Search Results (1,153)

Carbon deposits on the crown of engine pistons can markedly reduce combustion efficiency and shorten service life. Conventional cleaning techniques often fail to simultaneously ensure a high carbon removal efficiency and maintain optimal surface integrity. To enable efficient and precise carbon removal, this study proposes the application of hybrid laser cleaning—combining continuous-wave (CW) and pulsed lasers—to piston carbon deposit removal, and employs response surface methodology (RSM) for multi-objective process optimization. Using the N52B30 engine piston as the experimental substrate, this study systematically investigates the combined effects of key process parameters—including CW laser power, pulsed laser power, cleaning speed, and pulse repetition frequency—on surface roughness (Sa) and carbon residue rate (RC). Plackett–Burman design was employed to identify significant factors, the method of the steepest ascent was utilized to approximate the optimal region, and a quadratic regression model was constructed using Box–Behnken response surface methodology. The results reveal that the Y-direction cleaning speed and pulsed laser power exert the most pronounced influence on surface roughness (F-values of 112.58 and 34.85, respectively), whereas CW laser power has the strongest effect on the carbon residue rate (F-value of 57.74). The optimized process parameters are as follows: CW laser power set at 625.8 W, pulsed laser power at 250.08 W, Y-direction cleaning speed of 15.00 mm/s, and pulse repetition frequency of 31.54 kHz. Under these conditions, the surface roughness (Sa) is reduced to 0.947 μm, and the carbon residue rate (RC) is lowered to 3.67%, thereby satisfying the service performance requirements for engine pistons. This study offers technical insights into the precise control of the hybrid laser cleaning process and its practical application in engine maintenance and the remanufacturing of end-of-life components. Full article

The aim of this study was to analyze how recombinant rabbit NGF (Nerve Growth Factor) encapsulated in chitosan (rrβNGFch) affects sperm viability, motility, capacitation, acrosome reaction (AR), kinetic traits, and apoptosis after 30 min and 2 h of storage. Specific intracellular signaling pathways associated with either cell survival, such as protein kinase B (AKT) and extracellular signal-regulated kinases 1/2 (ERK1/2), or programmed cell death, such as c-Jun N-terminal kinase (JNK), were also analyzed. The results confirmed the effect of rrβNGFch on capacitation and AR, whereas a longer storage time (2 h) decreased all qualitative sperm traits. AKT and JNK did not show treatment-dependent activation and lacked a correlation with functional traits, as shown by ERK1/2. These findings suggest that rrβNGFch may promote the functional activation of sperm cells, particularly during early incubation. The increase in capacitation and AR was not linked to significant changes in pathways related to cell survival or death, indicating a specific action of the treatment. In contrast, prolonged storage negatively affected all sperm parameters. ERK1/2 activation correlated with capacitation, AR, and apoptosis, supporting its role as an NGF downstream mediator. Further studies should analyze other molecular mechanisms of sperm and the potential applications of NGF in assisted reproduction. Full article

Excessive fertilization is a widespread issue in onion (Allium cepa L.) production in Southwest China. This practice not only leads to environmental pollution but also decreases the marketable yield and fertilizer productivity of onions. Identifying an optimal fertilization rate is crucial for promoting high-yield and highly efficient onion cultivation. The objective of this research is to determine the appropriate amount of fertilizer by investigating the effects of different fertilization rates on the growth characteristics and bulb yield of onion. The study was conducted over two consecutive growing seasons utilizing a randomized complete block design, which included six treatments: local routine fertilizer application (F1), a 20% reduction from F1 (F2), a 40% reduction from F1 (F3), a 60% reduction from F1 (F4), an 80% reduction from F1 (F5), and no fertilizer application (F0). The results show that, at the mature stage, aboveground dry matter quantity and its accumulation rate of onion under treatment F2 were found to be the highest among all other treatments across both growing seasons. Following the onset of bulbing, dry matter accumulation initially increased but subsequently decreased with reduced fertilizer supply; notably, it was greater under treatment F2 compared to other treatments. Compared with F1, the PFPN (partial factor productivity of nitrogen fertilizer) under treatment F2 increased by 35.2% and 32.0%, and the marketable bulb yield under treatment F2 increased by 8.4% and 5.8% during the 2022–2023 and 2023–2024 growing seasons, respectively. The marketable bulb yield demonstrated extremely significant positive correlations with aboveground dry matter and the dry matter accumulation rate throughout all growth periods in both growing seasons. Furthermore, marketable bulb yield exhibited extremely significant positive correlations with dry matter translocation before the onset of bulbing and dry matter accumulation following bulbing initiation. It was concluded that the appropriate fertilizer application (F2), characterized by a fertilization rate of 339-216-318 kg ha⁻¹ for N-P₂O₅-K₂O, enhanced onion bulb yield and nitrogen fertilizer productivity by promoting post-bulbing dry matter accumulation. This study emphasizes the significance of optimizing the fertilization rate as a crucial factor in achieving high-yield and highly efficient onion cultivation by enhancing dry matter accumulation. Full article

