Abstract: This review summarizes mechanisms regulating mRNA translation under cellular stress and highlights design strategies to improve translation efficiency and stability in the gene therapy of human diseases. mRNA-based therapeutics are emerging as a versatile gene therapy platform enabling transient and controllable expression of therapeutic for the treatment of cancer, genetic disorders, and inflammatory diseases. The efficacy of mRNA-based gene therapy is strongly influenced by sequence design, chemical modifications, and structural features. Evidence shows that rational mRNA engineering can significantly enhance translation efficiency even under stress conditions that impair canonical protein synthesis, as observed in many pathological states. Cellular stress activates regulatory pathways that suppress global translation; however, optimized mRNA constructs can partially bypass these inhibitory mechanisms, enabling sustained protein expression. By improving mRNA stability and resistance to stress-responsive translational control, robust therapeutic protein production can be achieved even in challenging cellular environments. These advances position mRNA engineering as a promising component of next-generation gene therapy, offering new opportunities for effective treatments of human diseases.

Abstract: Mechanical locks were not quickly supplanted by electric locks. They are still being researched and improved, along with advanced electronic methods of attack. Reading pin lengths by detecting their natural frequencies (lock decoding) to forge a copy of the legitimate key can be done quickly using ultrasonic detectors, active or passive. Hence, advanced methods of defence must be further researched. One method is to make the lock’s pins out of functionally graded materials (FGM). A pin’s natural frequency (in the range 100 kHz-1 MHz) and hence its ultrasonic pulse transit/reflection time can be correlated to its length if it is made of a homogeneous material. The idea is to design pins made of functionally graded alloys, to achieve equal natural frequencies, but also desired positions of standing wave nodes regardless of the pin’s length. Mathematical models of pins vibrations must be devised first to enable calculations of FGM alloys composition. Two simple and fast mathematical models are first derived from finite-element model (FEM) of a pin. These models are used in an optimization procedure based on the Nelder-Mead simplex method to calculate optimal profiles of Young’s modulus and density along the pin’s longitudinal axis. A successful optimization procedure for 10 key pin lengths is performed, to make a pin-tumbler lock resistant to ultrasonic attacks.

Abstract: International green hydrogen regulations, standards, and guidelines are reviewed to assess their applicability to the Jordanian context. The analysis covers European Union directives, ISO and IEC standards, and best practices drawn from Germany, Japan, and Saudi Arabia. A gap analysis is conducted against Jordan's Draft National Hydrogen Strategy (2023) and the World Bank's 2025 policy note. Five regulatory gaps are identified: the absence of a dedicated hydrogen code, missing certification and quality standards, incomplete safety and technical codes, undefined infrastructure regulations, and weak policy integration. A two-phase adaptation roadmap is proposed, covering 2025 to 2030 and 2030 to 2050. Legislative reform, institutional capacity building, and regional cooperation are recommended to position Jordan as a green hydrogen production and export hub.

Abstract: To enhance immersion in virtual reality (VR) environments and improve the fidelity of virtual tactile interaction, this study presents a perceptually grounded haptic-rendering framework for fine surface-texture simulation. The framework is centred on a Perceptual Haptic Spectrum Model (PHSM), which maps virtual surface attributes (e.g., hardness, elasticity, roughness, and friction) to multi-band tactile targets defined in perceptual frequency space. The Just Noticeable Difference (JND) principle from psychophysics inspired parameterisation strategy is introduced to keep generated tactile cues within perceptually meaningful intervals. To account for anatomical heterogeneity across the fingertip, region-specific response functions are defined for the fingertip centre, finger pad, and lateral edge. The system further integrates flexible strain sensors for contact-state detection, rule-based channel allocation for multimodal feedback scheduling, and a short-horizon predictive feedforward module for anticipatory actuation. A dual-actuation prototype is described in which a glove-based primary actuation layer provides macroscopic force support and a finger-sleeve secondary actuation layer provides local texture cues. A representative virtual-fabric exploration scenario is reported to illustrate how the proposed framework handles concentrated, distributed, and slip contact states. The present manuscript therefore reports a prototype framework and proof-of-concept system operation rather than a completed large-scale psychophysical study. The results demonstrate internal consistency between perceptual modelling, sensing, and actuation, and suggest that the proposed approach is a promising basis for future quantitative evaluation of fine VR haptic interaction.

