Search Results (33,025)

Soil salinization and selenium (Se) deficiency threaten global food security. This study developed a composite bioinoculant combining ACC deaminase-producing Serratia liquefaciens and biogenically synthesized nano-selenium (SeNPs) to alleviate saline–alkali stress and enhance Se nutrition in rice (Oryza sativa L.). A strain of S. liquefaciens with high ACC deaminase activity was isolated and used to biosynthesize SeNPs with stable physicochemical properties. Pot experiments showed that application of the composite inoculant (S3: S. liquefaciens + 40 mmol/L SeNPs) significantly improved seedling biomass (fresh weight +53.8%, dry weight +60.6%), plant height (+31.6%), and root activity under saline–alkali conditions. S3 treatment also enhanced panicle weight, seed-setting rate, and grain Se content (234.13 μg/kg), meeting national Se-enriched rice standards. Moreover, it increased rhizosphere soil N, P, and K availability and improved microbial α-diversity. This is the first comprehensive demonstration that a synergistic bioformulation of ACC deaminase PGPR and biogenic SeNPs effectively mitigates saline–alkali stress, enhances soil fertility, and enables safe Se biofortification in rice. Full article

Divinylisoprene rubber, a copolymer of butadiene and isoprene, is used as raw material for rubber technical products, combining isoprene rubber’s elasticity and butadiene rubber’s wear resistance. These properties depend quantitatively on the copolymer composition, which depends on the kinetics of its synthesis. This work aims to theoretically describe how the monomer mixture composition in the butadiene–isoprene copolymerization affects the activity of the TiCl₄–Al(i-C₄H₉)₃ catalytic system (expressed by active sites concentration) via kinetic modeling. This enables development of a reliable kinetic model for divinylisoprene rubber synthesis, predicting reaction rate, molecular weight, and composition, applicable to reactor design and process intensification. Active sites concentrations were calculated from experimental copolymerization rates and known chain propagation constants for various monomer compositions. Kinetic equations for active sites formation were based on mass-action law and Langmuir monomolecular adsorption theory. An analytical equation relating active sites concentration to monomer composition was derived, analyzed, and optimized with experimental data. The results show that monomer composition’s influence on active sites concentration is well described by a two-step kinetic model (physical adsorption followed by Ti–C bond formation), accounting for competitive adsorption: isoprene adsorbs more readily, while butadiene forms more stable active sites. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature. Full article

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

Salinization of olive orchards constitutes a front-line agronomic challenge for farmers, consumers, and the scientific community as food security, olive logistics, and land use become more unsustainable and problematic. Plantlets of two olive varieties (var. Kalamon and var. Koroneiki) were tested for their performance under soil saline conditions, in which L-methionine, choline-Cl, and L-proline betaine were applied foliarly to alleviate adverse effects. The ‘Kalamon’ variety ameliorated its photosynthetic rates when L-proline betaine and L-methionine were administered at low saline exposure. The stressed varieties achieved higher leaf transpiration rates in the following treatment order: choline-Cl > L-methionine > L-proline betaine. Choline chloride supported stomatal conductance in stressed var. Kalamon olives without this pattern, which was also followed by var. Koroneiki. Supplementation regimes created a mosaic of responses on varietal water use efficiency under stress. The total phenolic content in leaves increased in both varieties after exogenous application only at the highest levels of saline stress. None of the substances applied to olive trees could stand alone as a tool to mitigate salinity stress in order to be recommended as a solid agronomic practice. The residual exploitation of amino acids by the olive orchard microbiome must also be considered as part of an environmentally friendly, integrated strategy to mitigate salinity stress. Full article