With their superior switching speed, GaN high-electron-mobility transistors (HEMTs) enable high power density, reduce energy losses, and increase power efficiency in a wide range of applications, such as power electronics, due to their high breakdown voltage. GaN-HEMT devices are subject to long-term reliability due to the self-heating effect and lattice mismatch between the SiC substrate and the GaN. Depletion-mode GaN HEMTs are utilized for radio frequency applications, and this work investigates three wide-bandgap (WBG) GaN HEMT fixed-frequency oscillators with output buffers. The first GaN-on-SiC HEMT oscillator consists of an HEMT amplifier with an LC feedback network. With the supply voltage of 0.8 V, the single-ended GaN oscillator can generate a signal at 8.85 GHz, and it also supplies output power of 2.4 dBm with a buffer supply of 3.0 V. At 1 MHz frequency offset from the carrier, the phase noise is −124.8 dBc/Hz, and the figure of merit (FOM) of the oscillator is −199.8 dBc/Hz. After the previous study, the hot-carrier stressed RF performance of the GaN oscillator is studied, and the oscillator was subject to a drain supply of 8 V for a stressing step time equal to 30 min and measured at the supply voltage of 0.8 V after the step operation for performance benchmark. Stress study indicates the power oscillator with buffer is a good structure for a reliable structure by operating the oscillator core at low supply and the buffer at high supply. The second balanced oscillator can generate a differential signal. The feedback filter consists of a left-handed transmission-line LC network by cascading three unit cells. At a 1 MHz frequency offset from the carrier of 3.818 GHz, the phase noise is −131.73 dBc/Hz, and the FOM of the 2nd oscillator is −188.4 dBc/Hz. High supply voltage operation shows phase noise degradation. The third GaN cross-coupled VCO uses 8-shaped inductors. The VCO uses a pair of drain inductors to improve the Q-factor of the LC tank, and it uses 8-shaped inductors for magnetic coupling noise suppression. At the VCO-core supply of 1.3 V and high buffer supply, the FOM at 6.397 GHz is −190.09 dBc/Hz. This work enhances the design techniques for reliable GaN HEMT oscillators and knowledge to design high-performance circuits. Full article

The low nitrogen-use efficiency (NUE) in sandy soils, due to high porosity and poor nutrient retention, necessitates proper management in fertilization. This study aims to evaluate the effect of biochar-coated urea (BCU) with different coating thicknesses and nitrogen doses on soil nitrogen content, nitrogen uptake, NUE, growth, and yield of sweet corn in sandy soil. The experiment used a factorial randomized block design with two factors, including biochar coating thicknesses (i.e., 14% and 29%) and fertilization doses (i.e., 50%, 100%, 150%, 200%, and 250%). The results showed that the 29% biochar coating thickness led to 9.9–21.3% higher plant height, N uptake, and N-use efficiency, but it led to 22.8% lower yield, as compared to the 14% biochar coating thickness. Additionally, the application of BCU doses of 100% and 150% (~161 and 241.5 kg N/ha) led to 9.2–97.3% higher maize growth, yield, N uptake, and NEU as compared to the other doses (i.e., 50%, 100%, 250%). This study confirmed that the combination of a 29% biochar coating thickness with 150% of the recommended BCU dose (~241.5 kg N/ha) was the best combination, resulting in the highest N uptake, growth, and yield of maize. Full article