Abstract: Grey cast iron brake discs remain standard in automotive braking systems due to their favorable thermal conductivity and mechanical strength. However, increasingly stringent environmental regulations, including Euro 7, necessitate enhanced surface durability to reduce particulate emissions and mitigate corrosion‑related degradation. In this context, Laser Metal Deposition (LMD) offers a promising route to engineer wear‑resistant coating systems with tailored microstructures. This study investigates phase formation and microstructural evolution in a 316L/430L‑WC multilayer coating deposited on grey cast iron (GJL) brake discs and subjected to brake‑shock testing to replicate thermomechanical load cycles representative of real braking conditions. X‑ray diffraction (XRD) performed on the interlayer region between the 316L and 430L‑WC layers revealed clear evidence of σ‑phase formation, indicating intermetallic transformations facilitated by thermal cycling. Microstructural characterization using scanning electron microscopy (SEM) and energy‑dispersive spectroscopy (EDS) identified localized enrichment of Cr‑ and Fe‑rich regions that support the XRD‑based interpretation of σ‑phase development. These results provide insights into phase transformations and elemental diffusion in LMD‑fabricated brake‑disc coatings. The findings advance the understanding of thermally induced transformations in multilayer steel systems and support the optimization of LMD coatings for high‑temperature and wear‑intensive applications through advanced analytical evaluation.

Abstract: The Classical Propositional Calculus CPC (zero-order logic, classical propositional logic), is the most fundamental two-valued logical system. Next, the Intuitionistic Propositional Calculus IPC differs from the CPC among others, that in IPC some laws of CPC are invalid (among others, the law of excluded middle and the law of double strong negation). Another difference is such that in IPC the principle of indirect proof (proof by contradiction) is rejected. In this paper, inconsistency (in the absolute sense i.e. Post’s sense) of the Classical Propositional Calculus is proved. From the inconsistency of CPC it follows immediately that the Intuitionistic Propositional Calculus is inconsistent in the absolute sense (Post’s sense), too.

Abstract: Pathogenic missense variants cause disease through two mechanistically distinct routes: structural destabilization leading to protein misfolding and degradation, or functional disruption of a stably folded protein. Despite the clinical importance of this distinction — pharmacological chaperones rescue misfolded proteins, while functionally defective proteins require gene therapy or enzyme replacement — no existing framework predicts which mechanism underlies a given variant. All current tools, from SIFT and PolyPhen-2 to AlphaMissense, predict pathogenicity but not mechanism.Here we show that the elemental composition of a protein’s amino acids, classified by dominant biophysical property, predicts misfolding mechanism from sequence alone. We map each amino acid to one of five elemental classes: charged residues (Agni), flexible residues (Vayu), hydrophobic residues (Prithivi), polar residues (Jal), and aromatic residues (Akasha). This classification is inspired by and named after the Panchamahabhuta system of classical Indian natural philosophy, whose five-element grouping aligns with the five dominant thermodynamic forces governing protein stability. The elemental composition profile of a domain — its elemental constitution (Prakriti) — explains 36.4% of variance in domain biological function across 14 functional classes (η² = 0.364, F = 18, p = 9.99×10⁻³³, n = 420 domains) without any machine learning.Applying this framework to 64,387 VAMP-seq stability measurements across 11 disease proteins, we derive a substitution risk hierarchy: mutations introducing charged residues into hydrophobic cores (Prithivi→Agni) cause misfolding in 57.5% of cases versus 21.1% for Agni→Prithivi. Secondary structure context reveals a 3.7-fold gradient — hydrophobic and aromatic residues in β-strands misfold at 51–52% when mutated; charged residues in turns at only 14%. Proteins whose domain composition conflicts with biological function — compositional discord (Vishamavet) — carry pathogenic variants at 91.8% versus 59.9% in concordant proteins (OR = 7.8, p < 10⁻¹⁵).Key finding: structured-position hydrophobic/aromatic residues misfold at 3.7× the rate of loop charged residues — mechanistic information absent from all conservation-based predictors.We introduce BhutaFormer, a transformer architecture that encodes sequence context as Bhuta tokens and learns elemental interaction grammar via multi-head self-attention, achieving AUROC = 0.77 overall and 0.76 on within-class (Tanmatra) variants — a 13.5 percentage point improvement over Random Forest. On ProteinGym DMS abundance assays, BhutaFormer achieves Spearman ρ = 0.29 for training-distribution proteins, exceeding the Site-Independent baseline (ρ = 0.175) without evolutionary alignment, structural data, or large language model pre-training.

