Abstract: Monitoring oil pipelines is crucial for effective infrastructure management and maintenance. It helps prevent threats such as vandalism and leaks, which can result in catastrophic events. Pipeline leaks pose significant environmental and economic risks, yet current detection methods are often expensive, slow, or unreliable, limiting their effectiveness for real-time applications. This research introduces a lightweight thermal imaging-based intelligent leak detection system that integrates Convolutional Neural Networks (CNNs), autoencoders, and knowledge distillation for deployment on edge devices. The proposed system addresses the challenges associated with existing pipeline detection techniques, such as large model sizes, high transmission latency, and excessive energy consumption. It utilizes thermal cameras to capture images of the pipeline, which are then compressed using an autoencoder. This compressed data is used to train a CNN model, which is further optimized through knowledge distillation. The model is trained and tested on real and synthetic data and deployed on a Raspberry Pi to simulate edge computing scenarios. Experimental results demonstrate improvement in detection accuracy, low inference latency, and an efficient transmission rate, confirming the system's suitability for real-time leak detection in remote and resource-constrained environments. This work contributes to the development of cost-effective, scalable, and energy-efficient solutions for pipeline monitoring.

Developing lead-free, eco-friendly dielectric materials is essential for the advancement of next-generation electronic and energy storage devices. Among these, perovskite-type oxides such as zinc stannate (ZnSnO₃) are particularly promising due to their high dielectric constant, wide bandgap, and flexible crystal structure. In this study, zirconium (Zr⁴⁺)-doped ZnSnO₃ ceramics with the general formula ZnSn₁₋ₓZrₓO₃ (x = 0.1–0.5) were synthesized using the chemical precipitation method to investigate the influence of Zr⁴⁺ substitution at the Sn⁴⁺ (B-site) position on the structural, optical, and dielectric properties. X-ray diffraction (XRD) confirmed the formation of a single-phase orthorhombic perovskite structure at all doping levels, with peak shifts indicating lattice distortion and unit cell modification due to Zr⁴⁺ incorporation. Fourier-transform infrared (FTIR) spectroscopy exhibited well-defined metal–oxygen bonds, supporting the structural stability and bonding environment of the perovskite framework. Ultraviolet–visible (UV–Vis) spectroscopy revealed shifts in absorption behavior, implying changes in the electronic structure and possible bandgap modulation. Dielectric analysis demonstrated a consistent and progressive increase in the dielectric constant with increasing Zr content, attributed to enhanced ionic polarization, lattice distortion, and subtle structural modifications. These findings underscore the suitability of Zr-doped ZnSnO₃ ceramics as versatile, lead-free dielectric materials with strong potential for use in advanced electronic systems and sustainable technologies.

The increasing trend of innovative plant-based milk alternatives (PBMAs) is driven by lactose intolerance, cholesterol risks, and ethical, environmental, religious and social beliefs. In this regard, walnuts can be considered a unique ingredient for the production of PBMAs due to their remarkable functional potential and nutritional value. In this context, this study formulated and evaluated walnut milk, employing Design-Expert software. The findings showed that the optimal walnut milk composition is walnuts (17%), water (79.8%), fibers (2%), sugar (0.5%), vanilla extract (0.5%), salt (0.1%), and stabilizer (0.1%). The proximate composition (g/100 g) of optimized walnut milk was proteins at 3.51 g, fats at 2.75 g, carbohydrates at 5.65 g and ash at 0.81 g. Furthermore, the microstructure analysis denoted that it is an oil-in-water emulsion with the particle size distribution of oil drops in walnut milk in the range of 0.45 … 5.40 microns. The largest part of the oil volume has an average diameter of 2.70 microns. A sensory study was also undertaken, where walnut milk was highly acceptable to the panellists. This study shows the high potential and provides a positive view of walnut milk production, which is in agreement with the current demand for sustainable alternatives to dairy milk.

Acknowledgments. This research was supported by the Institutional Project, subprogram 020405 “Optimizing food processing technologies in the context of the circular bioeconomy and climate change”, Bio-OpTehPAS, being implemented at the Technical University of Moldova.

Introduction. Heliomycin (resistomycin) is an antibiotic with broad-spectrum biological activity. However, its poor water solubility and the lack of feasible strategies for chemical modification have limited its potential as a drug scaffold. Recent advances in the design and synthesis of heliomycin-based compounds yielded water-soluble derivatives with strong i-motif stabilization and notable antitumor activity. This study investigates the effects of the heliomycin derivative LCTA-2614 on both hormone-dependent and hormone-independent breast cancer cell lines.

Methods. Aminoalkylamine side chains were introduced at positions 3, 5, and 7 of heliomycin through nucleophilic substitution of alkoxy groups, enabling the synthesis of a previously undescribed series of water-soluble derivatives. Biological activity was assessed using the MTT assay, flow cytometry, and immunoblotting.

