Advancing Open Science

Hyperspectral images (HSIs) have been broadly applied in remote sensing, environmental monitoring, agriculture, and other fields due to their rich spectral information and complex spatial properties. However, the inherent redundancy, spectral aliasing, and spatial heterogeneity of high-dimensional data pose significant challenges to classification accuracy. Therefore, this study proposes STM-Net, a hybrid deep learning model that integrates SSRE (Spectral–Spatial Residual Extraction Module), Transformer, and MDRM (Multi-scale Differential Residual Module) architectures to comprehensively exploit spectral–spatial features and enhance classification performance. First, the SSRE module employs 3D convolutional layers combined with residual connections to extract multi-scale spectral–spatial features, thereby improving the representation of both local and deep-level characteristics. Second, the MDRM incorporates multi-scale differential convolution and the Convolutional Block Attention Module mechanism to refine local feature extraction and enhance inter-class discriminability at category boundaries. Finally, the Transformer branch equipped with a Dual-Branch Global-Local (DBGL) mechanism integrates local convolutional attention and global self-attention, enabling synergistic optimization of long-range dependency modeling and local feature enhancement. In this study, STM-Net is extensively evaluated on three benchmark HSI datasets: Indian Pines, Pavia University, and Salinas. Additionally, experimental results demonstrate that the proposed model consistently outperforms existing methods regarding OA, AA, and the Kappa coefficient, exhibiting superior generalization capability and stability. Furthermore, ablation studies validate that the SSRE, MDRM, and Transformer components each contribute significantly to improving classification performance. This study presents an effective spectral–spatial feature fusion framework for hyperspectral image classification, offering a novel technical solution for remote sensing data analysis.

Waste haulage represents one of the most critical and cost-intensive operations in surface mining, accounting for up to 50% of the total operating costs. Under such operating conditions, the implementation of continuous systems such as In-Pit Crushing and Conveying (IPCC) is an alternative to truck haulage, as it demonstrates a higher degree of economic efficiency. In a theoretical and practical sense, due to its direct impact on the extraction plan, defining the optimal position of the crusher and consequently the system of conveyors is often the most challenging problem of this methodology. This paper introduces an innovative approach to determining the optimum waste haulage configuration by comparing conventional truck-based transport with IPCC systems. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem, explicitly incorporating spatial dimensions and the relocation costs of semi-mobile crushers. The model situates the crusher in a way that reduces transfer costs throughout production periods and it has been tested on a hypothetical open pit.

To tackle grape branch and leaf waste and alleviate global feed shortages, this study tested silage made from Xinjiang ‘Seedless White’ grape foliage. Three treatments were established: CK (control, only grape branches and leaves), PL (inoculated with 5 × 10⁶ CFU·g⁻¹ fresh weight Lactiplantibacillus plantarum), and PLC (inoculated with 5 × 10⁶ CFU·g⁻¹ L. plantarum and 0.3% cellulase). Silages were fermented at 18–23 °C and analyzed on days 7, 15, 30, and 60. PLC reduced dry matter loss in the late fermentation stage, while lowering Neutral detergent fiber (NDF) and Acid detergent fiber (ADF) contents to solve the high-fiber issue of grape foliage silage. It also maintained a lower pH in the mid-to-late stage and higher Lactic acid (LA) content to ensure anti-spoilage. Microbiologically, PLC had the highest Lactiplantibacillus abundance on day 7; on day 60, its Simpson index was higher, meaning stronger microbial community stability. Firmicutes replaced Cyanobacteria as the new dominant phylum, with Lactiplantibacillus remaining the absolute dominant genus, and the growth of molds and yeasts was effectively inhibited. In conclusion, the combined application of L. plantarum and cellulase enhances the quality of grape branch and leaf silage. This study turns low-value grape branches and leaves into high-quality feed, providing support for grape branch and leaf resource utilization and helping alleviate global feed shortages.

