Search Results (396)

Actinidia arguta is a dioecious plant, and the selection of superior male germplasm is crucial for ensuring effective pollination of female cultivars, maximizing their economic traits, and achieving high-quality yields. This study evaluated 30 male germplasms for pollen quantity, germination capacity, storage characteristics, and ultrastructural features. Results revealed significant variation in pollen germination rates (1.56–96.57%) among germplasms, with ‘Lvwang’, ‘TL20083’, and ‘TG06023’ performing best (all >90% germination). The storage characteristics study demonstrated that −80 °C is the optimal temperature for long-term pollen storage in A. arguta. Significant variations were observed in storage tolerance among different germplasms. Among them, Lvwang exhibited the best performance, maintaining a germination rate of 97.40% after 12 months of storage at −80 °C with no significant difference from the initial value, followed by TT07063. Pollen morphology was closely correlated with fertility. High-fertility pollen grains typically exhibited standard prolate or ultra-prolate shapes, featuring a tri-lobed polar view and an elliptical equatorial view, with neat germination furrows and clean surfaces. In contrast, low-fertility pollen grains frequently appeared shrunken and deformed, with widened germination furrows and visible exudates. Based on these findings, the following recommendations are proposed: ① Prioritize the use of germplasms with pollen germination rates >80% as pollinizers; ② Establish a rapid screening system based on pollen morphological characteristics. This study provides important scientific basis for both male germplasm selection and efficient cultivation practices in A. arguta (kiwiberry). Full article

Co-firing large-sized straw biomass in pulverized coal boilers is a potential pathway for carbon emission reduction in China’s thermal power plants. However, experimental data on large-sized straw combustion under pulverized coal boiler combustion conditions are critically lacking. This study selected typical large-sized wheat straw particles. Employing a two-mode experimental setup in a drop tube furnace (DTF) system simulating pulverized coal boiler conditions, we systematically investigated the combustion behavior and alkali metal release characteristics of this large-sized straw biomass, with combustion processes summarized for diverse particle types. The findings reveal asynchronous combustion progression across particle surfaces due to heterogeneous mass transfer and gas diffusion; unique behaviors distinct from denser woody biomass, including bending deformation, fiber branching, and fragmentation, occur; significant and morphology-specific deformations occur during devolatilization; fragmentation universally produces particles of varied shapes (needle-like, flaky, blocky, semi-tubular) during char combustion; and potassium release exceeds 35% after complete devolatilization and surpasses 50% at a burnout degree exceeding 80%. This work provides essential experimental data on the fundamental combustion characteristics and alkali metal release of large-sized wheat straw particles under pulverized coal boiler combustion conditions, offering engineering application guidance for the direct co-firing of large-sized flexible straw biomass in pulverized coal boilers. Full article

Thermal control in rolling mills motors is gaining importance as more and more hard-to-deform steel grades are rolled. The capabilities of diagnostics monitoring also expand as digital IIoT-based technologies are adopted. Electrical drives in modern rolling mills are based on synchronous motors with frequency regulation. Such motors are expensive, while their reliability impacts the metallurgical plant output. Hence, developing the on-line temperature monitoring systems for such motors is extremely urgent. This paper presents a solution applying to synchronous motors of the upper and lower rolls in the horizontal roll stand of plate mill 5000. The installed capacity of each motor is 12 MW. According to the digitalization tendency, on-line monitoring systems should be based on digital shadows (coordinate observers) that are similar to digital twins, widely introduced at metallurgical plants. Modern reliability requirements set the continuous temperature monitoring for stator and rotor windings and iron core. This article is the first to describe a method for calculating thermal loads based on the data sets created during rolling. The authors have developed a thermal state observer based on four-mass model of motor heating built using the Simscape Thermal Models library domains that is part of the MATLAB Simulink. Virtual adjustment of the observer and of the thermal model was performed using hardware-in-the-loop (HIL) simulation. The authors have validated the results by comparing the observer’s values with the actual values measured at control points. The discrete masses heating was studied during the rolling cycle. The stator and rotor winding temperature was analysed at different periods. The authors have concluded that the motors of the upper and lower rolls are in a satisfactory condition. The results of the study conducted generally develop the idea of using object-oriented digital shadows for the industrial electrical equipment. The authors have introduced technologies that improve the reliability of the rolling mills electrical drives which accounts for the innovative development in metallurgy. The authors have also provided recommendations on expanded industrial applications of the research results. Full article

This study investigates the excavation of the cataclastic loose rock slope at the mixing plant on the right bank of the BDa Hydropower Station, which is situated in the upper reaches of Lancang River. The dominant structural plane of the cataclastic loose rock mass was obtained using unmanned aerial vehicle tilt photography and 3D point cloud technology. The actual 3D numerical model of the study area was developed using the 3DEC discrete element numerical simulation software. The excavation response characteristics and overall stability of the cataclastic loose rock slope were analyzed. The support effect was evaluated considering the preliminary shaft micropile and Macintosh reinforced mat as slope support measures, and the stability was assessed by applying seismic waves. The results showed the main deformation and failure area after slope cleaning excavation at the junction of the cataclastic loose rock mass and Q^edl deposits in the shallow surface of the excavation face. Moreover, the maximum total displacement could reach 18.3 cm. Subsequently, the overall displacement of the slope was significantly reduced, and the maximum total displacement decreased to 2.78 cm. The support effect was significant. Under an earthquake load, the slope with support exhibited considerable displacement in the shallow surface of the excavation slope, with collapse deformation primarily occurring through shear failure. Full article

Mycelium-based composites (MBCs) have emerged as eco-friendly alternatives, utilizing fungal mycelium as a natural binder for agro-industrial residues. This study focuses on developing an MBC based on abundant waste in Colombia, pith Arboloco (A) (Montanoa quadrangularis), a plant endemic to the Colombian–Venezuelan Andes with outstanding insulating properties, and natural fiber of Kikuyu grass (G) (Cenchrus clandestinus), utilizing Ganoderma lucidum as an agent to form a mycelium network in the MBC. Three formulations, T (100% A), F1 (70% A/30% G), and F2 (30% A/70% G), were evaluated under two different Arboloco particle size ranges (1.0 to 5.6 mm) for their physical, mechanical, and thermal properties. The Arboloco particle sizes did not show significant differences in the MBC properties. An increase in Kikuyu grass proportion (F2) demonstrated superior density (60.4 ± 4.5 kg/m³), lower water absorption (56.6 ± 18.4%), and better compressive strength (0.1686 MPa at 50% deformation). Both mixing formulations (F1–F2) achieved promising average thermal conductivity and specific heat capacity values of 0.047 ± 0.002 W m⁻¹ K⁻¹ and 1714 ± 105 J kg⁻¹ K⁻¹, comparable to commercial insulation materials. However, significant shrinkage (up to 53.6%) and high water absorption limit their scalability for broader applications. These findings enhance the understanding of MBC’s potential for non-structural building materials made of regional lignocellulosic waste, promoting a circular economy in waste management for developing countries. Full article

In this paper, the effect of aluminosilicate microspheres (ASMs) from thermal power plant (TPP) ash on the properties of epoxy composites was studied. A method for modifying the ASMs’ surface using aminoacetic acid was developed to improve the adhesion at the polymer–filler interface. Complex analysis methods, including scanning electron microscopy, infrared spectroscopy, a thermogravimetric analysis, DSC, and DMA, showed that adding the optimal amount of ASMs significantly improved the physical and mechanical properties of the composites: the flexural strength increased by 112%, the elastic modulus by 198%, and the impact strength by 50%. Functionalization of the ASMs enhances their interaction with the matrix, providing the composites with the best strength and thermal stability indicators among the studied materials. The study of the curing kinetics showed the initiating effect of functionalized ASMs on the curing process of epoxy compositions, associated with the presence of active amino groups on the surface of the particles. The resulting composites demonstrate potential for application in structural and fire-resistant materials; have high-deformation and -strength characteristics; and facilitate the disposal of industrial waste. Full article

Selecting the type of lined rock cavern (LRC) is a critical aspect in the construction of compressed air energy storage (CAES) plants. Present research on CAES has mainly focused on site selection, sealing performance, and stability of underground LRCs. Insufficient attention has been given to the selection of LRC type, which is a prerequisite for further detailed analyses of LRCs. To overcome this limitation, based on reliable numerical simulation, in this study, we simulate the mechanical responses of two popular types of LRCs: tunnel-type and silo-type LRCs. Parameter sensitivity analysis is then conducted based on the mechanical response, including parameters such as the deformation modulus of the surrounding rock mass, Poisson’s ratio, cohesion, friction angle, crustal stress, and lateral stress coefficient. Based on the simulated results, the analytical hierarchy process (AHP) method is used to propose scoring systems for the two types of LRCs. This scoring system can be used for quantitative estimation of an appropriate LRC in CAES systems. Full article

The bio-fabrication of sustainable mycelium-based composites (MBCs) from renewable plant by-products offers a promising approach to reducing resource depletion and supporting the transition to a circular economy. In this research, MBCs were obtained by cultivating Ganoderma resinaceum GA1M on essential oils and agricultural by-products: hexane-extracted rose flowers (HERF), steam-distilled lavender straw (SDLS), wheat straw (WS), and pine sawdust (PS), used as single or mixed substrates. The basic physical and mechanical properties revealed that MBCs perform comparably to high-efficiency thermal insulating and conventional construction materials. The relatively low apparent density, ranging from 110 kg/m³ for WS-based to 250 kg/m³ for HERF-based composites, results in thermal conductivity values between 0.043 W/mK and 0.054 W/mK. Compression stress (40–180 kPa at 10% deformation) also revealed the good performance of the composites. The MBCs had high water absorption due to open porosity, necessitating further optimization to reduce hydrophilicity and meet intended use requirements. An aerobic biodegradation test using respirometry indicated ongoing microbial decomposition for all tested bio-composites. Notably, composites from mixed HERF and WS (50:50) showed the most rapid degradation, achieving over 46% of theoretical oxygen consumption for complete mineralization. The practical applications of MBCs depend on achieving a balance between biodegradability and stability. Full article

Background/Objectives: Cirsium setosum (commonly known as thistle) is a traditional Chinese medicinal plant with significant therapeutic potential, exhibiting hemostatic, antioxidant, and wound-healing properties. Electrospinning offers a versatile platform for fabricating nanoscale scaffolds with tunable functionality, making them ideal for drug delivery and tissue engineering. Methods: In this study, a bioactive extract from thistle was obtained and incorporated into a thermosensitive triblock copolymer (PNNS) and polycaprolactone (PCL) to develop a multifunctional nanofibrous scaffold for enhanced wound healing. The prepared nanofibers were thoroughly characterized using Fourier-transform infrared spectroscopy (FTIR), contact angle measurements, thermogravimetric analysis (TGA), and tensile fracture testing to assess their physicochemical properties. Results: Notably, the inclusion of PNNS imparted temperature-responsive behavior to the scaffold, enabling controlled deformation in response to thermal stimuli—a feature that may facilitate wound contraction and improve scar remodeling. Specifically, the scaffold demonstrated rapid shrinkage at a physiological temperature (38 °C) within minutes while maintaining structural integrity at ambient conditions (20 °C). In vitro studies confirmed the thistle extract’s potent antioxidant activity, while in vivo experiments revealed their effective hemostatic performance in a liver bleeding model when delivered via the composite nanofibers. Thistle extract and skin temperature-responsive contraction reduced the inflammatory outbreak at the wound site and promoted collagen deposition, resulting in an ideal wound-healing rate of above 95% within 14 days. Conclusions: The integrated strategy that combines mechanical signals, natural extracts, and electrospinning nanotechnology offers a feasible design approach and significant technological advantages with enhanced therapeutic efficacy. Full article