Results. Heliomycin exhibited potent antiproliferative activity across breast cancer cell lines of various molecular subtypes, with half-maximal inhibitory concentrations (IC₅₀) of 0.65±0.03 μM in MCF7, 0.79±0.01 μM in HCC1954, and 0.95±0.01 μM in MDA-MB-231 cells. The derivative LCTA-2614 showed comparable efficacy, with IC₅₀ values of 0.86±0.03 μM in MCF7, 0.60±0.03 μM in HCC1954, and 0.68±0.01 μM in MDA-MB-231. Importantly, both compounds maintained their antiproliferative activity under hypoxic conditions, a known driver of chemoresistance. Additionally, LCTA-2614 induced apoptosis in hormone-dependent MCF7 cells through a p53-dependent pathway.

Conclusions. These findings highlight heliomycin derivatives as promising molecular scaffolds for the development of new chemotherapeutic agents. Their retained activity under hypoxia suggests particular potential for the treatment of solid tumors with extensive hypoxic regions. Funding. This research was partly funded by the Russian Science Foundation (agreement 25-73-20069).

The production and consumption of plant-based products, including cheese alternatives, is constantly growing and demanding special attention in food science research. There are significant gaps in product development, as well as a need for assessing the nutritional composition, ingredients, and quality of plant-based cheese alternatives. This research focused on the development of a walnut-based cheese alternative with acceptable nutritious, physical, and sensory properties. Walnut cheese formulations were prepared with walnuts, water, instant yeast, agar-agar, walnut oil, salt, and sugar. Response surface models successfully predicted the optimal ingredients levels for the walnut cheese formulation: 79% walnut milk, with a walnut/water ratio of 1/1.26, 12% walnut oil, and 1.5% instant yeast. The results demonstrated an increase in the total polyphenol (164 mg GA/100g) and flavonoid (53 mg QE/100g) contents, as well as in DPPH, ABTS, and antioxidant activity values (83.51%, 91.38%) of walnut formulation, compared with animal cheese. In addition, walnut cheese demonstrated a hardness value of 1271.03g, similar to that of animal cheese 1304.27g (P ˂0.05). The walnut cheese recorded a lower L^* index (63.87) and higher a^* (2.00) and b^* (22.74) indexes compared with animal cheese. Overall, walnut cheese was found to be acceptable by sensory evaluation (≥8.27). This study provides a scientific basis for further production process of plant-based cheese.

Acknowledgments: This research was supported by an Institutional Project, subprogram 020405, “Optimizing food processing technologies in the context of the circular bioeconomy and climate change”, Bio-OpTehPAS, being implemented at the Technical University of Moldova.

This study investigated the effects of incorporating rice husk ash (RHA) as a partial replacement for cement on the properties of concrete. To determine the optimal replacement level, RHA was used to replace cement in varying proportions, ranging from 0% to 25% in 5% increments. The mix with 0% RHA served as the control. The properties evaluated included setting time, density, and compressive strength. The results revealed that blending RHA with cement increased the initial setting time. This was attributed to the lower calcium oxide (CaO₂) content of RHA, which slows early-age hydration reactions. Conversely, the final setting time was reduced due to the pozzolanic activity of RHA, which enhances later-stage reactions. Additionally, the inclusion of RHA resulted in a decrease in concrete density, owing to its lower specific gravity and bulk density compared to Portland cement. Despite this, RHA-modified specimens exhibited higher compressive strengths than the control specimens. This strength enhancement was linked to the formation of additional calcium-silicate-hydrate (C-S-H) gel due to the pozzolanic reaction between amorphous silica in RHA and calcium hydroxide (CaOH) from hydration reaction. The gel fills concrete voids at the microstructural level, producing a denser and more compact concrete matrix. Based on the balance between strength and durability, the optimal RHA replacement level was identified as 10%.

Building on the chemical absorption modeling of CO2 and H2S separation, this study further evaluates the feasibility of integrating the simulated process within Uzbekistan’s industrial carbon management strategy. The Aspen Plus rate-based simulations indicated that MEA and MDEA solvents offer distinct trade-offs between absorption capacity and regeneration energy demand. MDEA, for instance, showed lower reboiler duty (2.1 GJ/ton CO2) compared to MEA (2.5 GJ/ton), while maintaining comparable CO2 removal efficiency at elevated lean solvent loadings. Sensitivity analysis revealed that increasing the gas flow beyond 30 t/h led to marginal reductions in CO2 capture rate due to mass transfer limitations, suggesting the need for optimization of column internals in scaling scenarios. Additionally, integrating heat recovery between the absorber and regenerator units was estimated to lower total energy consumption by up to 14%. From a utilization perspective, the captured CO2 is intended for downstream conversion into calcined soda (Na2CO3) via the Solvay-like process implemented at the Dehkanabad plant. A mass balance indicates that 20 t/h of CO2 with 99.5% purity can yield ~37.1 t/h of soda, supporting annual outputs exceeding 290,000 tons. This valorization pathway not only offsets fossil-based soda production but also promotes circular carbon practices. Overall, the study reinforces the techno-economic potential of CCU in the Shurtan Gas Chemical Complex and advocates for pilot-scale demonstration to validate long-term operational stability and cost competitiveness.