Abstract: Simulations of intermediate-mass black holes (IMBHs) in dwarf galaxies within 10 Mpc that host bright nuclear star clusters (NSCs), prime candidates for IMBH formation, using the High Angular Resolution Monolithic Optical and Near-infrared Integral (HARMONI) field spectrograph on the Extremely Large Telescope probes black hole formation in the early Universe. Our approach combines observed surface brightness profiles from the Hubble Space Telescope (HST), synthetic stellar population spectra, and Jeans Anisotropic Modeling (JAM) for stellar dynamics. Mock HARMONI observations were generated with the HSIM simulator and analyzed in a Bayesian framework to infer IMBH masses down to 0.5% of the NSC mass. In this work, we extend these simulations by constructing improved stellar-mass models using SimCADO to simulate imaging with the Multi-AO Imaging Camera for Deep Observations (MICADO). The MICADO data are jointly analyzed with HARMONI kinematics via JAM to reassess IMBH masses and uncertainties. This combined framework enables us to examine how variations in the NSC inner surface-brightness slope influence IMBH mass estimates, providing tighter constraints on low-mass black holes and advancing models for IMBH detection in NSCs.

Abstract: Background Physical activity is a recommended first‑line treatment for the management of chronic low back pain, yet adherence to structured exercise often remains poor due to pain, fear, fatigue, and contextual barriers. Snacktivity™, which promotes brief, frequent bouts of movement embedded in daily routines, has emerged as a potentially feasible alternative. However, it remains unclear how, why, and for whom Snacktivity supports engagement in physical activity for people living with chronic low back pain. Objective To develop and refine programme theories explaining how Snacktivity‑type interventions support physical activity engagement and related outcomes in adults with chronic low back pain. Methods A realist review was conducted following RAMESES standards. Initial programme theories were developed and iteratively refined through synthesis of quantitative, qualitative, and mixed‑methods evidence from Snacktivity and related sedentary‑reduction interventions in low back pain and other adult populations in order to test developed programme theories. Evidence was analysed to identify context–mechanism–outcome configurations. Results A total of four studies met the inclusion criteria for Snacktivity-type studies related to low back pain and were included to develop and test the initial programme theories. This was supported by 38 studies that contributed evidence to programme theory refinement. Five refined programme theories were supported. Snacktivity appears to enable engagement by lowering perceived burden and threat rather than eliminating fear, generating mastery experiences that enhance self‑efficacy, and reducing symptom interference through brief, distributed activity. Education and coaching components supported meaning‑making by reframing movement as legitimate and achievable, while environmental cues and routines promoted habit formation. Psychosocial outcomes (confidence, mood, vitality) and habit formation improved more consistently than performance‑based outcomes, and engagement was sustained even when pain or fatigue persisted. ConclusionsSnacktivity functions as a participation‑enabling intervention rather than a traditional exercise prescription. Its effectiveness in chronic low back pain is explained by psychosocial and contextual mechanisms that support psychological safety, mastery, and habit formation. These findings support a shift from dose‑response exercise models toward interventions that prioritise feasibility, meaning, and sustained participation in daily life.

