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The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

Plant water-use strategies are critical for maintaining community stability during ecological restoration in arid regions. This study aims to quantify the proportional contributions of different water sources to dominant plant species across a restoration chronosequence and to assess their impact on the stability of shrub-grass ecosystems. The research was conducted within the Ningxia Baijitan National Nature Reserve, China, using a restoration chronosequence (1953-2020) that included natural vegetation areas. Samples of plant xylem water, soil water (0-120 cm depth), and precipitation were collected. Stable isotope ratios (δ²H and δ¹⁸O) were analyzed, and Bayesian mixing models (MixSIAR) were applied to quantify the proportional contributions of different soil layers to plant water uptake. The grass-to-shrub water use ratio (Rh/s​) was defined to characterize ecosystem stability, and its theoretical threshold was validated using a mathematical model. (1) Significant vertical differentiation in water sources existed among functional groups: shrubs predominantly relied on deep soil water (40-100 cm; 52.3% contribution), semi-shrubs primarily used intermediate depths (20-40 cm; 19.8%), while herbaceous species concentrated uptake in shallow layers (0-20 cm; 78.6%). (2) The proportion of deep-soil water used by shrubs increased significantly with vegetation age, whereas semi-shrubs showed a positive but non-significant trend for mid-layer water use, and herbs exhibited no significant differences across the restoration chronosequence. (3) Ecosystem stability thresholds based on Rh/s ​were identified: strong stability when Rh/s​<0.9, semi-stability when 0.91.4. This interval division was confirmed by a mathematical stability analysis calculating the real parts of eigenvalues. The results confirm that vegetation restoration facilitates a complementary water-use strategy. The stability-maintaining mechanism can be described as shrubs enhancing drought resilience by accessing deep water reserves, while herbaceous species foster community renewal through rapid exploitation of shallow resources. This underscores the key role of plant water-use strategies in the ecological reconstruction of arid regions.

Group target tracking (GTT) is a promising approach for countering unmanned aerial vehicles (UAVs). However, the complex distribution and high mobility of UAV swarms may limit GTTs performance. To enhance GTT performance for UAV swarms, this paper proposes potential solutions. An automatic measurement partitioning method based on ordering points to identify the clustering structure (OPTICS) is suggested to handle non-uniform measurements with arbitrary contour distribution. Maneuver modeling of UAV swarms using deep learning methods is proposed to improve centroid tracking precision. Furthermore, the group’s three-dimensional (3D) shape can be estimated more accurately by applying key point extraction and preset geometric models. Finally, optimized criteria are proposed to improve the spawning or combination of tracking groups. In the future, the proposed solutions will undergo rigorous derivations and be evaluated under harsh simulation conditions to assess their effectiveness.

Grassland restoration measures control soil degradation and improve soil quality (SQ) worldwide, but there is little knowledge about the effectiveness of restoration measures affecting SQ in arid areas, and the restoration rate of degraded grasslands to natural restoration grasslands and reseeded grasslands remains unclear. To establish a soil quality index (SQI) to evaluate the effects of different grassland restoration measures on SQ, continuous grazing grassland (CG) (as a reference), grazing exclusion grassland (EX), and reseeding grassland (RS) were selected and sampled in the arid desert steppe. Two soil indicator selection methods were conducted (total data set (TDS) and minimum data set (MDS)), followed by three SQ indices (additive soil quality index (SQI a ), weighted additive soil quality index (SQI w ), and Nemoro soil quality index (SQI n )). The results indicated that SQ was better assessed using the SQI w ( R 2  = 0.55) compared to SQI a and SQI n for indication differences among the treatments due to the larger coefficient of variance. The SQI w -MDS value in CG grassland was 46% and 68% lower than that of EX grassland and RS grassland, respectively. Our findings provided evidence that restoration practices of grazing exclusion and reseeding can significantly improve the SQ in the arid desert steppe, and native plant reseeded can accelerate soil quality restoration.

