Advancing Open Science

Aim: Stock price prediction remains a highly challenging task due to the complex and nonlinear nature of financial time series data. While deep learning (DL) has shown promise in capturing these nonlinear patterns, its effectiveness is often hindered by the low signal-to-noise ratio inherent in market data. This study aims to enhance the stock predictive performance and trading outcomes by integrating Singular Spectrum Analysis (SSA) with deep learning models for stock price forecasting and strategy development on the Australian Securities Exchange (ASX)50 index. Method: The proposed framework begins by applying SSA to decompose raw stock price time series into interpretable components, effectively isolating meaningful trends and eliminating noise. The denoised sequences are then used to train a suite of deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. These models are evaluated based on their forecasting accuracy and the profitability of the trading strategies derived from their predictions. Results: Experimental results demonstrated that the SSA-DL framework significantly improved the prediction accuracy and trading performance compared to baseline DL models trained on raw data. The best-performing model, SSA-CNN-LSTM, achieved a Sharpe Ratio of 1.88 and a return on investment (ROI) of 67%, indicating robust risk-adjusted returns and effective exploitation of the underlying market conditions. Conclusions: The integration of Singular Spectrum Analysis with deep learning offers a powerful approach to stock price prediction in noisy financial environments. By denoising input data prior to model training, the SSA-DL framework enhanced signal clarity, improved forecast reliability, and enabled the construction of profitable trading strategies. These findings suggested a strong potential for SSA-based preprocessing in financial time series modeling.

The flotation efficiency of magnesite in the slurry system is critically influenced by its surface wettability. In this work, molecular dynamics (MD) and density functional theory (DFT) calculations were employed to investigate the interactions between water molecules and the magnesite (104) surface. To elucidate the underlying mechanisms, systematic evaluations were conducted, encompassing frontier orbital energies, water molecule adsorption behavior, and the water wetting process. Results indicate that electrons readily transfer from the highest occupied molecular orbital (HOMO) of water to the lowest unoccupied molecular orbital (LUMO) of magnesite. Specifically, the chemisorption of a single water molecule onto the magnesite surface was observed, with a calculated adsorption energy of −91.6 kJ/mol. This process involves an interaction between the oxygen atom of water and a surface magnesium atom, leading to the formation of an Mg–O_W bond. This bond primarily arises from hybridization between the Mg 2p, Mg 2s, and O_W 2p orbitals. Furthermore, water molecules within the first adsorbed monolayer exhibited an average adsorption energy of −66.3 kJ/mol, which further confirms the occurrence of chemisorption. Notably, minimal changes were observed in the orbital interactions between water molecules and surface Mg atoms, a trend consistent with the single-molecule adsorption case. The average adsorption energies for the second and third water layers were calculated to be −63.2 kJ/mol and −45.6 kJ/mol, respectively. The stabilization of the hydration layer structure is attributed to the hydrogen-bonding network formed among water molecules in the outer layers. As the number of water layers increases, the structural disorder of water molecules on the magnesite surface progressively intensifies. This decrease in adsorption energy with increasing layer number is attributed to the progressively enhanced contribution of hydrogen-bonding interactions between water molecules across different layers. Consequently, the magnesite surface exhibits a low contact angle, indicating high intrinsic hydrophilicity. Collectively, these findings provide molecular-level insights into the wettability of the magnesite surface, thereby contributing to a more fundamental understanding of magnesite flotation mechanisms.

In skeletal muscles fibers, cellular respiration, excitation–contraction (EC) coupling (the mechanism that translates action potentials in Ca²⁺ release), and store-operated Ca²⁺ entry (SOCE, a mechanism that allows recovery of external Ca²⁺ during fatigue) take place in organelles specifically dedicated to each function: (a) aerobic ATP production in mitochondria; (b) EC coupling in intracellular junctions formed by association between transverse tubules (TTs) and sarcoplasmic reticulum (SR) named triads; (c) SOCE in Ca²⁺ entry units (CEUs), SR-TT junctions that are in continuity with membranes of triads, but that contain a different molecular machinery (see Graphical Abstract). In the past 20 years, we have studied skeletal muscle fibers by collecting biopsies from humans and isolating muscles from animal models (mouse, rat, rabbit) under different conditions of muscle inactivity (sedentary aging, denervation, immobilization by casting) and after exercise, either after voluntary training in humans (running, biking, etc.) or in mice kept in wheel cages or after running protocols on a treadmill. In all these studies, we have assessed the ultrastructure of the mitochondrial network and of the sarcotubular system (i.e., SR plus TTs) by electron microscopy (EM) and then collected functional data correlating (i) the changes occurring with aging and inactivity with a loss-of-function, and (ii) the structural improvement/rescue after exercise with a gain-of-function. The picture that emerged from this long journey points to the importance of the internal architecture of muscle fibers for their capability to function properly. Indeed, we discovered how the intracellular organization of the mitochondrial network and of the membrane systems involved in controlling intracellular calcium concentration (i[Ca²⁺]) is finely controlled and remodeled by inactivity and exercise. In this manuscript, we give an integrated picture of changes caused by inactivity and exercise and how they may affect muscle function.

Different topologies for individual stator teeth as modular electric motor components are investigated via several different metrics such as finite element analysis (FEA), winding methodologies, and thermal performance during electrical power loading. These are easily quantifiable metrics which allow for the direct comparison of the different topologies, particularly with respect to the concentrated windings of the copper wire around the stator teeth. The paper assesses temperature rise, heat dissipation, and the role of air gaps within the copper wire windings. The results show that the winding via robot resulted in 30 (±5)% lower temperature rises on average compared to commercial (hairpin) winding systems, due to more ordered winding which results in larger air gaps between the wires. The air gaps appear to play a critical role in the thermal performance of the stator windings. It is also shown that the different topologies affect thermal performance during electrical loading, suggesting that the different topologies could be useful in different applications.

The Mun River Basin, the largest Mekong tributary in Northeast Thailand, has experienced extensive agricultural expansion and forest decline, raising concerns over increasing soil erosion and sediment transfer. This study provides an integrated assessment of soil loss, sediment yield (SY), and sediment delivery ratio (SDR) across 19 sub-watersheds using the Universal Soil Loss Equation (USLE), field-based SY data, and multivariate statistical analyses in 2024. Basinwide soil loss was estimated at ~35 million t y⁻¹ (mean 4.96 t ha⁻¹ y⁻¹), with more than 80% of the basin classified in the no erosion to very low erosion classes. Despite substantial hillslope erosion, only 402,405 t y⁻¹ of sediment reaches the river network, corresponding to a low SDR of 1.15%, which falls within the range reported for large tropical watersheds with significant reservoir infrastructure. Soil loss is most strongly influenced by slope and forested terrain, while SY responds primarily to rainfall and tree plantations; urban land, croplands, and reservoirs act as sediment sinks. Principal Component Analysis (PCA) resolved multicollinearity and produced six components explaining over 90% of predictor variance. A PCA-based regression model predicted SY per unit area with high accuracy (r = 0.81). The results highlight the dominant roles of hydroclimate and land-use structure in shaping sediment connectivity, supporting targeted soil and watershed-management strategies.

The relationship between hormonal reactivity to acute stress and memory is well established, but the role of anticipatory cortisol and dehydroepiandrosterone (DHEA) levels remains underexplored. This study aimed to assess the psychobiological responses (anxiety, affect, cortisol and DHEA) to an academic examination, subsequent memory performance and associations between anticipatory hormonal response and memory retrieval. Seventy-nine undergraduates (10 males) completed an acquisition session involving picture encoding and immediate free recall. Forty-eight hours later, during the recall session, they sat a written examination followed by delayed free recall and recognition tasks. Results showed higher anticipatory anxiety, negative affect and cortisol levels in the recall session than in the acquisition session. Participants showed poorer delayed recall performance and reduced recognition of neutral pictures. In addition, after correction for multiple comparisons, exploratory hierarchical regression analyses indicated that anticipatory cortisol levels and the cortisol/DHEA ratio assessed prior to the recall session were negatively associated with total delayed free recall performance, with the cortisol/DHEA ratio also being negatively associated with delayed free recall of negative pictures. In the absence of a control group, these findings cannot be used to make causal inferences. However, they are consistent with theoretical accounts of DHEA’s anti-glucocorticoid role and highlight associations between cortisol/DHEA balance and delayed free recall performance, particularly for negative emotional material.

Altrenogest is a synthetic progestin used as a contraceptive method in various animal species, including bottlenose dolphins. This is a retrospective study based on the analysis of data collected between 2020 and 2025. Eighteen female dolphins (Tursiops truncatus) from four zoological parks on the Iberian Peninsula were included, treated with the progestin for variable periods. The animals were monitored through ultrasound examinations, behavioral observations, and hormonal assays for progesterone and estrogen. The results showed statistically significant changes in hormone levels, with a significant decrease in serum progesterone and a significant increase in estrogen levels in treated dolphins compared to untreated ones. These findings suggest that Altrenogest inhibits LH secretion, while it does not appear to directly affect FSH, allowing some degree of follicular activity to persist. Despite its demonstrated contraceptive efficacy, prolonged use of the drug was associated with the development of pyometra in four individuals and follicular cysts in three individuals. These results highlight the effectiveness of Altrenogest in reproductive management of dolphins, while also emphasizing the need for careful evaluation of treatment duration and the potential risk of long-term adverse effects.

This study examines how investment efficiency and risk jointly shape sustainable grain supply-chain upgrading. Using firm-level panel data for 25 listed grain supply-chain firms in China from 2015 to 2023, this study examines efficiency–risk structures and their heterogeneity across upstream, midstream, and downstream segments. A three-stage data envelopment analysis (DEA) is applied to measure investment efficiency while controlling for environmental heterogeneity and statistical noise, and a multidimensional investment risk index is constructed using principal component analysis (PCA), with an emphasis on sustainability metrics. The results reveal a clear supply-chain gradient: downstream firms exhibit the highest mean third-stage investment efficiency (crete = 0.633) and scale efficiency (scale = 0.634), midstream firms are intermediate (crete = 0.308; scale = 0.326), and upstream firms remain lowest (crete = 0.129; scale = 0.138). This ordering is also visible year by year, while risk profiles indicate higher exposure upstream and pronounced volatility midstream. Efficiency decomposition shows that upstream inefficiency is mainly driven by scale inefficiency rather than insufficient pure technical efficiency. Overall, efficiency–risk mismatch—manifested as persistent low scale efficiency and elevated risk exposure in upstream, volatility in midstream, and stability in downstream—constitutes a key micro-level barrier to long-term and resilient upgrading. The study thus offers policy-relevant insights for segment-specific interventions that align with sustainable agricultural development: facilitating land consolidation and integrated risk management for upstream scale inefficiency, promoting supply-chain finance and digital integration for midstream risk volatility, and leveraging downstream stability to drive coordinated upgrading and sustainable value creation through market-based incentives.