Search Results (4,380)

In this work, we consider a point-to-point underwater optical wireless communication scenario where an underwater sensor (US) transmits its sensing data to a remotely operated vehicle (ROV). Before the US transmits its data to the ROV, the ROV performs simultaneous lightwave information and power transfer (SLIPT), delivering both control data and lightwave power to the US. Under the considered scenario, our objective is to maximize energy harvesting at the US while supporting predetermined communication performance between the two nodes. To achieve this objective, we develop a hierarchical deep Q-network (DQN)–deep deterministic policy gradient (DDPG)-based online algorithm. This algorithm involves two reinforcement learning agents: the ROV and US. The role of the ROV agent is to determine an optimal beam-divergence angle that maximizes the received optical signal power at the US while ensuring a seamless optical link. Meanwhile, the US agent, which is influenced by the decision of the ROV agent, is responsible for determining the time-switching and power-splitting ratios to maximize energy harvesting without compromising the required communication performance. Unlike existing studies that do not account for adaptive parameter control in underwater SLIPT, the proposed algorithm’s adaptive nature allows for the dynamic fine-tuning of optimization parameters in response to varying underwater environmental conditions and diverse user requirements. Full article

Alterations to intestinal microbiota are assumed to occur in the pathogenesis of inflammatory bowel disease (IBD). This study aims to analyze the association of fecal microbiota composition, body composition, and lipid characteristics in patients with Crohn’s disease (CD). In our cross-sectional study, patients with CD were enrolled and blood and fecal samples were collected. Clinical and endoscopic disease activity and body composition were assessed and laboratory tests were made. Fecal bacterial composition was analyzed using the shotgun method. Microbiota alterations based on obesity, lipid parameters, and disease characteristics were analyzed. In this study, 27 patients with CD were analyzed, of which 37.0% were obese based on visceral fat area (VFA). Beta diversities were higher in non-obese patients (p < 0.001), but relative abundances did not differ. C. innocuum had a higher abundance at a high cholesterol level than Bacillota (p = 0.001, p = 0.0034). Adlercreutzia, B. longum, and Blautia alterations were correlated with triglyceride levels. Higher Clostridia (p = 0.009) and B. schinkii (p = 0.032) and lower Lactobacillus (p = 0.035) were connected to high VFA. Disease activity was coupled with dysbiotic elements. Microbiota alterations in obesity highlight the importance of gut microbiota in diseases with a similar inflammatory background and project therapeutic options. Full article

SiC-coated C/C composites with mechanical damage were repaired by the heat treatment method and slurry painting–preoxidation. The effects of different process parameters on the microstructure, interface bonding and oxidation resistance of Si-SiC repair coatings at 1773 K for 10 h were studied. The results show that the repair coating is tightly bonded to the original coating and the C/C substrate, and there is no obvious interface. Under the optimal parameters, the weight reduction in the repaired specimen merely amounted to 0.32% subsequent to oxidation at 1773 K for 10 h, and the mass loss was 74.79% lower than that of the damaged specimen, being proximate to that of the intact specimen. The objective of this work lies in achieving a greater density of the coating within the repair zone by manipulating the diverse powder ratios and preoxidation temperatures in the repair slurry, thereby safeguarding the C/C composite material against oxidation during its service. It offers a convenient and highly efficient approach for the repair of coatings with substantial size defects, significantly prolonging the service life of the material. Full article

Based on the principles of biomimicry, evolutionary algorithms (EAs) have been widely applied across diverse domains to tackle practical challenges. However, the inherent limitations of these algorithms call for further refinement to strike a delicate balance between global exploration and local exploitation. Thus, this paper introduces a novel multi-strategy enhanced hybrid algorithm called MHWACO, which integrates a Whale Optimization Algorithm (WOA) and Ant Colony Optimization (ACO). Initially, MHWACO employs Gaussian perturbation optimization for individual initialization. Subsequently, individuals selectively undertake either localized exploration based on the refined WOA or global prospecting anchored in the Golden Sine Algorithm (Golden-SA), determined by transition probabilities. Inspired by the collaborative behavior of ant colonies, a Flight Ant (FA) strategy is proposed to guide unoptimized individuals toward potential global optimal solutions. Finally, the Gaussian scatter search (GSS) strategy is activated during low population activity, striking a balance between global exploration and local exploitation capabilities. Moreover, the efficacy of Support Vector Regression (SVR) and random forest (RF) as regression models heavily depends on parameter selection. In response, we have devised the MHWACO-SVM and MHWACO-RF models to refine the selection of parameters, applying them to various real-world problems such as stock prediction, housing estimation, disease forecasting, fire prediction, and air quality monitoring. Experimental comparisons against 9 newly proposed intelligent optimization algorithms and 9 enhanced algorithms across 34 benchmark test functions and the CEC2022 benchmark suite, highlight the notable superiority and efficacy of MSWOA in addressing global optimization problems. Finally, the proposed MHWACO-SVM and MHWACO-RF models outperform other regression models across key metrics such as the Mean Bias Error (MBE), Coefficient of Determination ($R^2$), Mean Absolute Error (MAE), Explained Variance Score (EVS), and Median Absolute Error (MEAE). Full article