This study investigates the impact of widely used mineral fillers in self-compacting concrete compositions applied in vibration-free reinforced concrete production technology, as a means of enhancing rheological characteristics and cost-effectiveness. Three distinct types of mineral fillers, including the well-studied fillers microsilica and metakaolin, as well as the lesser-explored filler Kazakhstani natural opal-chalcedony opoka, are examined in this research. In addition to the evaluation of conventional rheological and performance properties of concretes containing these fillers, the internal processes within the cement–filler matrix are analyzed. This includes X-ray phase analysis and microstructural examination of cement hydration products in combination with a superplasticizer and each of the three minerals. The findings confirm the potential for optimizing the rheological parameters of the concrete mixture by substituting up to 15% of the cement with mineral fillers, achieving optimal viscosity and workability. It is established that compositions with the addition of microsilica and metakaolin have a more homogeneous structure, mainly represented by low-basicity calcium hydrosilicates of the CSH(B) type, along with an increase in compressive strength of up to 10%. The addition of these mineral fillers to C30/35 strength class self-compacting concrete resulted in improved frost resistance up to F300, a reduction in volumetric water absorption by up to 30%, and a decrease in shrinkage deformations by 32%. The developed SCC compositions have successfully passed production testing and are recommended for implementation in the operational processes of reinforced concrete product manufacturing plants. Full article

As an important economic crop, tomato is highly susceptible to diseases that, if not promptly managed, can severely impact yield and quality, leading to significant economic losses. Traditional diagnostic methods rely on expert visual inspection, which is not only laborious but also prone to subjective bias. In recent years, object detection algorithms have gained widespread application in tomato disease detection due to their efficiency and accuracy, providing reliable technical support for crop disease identification. In this paper, we propose an improved tomato leaf disease detection method based on the YOLOv10n algorithm, named BED-YOLO. We constructed an image dataset containing four common tomato diseases (early blight, late blight, leaf mold, and septoria leaf spot), with 65% of the images sourced from field collections in natural environments, and the remainder obtained from the publicly available PlantVillage dataset. All images were annotated with bounding boxes, and the class distribution was relatively balanced to ensure the stability of training and the fairness of evaluation. First, we introduced a Deformable Convolutional Network (DCN) to replace the conventional convolution in the YOLOv10n backbone network, enhancing the model’s adaptability to overlapping leaves, occlusions, and blurred lesion edges. Second, we incorporated a Bidirectional Feature Pyramid Network (BiFPN) on top of the FPN + PAN structure to optimize feature fusion and improve the extraction of small disease regions, thereby enhancing the detection accuracy for small lesion targets. Lastly, the Efficient Multi-Scale Attention (EMA) mechanism was integrated into the C2f module to enhance feature fusion, effectively focusing on disease regions while reducing background noise and ensuring the integrity of disease features in multi-scale fusion. The experimental results demonstrated that the improved BED-YOLO model achieved significant performance improvements compared to the original model. Precision increased from 85.1% to 87.2%, recall from 86.3% to 89.1%, and mean average precision (mAP) from 87.4% to 91.3%. Therefore, the improved BED-YOLO model demonstrated significant enhancements in detection accuracy, recall ability, and overall robustness. Notably, it exhibited stronger practical applicability, particularly in image testing under natural field conditions, making it highly suitable for intelligent disease monitoring tasks in large-scale agricultural scenarios. Full article

(1) Background: Rheumatoid arthritis (RA) is a chronic inflammatory condition known for its symptoms of joint damage and cartilage breakdown. Current treatments frequently result in adverse effects and show restricted efficacy in the long term. Dendropanax morbiferus, a plant recognized for its bioactive properties, demonstrates promise in the treatment of inflammatory conditions. The objective of this study was to examine the therapeutic properties of Dendropanax morbiferus Lév. water extract (DMWE) in RA through the utilization of in vitro and in vivo models. (2) Methods: Ultra-high-performance liquid chromatography (UPLC) analysis was used to identify bioactive compounds in DMWE. Antioxidant activity was evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) radical-scavenging assays. The in vitro experiments involved the treatment of CHON-001 cells with DMWE in order to assess its impacts on inflammation and matrix metalloproteinase (MMP) expression. The impact of DMWE on the Janus Kinase 2 (JAK2) and Signal Transducer and Activator of Transcription (STAT) signaling pathways was also assessed. RA was induced in Balb/c mice who were subsequently treated with varying doses of DMWE to assess its impact on joint morphology, edema, and body weight. (3) Results: DMWE demonstrated substantial antioxidant activity and hindered the expression of MMP-2 and MMP-8 in chondrocytes treated with IL-1β. It additionally inhibited the JAK2/STAT pathway and diminished inflammatory responses. Treatment with DMWE in living organisms led to a decrease in joint swelling, improved weight regains, and maintained joint structure, with higher doses exhibiting effects similar to those of the positive control, dexamethasone (Dexa). (4) Conclusions: DMWE was found to have excellent in vitro antioxidant and anti-inflammatory activities. In an RA-induced mouse model, DMWE-3 (500 mg/kg BW) was found to effectively treat RA by reducing the concentration of pro-inflammatory factors and preventing joint deformation. Full article

