy based on directed neighborhood perturbation. These strategies are designed to enhance the algorithm’s global exploration and local exploitation capabilities in tackling complex optimization problems. Subsequently, comparative experiments are conducted on the CEC2017 benchmark suite across three dimensions (30D, 50D, and 100D) against eight state-of-the-art algorithms proposed in recent years, including SBOA and DBO. The results demonstrate that IALA achieves superior performance across multiple metrics, ranking first in both the Wilcoxon rank-sum test and the Friedman ranking test. Analyses of convergence curves and data distributions further verify its excellent optimization performance and robustness. Finally, IALA and the comparative algorithms are applied to eight 3D UAV path planning scenarios and two amphibious UAV path planning models. In the independent repeated experiments across the eight scenarios, IALA attains the optimal performance 13 times in terms of the two metrics, Mean and Std. It also ranks first in the Monte Carlo experiments for the two amphibious UAV path planning models.

Tetrodotoxin (TTX) is a potent marine neurotoxin, necessitating sensitive and user-friendly on-site assays. To address long workflows of traditional immunoassays and limited signal amplification in colorimetric microfluidics, we developed a nanozyme-catalyzed colorimetric magnetic microfluidic immunosensor (Nano-CMI). This platform combines an aptamer–antibody sandwich capture format with catalytic amplification via AuNR@Pt@m-SiO₂ (APMS) nanozymes on a magnetically actuated microfluidic chip. Magnetic actuation simplifies sample handling and washing, while APMS catalysis enhances sensitivity and visual readout. The Nano-CMI has been used for the detection of TTX samples ranging from 0.2 to 20 ng/mL with a detection limit of 0.2 ng/mL in 10 min, following the linear equation: y = −31.14ln x + 110.15, and the entire “capture-reaction-detection” workflow can be completed within 1 h. With rapid response, minimal hands-on time, and robust performance, this platform offers a practical, high-sensitivity solution for on-site TTX screening in food safety and customs inspection.

Earth Observation (EO) and Remote Sensing (RS) data are widely used in various fields, including weather, environment, and natural disaster modeling and prediction. EO and RS done through geostationary satellite constellations in fields such as these are limited to a smaller region, while sun synchronous satellite constellations have discontinuous spatial and temporal coverage. This limits the ability of EO and RS data for near-real-time weather, environment, and natural disaster applications. To address these limitations, we introduce Now Observation Assemble Horizon (NOAH), a multi-modal, sensor fusion dataset that combines Ground-Based Sensors (GBS) of weather stations with topography, vegetation (land cover, biomass, and crown cover), and fuel types data from RS data sources. NOAH is collated using publicly available data from Environment and Climate Change Canada (ECCC), Spatialized CAnadian National Forest Inventory (SCANFI) and United States Geological Survey (USGS), which are well-maintained, documented, and reliable. Applications of the NOAH dataset include, but are not limited to, expanding RS data tiles, filling in missing data, and super-resolution of existing data sources. Additionally, Generative Artificial Intelligence (GenAI) or Generative Modeling (GM) can be applied for near-real-time model-generated or synthetic estimate data for disaster modeling in remote locations. This can complement the use of existing observations by field instruments, rather than replacing them. UNet backbone with Feature-wise Linear Modulation (FiLM) injection of GBS data was used to demonstrate the initial proof-of-concept modeling in this research. This research also lists ideal characteristics for GM or GenAI datasets for RS. The code and a subset of the NOAH dataset (NOAH mini) are made open-sourced.

The Blocking Job Shop Scheduling Problem (BJSSP) is a variant of the classical Job Shop Scheduling Problem in which a job completed on one machine cannot be transferred to the next machine until the latter becomes available, causing the current machine to remain blocked. Numerous real-world applications have been modeled as the BJSSP, which is classified as a strongly NP-hard problem. Previous studies indicate that several proposed approaches fail to guarantee the generation of feasible solutions during the search process, thereby requiring a solution reconstruction. In this study, we propose a Genetic Algorithm (GA) designed to operate strictly within the feasible solution space of the BJSSP, where the objective function is the minimization of the makespan. Experimental results show that no specific factor levels significantly influenced the solution quality obtained by the GA across all problem sets. On the other hand, incorporating an assignment operator into the solution representation enhanced the diversity of the population. The proposed GA yields solutions that outperform some of the best-known makespan values for the Lawrence benchmark problems. The runtime of the GA ranged from 20 s for instances with 10 jobs and five machines to 600 s for instances with 30 jobs and 10 machines.