Heart failure (HF) is becoming increasingly common, especially in older females, and displays marked sex-related differences in pathophysiology, treatment, and outcomes. Submaximal exercise capacity (SEC), frequently measured by the six-minute walk test (6MWT), is an important marker of aerobic function, prognosis, and quality of life in HF. However, evidence regarding sex differences in SEC remains limited and inconsistent. This single-centre, prospective cohort study included 1069 patients with chronic HF enrolled between 2004 and 2014. SEC was assessed using the 6MWT, and extensive clinical and psychosocial data were collected. Multivariate models evaluated the independent association between sex and SEC. Results showed that females had significantly shorter 6MWT distances (155 ± 149 m) than males (265 ± 164 m; p < 0.001). Female sex was an independent predictor of impaired SEC in both unadjusted and adjusted analyses (odds ratios 2.226–3.609; p < 0.001). Additional determinants of reduced SEC included advanced age, higher NYHA class, elevated heart rate, diabetes, iron deficiency, dependence in activities of daily living, cognitive impairment, and depressive symptoms. These findings demonstrate that female sex is a strong, independent predictor of reduced functional capacity in chronic HF and emphasize the need for sex-specific strategies addressing both clinical and psychosocial factors to improve outcomes.

Laterally loaded slender piles present a classic soil–structure interaction problem where pile displacements and flexural demands are governed by the mobilized lateral resistance of the surrounding soil and the axial-bending capacity of the reinforced concrete section. In response to increasing pressure to reduce embodied emissions, this study develops LAVERCO, an optimization framework for cost- and CO₂-efficient design of bored reinforced-concrete piles in cohesive soils subjected to combined lateral and axial actions. The framework integrates Eurocode-based geotechnical checks with full N–M section verification of the RC pile and applies a genetic algorithm over a multi-parametric grid of lateral load, vertical load, and undrained shear strength, using economic cost and embodied CO₂ as alternative single objectives. Rank-based (Spearman) sensitivity analysis quantifies how actions, soil strength, and design variables influence the optimal solutions. The results reveal two consistent geometry regimes: CO₂-optimal piles are systematically longer and slimmer, while COST-optimal piles are shorter and thicker. In both cases, the objective is dominated by pile length and is reduced by higher undrained shear strength; vertical load has a moderate direct effect, while horizontal load contributes mainly through deflection and bending checks. Feasibility improves significantly in stronger clays, and CO₂-optimal geometries generally incur higher costs, clarifying the trade-off between economic and environmental performance. The framework provides explicit geometry-level guidance for selecting bored pile designs that balance cost and embodied CO₂ across a wide range of soil and loading conditions and can be directly applied in both preliminary and detailed designs.

Lava tubes on Earth provide unique hydrogeological niches for life to proliferate. Orbital observations of the Martian surface indicate the presence of lava tubes, which could hold the potential for extant life or the preservation of past life within a subsurface environment protected from harsh conditions or weathering at the surface. Secondary minerals in lava tubes form as a combination of abiotic and biotic processes. Microbes colonize the surfaces rich in these secondary minerals, and their actions induce further alteration of the mineral deposits and host basalts. We conducted a biogeochemical investigation of basaltic lava tubes in the Medicine Lake region of northern California by characterizing the compositional variations in secondary minerals, organic compounds, microbial communities, and the host rocks to better understand how their biogeochemical signatures could indicate habitability. We used methods applicable to landed Mars missions, including Raman spectroscopy, X-ray diffraction (XRD), Laser-Induced Breakdown Spectroscopy (LIBS), and gas chromatography–mass spectrometry (GC-MS), along with scanning electron microscopy (SEM) and metagenomic DNA/RNA sequencing. The main secondary minerals, amorphous silicates, and calcite, formed abiotically from the cave waters. Two types of gypsum, large euhedral grains with halites, and cryptocrystalline masses near microbial material, were observed in our samples, indicating different formation pathways. The cryptocrystalline gypsum, along with clay minerals, was associated with microbial materials and biomolecular signatures among weathered primary basalt minerals, suggesting that their formation was related to biologic processes. Some of the genes and pathways observed indicated a mix of metabolisms, including those involved in sulfur and nitrogen cycling. The spatial relationships of microbial material, Cu-enriched hematite in the host basalts, and genetic signatures indicative of metal cycling also pointed to localized Fe oxidation and mobilization of Cu by the microbial communities. Collectively these results affirm the availability of bio-essential elements supporting diverse microbial populations on lava tube basalts. Further work exploring these relationships in lava tubes is needed to unravel the intertwined nature of abiotic and biotic interactions and how that affects habitability in these environments on Earth and the potential for life on Mars.

Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning.

This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide.