Abstract: This study investigates the energy-efficient mechanochemical activation of fly ash derived from Kazakh coals for the development of sustainable cementitious composites. The ap-proach aims to enhance the reactivity of aluminosilicate materials while reducing the en-ergy demand and carbon footprint associated with conventional clinker-based cement production. Mechanochemical activation was performed to increase the specific surface area and in-duce structural defects in the glassy phase of fly ash, thereby improving its reactivity. Chemical activation using sodium hydroxide (NaOH) was applied to promote intensive pozzolanic reactions and accelerate dissolution kinetics. The optimal activation conditions were identified as 15 min of mechanical treatment com-bined with 4% NaOH. Under these conditions, the compressive strength reached 35.5 MPa at 28 days, exceeding that of the reference cement (35.0 MPa). At fly ash contents of 15–20%, the composites maintained or improved strength, whereas an increase to 30% resulted in a reduction to 31.5 MPa. Mechanical activation increased the specific surface area to approximately 4800–5000 cm²/g; however, prolonged grinding (up to 30 min) led to particle agglomeration and a de-crease in strength to about 28 MPa. Chemical activation enhanced reaction kinetics without significantly affecting particle fineness. Microstructural analysis revealed the formation of a dense and homogeneous matrix dom-inated by C–S–H, C–A–S–H, and N–A–S–H gel phases with reduced porosity. The com-bined activation approach demonstrated a clear synergistic effect, enabling up to 20% ce-ment replacement without loss of performance. Importantly, the proposed method provides a low-energy pathway for the utilization of industrial waste, contributing to reduced clinker consumption and lower CO₂ emissions. The results highlight the significant potential of Kazakhstan’s industrial by-products for the production of energy-efficient, environmentally friendly, and cost-effective construction materials.

Abstract: Physics makes two questionable assumptions: (1) Distant galaxies are accelerating relative to Earth. (2) Entangled objects are spatially separated from each other. Why questionable? Acceleration relative to Earth has never been observed in a single galaxy. Observers perceive entangled objects as spatially separated, yet 3D space is relative. We show that physical realities are projections of a mathematical background reality: 4D Euclidean space (ES). In Euclidean relativity (ER), all objects move through ES at the speed C. There is no time coordinate in ES. All action is due to a monotonically increasing, absolute, external evolution parameter θ. An observer experiences two projections of ES as space and time. The axis of his current 4D motion is his proper time τ. Three orthogonal axes form his 3D space x₁, x₂, x₃. His physical reality is his spacetime x₁(ϑ), x₂(ϑ), x₃(ϑ), τ(ϑ), where τ is a natural time coordinate and θ converts to absolute parameter time ϑ. Without gravity, his spacetime is Minkowski-like. As in general relativity (GR), gravity in ER is the curvature of spacetime. Since coordinates in GR are merely labels, the Einstein field equations also hold in systems that use τ as the time coordinate. ER predicts time’s arrow, relativistic effects, galactic motion, the Hubble tension, and entanglement. Remarkably, ER manages without cosmic inflation, expanding space, dark energy, and non-locality. ER tells us: (1) Distant galaxies maintain their recession speeds. (2) From their perspective, entangled objects have never been spatially separated, yet their proper time flows in opposite 4D directions.

Abstract: The COVID-19 pandemic has disrupted the lives of the world's population, resulting in over 7 million deaths. It was immediately noted that obese and/or diabetic subjects and frail elderly individuals with multiple comorbidities were more likely to have a more severe disease course. The cause of the increased morbidity and mortality in obese and/or diabetic subjects was found to be related to the presence of insulin resistance in these individuals. Furthermore, it was also discovered that COVID-19, particularly in its more severe forms, was capable of causing de novo type 1 and type 2 diabetes as well as worsening the disease course, if already present. This review aims to highlight the most accredited possible mechanisms by which subjects with insulin resistance may have a more severe disease course and those by which SARS-CoV-2 infection may cause new onset of diabetes or worsening of existing diabetes. To write this manuscript, the authors independently reviewed and compared the results of peer-reviewed and impacted journal publications, written in English, selected from the most well-known search platforms such as PubMed, Scopus, Science Direct, Google Scholar, and ResearchGate, using the following keywords: SARS-CoV-2, COVID-19, Insulin resistance, Glucose metabolism, Obesity, Diabetes, Hospitalization, Mortality.

Abstract:

The increasing global frequency of harmful cyanobacterial blooms (CyanoHABs), driven by nutrient enrichment and climate change, poses a severe threat to aquatic ecosystems and public health. This study evaluates the effectiveness of novel clay-polymer nanocomposites—combining the charge-neutralizing capabilities of polydiallyldimethylammonium chloride (PolyDADMAC) with the high density of clay minerals (kaolinite and sepiolite) for the rapid removal of toxic cyanobacteria from water. Laboratory-scale experiments were conducted using Microcystis aeruginosa, Aphanizomenon ovalisporum, and Chlorella sp., with treatment doses determined by particle charge detector (PCD) measurements to identify the "nominal dose" required for full charge neutralization. Results demonstrate that clay-polymer nanocomposites achieve over 95% removal of turbidity and chlorophyll in M. aeruginosa at doses significantly lower (15–20%) than the calculated nominal dose, likely due to specific physical bridging interactions with the cyanobacteria’s external exopolysaccharide fibers. In contrast, A. ovalisporum and Chlorella sp. required doses closer to full charge neutralization for optimal removal. Among the materials tested, kaolinite-based nanocomposites (DKG24) showed slightly superior and more stable performance than sepiolite-based versions. Notably, application at or above the nominal dose was associated with increased soluble microcystin levels, suggesting that excessive polymer concentrations may compromise cell integrity and lead to toxin leakage. These findings suggest that engineered nanocomposites offer highly efficient, scalable technology for CyanoHAB management, provided that operational doses are carefully optimized to maximize biomass removal while minimizing toxin release.

Abstract: Overhead sports place high demands on the shoulder complex, making warm-up specificity relevant for acute readiness. This randomized controlled pilot trial compared the immediate effects of a shoulder-specific warm-up with a habitual routine in 24 youth competitive overhead athletes (14–20 years), allocated to an experimental group (EG = 12) and a standard warm-up group (SWG = 12). Outcome measures were collected before and immediately after warm-up and included shoulder flexion range of motion (ROM), handgrip strength, Closed Kinetic Chain Upper Extremity Stability (CKCUES) performance, and post-warm-up Rating of Perceived Exertion (RPE; Borg CR-10). A significant group-by-time interaction was found for right shoulder flexion ROM (p = 0.003, η²p = 0.346), with a significant increase in the EG from baseline to post-test (p = 0.008). No significant effects were observed for left shoulder flexion ROM, handgrip strength, or CKCUES performance. Post-warm-up RPE was significantly higher in the EG than in the SWG (p = 0.041). These preliminary findings support the practical value of more targeted warm-up strategies in overhead sports, while larger longitudinal studies are needed to confirm their broader functional relevance.

Abstract: The discrepancy between galactic rotation curves and visible baryonic mass persists despite empirical scaling relations like the Radial Acceleration Relation (RAR) and Baryonic Tully-Fisher Relation (BTFR). We explore a phenomenological framework where this discrepancy arises from the geometric misinterpretation of observables. Inspired by Painlevé-Gullstrand coordinates, we model the vacuum as a radially infalling compliant medium that induces an apparent compression of radial coordinates for distant observers, the "Mezzi effect". Assuming Newtonian dynamics govern an undistorted "true frame", we developed a discrete shell reconstruction method parameterized by a single universal compliance constant, tested against photometric and kinematic data from 175 late type galaxies in the SPARC database. This single parameter model yields universal scaling relations of Σtrue/Σobs∝(Rtrue/Robs)−0.5and Mobs/M.And reproduces observed rotation curves (RMS residual ∼ 34km/s). The geometric projection recovers the empirical RAR and shifts the BTFR slope from ∼ 2.8in the true frame to ∼ 3.7in the observer frame, and eliminating the normalization offset. Furthermore, The Mezzi scale factor ζ governs mass and lensing corrections via distinct power laws: Mtrue/Mtrue and αtrue/αobs∝ζ−1.26 , revealing that geometric scaling affects dynamical mass more strongly than lensing mass. These results indicate that geometric projection effects may offer a viable phenomenological explanation for galactic dynamics while remaining consistent with both Newtonian gravity and weak field general relativity. For reproducibility, the code used for this analysis is publicly available at https://github.com/Brahim-Benaissa/Zeta

Abstract: Gas hydrates are ice-like compounds formed from water and methane under high-pressure, low-temperature conditions in marine sediments. They influence sediment stability, fluid flow, and hydrocarbon distribution in continental margin settings. This study employs advanced seismic attribute analysis to investigate the gas hydrate stability zone (GHSZ) in the Gulf of Mexico and to assess the relationship between hydrate presence, subsurface fluid flow, and sediment deformation.Seismic attributes, including coherence, amplitude, and spectral decomposition, were applied to 3D seismic reflection datasets covering structurally complex regions of the northern Gulf of Mexico. These attributes were used to map bottom-simulating reflectors (BSRs), gas chimneys, and fault/fracture systems. Results indicate that gas hydrate stability zones are strongly associated with structural highs, fault intersections, and areas of enhanced deformation.The study finds that fault-controlled fluid pathways significantly influence hydrate distribution and sediment deformation patterns, highlighting the need to integrate seismic attribute analysis in hydrate resource assessment and geohazard evaluation. These findings provide new insights into fluid migration mechanisms and sediment dynamics in hydrate-bearing marine environments.

Abstract: The composition of partially ionised plasmas is investigated for densities and temperatures at which the free electrons are degenerate. Based on a quantum statistical approach, the effect of Pauli blocking is addressed. Specifically, one- and two-electron ions are studied. New results regarding the degree of ionisation and the Mott effect are presented. Standard codes for plasma properties do not take Pauli blocking effects into account and are therefore unable to explain the experiments in the high-density regime, where the electrons are degenerate.

Abstract: Extracting Alpha in extreme low signal-to-noise ratio (SNR) environments, such as the Chinese A share market, remains a notoriously unsolved challenge for deep learning. Traditional heavily parameterized models, including Transformers, inevitably fall into the ”dimensionality disaster,” memorizing market noise rather than fundamental mechanics. To break this overfitting curse, we propose a novel, ultra-lightweight architecture inspired by thermody namics and Neural Turing Machines (NTMs): the Physics-Informed Ghost Operator. By mapping the cross-sectional stock market into a high-dimensional physical manifold, our Ghost Operators navigate the feature space driven by gravitational routing. Crucially, we enforce a minimal action principle via a Boltzmann-distributed temperature scaling and Pauli-exclusion-like potential well clamping. Walk forward validation on 10 years of real A-share data reveals that our architecture achieves a substantial Sharpe ratio improvement (up to 3.2x) and cuts the maximum drawdown by nearly half compared to native NTMs. Furthermore, network sparsity is reduced by 66%, proving that physical constraints compel the model to aggressively filter noise and focus strictly on high-potential Alpha regions.

Abstract: Low-light video enhancement aims to restore clear, color-faithful, and temporally consistent visual content from video sequences captured under extremely low signal-to-noise ratios and high dynamic range constraints. Existing multi-frame enhancement methods typically adopt uniform spatio-temporal sampling and feature extraction strategies for all frames, making it challenging to simultaneously achieve long-range temporal denoising and accurate fast-motion modeling. To address this trade-off, we propose a low-light video enhancement framework based on a Fast–Slow dual-branch architecture. The video signal is decomposed into two complementary feature streams: a Slow branch with sparse temporal sampling and high spatial resolution, built on a Vision Transformer backbone, which focuses on long-range temporal denoising and high-frequency texture restoration for static and slow-moving regions; and a Fast branch with dense temporal sampling and low spatial resolution, built on a ViT-Tiny backbone, which efficiently captures large-scale motion and rapid illumination changes. To mitigate the discrepancy in sampling rates and spatial resolutions between the two branches, we further introduce a flow branch based on a pre-trained StreamFlow model and design a Flow-Guided Cross-Attention (FGCA) module. FGCA first uses optical flow to geometrically modulate and progressively align Fast-branch features, and then injects the flow-enhanced Fast features into the Slow branch at each space-time location via lightweight pixel-wise cross-attention. This mechanism achieves a cascade of coarse geometric alignment and fine semantic fusion. Experiments on two real-world low-light video datasets, SDSD-indoor and SDSD-outdoor, demonstrate that our method consistently outperforms several representative approaches in terms of PSNR, SSIM, AB(Var), and MABD, while effectively suppressing motion blur and ghosting artifacts in dynamic night scenes, yielding temporally stable and perceptually pleasing results.

