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During loading and unloading operations of electric loaders, the traveling system requires frequent starting and braking, a process that has high energy consumption and requires high installed power of the system drive motor. This paper proposes a hydraulic-electric hybrid loader traveling energy-saving system, describes its working principle, designs a hydraulic regenerative braking strategy and an energy-assisted starting strategy, analyzes the braking system in detail, gives a detailed analysis of the braking system, gives how to determine the parameters of the key components such as accumulators and hydraulic motors, and coordinates the powertrain of electric motors and hydraulic pumps/motors. Simulation results show that the scheme can effectively recover and reuse the kinetic energy of the loader during braking, and that it reduces the peak power of the original drive motor by about 19%, and the energy consumption of the whole machine operation by about 30%.

Rice terrace ecosystems (RTEs) offer a wealth of ecosystem services vital to human well-being. Regrettably, the recent period has witnessed a growing trend of abandonment and habitat degradation within RTEs. This study focuses on the Longji Terraces in Guilin, China, using remote sensing data from 1985–2020 to assess habitat quality (HQ) and its spatial-temporal dynamics. Furthermore, the driving factors of HQ is explored through ordinary least squares (OLS) and geographically weighted regression (GWR) models. The research reveals that forested areas are crucial for HQ, with an overall high average HQ, but significant degradation in certain areas. The growth of the tourism industry and the arrangement of the landscape are recognized as the key drivers of RTE-HQ (average annual R2 = 0.75). The findings provide a scientific basis for the conservation and sustainable use of global rice terraces.

Underwater acoustic target recognition (UATR) technology has been implemented widely in the fields of marine biodiversity detection, marine search and rescue, and seabed mapping, providing an essential basis for human marine economic and military activities. With the rapid development of machine-learning-based technology in the acoustics field, these methods receive wide attention and display a potential impact on UATR problems. This paper reviews current UATR methods based on machine learning. We focus mostly, but not solely, on the recognition of target-radiated noise from passive sonar. First, we provide an overview of the underwater acoustic acquisition and recognition process and briefly introduce the classical acoustic signal feature extraction methods. In this paper, recognition methods for UATR are classified based on the machine learning algorithms used as UATR technologies using statistical learning methods, UATR methods based on deep learning models, and transfer learning and data augmentation technologies for UATR. Finally, the challenges of UATR based on the machine learning method are summarized and directions for UATR development in the future are put forward.

Key message Overexpression of the tea plant gene CsbZIP18 in Arabidopsis impaired freezing tolerance, and CsbZIP18 is a negative regulator of ABA signaling and cold stress. Basic region/leucine zipper (bZIP) transcription factors play important roles in the abscisic acid (ABA) signaling pathway and abiotic stress response in plants. However, few bZIP transcription factors have been functionally characterized in tea plants ( Camellia sinensis ). In this study, a bZIP transcription factor, CsbZIP18, was found to be strongly induced by natural cold acclimation, and the expression level of CsbZIP18 was lower in cold-resistant cultivars than in cold-susceptible cultivars. Compared with wild-type (WT) plants, Arabidopsis plants constitutively overexpressing CsbZIP18 exhibited decreased sensitivity to ABA, increased levels of relative electrolyte leakage (REL) and reduced values of maximal quantum efficiency of photosystem II ( F v /F m ) under freezing conditions. The expression of ABA homeostasis- and signal transduction-related genes and abiotic stress-inducible genes, such as RD22 , RD26 and RAB18 , was suppressed in overexpression lines under freezing conditions. However, there was no significant change in the expression of genes involved in the C-repeat binding factor (CBF)-mediated ABA-independent pathway between WT and CsbZIP18 overexpression plants. These results indicate that CsbZIP18 is a negative regulator of freezing tolerance via an ABA-dependent pathway.

Tea plants (Camellia sinensis) are commercially cultivated in >60 countries, and their fresh leaves are processed into tea, which is the most widely consumed beverage in the world. Although several chromosome-level tea plant genomes have been published, they collapsed the two haplotypes and ignored a large number of allelic variations that may underlie important biological functions in this species. Here, we present a phased chromosome-scale assembly for an elite oolong tea cultivar, “Huangdan”, that is well known for its high levels of aroma. Based on the two sets of haplotype genome data, we identified numerous genetic variations and a substantial proportion of allelic imbalance related to important traits, including aroma- and stress-related alleles. Comparative genomics revealed extensive structural variations as well as expansion of some gene families, such as terpene synthases (TPSs), that likely contribute to the high-aroma characteristics of the backbone parent, underlying the molecular basis for the biosynthesis of aroma-related chemicals in oolong tea. Our results uncovered the genetic basis of special features of this oolong tea cultivar, providing fundamental genomic resources to study evolution and domestication for the economically important tea crop.