Considerable biological decline of continuously cropped alfalfa may be tightly linked to rhizosphere metabolism. However, plant-soil feedbacks and age-related metabolic changes in alfalfa stands remain unexplored. The aim of this study was to identify the linkages of rhizosphere and root metabolites, particularly autotoxins and prebiotics, to alfalfa decline under continuous cropping. We performed liquid chromatography–mass spectrometry for non-targeted metabolomic profiling of rhizosphere soils and alfalfa roots in 2- and 6-year-old stands. Differentially abundant metabolites that responded to stand age and associated metabolic pathways were identified. Compared with bulk soils, rhizosphere soils were enriched with more triterpenoid saponins (e.g., medicagenic acid glycosides), which showed inhibitory effects on seed germination and seedling growth. These autotoxic metabolites were accumulated in the old stand age, and their relative abundances were negatively correlated with plant growth, yield, and quality traits, as well as soil total nitrogen and alkali-hydrolyzable nitrogen concentrations. In contrast, prebiotic metabolites, represented by glycerolipids (e.g., glycerophosphocholine) and fatty acyls (e.g., colnelenic acid), were depleted in rhizosphere soils in the old stand. The relative abundances of glycerolipids and fatty acyls were positively correlated with plant traits and soil available phosphorus and alkali-hydrolyzable nitrogen concentrations. Age-induced changes in the rhizosphere metabolome mirrored the reprogramming patterns of root metabolome. The pathways of terpenoid backbone biosynthesis and plant hormone signal transduction, as well as metabolism of galactose, glycerophospholipid, and ɑ-linolenic acid in alfalfa roots were affected by stand age. The upregulation of terpenoid backbone biosynthesis in alfalfa roots of old plants, which stimulated triterpenoid saponin biosynthesis and exudation. Rhizosphere accumulation of autotoxins was accompanied by depletion of prebiotics, leading to soil degradation and exacerbating alfalfa decline. This research aids in the development of prebiotics to prevent and manage continuous cropping obstacles in alfalfa.

With the maturation of reusable launch vehicle technology and satellite mass-production capabilities, global mega-constellation projects have entered a phase of rapid expansion. Inter-satellite networking is a key approach for enhancing constellation performance, as it crucially impacts overall constellation effectiveness. However, existing studies mostly focus on the network layer protocol optimization, with insufficient attention to topological structure design, and fail to fully consider the engineering challenges associated with inter-orbit Inter-Satellite Links (ISLs). To address these issues, this paper proposes a heterogeneous ISL topology architecture for mega-constellations, centered on “stable high-speed laser backbone connection within intra-orbit planes + dynamic and flexible radio network between inter-orbit planes”. First, we clarify the optimization objectives for mega-constellation topological design under this architecture and theoretically prove that the optimization problem is NP-hard. Building on this, we introduce Topological Structure Units (TSUs) and employ a unit reuse strategy to simplify topological design. Furthermore, we propose a TSU-based heterogeneous ISL topological design algorithm. Considering the uneven satellite distribution across latitude zones within the constellation, we further propose a regional TSU-based topological design algorithm. Finally, through simulation experiments in Starlink and GW constellation scenarios, we conduct multi-dimensional verification to demonstrate the effectiveness of the proposed algorithms in reducing end-to-end delay and decreasing ISL hops.