Abstract: Aligned large language models (LLMs) often react very differently to the same jailbreak prompt: one model may refuse, another may partially comply, and a third may produce unsafe content. This variability suggests that jailbreak vulnerability is not determined by a single factor. Instead, it likely emerges from the interaction of backbone architecture, tokenization, prompt-template structure, post-training alignment, and internal representation-level mechanisms governing refusal and compliance. This concept paper argues that cross-model jailbreak variability should be studied as a mechanistic problem rather than only a benchmarking problem. Drawing on prior work on safety-training failure modes, optimization-based jailbreaks, shallow safety alignment, prompt-template effects, refusal directions, attention manipulation, and token-position sensitivity, this paper proposes a unified research agenda for explaining why aligned LLMs exhibit different internal responses to the same jailbreak prompt. The central thesis is that architecture matters, but many practically important differences arise from post training alignment and from how refusal and helpfulness are represented and routed internally.The paper formulates testable hypotheses, proposes an experimental framework spanning models such as Llama-2-Chat, Vicuna, and Mistral-Instruct, and outlines a methodology combining attack evaluation with attention analysis, hidden-state analysis, refusal-direction probing, tokenizer analysis, and causal interventions. The goal is to move from measuring jailbreak success toward understanding the internal mechanisms that produce it.

Abstract: Metagenomic analysis of Antarctic soils provides a key perspective on the microbial diversity of one of the most extreme ecosystems on Earth, where microorganisms dominate ecological processes, although their diversity and distribution remain less understood than those of macroscopic eukaryotes. This study aimed to identify bacteria present in Antarctic soils using a metagenomic approach. Soil samples were obtained from the sample bank of the Escuela Superior Politécnica Agropecuaria de Manabí and were collected during an expedition conducted in 2014 on four islands: Greenwich, Dee, Barrientos, and Torre. The analysis included DNA extraction, amplification of the 16S rRNA gene by polymerase chain reaction (PCR) using specific primers, and sequencing on the Illumina MiSeq platform (Macrogen©). These techniques enabled reliable identification of the microbial populations present. The results revealed the predominance of several bacterial phyla across all four islands, with Actinomycetales (36.60%), Proteobacteria (20.03%), Firmicutes (17.53%), and Bacteroidetes (6.91%) being the most abundant. In addition, Antarctic soil metagenomes indicated the potential access to novel genes encoding enzymes with possible biotechnological and industrial applications. Overall, these findings highlight Antarctic ecosystems as reservoirs of valuable genetic resources and underscore their importance for the long-term development of biotechnological applications, without extrapolating conclusions beyond the results obtained.

Abstract: Spent mushroom substrate (SMS), the main by-product of mushroom production, is rich in valuable compounds that could be recovered by ultrasound-assisted extraction (UAE) and exploited as fat-mimetic functional ingredients in food formulations. In this study, low-fat cookies prototypes were developed by incorporating a dietary fiber extract obtained from SMS using UAE. The extraction process was optimized following a Box–Behnken experimental design, identifying optimal conditions at a specific energy input of 200 J/mL, a particle size of 2 mm, and a solute-to-solvent ratio of 1:27, yielding a dietary fiber recovery of 30.82%. The optimized SMS extract exhibited high oil-holding capacity (1.39 g/g), emulsion stability (80%), and foaming capacity (83.55%). Four cookie formulations were evaluated, among which G1 (50% fat replacement) showed the best balance between consumer acceptability and an improved nutritional profile, characterized by higher protein (8.4 g/100 g), total dietary fiber (7.10 g/100 g), and mineral contents. Notably, G1 cookies displayed a significant reduction in predicted glycemic index, decreasing from 83.84 in the control to 69.65. Overall, these results demonstrate that optimized SMS-derived dietary fiber is an effective functional ingredient for the development of low-fat, high-fiber, and reduced-glycemic cookies, contributing to the valorization of agro-industrial by-products within a circular economy framework.