This study introduces the Skeptical Optimism Scale (SkO) and presents preliminary evidence of its content, construct, and criterion validity. Skeptical optimism refers to dispositional tendencies of having general positive expectations about the future, conditional on critical analysis and in-depth exploration of (potential negative) outcomes. We developed an initial pool of 31 items that explore positive expectations in three main life domains (finding solutions to difficult problems, mastering novel and challenging tasks, and effectively dealing with general life challenges) that were subject to content analysis by eight independent raters. The remaining items were tested for criterion and predictive validity in two samples (N = 198 and N = 417 participants). Factor analyses supported a three-factor structure and the refined 17-item version of the scale showed good reliability and validity. To support applications in settings requiring brief instruments, we also developed a 9-item version, preserving the factorial structure and psychometric qualities of the original scale. The results show that the 17 as well as 9-item SkO scales have a good criterion validity as they positively and significantly correlate with the core self-evaluation scale, critical thinking disposition, and grit. Moreover, our results show that the SkO has good predictive validity as it is the only significant predictor of the creativity quotient in our sample. Full article

Investigating the factors influencing rice grain yield (GY) is critical for optimizing nitrogen (N) management and enhancing resource use efficiency in rice cultivation. However, few studies have comprehensively investigated the factors affecting rice GY, considering an entire influence chain encompassing rice N uptake, growth indicators, and GY components. In this study, field experiment with six different N fertilizer rates (0, 60, 120, 180, 225, and 300 kg N ha⁻¹, i.e., N0, N60, N120, N180, N225, and N300) was conducted in the Jianghan Plain in the Middle Reaches of the Yangtze River, China, to comprehensively elucidate the factors influencing rice GY from aspects of rice N uptake, growth indicators, and GY components and determine the optimal N fertilizer rate. The results showed that rice GY and N uptake initially increased and then either stabilized or declined with higher N fertilizer rate, while apparent N loss escalated with increased N fertilizer rate. The application of N fertilizer significantly promoted the increase in straw N uptake, which was significantly positively correlated with growth indicators (p < 0.05). Among all GY components, panicle number per hill was the most significant positive factor influencing rice GY, and it was significantly positively correlated with all rice growth indicators (p < 0.05). In addition, N180 was the optimal N fertilizer rate, ensuring more than 95% of maximum GY and reducing N loss by 74% and 39% compared to N300, respectively. Meanwhile, the average N balance for N180 remained below 60 kg N ha⁻¹. In conclusion, optimizing the N fertilizer application in paddy fields can effectively maintain stable rice GY and minimize environmental pollution. Full article

Creating a comfortable microclimate in the premises of buildings is currently becoming one of the priorities in the field of architecture, construction and engineering systems. The increased attention from the scientific community to this topic is due not only to the desire to ensure healthy and favorable conditions for human life but also to the need for the rational use of energy resources. This area is becoming particularly relevant in the context of global challenges related to climate change, rising energy costs and increased environmental requirements. Practice shows that any technical solutions to ensure comfortable temperature, humidity and air exchange in rooms should be closely linked to the concept of energy efficiency. This allows one not only to reduce operating costs but also to significantly reduce greenhouse gas emissions, thereby contributing to sustainable development and environmental safety. In this connection, this study presents a parametric assessment of the influence of climatic and geometric factors on the aerodynamic characteristics of the air cavity, which affect the heat exchange process in the ventilated layer of curtain wall systems. The assessment was carried out using a combined analytical calculation method that provides averaged thermophysical parameters, such as mean air velocity ($V_s$), average internal surface temperature ($t_{in.s}^{av}$), and convective heat transfer coefficient (${\alpha }_s$) within the air cavity. This study resulted in empirical average values, demonstrating that the air velocity within the cavity significantly depends on atmospheric pressure and façade height difference. For instance, a 10-fold increase in façade height leads to a 4.4-fold increase in air velocity. Furthermore, a three-fold variation in local resistance coefficients results in up to a two-fold change in airflow velocity. The cavity thickness, depending on atmospheric pressure, was also found to affect airflow velocity by up to 25%. Similar patterns were observed under ambient temperatures of +20 °C, +30 °C, and +40 °C. The analysis confirmed that airflow velocity is directly affected by cavity height, while the impact of solar radiation is negligible. However, based on the outcomes of the analytical model, it was concluded that the method does not adequately account for the effects of solar radiation and vertical temperature gradients on airflow within ventilated façades. This highlights the need for further full-scale experimental investigations under hot climate conditions in South Kazakhstan. The findings are expected to be applicable internationally to regions with comparable climatic characteristics. Ultimately, a correct understanding of thermophysical processes in such structures will support the advancement of trends such as Lightweight Design, Functionally Graded Design, and Value Engineering in the development of curtain wall systems, through the optimized selection of façade configurations, accounting for temperature loads under specific climatic and design conditions. Full article