(1) Background: The scientific development in the field of industrialization demands the automization of electronic shelf labels (ESLs). COVID-19 has limited the manpower responsible for the frequent updating of the ESL system. The current ESL uses QR (quick response) codes, NFC (near-field communication), and RFID (radio-frequency identification). These technologies have a short range or need more manpower. LoRa is one of the prominent contenders in this category as it provides long-range connectivity with less energy harvesting and location tracking. It uses many gateways (GWs) to transmit the same data packet to a node, which causes collision at the receiver side. The restriction of the duty cycle (DC) and dependency of acknowledgment makes it unsuitable for use by the common person. The maximum efficiency of pure ALOHA is 18.4%, while that of slotted ALOHA is 36.8%, which makes LoRa unsuitable for industrial use. It can be used for applications that need a low data rate, i.e., up to approximately 27 Kbps. The ALOHA mechanism can cause inefficiency by not eliminating fast saturation even with the increasing number of gateways. The increasing number of gateways can only improve the global performance for generating packets with Poisson law having a uniform distribution of payload of 1~51 bytes. The maximum expected channel capacity usage is similar to the pure ALOHA throughput. (2) Methods: In this paper, the improved ALOHA mechanism is used, which is based on the orthogonal combination of spreading factor (SF) and bandwidth (BW), to maximize the throughput of LoRa for ESL. The varying distances (D) of the end nodes (ENs) are arranged based on the K-means machine learning algorithm (MLA) using the parameter selection principle of ISM (industrial, scientific and medical) regulation with a 1% DC for transmission to minimize the saturation. (3) Results: The performance of the improved ALOHA degraded with the increasing number of SFs and as well ENs. However, after using K-mapping, the network changes and the different number of gateways had a greater impact on the probability of successful transmission. The saturation decreased from 57% to 1~2% by using MLA. The RSSI (Received Signal Strength Indicator) plays a key role in determining the exact position of the ENs, which helps to improve the possibility of successful transmission and synchronization at higher BW (250 kHz). In addition, a high BW has lower energy consumption than a low BW at the same DC with a double-bit rate and almost half the ToA (time on-air).

The effect of the molecular layering at liquid–solid interface on the thermal conductivity of the nanofluid is investigated by an equilibrium molecular dynamics simulation. By tracking the position of the nanoparticle and the liquid atoms around the spherical nanoparticle, it was found that a thin layer of liquid is formed at the interface between the nanoparticle and liquid; this thin layer will move with the Brownian motion of the nanoparticle. Through the analysis of the density distribution of the liquid near the nanoparticle, it is found that more argon atoms are attracted to form the layer around the nanoparticle when the diameter of the nanoparticle is larger, and therefore lead to the more significant enhancement of the thermal conductivity of the nanofluid.

Genomic DNA is folded into a higher-order structure that regulates transcription and maintains genomic stability. Although progress has been made on understanding biochemical characteristics of epigenetic modifications in cancer, the in-situ higher-order folding of chromatin structure during malignant transformation remains largely unknown. Here, using optimized stochastic optical reconstruction microscopy (STORM) for pathological tissue (PathSTORM), we uncover a gradual decompaction and fragmentation of higher-order chromatin folding throughout all stages of carcinogenesis in multiple tumor types, and prior to tumor formation. Our integrated imaging, genomic, and transcriptomic analyses reveal functional consequences in enhanced transcription activities and impaired genomic stability. We also demonstrate the potential of imaging higher-order chromatin disruption to detect high-risk precursors that cannot be distinguished by conventional pathology. Taken together, our findings reveal gradual decompaction and fragmentation of higher-order chromatin structure as an enabling characteristic in early carcinogenesis to facilitate malignant transformation, which may improve cancer diagnosis, risk stratification, and prevention. Aberrant chromatin structure is often found in cancer. Here, the authors optimise super-resolution microscopy for pathological tissue and discovered a significant decompaction of chromatin folding in early carcinogenesis prior to tumour formation.

A short-range, compact, real-time pulsed laser rangefinder is constructed based on pulsed time-of-flight (ToF) method. In order to reduce timing discrimination error and achieve high ranging accuracy, gray-value distance correction and temperature correction are proposed, and are realized with a field programmable gate array (FPGA) in a real-time application. The ranging performances—such as the maximum ranging distance, the range standard deviation, and the ranging accuracy—are theoretically calculated and experimentally studied. By means of these proposed correction methods, the verification experimental results show that the achievable effective ranging distance can be up to 8.08 m with a ranging accuracy of less than ±11 mm. The improved performance shows that the designed laser rangefinder can satisfy on-line ranging applications with high precision, fast ranging speed, small size, and low implementation cost, and thus has potential in the areas of robotics, manufacturing, and autonomous navigation.