Abstract: The optical counterpart of the gravitational wave event GW170817, known as kilonova, has provided strong evidence that binary neutron star mergers are favourable sites to host the r-process nucleosynthesis. Kilonova is a quasi-thermal electromagnetic emission powered by the radioactive decay of heavy neutron-rich nuclei produced by the r-process. Considering the variety of elements contributing to kilonova ejecta, essential information about its composition can be achieved through spectral characterisation, radiative transfer simulations, and opacities. The latter represents one of the most challenging aspects of the modelling, as it relies on accurate atomic structure calculations of energy levels and transitions. Since light r-process elements are major opacity contributors in early (< 2 days) scenario, this work focuses on atomic calculations for Zr I-IV. Energy levels and bound-bound transitions are determined using the GRASP2018 code, assuming two different multi-reference sets for each ionisation stage: one including, and one excluding core-core and core-valence correlations. Results demonstrate that the inclusion of f shell and core correlations impacts on both energy levels and transitions. A systematic assessment of the accuracy is performed through detailed comparisons with the NIST ASD. Finally, these Zr data are integrated on the open access MARTINI platform.

Abstract: Background/Objectives: Large language models (LLMs) are increasingly applied to medical image interpretation; however, their diagnostic accuracy and reliability in musculoskeletal radiology remain uncertain. This study evaluates the diagnostic performance and confidence calibration of LLMs in detecting and classifying bone tumors on radiographs. Methods: This retrospective observational study analyzed a dataset of 257 radiographs with confirmed diagnoses obtained from Radiopaedia, including normal studies and a spectrum of benign and malignant bone tumors. Cases were selected to ensure representation across multiple tumor types. Three LLMs (ChatGPT 5.3, X-ray Interpreter GPT-4.1, and X-ray Interpreter Gemini) evaluated each image using a standardized prompt assessing abnormality detection, tumor detection, classification, and confidence. Outcomes included diagnostic accuracy, false positive abnormality rates, false negative rates, tumor hallucination rates, and confidence calibration. Results: Abnormality detection was high across models, with Gemini demonstrating the highest sensitivity (up to 100%). Tumor detection was strongest in lesions with characteristic features, including osteosarcoma and osteochondroma. False negative rates varied substantially, with GPT-4.1 demonstrating the highest rate (29.9%), followed by ChatGPT (24.8%) and Gemini (6.6%). Primary diagnostic accuracy was highest for osteosarcoma in GPT-4.1 (80%), while ChatGPT 5.3 performed best in benign lesions, including osteochondroma (84.6%) and non-ossifying fibroma (76.9%). Tumor subtype classification remained limited across all models and was poorest for Ewing sarcoma (0% in ChatGPT and GPT-4.1; 10.3% in Gemini). False positive abnormality rates were highest in GPT-4.1 (40.7%), followed by Gemini (25.9%) and ChatGPT (13.5%). Tumor hallucination occurred only in Gemini (12.3%). All models demonstrated confidence miscalibration, with higher confidence observed in incorrect predictions and in tumor-negative cases. Conclusions: LLMs demonstrate strong performance in detecting radiographic abnormalities but remain limited in tumor subtype classification, particularly for diagnostically challenging lesions such as Ewing sarcoma. Elevated false positive and false negative rates, along with systematic overconfidence—especially in GPT-4.1—highlight important limitations for clinical use. These findings support the role of LLMs as adjunctive tools rather than independent diagnostic systems.