Two solvents, n-hexane and ethyl acetate, were employed to extract oil from Asphodelus tenuifolius Cav. seeds using the Soxhlet extraction technique. The process was optimized using Central Composite Design (CCD) and Response Surface Methodology (RSM). ANOVA and a second-order polynomial equation were applied to evaluate the effects of key operational factors, including extraction time (20–60 min) and solvent-to-solid ratio (0.2–0.6 g/mL), on oil yield. The physicochemical properties, fatty acid composition, and functional groups of the extracted oil were analyzed. While both solvents influenced oil yield and quality, the fatty acid composition remained consistent, with unsaturated fatty acids, particularly linoleic acid, identified as the main components. Under optimized conditions, the highest oil yields were 22% with n-hexane and 19.91% with ethyl acetate. FTIR spectroscopy confirmed the presence of ester groups, suggesting potential applications in biodiesel production. These findings offer valuable insights for producing oils rich in unsaturated fatty acids for food, cosmetic and renewable energy industries. These findings pave the way for further advancements in industrial applications by promoting the sustainable use of plant-derived oils. Full article

Wireless sensor networks provide a solution for structural health monitoring of aviation pipelines. In the installation environment of aviation pipelines, widespread vibrations can be utilized to extract energy through vibration energy harvesting technology to achieve self-powering of sensors. This study analyzed the vibration characteristics of aviation pipeline structures. The vibration characteristics and influencing factors of typical aviation pipeline structures were obtained through simulations and experiments. An N-shaped symmetric vibration energy harvester was designed considering the limited space in aviation pipeline structures. To improve the efficiency of electrical energy extraction from the vibration energy harvester, expand its operating frequency band, and achieve efficient vibration energy harvesting, this study first analyzed its natural frequency characteristics through theoretical analysis. Finite element simulation software was then used to analyze the effects of the external excitation acceleration direction, mass and combination of counterweights, piezoelectric sheet length, and piezoelectric material placement on the output power of the energy harvester. The structural parameters of the vibration energy harvester were optimized, and the optimal working conditions were determined. The experimental results indicate that the N-shaped symmetric vibration energy harvester designed and optimized in this study improves the efficiency of vibration energy harvesting and can be arranged in the limited space of aviation pipeline structures. It achieves efficient energy harvesting under multi-modal conditions, different excitation directions, and a wide operating frequency band, thus meeting the practical application requirement and engineering feasibility of aircraft design. Full article

The present study aimed to develop biodegradable films formulated with pectin/carboxymethyl cellulose (CMC) and cinnamon essential oil, investigating the effects of CP treatment time on the properties of the films. The developed films were used as packaging to evaluate the shelf life of chicken meat. Biodegradable films were produced from a film-forming solution containing pectin/CMC, glycerol (30%), and cinnamon essential oil (2%). All formulations included the essential oil, and the control group corresponded to the film that was not subjected to CP treatment. The CP treatments were applied at 22.5 L/min, 20 kV, and 80 kHz for 10, 20, and 30 min. The results showed that increasing CP treatment time led to a progressive reduction in apparent viscosity, indicating improved homogeneity of the polymer system. Hydrophobicity increased with treatment time, as shown by a higher contact angle (from 51.15° to 62.38°), resulting in lower water solubility. Mechanical properties were also enhanced, with tensile strength rising from 3.29 MPa to 6.74 MPa after 30 min of CP. Biodegradability improved with treatment time, reaching 99.51% mass loss after 15 days for the longest exposure. Films produced from the solution treated for 30 min (FCP30) were most effective in extending the shelf life of chicken breast fillets, reducing lipid oxidation (TBARS: 61.9%), peroxide content (58.7%), and microbial spoilage (TVB-N: 59.2%) compared to the untreated film. Overall, the results highlight the importance of CP treatment time as a key factor in enhancing film performance, supporting its application in sustainable active packaging. Full article

This research presents the development and management principles of mobile resonant electrical systems designed for high-voltage generation, intended for non-destructive diagnostics of insulation in high-power electrical equipment. The core of the system is a series inductive–capacitive (LC) circuit characterized by a high quality (Q) factor and operating at high frequencies, typically in the range of 40–50 kHz or higher. Practical implementations of the LC circuit with Q-factors exceeding 200 have been achieved using advanced materials and configurations. Specifically, ceramic capacitors with a capacitance of approximately 3.5 nF and Q-factors over 1000, in conjunction with custom-made coils possessing Q-factors above 280, have been employed. These coils are constructed using multi-core, insulated, and twisted copper wires of the Litzendraht type to minimize losses at high frequencies. Voltage amplification within the system is effectively controlled by adjusting the current frequency, thereby maximizing voltage across the load without increasing the system’s size or complexity. This frequency-tuning mechanism enables significant reductions in the weight and dimensional characteristics of the electrical system, facilitating the development of compact, mobile installations. These systems are particularly suitable for on-site testing and diagnostics of high-voltage insulation in power cables, large rotating machines such as turbogenerators, and other critical infrastructure components. Beyond insulation diagnostics, the proposed system architecture offers potential for broader applications, including the charging of capacitive energy storage units used in high-voltage pulse systems. Such applications extend to the synthesis of micro- and nanopowders with tailored properties and the electrohydropulse processing of materials and fluids. Overall, this research demonstrates a versatile, efficient, and portable solution for advanced electrical diagnostics and energy applications in the high-voltage domain. Full article

Background: Gastric signet-ring cell carcinoma (GSRCC) is associated with a poor prognosis, and the effectiveness of neoadjuvant chemotherapy (NAC) in improving survival outcomes remains inconclusive. This study aimed to evaluate the impact of NAC on survival in patients with GSRCC. Methods: This retrospective cohort study included GSRCC patients from two databases: the National Cancer Center (n = 1289) and SEER (n = 1773), all of whom underwent radical surgery between January 2011 and January 2018. The primary endpoint was overall survival (OS) after surgery. Kaplan–Meier survival curves were generated, and multivariate Cox regression analyses were performed to adjust for confounding factors. Additionally, subgroup analyses were conducted to assess the potential survival benefits of NAC in specific patient subsets. Results: NAC use was limited, with 24.6% (436/1773) of patients in the SEER cohort and 22.6% (292/1289) of patients in the NCC cohort receiving NAC. The median follow-up duration was 30 months (range: 8–131 months; IQR: 24–70 months). In the SEER cohort, the 3-year and 5-year survival rates were 47.4% and 41.3%, respectively, whereas in the NCC cohort, they were 82.4% and 73.9%. Multivariate analysis identified race, tumor size, cTNM stage, pT stage, and pN stage as independent predictors of survival in the SEER cohort (all p < 0.05). In the NCC cohort, age, tumor size, and cTNM stage were significant predictors (p < 0.05). NAC did not demonstrate a significant OS benefit in either cohort (SEER: p = 0.653; NCC: p = 0.139). Subgroup analyses focusing on mid/distal tumor locations and cTNM stages II/III indicated a significant trend towards improved survival with NAC (all p < 0.001). Conclusions: NAC showed limited efficacy in unselected GSRCC patients. However, its selective application in patients with mid/distal tumors or locally advanced tumors (cTNM II/III) may offer potential survival benefits. Further studies are needed to explore tailored NAC strategies as a means to improve outcomes in this highly aggressive cancer. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge of non-stationary vibration signals. To achieve this, a novel hybrid method is proposed that integrates the Gazelle Optimization Algorithm (GOA), Feature Mode Decomposition (FMD), and a Transformer-based classification model. Specifically, GOA is employed to automatically optimize key FMD parameters, including the number of filters (K), filter length (L), and number of decomposition modes (N), enabling high-resolution signal decomposition. From the resulting intrinsic mode functions (IMFs), statistical time domain features—peak factor, impulse factor, waveform factor, and clearance factor—are extracted to form feature vectors. After feature extraction, the resulting vectors are utilized by a Transformer to classify fault types. Benchmark comparisons with other decomposition and learning approaches highlight the enhanced performance of the proposed framework. The model achieves a 95.83% classification accuracy on the test set and an average of 96.7% under five-fold cross-validation, demonstrating excellent accuracy and generalization. What distinguishes this research is its incorporation of a GOA–FMD and a Transformer-based attention mechanism for pattern recognition into a unified and efficient diagnostic framework. With its high effectiveness and adaptability, the proposed framework shows great promise for real-world applications in the smart fault monitoring of power systems. Full article