This article discusses the current problem of industrial waste disposal and its use in the production of building materials, which corresponds to the global concept of sustainable development. Attention is mainly paid to the development of a gruntosilicate composite (concrete) based on a mineral slag binder using drilling sludge from the mining industry, ashes from thermal power plants and electrothermophosphoric slag. Physico-chemical studies of man-made raw materials have been carried out, including analysis of chemical and mineralogical composition, granulometric characteristics, radiation safety and other parameters. It has been established that drilling mud, thermal power plant ash and electrothermophosphoric slag meet the requirements for use in building materials and belong to non-hazardous waste. The optimal ratios of the components in the composition of gruntosilicate concrete have been experimentally determined. The highest compressive strength (3.0–3.5 MPa) is achieved with a drilling mud content of 15–23% and a mineral slag binder of 10–20%. It is shown that the introduction of these wastes improves the structure of the material, reduces shrinkage deformations and ensures compliance with the requirements of road surfaces of the II–III classes. The use of industrial waste in construction will reduce the cost of raw materials by approximately 10–30%, reduce the environmental burden and solve the problem of waste disposal. The results of the study demonstrate the prospects of creating a waste-processing industry capable of processing up to 40% of industrial waste into building materials. Full article

With the accelerated transition of the global energy structure towards decarbonization, the share of PV power generation in the power system continues to rise. IEA predicts PV will account for 80% of new global renewable installations during 2025–2030. However, latent faults emerging from the long-term operation of photovoltaic (PV) power plants significantly compromise their operational efficiency. The existing EL detection methods in PV plants face challenges including grain boundary interference, probe band artifacts, non-uniform luminescence, and complex backgrounds, which elevate the risk of missing small defects. In this paper, we propose a high-precision defect detection method based on BiFDRep-YOLOv8n for small target defects in photovoltaic (PV) power plants, aiming to improve the detection accuracy and real-time performance and to provide an efficient solution for the intelligent detection of PV power plants. Firstly, the visual transformer RepViT is constructed as the backbone network, based on the dual-path mechanism of Token Mixer and Channel Mixer, to achieve local feature extraction and global information modeling, and combined with the structural reparameterization technique, to enhance the sensitivity of detecting small defects. Secondly, for the multi-scale characteristics of defects, the neck network is optimized by introducing a bidirectional weighted feature pyramid network (BiFPN), which adopts an adaptive weight allocation strategy to enhance feature fusion and improve the characterization of defects at different scales. Finally, the detection head part uses DyHead-DCNv3, which combines the triple attention mechanism of scale, space, and task awareness, and introduces deformable convolution (DCNv3) to improve the modeling capability and detection accuracy of irregular defects. Full article

Non-destructive evaluation (NDE) is highly relevant to assessing micro- and macrostructural changes in ferritic and ferritic/martensitic steels subjected to high temperature loading. These materials are widely used in energy generation, where they undergo extreme thermal and mechanical loads. This study examines the feasibility of micromagnetic NDE techniques, i.e., micromagnetic measurements, supported by machine learning methods, to identify and characterize the micro- and macrostructural changes caused by the mechanical loading at high temperatures of power plant steels, i.e., ferritic/martensitic P91 and the high chromium ferritic steel HiperFer-17Cr2. While the P91 did not show any systematic changes in micromagnetic measurements, which generally correlate with the evolution of the microstructure and the mechanical properties, for the HiperFer-17Cr2, pronounced changes in the micromagnetic properties were observed. In correlation with the evolution of the hardness and cyclic deformation behavior, which are both mainly attributed to Laves phase precipitation, the micromagnetic measurements significantly changed depending on the temperature, number of load cycles and load amplitude applied. Thus, these NDE methods can be used for early diagnosis and preventive maintenance strategies for HiperFer-17Cr2, potentially extending the lifespan of the components and mitigating safety risks. Full article