Abstract: Depression, anxiety, and stress-related disorders continue to rise globally, with South Africa’s burden intensified by structural inequalities and a 91% mental health treatment gap. Accessible complementary interventions are urgently needed. This exploratory mixed-methods pilot study examined the feasibility, acceptability, and preliminary effectiveness of astronomy-based mental health support grounded in Attention Restoration Theory and awe research. Two retreats combined guided naked-eye and telescope-based stargazing with nature immersion: a proof-of-concept peer camp (n=19, Glencairn) and a family-focused retreat (n=27, Sutherland). Quantitative outcomes using the Depression Anxiety Stress Scale (DASS-21) were collected in the Glencairn cohort, alongside qualitative data from Most Significant Change focus groups and asynchronous text-based interviews. Significant reductions in depression, anxiety, and stress were observed in the Glencairn cohort, while qualitative findings across both settings indicated experiences of calm, perspective shifts, and relational connection. However, increased environmental novelty and family dynamics introduced competing cognitive demands in the Sutherland setting. These findings provide preliminary evidence that astronomy-based interventions may support short-term psychological well-being, while highlighting key design considerations, including cognitive spaciousness, contextual onboarding, and relational facilitation, for implementation in diverse African contexts.

Abstract: Today, the aviation industry is transitioning from fossil fuel to renewable energy. Re-newable energy systems have advantages, such as cleanliness and reduced emissions, but also face limitations in battery energy density and aerodynamic performance dur-ing operation. Therefore, electric ducted propulsion fans (eDPFs) are a promising so-lution that uses duct components to enhance aerodynamic efficiency and operational safety. This study utilizes average Navier-Stokes analysis, incorporating Reynolds numbers and a k-ω SST turbulence model, to examine eDPF configurations both with and without a secondary air intake channel, concentrating on internal flow dynamics and aerodynamic efficiency. The air intake channel, which is located close to the tip of the rotor blade, helps the eDPF move more mass and create more thrust. Several dif-ferent configurations of the secondary air intake channel were tested by varying the intake channel position, curvature, and size of the inlet and outlet ports under static conditions at 6000 rpm. The best design improved thrust by an additional 2.2% com-pared to the baseline case without the auxiliary intake port

Abstract: Background/Objectives: Obesity and menopause are major determinants of skeletal deterioration; however, their combined effects on bone remodeling and associated cellular bioenergetics remain incompletely understood. This study aimed to determine whether obesity induces osteoporotic alterations under both estrogen-replete and estrogen-deficient conditions and to evaluate the therapeutic potential of dental tissue–derived mesenchymal stem cells (D-MSCs). Methods: Female mice were subjected to ovariectomy (OVX) and/or high-fat diet (HFD) feeding for 16 weeks to establish obesity-associated osteoporosis models. D-MSCs were administered intraperitoneally at defined intervals. Body weight and serum leptin levels were measured to assess metabolic status. Femoral tissues were analyzed by quantitative real-time PCR for estrogen receptors (ERα, ERβ), inflammatory markers (Il-1β, Tnf-α), mitochondrial regulators (Pgc1α, Pgc1β), and the OPG/RANKL ratio. Histological analysis was performed to evaluate bone marrow adiposity. Results: HFD significantly increased body weight and serum leptin levels in both intact and OVX mice. Obesity was associated with reduced expression of ERα and ERβ, decreased Pgc1α levels, and a lower OPG/RANKL ratio, accompanied by increased Il-1β, Tnf-α, and Pgc1β expression. D-MSC administration attenuated body weight gain and reduced leptin levels, particularly in OVX mice. In femoral tissue, D-MSC treatment restored estrogen receptor expression, increased Pgc1α, decreased Pgc1β, and normalized the OPG/RANKL ratio. In addition, inflammatory marker expression and bone marrow adiposity were reduced following MSC administration. Conclusions: Obesity induces bone remodeling dysregulation under both intact and estrogen-deficient conditions, characterized by altered estrogen signaling, inflammatory activation, and mitochondrial imbalance. D-MSC administration was associated with partial restoration of these alterations, suggesting a potential role in modulating metabolic and skeletal homeostasis in obesity-associated bone loss.

