Angel Kanchev University of Ruse

Climate variability amplifies temporal heterogeneity in crop production, challenging uniform varietal recommendations and highlighting the need to integrate genotype × environment interactions. This study evaluated the yield performance and stability of sixteen triticale (×Triticosecale Wittmack) genotypes over three consecutive growing seasons (2022/2023, 2023/2024, 2024/2025) at a single location with pronounced interannual climatic variability. Grain yield ranged from 3.49 to 6.68 t/ha in the least productive season (2022/2023) and from 7.71 to 9.92 t/ha in the most favorable season (2024/2025), with overall genotype means varying between 6.67 and 8.12 t/ha. Stability was assessed using regression-based parameters (regression coefficient and variance of deviations from regression), Shukla’s stability variance, and derived indices describing responsiveness (RI), predictability (PI), genetic risk (GRI), stress robustness (SRI), and yield opportunity (YOI). Results revealed substantial genotype × year interaction, with yield strongly dependent on seasonal conditions. Four genotypes combined high mean yield with stable performance and low interaction-related risk, indicating broad adaptability across years. Another four exhibited strong responsiveness to favorable seasons or elevated instability, increasing production risk despite high yield potential. The derived indices enabled risk-oriented genotype profiling, identifying contrasting adaptation strategies. Multivariate AMMI and GGE biplot analyses confirmed these patterns, providing a comprehensive view of interaction structure and stability. This integrated framework translates stability metrics into practical, decision-oriented descriptors, supporting risk-aware genotype selection under variable climates.

The paper proposes an adaptive architecture of an electromechanical drive with internally controlled energy exchange, implemented through the integration of an inertial flywheel and a controlled clutch into the structure of a planetary transmission. A multi-mass dynamic and energy model of the system is developed, and the power balance is verified. Based on the energy formulation, adaptive energy and predictive energy control strategies are implemented. The results of numerical simulation confirm that the use of the internal energy exchange loop increases system stability, reduces peak motor torque by 30–40%, decreases maximum output speed deviations by 35–45% under step load conditions, and reduces the root-mean-square tracking error by 20–30% compared with reactive energy-based control, demonstrating improved tracking performance and reduced actuator load compared to the classical drive architecture.

Abstract This work investigates the impact of electron beam treatment (EBT) on Ti–Cu oxide coatings deposited on Ti6Al4V via reactive DC magnetron sputtering (MS). The as-deposited films, mainly anatase TiO 2 with minor Cu phases, were post-treated using beam currents of 1 mA and 2 mA. Surface and structural analyses (SEM/EDS, XRD) revealed densification and partial anatase-to-rutile transformation at higher energy input, accompanied by grain coarsening. Mechanical tests revealed a slight decrease in hardness but a significant improvement in adhesion, with critical loads exceeding 30 N following post-EBT treatment. Electrochemical studies in simulated body fluid indicated a strong reduction in corrosion current density for all coatings, with protection efficiencies above 98% at low beam energy. Additionally, EBT reduced copper ion release during immersion, linked to surface densification and phase evolution. The combined MS deposition + EBT treatment enhances adhesion, corrosion resistance, and ion release control, making these coatings promising for biomedical implant applications.

Is it reasonable to use allogeneic mesenchymal stem cells (MSCs) therapy for thin endometrium and recurrent implantation failure? Thin endometrium (TE) and recurrent implantation failure (RIF) are associated with poor reproductive outcomes. Single-cell RNA sequencing (scRNA-seq) shows that such pathologies involve functional impairment of endometrial stromal, vascular, immune cells rather than reductions in cell numbers. MSCs exert regenerative and immunomodulatory effects and are proposed as candidates for endometrial repair. scRNA-seq studies indicate that TE and RIF are characterized by stromal progenitor dysfunction, impaired angiogenesis, immune dysregulation, and cellular senescence, providing a biological rationale for investigating allogeneic MSC-based therapies. scRNA-seq studies of human endometrium in patients with TE and RIF are reviewed alongside experimental and clinical studies evaluating autologous and allogeneic MSCs, with particular emphasis on umbilical cord-derived MSCs. Transcriptomic analyses consistently demonstrate reduced proliferation and decidualization of endometrial stromal cells, suppression of angiogenesis, immune dysregulation, and activation of senescence-associated genes. Preclinical studies show that MSC administration restores endometrial structure, vascularization, and receptivity markers. Early clinical studies suggest potential benefit, although data remain limited and heterogeneous due to non-randomized studies. Allogeneic MSCs are promising as therapy, but further studies on mechanisms and clinical validation are needed.

This paper conceptualises burnout in Project-Based Organisations (PBOs) as a systemic emergent property arising from the non-linear interaction between structural demands and human capital. Utilising a System Dynamics (SD) methodology, the study constructs a Causal Loop Diagram (CLD) to visualise the feedback architecture governing the burnout cycle. The analysis identifies the dynamic tension between the Reinforcing Loop of exhaustion (R1) and the Balancing Loop of adaptation (B1). A key theoretical contribution is the positioning of the Project Manager’s Emotional Intelligence (EI) not merely as a soft skill but as a systemic control lever (B2) capable of reducing information delays and shifting the system from reactive to proactive homeostasis. Crucially, the study operationalises these conceptual findings into a Burnout Risk Management Framework (BRMF), accompanied by a practical diagnostic dashboard. This tool offers managers a set of leading and lagging indicators for early detection, bridging the gap between theoretical plausibility and applied risk management in high-entropy project environments.

Ki-67 is a nuclear protein linked to cellular proliferation and is used as a prognostic and predictive biomarker in breast carcinoma. However, variability in its assessment limits its clinical utility. This study evaluated interobserver reproducibility in Ki-67 scoring and compared proliferative indices between core needle biopsy (CNB) and corresponding resection specimens in invasive breast carcinoma. Sixty-three cases with matched CNB and resection specimens were retrospectively analyzed. Ki-67 immunohistochemistry was independently evaluated by two pathologists using manual image-based counting. Interobserver agreement and CNB-resection concordance were assessed using the intraclass correlation coefficient (ICC), Cohen's kappa, Bland-Altman analysis, and Spearman's correlation. Tumor characteristics were analyzed for their association with variability. Interobserver agreement was excellent, with ICCs of 0.89 for CNB and 0.91 for resection specimens. Cohen's kappa for binary classification (<20% vs. ≥20%) showed moderate agreement for CNB (κ=0.54) and substantial agreement for resections (κ=0.78). Bland-Altman analysis revealed small but consistent bias, with CNB values slightly higher (+2.89% and+2.25% for raters 1 and 2, respectively). However, discrepancies >10% were observed in some cases. Tumor characteristics had minimal to no association with scoring variability. Despite excellent statistical agreement, clinically significant variability in Ki-67 scoring may occur. These findings support interpreting Ki-67 as a continuous variable rather than relying on fixed cutoffs. Pathologists should consider specimen type and scoring limitations when reporting Ki-67, and may recommend which value is more reliable or suggest retesting when appropriate.

The aim of the current manuscript is to study a coupled system of nabla fractional equations with general summation boundary conditions depending on parameters. We deduce the expression of the Green’s function and we obtain useful bounds of it. Using these bounds, under suitable conditions, we obtain existence results by implying a few classical fixed point theorems. Then, we show that there exists an interval for the parameters, where the Green’s function is strictly positive, and we are able to deduce existence, nonexistence, and multiplicity results for our studied problem using the same theory. At the end of this work, we deliver some numerical examples to clarify our theoretical results.

This paper presents the design, modeling and control of a flywheel actuator for mobile platform stabilization. A Lagrangian-based model couples platform mechanics with DC-motor electromechanics. Analytical calculations estimate natural frequencies, damping and actuator limits. Numerical simulations in Python 3.12 evaluate cascade and state-feedback controllers for suppressing free oscillations and rejecting external disturbances. Additional studies examine filtering to improve measurement quality and unloading strategies to avoid actuator saturation. The results validate the proposed control architecture and demonstrate its applicability to robotic and energy systems operating under dynamic loads.

Fractional calculus, the study of derivatives and integrals of arbitrary order [...]

Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes from Kazakhstan is discussed in the paper. Ten representative varieties of Kazakhstani potatoes were selected as objects of study: Alians, Alians mini, Astana, Astana mini, Edem, Edem mini, Nerli, Nerli mini, Zhanaisan, and Zhanaisan mini. Two convolutional neural network (CNN) models, SqueezeNet and GoogLeNet, were refined via transfer learning employing three optimization approaches. Then, they were used to classify the potato images. A comparison of the two neural networks’ classification performance was conducted using common evaluation criteria—accuracy, precision, F1 score, and recall—alongside a confusion matrix to highlight misclassified samples. The comparative analysis demonstrated that both CNN architectures—SqueezeNet and GoogLeNet—achieve high classification accuracy for Kazakhstani potato varieties, with the best performance on Astana and Zhanaisan (&gt;97%). The study confirms the applicability of lightweight CNNs for digital varietal identification and automated quality assessment of seed potatoes under controlled imaging conditions. The developed approach is the first comparative CNN-based varietal identification of Kazakhstani potato tubers using transfer learning and contributes to the digitalization of potato breeding, and provides a baseline for future real-time sorting systems using deep learning.