The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumption, an improved multi-objective evolutionary algorithm with decomposition (MOEA/D) based on reinforcement learning is proposed. Firstly, three initialization strategies are used to generate the initial population in a certain ratio, and four variable neighborhood search strategies are combined to increase the local search capability of the algorithm. Second, a parameter adaptation strategy based on Q-learning is proposed to guide the population to select the optimal parameters to increase diversity. Finally, the performance of the proposed algorithm is analyzed and evaluated by comparing Q-MOEA/D with IMOEA/D and NSGA-II through different sizes of Kacem and BRdata benchmark cases and production examples of automotive engine cooling system manufacturing. The results show that the Q-MOEA/D algorithm outperforms the other two algorithms in solving the energy-efficient scheduling problem for flexible job shops. Full article

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1–110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system’s performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets. Full article

Yeasts play an essential role in the aroma and sensory profiles of wines. Spontaneous fermentations were carried out at the newly built winery of Granxa D’Outeiro. Yeasts were isolated from must at different stages of fermentation. Colonies belonging to Saccharomyces cerevisiae were characterised at the strain level by mtDNA-RFLPs. General chemical parameters and aroma profiles of the wines were determined using official OIV methodology and GC-MS analysis, respectively. The diversity of S. cerevisiae per fermentation ranged from 5 to 13 different strains depending on the grapevine variety. Out of 24 strains, strain B was the dominant yeast in most fermentations at different proportions, but strains D, E, and H also reached up to 25% of the total population in some fermentations. The yeast diversity was higher in the Lado fermentation than in those containing Treixadura. The chemical compositions of the wines revealed differences among them, with Loureira and Albariño wines showing the highest content of volatile compounds. The evaluation of their technological properties revealed the oenological potential of some strains of S. cerevisiae. The strains showing the best scores were selected to be used in future vintages to enhance the typicality of wines in the Granxa D’Outeiro winery. Full article

In patients with heart failure, evaluating left ventricular (LV) diastolic function is vital, offering crucial insights into hemodynamic impact and prognostic accuracy. Echocardiography remains the primary imaging modality for diastolic function assessment, and using it effectively requires a profound understanding of the underlying pathology. This review covers four main topics: first, the fundamental driving forces behind each phase of normal diastolic dynamics, along with the physiological basis of two widely used echocardiographic assessment parameters, E/e’ and mitral annulus early diastolic velocity (e’); second, the intricate functional relationship between the left atrium and LV in patients with varying degrees of LV diastolic dysfunction (LVDD); third, the role of stress echocardiography in diagnosing LVDD and the significance of echocardiographic parameter changes; and fourth, the clinical utility of evaluating diastolic function from echocardiography images across diverse cardiovascular care areas. Full article