Network connectivity and area coverage are the most important aspects in the applications of wireless sensor networks (WSNs). The resource and energy constraints of sensor nodes, operational conditions, and network size pose challenges to the optimal coverage of targets in the region of interest (ROI). The main idea is to achieve maximum area coverage and connectivity with strategic deployment and the minimal number of sensor nodes. This work addresses the problem of network area coverage in randomly distributed WSNs and provides an efficient deployment strategy using an enhanced version of cuckoo search optimization (ECSO). The “sequential update evaluation” mechanism is used to mitigate the dependency among dimensions and provide highly accurate solutions, particularly during the local search phase. During the preference random walk phase of conventional CSO, particle swarm optimization (PSO) with adaptive inertia weights is defined to accelerate the local search capabilities. The “opposition-based learning (OBL)” strategy is applied to ensure high-quality initial solutions that help to enhance the balance between exploration and exploitation. By considering the opposite of current solutions to expand the search space, we achieve higher convergence speed and population diversity. The performance of ECSO-OBL is evaluated using eight benchmark functions, and the results of three cases are compared with the existing methods. The proposed method enhances network coverage with a non-uniform distribution of sensor nodes and attempts to cover the whole ROI with a minimal number of sensor nodes. In a WSN with a 100 m² area, we achieved a maximum coverage rate of 98.45% and algorithm convergence in 143 iterations, and the execution time was limited to 2.85 s. The simulation results of various cases prove the higher efficiency of the ECSO-OBL method in terms of network coverage and connectivity in WSNs compared with existing state-of-the-art works. Full article

Lung cancer is one of the most common cancers and the leading cause of cancer-related death worldwide. Despite advancements, the overall survival rate for lung cancer remains between 10% and 20% in most countries. However, recent progress in diagnostic tools and therapeutic strategies has led to meaningful improvements in survival outcomes, highlighting the growing importance of personalized management based on accurate disease assessment. ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become essential in the management of lung cancer, serving as a key imaging modality for initial diagnosis, staging, treatment response assessment, and follow-up evaluation. Recent developments in radiomics and artificial intelligence (AI), including machine learning and deep learning, have revolutionized the analysis of complex imaging data, enhancing the diagnostic and predictive capabilities of FDG PET/CT in lung cancer. However, the limitations of FDG, including its low specificity for malignancy, have driven the development of novel oncologic radiotracers. One such target is fibroblast activation protein (FAP), a type II transmembrane glycoprotein that is overexpressed in activated cancer-associated fibroblasts within the tumor microenvironment of various epithelial cancers. As a result, FAP-targeted radiopharmaceuticals represent a novel theranostic approach, offering the potential to integrate PET imaging with radioligand therapy (RLT). In this review, we provide a comprehensive overview of FDG PET/CT in lung cancer, along with recent advances in AI. Additionally, we discuss FAP-targeted radiopharmaceuticals for PET imaging and their potential application in RLT for the personalized management of lung cancer. Full article

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

As a new type of alloy system composed of five or more principal components, high-entropy alloys demonstrate outstanding comprehensive performance in the field of friction and wear through the synergistic effects of the high-entropy effect, lattice distortion effect, hysteresis diffusion effect and cocktail effect. This paper systematically reviews the research progress on the friction and wear properties of high-entropy alloys. The mechanisms of metal elements such as Al, Ti, Cu and Nb through solid solution strengthening, second-phase precipitation and oxide film formation were analyzed emphatically. And non-metallic elements such as C, Si, and B form and strengthen the regulation laws of their tribological properties. The influence of working conditions, such as high temperature, ocean, and hydrogen peroxide on the friction and wear behavior of high-entropy alloys by altering the wear mechanism, was discussed. The influence of test conditions such as load, sliding velocity and friction pair matching on its friction coefficient and wear rate was expounded. It is pointed out that high-entropy alloys have significant application potential in key friction components, providing reference and guidance for the further development and application of high-entropy alloys. Full article