Tea plant breeding is a topic of great economic importance. However, disease remains a major cause of yield and quality losses. In this study, an anthracnose-resistant cultivar, ZC108, was developed. An infection assay revealed different responses to Colletotrichum sp. infection between ZC108 and its parent cultivar LJ43. ZC108 had greater resistance than LJ43 to Colletotrichum camelliae. Additionally, ZC108 exhibited earlier sprouting in the spring, as well as different leaf shape and plant architecture. Microarray data revealed that the genes that are differentially expressed between LJ43 and ZC108 mapped to secondary metabolism-related pathways, including phenylpropanoid biosynthesis, phenylalanine metabolism, and flavonoid biosynthesis pathways. In addition, genes involved in plant hormone biosynthesis and signaling as well as plant-pathogen interaction pathways were also changed. Quantitative real-time PCR was used to examine the expression of 27 selected genes in infected and uninfected tea plant leaves. Genes encoding a MADS-box transcription factor, NBS-LRR disease-resistance protein, and phenylpropanoid metabolism pathway components (CAD, CCR, POD, beta-glucosidase, ALDH and PAL) were among those differentially expressed in ZC108.

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications.

Ecological quality (EQ) protection constitutes a cornerstone of sustainable development. This study develops a Remote Sensing Ecological Index (RSEI) via the Google Earth Engine (GEE) platform, integrating Theil-Sen slope estimation, spatial autocorrelation, GeoDetector, geographically weighted regression (GWR), and text mining to investigate EQ dynamics in the Li River Basin (1995–2021). Key findings reveal: (1) The basin exhibited a “low-central, high-peripheral” RSEI pattern with a fluctuating upward EQ trend that peaked in 2003 (forest conservation and urban green policies) and 2009 (accelerated farmland-to-forest conversion and energy-saving initiatives). Mean RSEI values progressed from 0.59 to 0.60 and 0.62 across three phases, with 61.06% of areas showing improvement (predominantly construction/agricultural zones) versus 31.52% degradation (concentrated in water-source forests). (2) Land use intensity and elevation emerged as primary determinants of RSEI variability, while tourism activity intensity demonstrated escalating influence over time. (3) RSEI exhibits spatial autocorrelation, with the effects of slope, elevation, land use intensity, and tourism activity varying geographically and undergoing dynamic transitions across regions. (4) Policy evolution in the basin reflects a progressive shift toward sustainable landscape resource management and ecosystem conservation. This study provides crucial insights for preserving karst ecosystems and promoting landscape resource sustainability worldwide.

This paper proposes a method for visualizing three-dimensional non-uniformly sampled data from multibeam echosounder systems (MBESs), aimed at addressing the requirements of monitoring complex and dynamic underwater flow fields. To tackle the challenges associated with spatially non-uniform sampling, the proposed method employs linear interpolation along the radial direction and arc length weighted interpolation in the beam direction. This approach ensures consistent resolution of three-dimensional data across the same dimension. Additionally, an opacity transfer function is generated to enhance the visualization performance of the ray casting algorithm. This function leverages data values and gradient information, including the first and second directional derivatives, to suppress the rendering of background and non-interest regions while emphasizing target areas and boundary features. The simulation and experimental results demonstrate that, compared to conventional two-dimensional beam images and three-dimensional images, the proposed algorithm provides a more intuitive and accurate representation of three-dimensional data, offering significant support for the observation and analysis of spatial flow field characteristics.

Volatile organic compounds (VOCs) are important for teas’ quality and act as a critical evaluative criterion in teas. The distinctive aromatic profile of tea not only facilitates tea classification but also has potential applications in aroma-driven product innovation. In this review, we summarized the tea aroma from tea classification, VOCs extraction methodologies, and VOCs detection techniques. Moreover, the potential utilization of tea aroma in the future, such as applications in essential oil refinement, food flavor enhancement, and functional fragrance for personal health care, was proposed. Our review will provide a solid foundation for further investigations in tea aroma and offer significant insights into the development and application of tea fragrance.