With the advancement of computer vision technology, the demand for fruit recognition in agricultural automation is increasing. To improve the accuracy and efficiency of recognizing young red pears, this study proposes an improved model based on the lightweight YOLOv9s, termed YOLOv9s-Pear. By constructing a feature-rich and diverse image dataset of young red pears and introducing spatial-channel decoupled downsampling (SCDown), C2FUIBELAN, and the YOLOv10 detection head (v10detect) modules, the YOLOv9s model was enhanced to achieve efficient recognition of small targets in resource-constrained agricultural environments. Images of young red pears were captured at different times and locations and underwent preprocessing to establish a high-quality dataset. For model improvements, this study integrated the general inverted bottleneck blocks from C2f and MobileNetV4 with the RepNCSPELAN4 module from the YOLOv9s model to form the new C2FUIBELAN module, enhancing the model’s accuracy and training speed for small-scale object detection. Additionally, the SCDown and v10detect modules replaced the original AConv and detection head structures of the YOLOv9s model, further improving performance. The experimental results demonstrated that the YOLOv9s-Pear model achieved high detection accuracy in recognizing young red pears, while reducing computational costs and parameters. The detection accuracy, recall, mean precision, and extended mean precision were 0.971, 0.970, 0.991, and 0.848, respectively. These results confirm the efficiency of the SCDown, C2FUIBELAN, and v10detect modules in young red pear recognition tasks. The findings of this study not only provide a fast and accurate technique for recognizing young red pears but also offer a reference for detecting young fruits of other fruit trees, significantly contributing to the advancement of agricultural automation technology. Full article

Artificial water bodies in Central Asia offer unique environments in which to study plankton diversity influenced by topographic barriers. However, the complexity of these ecosystems and limited comprehensive studies in the region challenge our understanding. In this study, we systematically investigated the water environment parameters and phytoplankton community structure by surveying 14 artificial waters on the southern side of the Altai Mountains and the northern and southern sides of the Tianshan Mountains in the Xinjiang region. The survey covered physical and nutrient indicators, and the results showed noticeable spatial differences between waters in different regions. The temperature, dissolved oxygen, total nitrogen, and total phosphorus of artificial water in the southern Altai Mountains vary greatly. In contrast, the waters in the northern Tianshan Mountains have more consistent physical indicators. The results of phytoplankton identification showed that the phytoplankton communities in different regions are somewhat different, with diatom species being the dominant taxon. The cluster analysis and the non-metric multidimensional scaling (NMDS) results also confirmed the variability of the phytoplankton communities in the areas. The variance partitioning analysis (VPA) results showed that climatic and environmental factors can explain some of the variability of the observed data. Nevertheless, the residual values indicated the presence of other unmeasured factors or the influence of stochasticity. This study provides a scientific basis for regional water resource management and environmental protection. Full article

Atherosclerosis (AS) is a chronic inflammatory vascular disease. Dysregulated lipid metabolism, oxidative stress, and inflammation are the major mechanisms implicated in the development of AS. In addition, evidence suggests that gut dysbiosis plays an important role in atherogenesis, and modulation of the gut microbiota with probiotics and phenolic compounds has emerged as a promising strategy for preventing and treating AS. It has been shown that probiotics and phenolic compounds can improve atherosclerosis-related parameters by improving lipid profile, oxidative stress, and inflammation. In addition, these compounds may modulate the diversity and composition of the gut microbiota and improve atherosclerosis. The studies evaluated in the present review showed that probiotics and phenolic compounds, when consumed individually, improved atherosclerosis by modulating the gut microbiota in various ways, such as decreasing gut permeability, decreasing TMAO and LPS levels, altering alpha and beta diversity, and increasing fecal bile acid loss. However, no study was found that evaluated the combined use of probiotics and phenolic compounds to improve atherosclerosis. The available literature highlights the synergistic potential between phenolic compounds and probiotics to improve their health-promoting properties and functionalities. This review aims to summarize the available evidence on the individual effects of probiotics and phenolic compounds on AS, while providing insights into the potential benefits of nutraceutical approaches using probiotic strains, quercetin, and resveratrol as potential adjuvant therapies for AS treatment through modulation of the gut microbiota. Full article

This study proposes a compact, low-profile, four-port dual-band monopole multiple-input-multiple-output (MIMO) antenna array for unmanned aerial vehicle (UAV) communication systems. Each monopole antenna of the array features a modified T-shaped radiator configuration and is printed on a Rogers RT5880 substrate with compact dimensions of 134.96 mm × 134.96 mm × 0.8 mm. A four-element square MIMO configuration with sequential 0°, 90°, 180°, and 270° rotations was integrated smoothly into the UAV body. A prototype of the MIMO array was fabricated and experimentally evaluated, with measured results showing a close correlation to simulated results. The proposed dual-band monopole antenna demonstrated one of the widest impedance bandwidths of 46.15% at 2.4 GHz (2.04 to 3.25 GHz) IEEE 802.11b and 31.85% at 5.8 GHz (5.37 to 7.38 GHz) IEEE 802.11a on a thin 0.0064 λ_o substrate while achieving high transmission efficiency. The isolation of the proposed four-port MIMO design was measured at 23 dB at 2.4 GHz and 19 dB at 5.8 GHz. The MIMO array’s total efficiency of each monopole antenna was measured at 96% at 2.4 GHz and 89% at 5.8 GHz. The design has measured diversity parameters such as an ECC below 0.01 and a DG of approximately 10. Based on these results, the proposed design suits the UAV communication system. Full article