Sapium discolor is a valuable native species in southern China, valued for its rapid growth and vibrant foliage, and widely used in ecological restoration and landscaping. To identify superior propagation materials with fast growth and red leaves, regional open-pollinated progeny trials of 10 elite trees were established in Nanping, Sanming, and Zhangzhou (Fujian Province) in 2015. Growth (tree height and diameter) was monitored from 2015 to 2023, and leaf color (the proportion of red in leaf color) was assessed in 2024. The species showed early fast growth, with mean tree height and diameter at breast height (DBH) reaching 7.98 m and 9.99 cm at six years, then slowing. Family-level phenotypic variation was limited. ANOVA revealed highly significant differences among families for growth traits from 2016 onward and for leaf color in 2024. Broad-sense heritability was moderate for 2023 tree height (0.3839), DBH (0.1879), and 2024 leaf color (0.2102), with low narrow-sense heritability, indicating non-additive genetic effects. Clonal selection based on genotypic values achieved notable genetic gains, especially for growth. One superior clone combined improvements in height (13.1%), diameter (10.1%), and red coloration (8.3%). These results highlight the value of clonal selection and the need to consider genotype × environment interactions in breeding programs.

Bait costs constitute 40–50% of the total expenditure in river crab aquaculture, highlighting the critical need for accurately assessing crab growth and scientifically determining optimal feeding regimes across different farming stages. Current traditional methods rely on periodic manual sampling to monitor growth status and artificial feeding platforms to observe consumption and adjust bait input. These approaches are inefficient, disruptive to crab growth, and fail to provide comprehensive growth data. Therefore, this study proposes a machine vision-based monitoring system for river crab feeding platforms. Firstly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is applied to enhance underwater images of river crabs. Subsequently, an improved YOLOv11 (You Only Look Once) model is introduced and applied for multi-target detection and counting in crab ponds, enabling the extraction of information related to both river crabs and bait. Concurrently, underwater environmental parameters are monitored in real-time via an integrated environmental information sensing system. Finally, an information processing platform is established to facilitate data sharing under a “detection–processing–distribution” workflow. The real crab farm experimental results show that the river crab quality error rate was below 9.57%, while the detection rates for both corn and pellet baits consistently exceeded 90% across varying conditions. These results indicate that the proposed system significantly enhances farming efficiency, elevates the level of automation, and provides technological support for the river crab aquaculture industry.

Production of ferrochrome alloy is carried out using carbon as a reductant in a Submerged Arc Furnace (SAF). Carbothermic reduction of chromite ore results in high CO₂ emissions, and alternative reductants such as H₂, wherein H₂O is the only by-product, have become attractive potential alternatives. Before utilizing H₂ as a reductant, it is crucial to carry out a comprehensive study on the reaction kinetics with the view to aid the design and operation of reactors that facilitate the reduction process. The current study determined the kinetic parameters for isothermal and non-isothermal pre-reduction of chromite with H₂ in a thermogravimetric furnace. Results from powder X-ray diffraction and scanning electron microscopy determined the mineralogical variations between the feed and the pre-reduced samples, as well as the variation between isothermally and non-isothermally treated samples. The mass loss data indicates that longer reduction times are required to reach complete reduction. The apparent activation energy for the isothermal and non-isothermal pre-reduction tests was found to be 105 and 124 kJ/mol, respectively. The mineralogical observations for pre-reduced samples at 1300 °C and 1500 °C showed that samples treated at lower temperatures (1300 °C) displayed consistent textures and Fe-Cr droplets along rims of partially altered chromite (PAC), which suggested higher metallization at this temperature. Higher temperatures (1500 °C), on the other hand, resulted in poor metallization, possibly because higher temperatures are often associated with a collapsed pore network, which results in poor diffusion rates, thus hindering complete reduction.

The increasing ability of Large Language Models (LLMs) to generate fluent and coherent text has heightened the need for resources to analyze and detect synthetic content, particularly in Spanish, where the scarcity of datasets hinders the development of reliable detection systems. This work presents a Spanish-language dataset of 18,236 synthetic news descriptions generated from real journalistic headlines using a fully reproducible, open-source pipeline. The methodology used to produce the dataset includes both a Retrieval Augmented Generation (RAG) approach, which incorporates contextual information from recent news descriptions, and a NO-RAG approach, which relies solely on the headline. Texts were generated with the instruction-tuned Mistral 7B Instruct model, systematically varying temperature to explore the effect of generation parameters. The dataset includes detailed metadata linking each synthetic description to its source headline, generation settings, and, when applicable, retrieved contextual content. By combining contextual grounding, controlled parameter variation, and source-level traceability, this dataset provides a reproducible and richly annotated resource that supports research in Spanish synthetic text and evaluation of LLM-based generation.