Abstract: Background: Genetic variations such as single nucleotide polymorphisms (SNPs) as part of pharmacogenomics play an important role in the metabolism of drug and hence their active concentrations in blood plasma. Objectives: The aim of this study is to select candidate compounds from a TCM dataset that may be repurposed for arterial and venous thromboses management. This shall be achieved through development and evaluation of an ensemble deep learning model that taking into account the genetic variations in protein sequences. Methods: BIOSNAP dataset was supplemented with 321,657 drug–target pairs consisting of SNP variants of wild-type proteins. The application dataset consisted of a TCM dataset containing 35,553 ingredients. The control group was set as the pathogenic group, whilst the treatment group was set as the non-pathogenic group. Contrastive and non-contrastive deep cross-modal attention ensemble modelling was developed, evaluated and applied. Results: Contrastive regularisation effect improved the performance of the Contrastive Learning (CL) Ensemble over the Non-CL Ensemble model as well as the Dong et al. (May 2025) CL model in the test set (AUPR 0.919 vs. 0.894 vs. 0.813). Safflower yellow A, Paeoniflorin and Notoginsenoside R6 were associated with existing TCM and highly ranked for interaction with Factor Xa genetic variants. Highly interacting protein targets were identified. Conclusions: Ensemble modelling with contrastive learning resulted in performance improvements and can be useful for selecting TCM compounds for antithrombotic management. This is a step towards personalised drug selection and can simultaneously facilitate interpretation of the biological rationales for risk vs benefit evaluations during decision making.

Abstract: Background: The coexistence of Myasthenia Gravis (MG) and Rheumatoid Arthritis (RA) represents a rare but clinically challenging form of polyautoimmunity, raising interesting questions about shared immunopathogenic mechanisms and the safety of long-term immunomodulatory therapies. Methods: The article describes a case report of a 66-year-old female with a 12-year history of seropositive RA who subsequently developed seropositive MG during long-term exposure to hydroxychloroquine (HCQ) therapy. Following discontinuation of HCQ, methotrexate (MTX) therapy was initiated and stable control of both diseases was temporally obtained. Results: Three years later, the patient presented with upper gastrointestinal bleeding and severe microcytic anemia. Further evaluation revealed advanced liver fibrosis (F4) and severe gastropathy, consistent with Child–Pugh class A cirrhosis. Viral, alcoholic, and autoimmune causes of chronic liver disease were excluded. In the absence of alternative etiologies, this was considered possibly associated with MTX therapy, in the context of additional metabolic risk factors, including type 2 diabetes mellitus and increased body mass index. Conclusions: The complex interplay between polyautoimmunity and treatment-related toxicity is underscored in this article. Overlapping autoimmune diseases may arise on a shared immunological background, while therapeutic agents may contribute to disease expression or long-term complications. These findings highlight the need for individualized therapeutic strategies and vigilant monitoring, particularly in patients with coexisting metabolic risk factors.

Abstract: In the paper, a systematic treatment of sensitivity analysis of multivariable control systems from a perspective of the characteristic transfer functions (CTFs) method is given. The CTFs method (also called Characteristic Gain Loci method) allows one to associate with an N-dimensional multi-input multi-output (MIMO) system a set of N independent single-input single-output (SISO) characteristic systems and thereby to reduce the analysis and design of a MIMO system to analysis and design of N SISO systems. The formulas are derived determining the sensitivity functions of the CTFs and sensitivity vectors of the canonical basis axes to small variations of parameters of general type MIMO systems. The relations between the sensitivity functions of the open-loop and closed-loop MIMO systems are established. Two illustrative examples are considered. The first of them concerns the sensitivity of a two-dimensional not robust system with large degree of skewness of the canonical basis axes. In the second example, the sensitivity of the control system of a hexacopter (multirotor UAV with six rotors) to small degradations of the motors’ efficiency is analyzed.

Abstract: (1) Background: Retinol has consistently demonstrated efficacy in improving signs of skin aging. However, recent European Union regulations have limited its cosmetic concentration to 0.3%, creating the need for new formulations to be capable of maintaining high efficacy, safety, and tolerance. (2) Material and Methods: This clinical study aimed to evaluate and compare the rejuvenating effects and tolerance of a 0.5% retinol serum with a new equivalent technology, Retinduo®, which previously showed promising preclinical results. A single-center, prospective, randomized, controlled, double-blind, two-arm parallel study was conducted in 40 Caucasian women aged 38–60 years with moderate photoaging (Glogau II). 20 participants applied Retinduo® serum and 20 applied retinol 0.5%, following a progressive ap-plication protocol. Clinical and instrumental assessments measured hydration, firmness, elasticity, tone homogeneity, melanin levels, skin roughness, wrinkle parameters, and stratum corneum thickness. (3) Results: Both formulations signifi-cantly improved hydration, firmness, and elasticity from day 28 onward. Retinduo® showed a significant increase in viscoelasticity (R8) from day 56, while retinol 0.5% did not demonstrate significant changes in this parameter. Melanin reduction was observed with Retinduo® at days 28 and 56 and with retinol 0.5% just at day 28. Although a reduction in melanin was observed with both ingredients, the reduction was more significant with Retinduo® at 56 days. Both treatments reduced the thickness of the stratum corneum; however, with Retinduo®, a significant and more pronounced reduction was achieved after 3 months of treatment (30% (p=0.0001) vs. 12% (p=0.033). Retinduo® demonstrated significant wrinkle depth reduction at day 28 and in wrinkle amplitude (width and length of wrinkles) at the end of treatment, while 0.5% retinol showed a positive trend in this parameter. Both products exhibited excellent tolerance. (4) Conclusions: Overall, Retinduo® achieved comparable or slightly superior anti-aging effects while aligning with current European regulatory limits.