Autochthonous Italian pomegranate accessions are still underexplored, although they could be an important resource for fresh consumption, processing, and nutraceutical uses. Therefore, it is necessary to characterize the local germplasm to identify genotypes with desirable traits. In this study, six old Italian pomegranate landraces and a commercial cultivar (Dente di Cavallo) were investigated, evaluating their fruit pomological parameters, physicochemical (TSS, pH, TA, and color) characteristics, sugar content, and aromatic profiles (HeadSpace Solid-Phase MicroExtraction (HS-SPME)) coupled with Gas Chromatographyass Spectrometry (GC–MS) of pomegranate juices. Significant differences were observed in the size and weight of the seed and fruits (127.50–525.1 g), as well as the sugar content (100–133.6 gL⁻¹), the sweetness (12.9–17.6 °Brix), and the aroma profiles. Over 56 volatile compounds, predominantly alcohols (56%), aldehydes (24%), and terpenes (9%), were simultaneously quantified. Large variability among the genotypes was also statistically confirmed. The results indicate a strong potential for commercial exploitation of this germplasm, both as fresh and processed fruit, and highlight its versatility for diverse applications. The genetic diversity of the autochthonous pomegranate accessions represents a precious heritage to be preserved and enhanced. This work represents a preliminary step toward a more comprehensive characterization and qualitative valorization of the Italian pomegranate germplasm. Full article

The multi-functional integrated RF system (MIRFS) is a crucial component of aircraft onboard systems. In the testability design process, traditional methods cannot effectively deal with the inevitable differences between system designs and usage requirements. By considering the MIRFS’s full lifecycle characteristics, a new testability allocation method based on multi-criteria decision-making (MCDM) is proposed in this paper. Firstly, the testability framework was constructed and more than 100 indicators were given, which included both different system-level and inter-system indicators. Secondly, to manage parameter diversity and calculate complexity, the basic 12 testability indicators were optimized through the Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) method. Thirdly, the detailed testability parameters were obtained by using the Decision-Making Trial and Evaluation Laboratory and Analytic Network Process (DEMATEL-ANP) to reduce the subjectivity and uncertainty. Finally, an example was utilized, and the results show that the MCDM method is significantly better than traditional methods in terms of accuracy and effectiveness, which will provide a more scientific basis for the MIRFS testability design process. Full article

The high spatial complexities of soil temperature modeling over semiarid land have challenged the calibration–forecast framework, whose composited objective lacks comprehensive evaluation. Therefore, this study, based on the Noah land surface model and its full parameter table, utilizes two global searching algorithms and eight kinds of objectives with dimensional-varied metrics, combined with dense site soil moisture and temperature observations of central Tibet, to explore different metrics’ performances on the spatial heterogeneity and uncertainty of regional land surface parameters, calibration efficiency and effectiveness, and spatiotemporal complexities in surface forecasting. Results have shown that metrics’ diversity has shown greater influence on the calibration—predication framework than the global searching algorithm’s differences. The enhanced multi-objective metric (EMO) and the enhanced Kling–Gupta efficiency (EKGE) have their own advantages and disadvantages in simulations and parameters, respectively. In particular, the EMO composited with the four metrics of correlated coefficient, root mean square error, mean absolute error, and Nash–Sutcliffe efficiency has shown relatively balanced performance in surface soil temperature forecasting when compared to other metrics. In addition, the calibration–forecast framework that benefited from the EMO could greatly reduce the spatial complexities in surface soil modeling of semiarid land. In general, these findings could enhance the knowledge of metrics’ advantages in solving the complexities of the LSM’s parameters and simulations and promote the application of the calibration–forecast framework, thereby potentially improving regional surface forecasting over semiarid regions. Full article