Abstract: Sustainable urban transportation is fundamentally linked to public health outcomes, specifically the mitigation of fatal traffic risks under environmental stress. While stressors like adverse weather affect entire cities, traditional road safety models often assume uniform risk, thereby masking the spatial inequalities inherent in the urban fabric. This study addresses this gap by investigating the geographically heterogeneous impact of environmental stressors—including rainfall, surface moisture, and lighting conditions—on the conditional probability of fatal crash outcomes in Melbourne, Australia. Analyzing 43,075 severe crashes through a multi-stage geospatial framework (Getis-Ord Gi* and Geographically Weighted Logistic Regression), this research diagnoses how varying urban development patterns mediate the lethality of these stressors. The findings unmask a critical “threshold-crossing” effect for wet surfaces, where risk transitions from protective to hazardous based on local infrastructure form and street geometry. Significant spatial inequalities are identified: high-density inner-urban cores and adjacent coastal corridors exhibit a heightened sensitivity to visibility failures and moisture, whereas newer industrial peripheries show stronger protective “risk compensation” effects. These results reveal a systemic mismatch between historical urban form and contemporary climate-driven public health risks. By identifying localized “lethality thresholds”, this study provides a robust evidence base for integrated planning and equitable resource allocation. It enables urban planners to move beyond generalized safety warnings toward targeted structural interventions, ensuring that sustainable transportation networks prioritize safety equity for all citizens regardless of their location within the urban environment.

Abstract: Triangular mesh is one of the most widely used representations for 3D surfaces. However, high-resolution mesh models often contain a large number of triangles, leading to significant burdens in storage, transmission, and real-time rendering. Mesh simplification aims to reduce model complexity while preserving geometric fidelity and structural features. Classical methods, such as quadric error metrics (QEM), rely solely on local geometric errors, making them difficult to distinguish between redundant regions and structurally important features, often resulting in feature loss and topological degradation. To address these limitations, this study proposes a structure-aware triangular mesh simplification framework based on graph neural networks (GNNs)-guided QEM. GNNs are employed as a structural importance estimator to predict geometric saliencies of mesh edges. The predicted importances are incorporated into the classical QEM edge collapse cost through a soft modulation mechanism. Furthermore, a geometry-saliency driven dynamic cost modulation strategy is designed, enabling the simplification process to prioritize critical features in early stages and gradually transition to global error minimization in later stages, without compromising the geometric optimality of QEM. In terms of model design, hybrid structural representation GNNs are constructed by integrating spectral geometry and a dual-branch architecture. Laplacian positional encoding is introduced to capture global topological information, while 1-hop and 2-hop message passing branches enable multi-scale representation of complex geometric structures. In addition, a staged inference strategy is adopted to dynamically update graph structural features during simplification, effectively mitigating topological drift. Experimental results on the TOSCA dataset demonstrate that the proposed method achieves stable performance across various simplification ratios. It consistently outperforms FQMS and remains comparable to classical QEM in terms of geometric error (P_CD) and normal consistency (P_NE). For structural preservation (P_LE), the method shows clear advantages, with win-rates generally exceeding 70%. Moreover, it significantly improves the preservation of local geometric details at low to moderate simplification ratios. In summary, the proposed method effectively enhances local structural preservation while maintaining global geometric topology, providing an interpretable and practical solution for integrating learning-based structural awareness with classical geometric optimization in mesh simplification.