Abstract: In this study we develop a Land-Use Random Forest (LURF) model for the Campania Region (southern Italy) that combines 2022 daily PM10 observations from 13 quality-controlled ARPA Campania stations with a rich set of spatial predictors to produce daily concentration maps at 1000 m × 1000 m resolution, from which annual statistics (mean, percentiles, and exceedances) are derived through temporal aggregation. The predictor space includes resident population, land-cover and imperviousness indicators, road-network metrics derived from OpenStreetMap, meteorological fields from the ERA5 reanalysis, satellite aerosol optical depth (AOD) from MODIS Terra and Aqua—scaled by ERA5 boundary-layer height (AOD/pbl)—daily mean PM10 from a nested CHIMERE simulation, and a binary categorical predictor (IdDust) flagging days affected by Saharan dust transport events. The hyperparameters for the LURF model are selected via a nested inner grid search; generalisation performance is assessed through a spatially aware leave-location-out cross-validation (LLO-CV) scheme, which prevents optimistic bias arising from spatial autocorrelation among neighbouring stations. Under LLO-CV, the LURF achieves R2=0.54, RMSE =11.1 μg m−3, and MAE =8.0 μg m−3, against R2=−1.11, RMSE =23.6 μg m−3, and MAE =19.1 μg m−3 for the raw CHIMERE output evaluated on the same observations. The inclusion of IdDust as a categorical covariate allows the Random Forest to partition the training distribution between dusty and non-dusty regimes, improving the representation of episodic high-PM10 events and reducing systematic underestimation at the upper tail of the concentration distribution. CTM-derived PM10 and ERA5 boundary-layer and pressure fields emerge as the dominant predictors, collectively accounting for the majority of explained variability, while IdDust ranks among the physically interpretable secondary predictors. The 1000 m maps highlight marked urban–rural contrasts, resolving hotspots in the Naples metropolitan area and along major motorway corridors that remain unresolved at typical CTM grid spacings. By embedding physically based CTM output, satellite aerosol diagnostics, and dust-event classification within a flexible machine-learning framework, the proposed approach offers a low-cost, operationally tractable tool for high-resolution PM10 exposure assessment in regions characterised by complex terrain and heterogeneous emission sources.