This study investigates the bond behavior of fabric-reinforced cementitious matrix (FRCM) composites with three common masonry substrates—solid clay bricks (SBs), perforated bricks (PBs), and concrete hollow blocks (HBs)—using knitted polyester grille (KPG) fabric. Through uniaxial tensile tests of the KPG fabric and FRCM system, along with single-lap and double-lap shear tests, the interfacial debonding modes, load-slip responses, and composite utilization ratio were evaluated. Key findings reveal that (i) SB and HB substrates predominantly exhibited fabric slippage (FS) or matrix–fabric (MF) debonding, while PB substrates consistently failed at the matrix–substrate (MS) interface, due to their smooth surface texture. (ii) Prism specimens with mortar joints showed enhanced interfacial friction, leading to higher load fluctuations compared to brick units. PB substrates demonstrated the lowest peak stress (69.64–74.33 MPa), while SB and HB achieved comparable peak stresses (133.91–155.95 MPa). (iii) The FRCM system only achieved a utilization rate of 12–30% in fabric and reinforcement systems. The debonding failure at the matrix–substrate interface is one of the reasons that cannot be ignored, and exploring methods to improve the bonding performance between the matrix–substrate interface is the next research direction. HB bricks have excellent bonding properties, and it is recommended to prioritize their use in retrofit applications, followed by SB bricks. These findings provide insights into optimizing the application of FRCM reinforcement systems in masonry structures. Full article

The present paper reports for the first time the comparison of radar-derived eddy dissipation rate (EDR) and vertical velocity with measurements from six aircraft for an intense squall line crossing Hong Kong. The study objectives are three-fold: (i) to characterise the structural dynamics of the intense squall line; (ii) to identify the dynamical change in EDR and vertical velocity during its eastward propagation across Hong Kong with a view to gaining insight into the intensity change of the squall line and the severity of its impact on aircraft flying near it; (iii) to carry out quantitative comparison of EDR and vertical velocity derived from remote sensing instruments, i.e., weather radars and in situ measurements from aircraft, so that the quality of the former dataset can be evaluated by the latter. During the passage of the squall line and taking reference of the radar reflectivity, vertical circulation and the subsiding flow at the rear, it appeared to be weakening in crossing over Hong Kong, possibly due to land friction by terrain and urban morphology. This is also consistent with the maximum gusts recorded by the dense network of ground-based anemometers in Hong Kong. However, from the EDR and the vertical velocity of the aircraft, the weakening trend was not very apparent, and rather severe turbulence was still recorded by the aircraft flying through the squall line into the region with stratiform precipitation when the latter reached the eastern coast of Hong Kong. In general, the radar-based and the aircraft-based EDRs are consistent with each other. The radar-retrieved maximum vertical velocity may be smaller in magnitude at times, possibly arising from the limited spatial and temporal resolutions of the aircraft data. The results of this paper could be a useful reference for the development of radar-based turbulence products for aviation applications. Full article

China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F₁ score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning. Full article

Biocatalysis is one of the oldest fields that has been used in industrial applications, with one of the earliest purposeful examples being the mass production of acetic acid from an immobilized Acinetobacter strain in the year 1815. Efficiency, specificity, reduced reaction times, lower overall costs, and environmental friendliness are some advantages biocatalysis has over conventional chemical synthesis, which has made biocatalysis increasingly used in industry. We highlight three necessary fields that are fundamental to advancing industrial biocatalysis, including biocatalyst engineering, solvent engineering, and mechanistic engineering. However, the fundamental mechanism of enzyme function is often overlooked or given less attention, which can limit the engineering process. In this review, we describe how mechanistic enzymology benefits industrial biocatalysis by elucidating key fundamental principles, including the k_cat and k_cat/K_m parameters. Mechanistic enzymology presents a unique field that provides in-depth insights into the molecular mechanisms of enzyme activity and includes areas such as reaction kinetics, catalytic mechanisms, structural analysis, substrate specificity, and protein dynamics. In line with the objective of protein engineering to optimize enzyme activity, we summarize a range of strategies reported in the literature aimed at improving the product release rate, the chemical step of catalysis, and the overall catalytic efficiency of enzymes. Further into this review, we delineate kinetic solvent viscosity effects (KSVEs) as a very efficient, cost-effective, and easy-to-perform method to probe different aspects of enzyme reaction mechanisms, including diffusion-dependent kinetic steps and rate-limiting steps. KSVEs are cost-effective because simple kinetic enzyme assays, such as the Michaelis–Menten kinetic approach, can be combined with them without the need for specialized and costly equipment. Other techniques in protein engineering and genetic engineering are also covered in this review. Additionally, we provide information on solvent systems in enzymatic reactions, details on immobilized biocatalysts, and common misconceptions that misguide enzyme design and optimization processes. Full article