Abstract: This systematic review provides a comprehensive and quantitatively grounded synthesis of machine learning (ML) approaches for electrocardiography (ECG)-based detection of dysglycemia, with a specific focus on translational readiness for clinical screening. A structured literature search across PubMed, Scopus, Web of Science, and IEEE Xplore (February 2025) identified 183 records, of which 17 studies met predefined inclusion criteria following PRISMA-guided screening. The included studies demonstrate substantial heterogeneity in dataset size (ranging from &lt;50 to &gt;25,000 subjects), ECG acquisition modalities (single-lead, 12-lead, wearable), feature representations (raw signals, heart rate variability, engineered features), and ML strategies (classical algorithms, deep learning, and multimodal models). Reported model performance is generally high, with accuracy values frequently exceeding 0.85 and area under the curve (AUC) ranging from 0.78 to 0.99. Smaller experimental studies often report inflated performance (up to 96–99% accuracy), whereas large-scale population-based investigations demonstrate more moderate but clinically plausible results (AUC ≈ 0.80–0.85). External validation, a key requirement for clinical applicability, was performed in only a limited subset of studies (approximately 12%). From a physiological perspective, ML models exploit ECG alterations associated with dysglycemia, including reduced heart rate variability, QT interval prolongation, and changes in ventricular depolarization and repolarization dynamics. However, the relationship between metabolic dysfunction and ECG signals remains indirect. A key finding of this review is the mismatch between reported predictive performance and model maturity. The majority of studies (≈65–70%) are classified as early-stage (Level 1–2 or 2–3), relying on small, single-center datasets and internal validation. Only a minority of studies achieve near-translational maturity (Level 4), characterized by large-scale datasets and external validation. ECG-based dysglycemia detection represents a promising non-invasive and scalable screening paradigm. However, its clinical translation is constrained by the lack of standardized ECG acquisition protocols, limited dataset diversity, insufficient external validation, and fragmented methodological frameworks. Future research should prioritize large multi-center datasets, standardized feature extraction pipelines, hybrid interpretable models, and prospective validation to enable robust, generalizable, and clinically deployable screening systems.

Abstract: One-dimensional fibrous scaffolds with tunable bioactivity offer promise for bone tissue regeneration, yet optimal calcium phosphate phases for enhancing osteogenic perfor-mance remain underexplored. This study aimed to evaluate the impact of monetite, brushite, and cerium-doped phosphates deposition on electrospun nylon nanofibres func-tionalized via matrix-assisted pulsed laser evaporation (MAPLE). Six nylon fibre composi-tions were synthesized, coated with three calcium phosphate phases, calcined at varying temperatures (500–800 °C) before laser deposition. Physicochemical properties were as-sessed using energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and fibre diameter measurements. Biocompatibility assays following MC3T3 pre-osteoblast seeding and incubation evaluated biological performance. EDX confirmed homogeneous phase deposition; SEM showed phase- and temperature-dependent mor-phology, with monetite yielding uniform granular structures and cerium-doped phos-phate at 800 °C forming dense aggregates. Brushite-coated fibres exhibited superior preos-teoblast metabolic activity versus monetite variants, indicating phase-specific stimulation of bone cells growth. These phosphate-functionalized nylon fibres retain structural integ-rity, hierarchical porosity, and enhanced bioactivity, providing a versatile electrospin-ning-MAPLE platform for customizable bone grafts with clinical potential.

Abstract: Reliable photovoltaic (PV) power forecasting based on deep learning typically requires large historical datasets to capture the high temporal and spatial variability of solar irradiance. However, in many real-world applications, data availability is limited to short observation periods, hindering the effective training of deep learning models. This paper investigates how sky image data augmentation techniques can improve the generalization capability of Convolutional Neural Networks (CNNs) trained under data scarcity. Three augmentation-based oversampling methods—SMOTE, Mixup-kNN, and Mixup-RP—are evaluated, along with two novel hybrid strategies that combine them in-parallel and in-series configurations. The proposed framework is validated on two distinct PV power nowcasting case studies, in which the original sky image training datasets span less than one month. Experimental results show average performance improvements of up to 50% on external validation data when training the CNN on the augmented datasets compared to the original base datasets, demonstrating that accurate PV power nowcasting is feasible even under data-scarce conditions typical of newly installed PV systems, and highlighting the potential of data-efficient learning approaches for renewable energy applications.