Introduction:
Electrical distribution networks often traverse remote or hazardous terrains, making conventional ground-based inspections both risky and inefficient. Recent advances in UAV technology and AI-based computer vision have opened new avenues for remote asset monitoring (Shi et al., 2022). In this study, we introduce Powerline AI, an integrated system leveraging drones and object detection to automate powerline inspection tasks.

Methods:
Using drone-mounted high-resolution cameras, field images are captured from previously inaccessible areas. A deep learning-based object detection module, trained on annotated electrical infrastructure datasets, is employed to extract inventory features (e.g., pole types, insulators) and detect anomalies such as broken elements, corrosion, or vegetation encroachment (Zhang et al., 2021; Wang et al., 2020). The system is integrated into a GIS-backed web and mobile application, enabling real-time reporting and visualisation.

Results:
Field deployment across rural regions revealed that Powerline AI achieved over 92% mean Average Precision (mAP) in anomaly detection. Time spent on routine inspections decreased by 60% compared to manual methods, while early anomaly alerts enabled preemptive maintenance actions. In mountainous terrain, drone accessibility has significantly improved inspection coverage.

Conclusion:
This work demonstrates that AI-powered UAV inspection systems can enhance the accuracy, safety, and operational efficiency of powerline monitoring. Their integration with enterprise systems ensures daily usability, contributing to predictive maintenance frameworks and reducing long-term asset failure risks (Chen et al., 2020).

Silica is one of the major wastes produced as a result of mining in the clay industries. With the goal of turning industrial waste into valuable chemical materials, this project investigates the sustainable synthesis of sodium silicate from silica-rich waste produced by the clay industry. Waste silica sand is roasted at high temperatures with potassium hydroxide (KOH) and sodium hydroxide (NaOH) to transform inert silica into soluble silicate compounds. To recover solid sodium silicate, the roasted mass is subsequently leached with hot water, filtered, and the filtrate is then concentrated by evaporation. The synthesized material was thoroughly characterized using X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM), and Raman spectroscopy to verify its composition, structure, and purity. This technique shows a practical, environmentally responsible way to value the waste from the clay industry while supporting resource recovery and circular economy principles. The elemental composition, surface morphology, molecular structure, and successful preparation of sodium silicate were all confirmed by these analyses. The study shows how to turn industrial waste into a highly sought-after chemical product in an efficient and environmentally responsible manner.

The need of the present study is to investigate a novel approach to synthesize sodium silicate from an unusual, underutilized source clay industry waste. It draws attention to the potential of industrial waste products as substitute raw materials, advancing green chemistry and material science.

The conversion of CO₂ into gasoline-range hydrocarbons (C₅–C₁₂) using Fischer–Tropsch synthesis (FTS) represents a compelling pathway toward sustainable fuel production. In this study, we compiled and statistically analyzed a dataset of over 100 experimental records from the published literature focused on CO₂-FTS performance, predominantly featuring Co‑ and Fe‑based catalysts, which are the most frequently reported in gasoline-range studies. We evaluated four machine learning models—XGBoost, CatBoost, Random Forest, and Neural Networks—to predict CO₂ conversion and gasoline-range selectivity. CatBoost achieved the highest predictive accuracy with a test R² score of approximately 0.8, and was selected for further interpretation using SHAP-based post hoc analysis. The model revealed that the optimal operational conditions for maximizing gasoline-range hydrocarbon yield are aligned with ranges commonly reported: a temperature of 280–320 °C, pressure of around 2 MPa, and space velocity (GHSV) between 900 and 120,000 mL  h⁻¹ g⁻¹ (most studies cluster in the 1,000–5,000 range). Conditions were associated with enhanced chain growth probability and suppressed methane formation, especially in Co-based systems. The SHAP analysis also highlighted the principal role of catalysts containing cobalt (often supported on γ-Al₂O₃ with Re promoter) in increasing C₅⁺ chain growth and gasoline-range selectivity. Additionally, Co-based catalysts demonstrated clear benefits: increased chain-growth probability, reduced methane selectivity, and higher selectivity toward gasoline fractions under the identified optimal conditions. Our ML-driven framework not only predicts performance but also provides mechanistic insights into the influence of catalyst composition and reaction parameters. This integrated approach accelerates rational catalyst and process design for CO₂‑to‑fuel technology.