Abstract: This study evaluated the use of cholesterol-loaded cyclodextrin (CLC) to mitigate cryo-damage, aiming to enhance sperm cryo-survivability and reduce cryo-capacitation. Different CLC levels were tested, and post-thaw motility was higher (p < 0.05) at 1.5-2 mg/mL CLC added to extended semen containing 120 million sperm/mL. Subsequent investigations compared untreated and 2 mg/mL CLC-treated bovine sperm (n=5) and assessed membrane and acrosome integrity, mitochondrial potential, and capacitation status using flow cytometry. Moreover, this study is the first to quantify the post-thaw binding ability of CLC-treated bovine sperm and evaluate their capacitation window using an IVF approach. CLC-treated sperm showed no differences in flow cytometric results. CLC treatment significantly (p < 0.0001) increased the number of viable bound sperm cells to oviduct cells (1.65 × 10⁶ µm²) from 118 ± 15 to 267 ± 15 after three hours, and from 70 ± 15 to 127 ± 15 after seven hours (p < 0.05) of coincubation. When equal numbers of motile sperm were incubated with oocytes, CLC-treated sperm showed penetration and pronuclei formation rates comparable to the control, indicating a similar capacitation window. In conclusion, CLC addition significantly improved post-thaw sperm motility and binding ability, while maintaining sperm capacitation and fertilizing ability.

Abstract: The accelerating integration of artificial intelligence (AI) into cybersecurity operations has introduced new challenges and opportunities for modernizing incident response (IR) practices. This study explores how cybersecurity practitioners perceive the adoption of intelligent automation and the readiness of legacy frameworks to address AI-driven threats. A structured, two-part quantitative survey was conducted among 194 U.S.-based professionals, capturing perceptions on operational effectiveness, trust in autonomous systems, and the adequacy of frameworks such as NIST and SANS. Using binary response formats and psychometric validation items, the study quantified views on AI’s role in reducing mean time to detect and respond, willingness to delegate actions to autonomous agents, and the perceived obsolescence of static playbooks. Findings indicate broad support for the modernization of incident response frameworks to better align with emerging AI capabilities and evolving operational demands. The results reveal a clear demand for modular, adaptive frameworks that integrate AI-specific risk models and decision auditability. These insights provide empirical grounding for the design of next-generation IR models and contribute to the strategic discourse on aligning automation capabilities with ethical, scalable, and operationally effective cybersecurity response.

Abstract: The purpose of the article is represented by the automation of the process of drawing up the records of agricultural holdings through the necessary registers for the documentation and planning of plant protection works and the prevention of pollution with nitrates from agricultural sources. Through Robotic Process Automation (RPA) in agriculture, we can manage a wide range of repetitive or routine tasks. Using RPA to automate various agricultural operations allows companies and farmers to reduce unnecessary expenses while increasing production and profits. RPA is about simplifying complex agricultural processes that help save time and improve overall operational efficiency towards an important level of process planning and control that will enable farms to maximize their profitability with minimal losses. We carried out the Extraction Transformation and Loading (ETL) of the data related to the declared parcels from the files downloaded from the holdings’ account, later by means of the algorithms the summation of the areas related to the crops and categories of use found in the single payment request was realized. The sheets containing the data related to the animals declared by the holding and the identification data of the holding were identified, and finally the sheets intended for storing the extracted data and the calculated values were created. This dual implementation—desktop-based (Python) and web-based (R Shiny)—demonstrates the adaptability of RPA workflows across platforms, reducing document processing time by approximately 80% and supporting digital inclusivity for small farms.

Abstract: Reaction time (RT) is a key indicator of cognitive and motor processing speed, and its age-related decline has important implications for everyday activities such as driving. However, conventional Psychomotor Vigilance Tests (PVTs) assess hand responses and do not capture lower-limb reaction characteristics relevant to pedal operations. This study used a foot-response version of the PVT (Foot PVT) to compare RTs between younger and older adults and to examine the influence of height, sleep factors, and physical activity level (PAL). Twenty younger adults (24 ± 3 years) and twenty-four older adults (73 ± 5 years) performed a 10-minute Foot PVT between 11:00 and 14:00. Participants responded to visual stimuli by moving the right foot laterally from a central pedal to the left or right pedals. RT mean, RT median, RT SD, skewness, and kurtosis were calculated, and correlation and multiple regression analyses were conducted using height, five OSA Sleep Inventory factors, and PAL as predictors. RT mean was significantly slower in older adults (818 ± 105 ms) than in younger adults (700 ± 73 ms), indicating an age-related delay of approximately 120 ms. Older adults showed lower skewness and kurtosis, suggesting more homogeneous responses and a cautious response strategy. In younger adults, height correlated negatively with RT (r = −0.593), and multiple regression identified height as the only significant predictor (adjusted R² = 0.316). No significant predictors were found in older adults. In the combined sample, height and age jointly explained 37.2% of RT variance. These findings indicate that Foot PVT performance reflects both biomechanical characteristics and age-related declines in reaction speed. Height strongly influences RT in younger adults, whereas RT in older adults appears to be shaped by multifactorial age-related changes. The Foot PVT provides a practical tool for assessing lower-limb reaction capabilities relevant to driving and aging.

Abstract: It has been 125 years since Dr. Alzheimer of Germany first announced Alzheimer's disease to the world in 1900, yet the causative agent of this disease remains unidentified to this day. Through the efforts of many researchers, deposits of amyloid beta protein outside nerve cells and tau protein within nerve cells have been identified as the pathological hallmarks. These proteins have been suggested as the cause of Alzheimer's disease. However, even when amyloid is reduced through amyloid therapy, no recovery of cognitive function is observed. Currently, attention is focused on whether treatment targeting tau protein might lead to cognitive recovery. Yet, the reason why this tau protein accumulates within nerve cells remains unknown. We previously published in the 2010 issue of PLOS ONE that homocysteic acid (HCA), generated by the super-oxidation of the amino acid methionine via OH radicals, is a causative agent that produces amyloid and tau proteins and further induces cognitive decline. This time, we comprehensively reviewed how this HCA is related to various lifestyle-related diseases associated with aging, and is a factor determining lifespan beyond Alzheimer's disease, cancer, and aging itself. By reducing this HCA, we will explore the potential to extend healthy lifespan and curb the escalating costs of healthcare.

Abstract: This study examines the determinants of goodwill impairment recognition under IFRS 3 in the context of Moroccan listed firms. Using an unbalanced panel of 62 companies observed from 2006 to 2024, we employ a three-stage empirical strategy that integrates a Probit model to estimate the likelihood of impairment, a Tobit model to assess the magnitude of the loss, and a Heckman two-step procedure to correct for potential self-selection. The results show that goodwill impairment reflects key economic and financial fundamentals, including revenue growth, book-to-market ratios, and operating performance. However, both real and accrual-based earnings management significantly influence the probability and intensity of impairment, particularly through abnormal cash flows and income-smoothing behavior. Discretionary accruals become significant only after correcting for selection bias, indicating that they do not drive the recognition decision but contribute to determining the size of the impairment once it has been recorded. The findings are robust across multiple specifications and contribute to the broader literature on financial reporting quality under IAS/IFRS, while enriching empirical evidence on managerial discretion and earnings management in emerging-market environments.

Abstract: We investigate the impact of government economic policy uncertainty (GEPU) on bank risk, distinguishing short- and long-term effects. We argue that heightened GEPU increases bank risk in the short run by raising borrowers’ default probabilities under adverse economic conditions, while reducing risk in the long run by discouraging banks from extending risky loans due to the higher option value of waiting under uncertainty. Using bank-level data from 22 countries over 1998–2017, we find that elevated GEPU raises bank risk contemporaneously but lowers it with a lag of two to four years. These results are robust to endogeneity concerns, alternative measures of bank risk and GEPU, variations in sample composition, and different estimation techniques. Our findings highlight the dual role of policy uncertainty in shaping bank risk-taking behavior and have implications for regulatory design and macroprudential policy.

Abstract: The paper presents a solution to the limited accuracy of automated diagnostics for retinal pathologies, such as diabetic retinopathy and age-related macular degeneration. These challenges arise from difficulties in modeling comorbidities, a reliance on paired multimodal data, and issues related to class imbalance. The proposed solution features a novel hierarchical deep learning architecture designed for multi-label classification of optical coherence tomography (OCT) data. This architecture facilitates cross-modal knowledge transfer from fundus images without the need for paired fundus images. It was accomplished through the modular specialization of the architecture and the application of contrast equalization, which creates a latent “bridge” between the OCT and fundus data. The results demonstrate that the proposed approach achieves high accuracy (macro-F1 score of 0.989) and good calibration (Expected Calibration Error of 2.1%) in classification and staging tasks. Notably, it eliminates the need for fundus images for diabetic retinopathy staging in 96.1% of cases and surpasses traditional monolithic architectures on the macro-AUROC metric.

Abstract: Rutin, a naturally occurring flavonoid, is widely recognized for its potent antioxidant, anti-inflammatory, and cardiovascular-protective properties. However, its therapeutic potential is significantly limited by poor aqueous solubility, low dissolution rate, and inadequate bioavailability. Commercially available rutin formulations, such as Nature Plus® 500 mg tablets, exhibit rapid disintegration but fail to achieve satisfactory dissolution, releasing less than 50% of the drug within one hour. This study aims to address these limitations through the development of novel rutin herbosomes—a phospholipid-based delivery system—and subsequent conversion into freeze-dried granules for improved stability, flowability, and dissolution performance. In an innovative approach, probiotic-derived excipients were incorporated to enhance gut absorption and provide synergistic nutraceutical benefits. Herbosomes were prepared using rutin and phosphatidylcholine in aprotic solvents, followed by freeze granulation with Eudragit S100 and maltodextrin. Comprehensive evaluation included vesicle characterization, flow properties, dissolution testing, and cytotoxicity assessment. Results demonstrated that herbosomal formulations achieved >90% drug release within 45 minutes, with excellent flow characteristics (Carr’s Index: 8–10; Hausner’s Ratio: 1.1–1.2) and non-toxic profiles. Probiotic enrichment further enhanced dissolution and stability. This study presents a promising, multifunctional strategy for enhancing the oral bioavailability of poorly soluble nutraceuticals.

Abstract: Fraser Syndrome (FS) is an extremely rare genetic disorder with a strong pattern of inheritability that follows the autosomal recessive fashion; the fundamental clinical features include congenital anomalies such as cryptophthalmos, syndactyly, as well as, renal damage. The FRAS1 gene is one of the principal genes implicated in FS. The role of genetic mutations in the development of FS has not been comprehensively elucidated for the present, and novel mutations are still being identified. Hence, the current article addresses two patients who were clinically diagnosed with Fraser Syndrome and included two unique mutations (c.7777C˃T; (p. Q2593X) and c.9821G˃C; (p. R3274P) that are found in the FRAS1 gene. Both patients had clinical features concerning Fraser Syndrome including renal abnormalities and cryptophthalmos, which addresses the severe phenotype associated with FRAS1 mutations. These two novel mutations discovered have expanded the genetic heterogeneity of Fraser Syndrome and have extended the mutational list of the FRAS1 gene. These outcomes might have a profound impact on the further approaches to the therapy of FS and methods of genetic counseling and diagnosis.

Abstract: Rett syndrome (RTT), an X-linked neurodevelopmental disorder predominantly arising from de novo MECP2 mutations, manifests with psychomotor regression, stereotypic hand movements, gait apraxia, and expressive aphasia, driven by dosage-sensitive epigenetic dysregulation via MeCP2's methyl-CpG-binding domain (MBD) and transcriptional repression domain (TRD). Isoform-specific expression (MeCP2-E1 neuronal predominance) and X-chromosome inactivation mosaicism underpin phenotypic variability, with missense (R133C, T158M) and nonsense (R168X, R255X) variants correlating to severity gradients. Multisystem pathophysiology encompasses brainstem-mediated respiratory dysrhythmias, QTc prolongation via ion channel perturbations, enteric hypomotility, osteopenic fractures, and mitochondrial bioenergetic deficits, exacerbated by glial-neuronal crosstalk and oxidative stress. Preclinical platforms, including Mecp2-null rodents, patient-derived iPSCs, and cerebral organoids, elucidate synaptic hyperexcitability, dendritic arborization deficits, and reversibility upon Mecp2 reactivation. Therapeutic modalities span supportive multidisciplinary interventions, FDA-approved trofinetide (IGF-1 analog modulating neurotrophic cascades), AAV-mediated gene replacement (NGN-401, TSHA-102 with miRARE autoregulation), ASOs for dosage normalization, and emerging PPAR-γ agonists targeting metabolic homeostasis. Prioritized research agendas emphasize validated biomarkers (BDNF/IGF-1 axes, miRNA signatures), combinatorial regimens, and equitable global access to mitigate caregiver burden and phenotypic heterogeneity.

Abstract: The morphology of solid surfaces encodes fundamental information about the physical mechanisms that govern their formation. Here, we reinterpret scanning electron microscopy (SEM) micrographs of oxide thin films as two-dimensional self-affine surfaces and analyze them using a multiscale statistical-physics framework that integrates spectral, multifractal, geometric, and topological descriptors. Fourier-based power spectral density (PSD) provides the spectral slope β and apparent Hurst exponent H, while multifractal scaling yields the information dimensions D_q, the singularity spectrum f(α), and its width Δα, which quantify scale hierarchy and intermittency. Lacunarity captures intermediate-scale heterogeneity, and Minkowski functionals—especially the Euler characteristic χ(θ)—probe connectivity and identify the onset of a percolation-like network structure. Two representative surfaces with contrasting morphologies are used as model systems: one exhibiting an anisotropic, porous, strongly multifractal structure with fragmented domains; the other showing a compact, nearly isotropic, and nearly monofractal organization. The porous regime displays steep PSD decay, broad multifractal spectra, and positive χ, consistent with a sub-percolated, diffusion-limited, Edwards–Wilkinson-like (EW-like) growth regime. Conversely, the compact regime exhibits gentler spectral slopes, narrower f(α), enhanced lacunarity at intermediate scales, and a χ(θ) zero-crossing indicative of a connectivity transition where a surface becomes a percolating network, consistent with a Kardar–Parisi–Zhang-like (KPZ-like) correlated growth regime. These results demonstrate that individual SEM micrographs encode quantitative fingerprints of nonequilibrium universality classes and topology-driven transitions from fragmented surfaces to connected networks, establishing SEM as a quantitative probe for testing theories of rough surfaces and kinetic growth in experimental thin-film systems.

Abstract: The BCS theory of superconductivity, which relies on the formation of Cooper pairs mediated by lattice phonons, has stood for decades as the cornerstone of our understanding of superconductivity in conventional metals. However, critical inspection reveals that several theoretical and experimental inconsistencies persist in this framework, especially when extended to high-temperature and unconventional superconductors. This paper rigorously analyzes these inconsistencies, with emphasis on the inadequacy of phonon-mediated interactions to overcome Coulomb repulsion, the questionable nature of the long-range coherence implied by the size of Cooper pairs, and the breakdown of BCS predictions in strongly correlated systems. We present a calculation-intensive critique, highlighting the need for a deeper, possibly non-phononic mechanism for electron pairing or collective quantum behavior in superconductors. The BCS theory of superconductivity, premised on the formation of Cooper pairs via weak electron-phonon coupling, has long served as the canonical framework for understanding low-temperature superconductors. However, we argue that this framework is conceptually and physically insufficient—even for conventional materials. This paper presents a detailed theoretical critique grounded in explicit calculations, exposing contradictions in the length and energy scales involved, the lack of real-space localization of paired electrons, and the incompatibility between the BCS ground state and a physically bound pair in position space. We emphasize that the superconducting energy gap may better reflect a many-body correlation scale rather than a two-body binding energy. Further, we discuss topological and quantum field theoretic obstructions to pairing and reframe superconductivity as a macroscopic quantum coherent state independent of pair formation. Our approach challenges the narrative that Cooper pairing is a necessary cause of superconductivity and instead highlights the role of collective phase coherence, entanglement, and broken gauge symmetry as possible fundamental mechanisms.

Abstract: The rapid evolution of multi-scale energy systems (spanning electricity, hydrogen, and renewable integration) has introduced unprecedented complexity, making robust anomaly detection a critical challenge. The vast heterogeneity and dynamic nature of these systems expose them to faults and cyber–physical risks, where timely detection is vital to ensure resilience, safety, and uninterrupted operation. Nowadays, deep learning (DL) techniques have emerged as powerful tools for modeling large-scale, non-linear, and high-dimensional energy data, enabling the extraction of latent spatio-temporal patterns. In this paper, we proposed an optimized deep reinforcement learning–generative adversarial network (ODRL-GAN) framework for reliable anomaly detection in multi-scale energy systems. The integration of DRL and GAN brings a key innovation: while DRL enables adaptive decision-making under dynamic operating conditions, GAN enhances detection by reconstructing normal patterns and exposing subtle deviations. To further strengthen the model, a novel multi-objective chimp optimization algorithm (NMOChOA) is employed for hyper-parameter tuning, improving accuracy, and convergence. This design allows the ODRL–GAN to effectively capture high-dimensional spatio-temporal dependencies while maintaining robustness against diverse anomaly patterns. The framework is validated on two benchmark datasets, PSML and LEAD1.0, and compared against state-of-the-art baselines including transformer, deep belief network (DBN), convolutional neural network (CNN), gated recurrent unit (GRU), and support vector machines (SVM). Experimental results demonstrate that the proposed method achieves a maximum detection accuracy of 99.58% and recall of 99.75%, significantly surpassing all baselines. Furthermore, the model exhibits superior runtime efficiency, faster convergence, and lower variance across trials, highlighting both robustness and scalability. The optimized DRL–GAN framework provides a powerful and generalizable solution for anomaly detection in complex energy systems, offering a pathway toward secure and resilient next-generation energy infrastructures.

Abstract: This paper argues that the moment-to-moment content of phenomenal consciousness is identical to whichever neural or mnemonic representation is, at that instant, transiently the most accessible given the system’s causal history and current embedding context. Building on Tulving’s distinction between availability and accessibility, together with empirical work on working-memory constraints, attentional blink, priming, and neuromodulation, we argue that the “stream of consciousness” (James, 1890) is a serial, determined sequence of state transitions governed by relative accessibility. A broader claim is advanced: the stream of consciousness is not an accidental by-product of slower adaptive processes but the real-time continuation of the same abstract dynamic that operates across evolutionary, developmental, and cultural timescales, only here unfolding at psychological speed. The account is deterministic (or near-deterministic) at the psychological level, reframes the hard problem of consciousness as a tractable question about accessibility mechanisms, and remains neutral on low-level physical indeterminism. It is compatible with major neuroscientific findings and generates contrasting predictions about priming effects, mind-wandering sequences, conscious transitions under neuromodulation, and clinical disruptions of conscious seriality.

Abstract: The challenge of insufficient monitoring accuracy in vision-based multi-point dis-placement measurement of bridges using Unmanned Aerial Vehicles (UAVs) stems from camera motion interference and the limitations in camera performance. Existing methods for UAV motion correction often fall short of achieving the high precision necessary for effective bridge monitoring, and there is a deficiency of high-performance cameras that can function as adaptive sensors. To address these challenges, this paper proposes a UAV vision-based method for multi-point displacement measurement of bridges and introduces a monitoring system that includes a UAV-mounted camera, a computing terminal, and targets. The proposed technique was applied to monitor the dynamic displacements of the Lunzhou Highway Bridge in Qingyuan City, Guangdong Province, China. The research reveals the deformation behavior of the bridge under vehicle traffic loads. Field test results show that the system can accurately measure vertical multi-point displacements across the entire span of the bridge, with monitoring results closely matching those obtained from a Scheimpflug camera. With a root mean square error (RMSE) of less than 0.3 mm, the proposed method provides essential data necessary for bridge displacement monitoring and safety assessments.

Abstract: Background/Objectives: Health care in preschools has gained increasing attention, particularly in the post-pandemic era, as educators face dual challenges in detecting emotional and physiological abnormalities among young children. Observation-based assessments are subjective and lack real-time data, often delaying the identification of potential health risks. This study aimed to construct an artificial intelligence (AI)-based model capable of recognizing the potential association between emotional abnormalities and physiological illnesses in preschool children. Methods: A mixed-method design was employed, integrating a literature review and the Delphi method. The literature review identified trends and feasibility in AI-assisted child health monitoring. Nine interdisciplinary experts in pediatrics, AI sensing, and early childhood education participated in three Delphi rounds to establish consensus on key physiological and behavioral indicators. Results: Experts reached consensus on five primary indicators—facial expression, speech prosody, heart rate variability (HRV), galvanic skin response (GSR), and skin temperature—and recommended using noninvasive wearable devices. A real-time risk alert system using red, yellow, and green levels was proposed. The final AI model included four modules: sensor input, data pre-processing, AI integration and analysis, and feedback interface. Conclusions: The developed AI-based recognition model demonstrates strong potential for early detection of emotional and physiological abnormalities in preschoolers. It provides timely, objective, and science-based support for caregivers, facilitating early intervention and individualized care. This model may serve as a practical framework for advancing digital transformation in preschool health care.