Abstract: To explore the genetic diversity and adaptive evolutionary mechanism of Mastacembelus armatus in the Dongjiang and Ganjiang River Sources, whole-genome resequencing was performed on three populations of M. armatus from Xunwushui (XW) and Jiuqu River (DN) in the Dongjiang River Source, and Taojiang (XF) in the Ganjiang River Source. Population genetics methods were integrated to analyze their genetic structure, differentiation characteristics and selection signals. The results showed that a total of 209.05 Gbp of Clean Data was obtained from the three populations, with the Q30 base percentage reaching 94.42% and the average mapping rate to the reference genome being 97.85%, indicating high reliability of the sequencing data. A mean of 7,459,686 single nucleotide polymorphisms (SNPs) were detected, with a transition/transversion ratio of 1.52 and a heterozygous SNP ratio of 2.22%. The total number of genome-wide insertions and deletions (InDels) was 1,902,722±23,247. Gene Ontology (GO) functional annotation revealed a consistent variation pattern of core genes among the three populations. Phylogenetic tree, Admixture and principal component analysis (PCA) confirmed that the three populations belonged to a single evolutionary clade and shared a genetic origin from two ancestral populations (the lowest cross-validation error at K=2), while significant genetic differentiation was observed among populations: XW and DN populations had similar genetic backgrounds and closer genetic relationships, both biased towards the blue ancestral component, whereas XF population was inclined to the red ancestral component, with the DN population showing the highest degree of genetic admixture. Individuals within the XF population had more distant genetic relationships and the longest linkage disequilibrium (LD) decay distance, which was speculated to be associated with its small population size and low recombination rate; in contrast, the XW population had the shortest LD decay distance, corresponding to the characteristics of large population size and high recombination rate. Analysis of population genetic diversity indicated that XW and DN populations were classified as the high-diversity group (with more than 440,000 polymorphic markers, expected heterozygosity >0.31 and polymorphism information content (PIC) ≈0.25), while the XF population was the low-diversity group (with 342,646 polymorphic markers, expected heterozygosity of 0.2608 and PIC of 0.2073). Only the minor allele frequency (MAF) of the XF population (0.2829) was slightly higher than that of the other two populations. This study systematically elucidated the characteristics of genetic differentiation and diversity differences of M. armatus in the Dongjiang and Ganjiang River Sources, providing a genome-level scientific basis for the conservation of genetic resources, development of molecular markers and analysis of environmental adaptive mechanisms of this species.

Abstract: Artificial Intelligence (AI) is increasingly central to modern system engineering and service operations, enabling real-time decision-support in cyber-physical and data-intensive environments. This study develops an Extended Deep Neural Network–Logistic Regression (EDNN–LR) hybrid framework as a scalable AI solution for predictive optimisation within Industry 4.0 decision systems. The model integrates the nonlinear learning capability of deep neural networks with the interpretability and convergence stability of logistic regression, thereby enhancing transparency, robustness, and computational efficiency in engineering applications characterised by uncertainty and behavioural variability. The proposed framework is validated using a publicly available financial–cyber dataset comprising over 4.44 million records from CoinMarketCap (2013–2025), representing a dynamic cyber-physical decision environment analogous to complex industrial ecosystems. Implemented in MATLAB R2024a and TensorFlow 2.17, the model achieves rapid convergence by epoch 142 and 98 % classification accuracy (AUC = 0.846, MSE = 0.79, recall = 90.6 %) on selected high-liquidity assets. These results confirm the framework’s ability to model nonlinear dependencies and adapt to stochastic disturbances typical of service-oriented and engineering-operation contexts. Beyond predictive precision, the EDNN–LR framework provides explainable probabilistic outputs that can be directly incorporated into decision variables such as resource allocation, demand forecasting, and dynamic scheduling under real-time constraints. Its hybrid design reduces computational cost, enhances interpretability, and enables cross-domain adaptability—from financial risk management to logistics, supply-chain coordination, and energy-system optimisation. By bridging deep learning, system engineering, and behavioural decision analytics, this study contributes a generalised AI-driven architecture for intelligent and transparent decision-support across Industry 4.0 service and production ecosystems.

Abstract: Autumn-winter forage scarcity limits subtropical livestock systems. This study aimed to: (1) develop a segregating F₁ population derived from parents contrasting in autumn-winter biomass yield (WBY) in tetraploid Paspalum notatum; (2) estimate phenotypic and genetic variability for WBY across environments; (3) determine the relationship between WBY and spring-summer biomass yield (SBY); and (4) assess the feasibility of UAV-derived vegetation indices as non-destructive estimators of dry autumn-winter biomass yield (WBY) for future breeding. A population of 182 tetraploid F1 hybrids was evaluated at two sites in Corrientes Province, Argentina (2022-2024). WBY exhibited wide genotypic variability across locations and years (p < 0.001), with significant effects of genotype, location, and genotype × location interaction. Broad-sense heritability (H²) ranged from 0.41 to 0.64, reflecting sensitivity to thermal and moisture conditions of each environment. WBY showed a positive, moderate association with SBY (R² = 0.20 - 0.26), indicating that selection for cool-season yield does not compromise summer productivity. Among the indices evaluated, the Normalized Difference Red Edge Index (NDRE) was the most robust predictor of WBY (R² up to 0.67), though predictive accuracy varied with environmental conditions. Overall, the results demonstrate substantial and exploitable genetic variation for cool-season forage yield in P. notatum.