Abstract:

Multiferroic BaTiO₃ (BTO, piezoelectric)/CoFe₂O₄ (CFO, magnetostrictive) bilayer thin films were prepared by laser ablation on conductive Nb-doped SrTiO₃ (100) substrates to investigate the influence of BTO layer thickness on their structural, microstructural, dielectric, and electrical (DC and AC) properties. X-ray diffraction confirmed the coexistence of the cubic spinel CoFe₂O₄ phase and the tetragonal ferroelectric BaTiO₃ phase. The films exhibit preferred orientation, with CFO showing the [400] direction along the growth axis and BTO displaying (100)/(001) planes stacked parallel to it. The CFO unit cell is compressed along the growth direction, while BTO presents the ferroelectric distortion with a tetragonality ratio (c/a) slightly below, but close to, the bulk value. Second harmonic generation studies further verified the non-centrosymmetric ferroelectric nature of BTO at room temperature. The temperature-dependent dielectric permittivity was modeled using the Havriliak–Negami function with an additional conductivity term to extract relaxation dynamics, DC conductivity, Curie temperature (Tc), and activation energies. The Curie temperature increases with BTO thickness, approaching the bulk value for thicker layers. DC conductivity activation energies exhibit a change at Tc, from below 0.5 eV for T < Tc to above 0.5 eV for T > Tc, consistent with small-polaron tunneling. The AC conductivity follows a Jonscher-type frequency dependence with two power-law contributions reflecting the behavior of both layers. Temperature-dependent analysis of the power-law exponents reveals that small-polaron tunneling dominates conduction in BTO, while ion hopping between octahedral sites governs conduction in CFO. Underoxidation leads to a more complex transport regime in BTO, showing a transition from quantum-mechanical tunneling below Tc to correlated barrier hopping above it. By revealing how transport processes operate within multiferroic oriented bilayer systems, these findings advance our understanding of material interactions and pave the way for the design of innovative multifunctional platforms optimized for spintronic technologies.

Abstract: This paper presents an outline of the problems facing the Polish energy sector. It high-lights the significant role of wind energy in the National Power System, while limiting the possibility of installing new wind farms. It is suggested that repowering and ex-tending the operational life of wind turbines will be an important solution to this problem. The possibility of using data from existing turbines to inform operational strategies was analyzed. Historical data was obtained for selected wind turbines and statistically analyzed. The main goal of the study was to develop regression models for wind conditions and electricity production. The best fit between the actual distribu-tions of the analyzed variables and selected theoretical distributions was determined. It was demonstrated that in the analyzed case, the Log-Normal distribution provided a better fit than the Weibull distribution, preferred by the energy industry.

Abstract:

The production of pear (Pyrus communis L.) nurseries is essential to providing high-quality planting material for the establishment of a successful orchard. Thus, encouraging early vegetative growth and seedling vigor during the nursery period requires optimal fertilization. Under temperate continental circumstances in northwest Romania, this study assessed the impact of various NPK fertilizer rates on the shoot fresh weight of pear nursery trees. The study was carried out in 2025 using a factorial design with two Romanian cultivars (‘Napoca’ and ‘Monica’) and four fertilization treatments (N₀P₀K₀, N₈P₈K₈, N₁₆P₁₆K₁₆, and N₂₄P₂₄K₂₄), set up in a randomized block system with five replications. At progressively higher rates of 50, 100, and 150 kg ha⁻¹, a totally water-soluble 16–16–16 fertilizer was applied. At the conclusion of the growing season, the fresh weight of the shoots was measured. The accumulation of shoot biomass was significantly and gradually impacted by fertilization. The fresh weight of the shoots rose by 29%, 45%, and 59% as compared to the unfertilized control (0.42 kg tree⁻¹) under the treatments of N₈P₈K₈, N₁₆P₁₆K₁₆, and N₂₄P₂₄K₂₄. There were no discernible cultivar-dependent variations at any fertilization level, and both cultivars showed almost equal growth responses. These findings show how strongly the growth of pear nursery shoots depends on the availability of NPK and offer helpful advice for maximizing fertilization techniques to enhance the quality of planting materials.