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Salinity management in estuarine systems is crucial for developing effective water-management strategies to maintain compliance and understand the impact of salt intrusion on water quality and availability. Understanding the temporal and spatial variations of salinity is a keystone of salinity-management practices. Process-based numerical models have been traditionally used to estimate the variations in salinity in estuarine environments. Advances in data-driven models (e.g., deep learning models) make them effective and efficient alternatives to process-based models. However, a discernible research gap exists in applying these advanced techniques to salinity modeling. The current study seeks to address this gap by exploring the innovative use of deep learning with data augmentation and transfer learning in salinity modeling, exemplified at 23 key salinity locations in the Sacramento–San Joaquin Delta which is the hub of the water-supply system of California. Historical, simulated (via a hydrodynamics and water quality model), and perturbed (to create a range of hydroclimatic and operational scenarios for data-augmentation purposes) flow, and salinity data are used to train a baseline multi-layer perceptron (MLP) and a deep learning Residual Long-Short-Term Memory (Res-LSTM) network. Four other deep learning models including LSTM, Residual Network (ResNet), Gated Recurrent Unit (GRU), and Residual GRU (Res-GRU) are also examined. Results indicate that models pre-trained using augmented data demonstrate improved performance over models trained from scratch using only historical data (e.g., median Nash–Sutcliffe efficiency increased from around 0.5 to above 0.9). Moreover, the five deep learning models further boost the salinity estimation performance in comparison with the baseline MLP model, though the performance of the latter is acceptable. The models trained using augmented data are then (a) used to develop a web-based Salinity Dashboard (Dashboard) tool that allows the users (including those with no machine learning background) to quickly screen multiple management scenarios by altering inputs and visualizing the resulting salinity simulations interactively, and (b) transferred and adapted to estimate observed salinity. The study shows that transfer learning results more accurately replicate the observations compared to their counterparts from models trained from scratch without knowledge learned and transferred from augmented data (e.g., median Nash–Sutcliffe efficiency increased from around 0.4 to above 0.9). Overall, the study illustrates that deep learning models, particularly when pre-trained using augmented data, are promising supplements to existing process-based models in estuarine salinity modeling, while the Dashboard enables user engagement with those pre-trained models to inform decision-making efficiently and effectively.

Salinity in estuarine environments has been traditionally simulated using process-based models. More recently, data-driven models including artificial neural networks (ANNs) have been developed for simulating salinity. Compared to process-based models, ANNs yield faster salinity simulations with comparable accuracy. However, ANNs are often purely data-driven and not constrained by physical laws, making it difficult to interpret the causality between input and output data. Physics-informed neural networks (PINNs) are emerging machine-learning models to integrate the benefits of both process-based models and data-driven ANNs. PINNs can embed the knowledge of physical laws in terms of the partial differential equations (PDE) that govern the dynamics of salinity transport into the training of the neural networks. This study explores the application of PINNs in salinity modeling by incorporating the one-dimensional advection–dispersion salinity transport equation into the neural networks. Two PINN models are explored in this study, namely PINNs and FoNets. PINNs are multilayer perceptrons (MLPs) that incorporate the advection–dispersion equation, while FoNets are an extension of PINNs with an additional encoding layer. The exploration is exemplified at four study locations in the Sacramento–San Joaquin Delta of California: Pittsburg, Chipps Island, Port Chicago, and Martinez. Both PINN models and benchmark ANNs are trained and tested using simulated daily salinity from 1991 to 2015 at study locations. Results indicate that PINNs and FoNets outperform the benchmark ANNs in simulating salinity at the study locations. Specifically, PINNs and FoNets have lower absolute biases and higher correlation coefficients and Nash–Sutcliffe efficiency values than ANNs. In addition, PINN models overcome some limitations of purely data-driven ANNs (e.g., neuron saturation) and generate more realistic salinity simulations. Overall, this study demonstrates the potential of PINNs to supplement existing process-based and ANN models in providing accurate and timely salinity estimation.

Global climate change, especially extreme drought events, presents a complicated challenge to humanity and Earth’s system in the 21st century. As an extremely important carbon sink region in China, Southwest China has encountered frequent drought disasters in recent decades. It is critical to explore the frequency, duration, severity, and other associated characteristics of drought events as well as their spatial and temporal patterns in the region from a long-term perspective. In this study, we used the latest dataset from the Spanish National Research Council (CSIC) between 1901 and 2018 to extract all drought events by calculating the standardized anomaly of the Standardized Precipitation Evapotranspiration Index (SPEI). Theil–Sen median trend analysis, the Mann–Kendall test, and the moving t-test were used to reveal the spatial trend and mutation point of drought severity. The results showed that (1) The standardized anomaly of the 3-month SPEI can accurately identify drought events in Southwest China. In total, 72 drought events occurred during this period, of which the consecutive drought in autumn, winter, and spring from 2009 to 2010 lasted the longest, having the most substantial severity and the most extensive damage range. (2) Drought events mainly started in spring and early summer and ended in autumn and winter. The distribution of drought was the most expansive and the drought severity was the most serious in September. (3) In terms of spatial pattern, Guangxi has the highest frequency of drought events, with some areas experiencing up to 100 events. The average duration of drought events ranged between 3.5 and 5.5 months, with most lasting for 4–5 months. The most severe drought areas are mainly concentrated in southern Sichuan and western Yunnan. Overall, the severity of drought events in the west were generally higher compared to that in the east. (4) Over the past 120 years, most of the region (82.46%) showed an increasing trend in drought severity, with a slope of up to −0.01. About 15.12% of the areas exhibited a significant drying trend (p < 0.05), particularly in southern Sichuan, eastern Guizhou, and northern and southern Yunnan. Such analyses can serve as a scientific foundation for developing drought prevention and mitigation measures as well as exploring how drought events affect the structure and function of terrestrial ecosystems in Southwest China.

Understanding the genetic mechanisms of phenotypic variation in hybrids between domestic animals and their wild relatives may aid germplasm innovation. Here, we report the high-quality genome assemblies of a male Pamir argali (O. ammon polii, 2n = 56), a female Tibetan sheep (O. aries, 2n = 54), and a male hybrid of Pamir argali and domestic sheep, and the high-throughput sequencing of 425 ovine animals, including the hybrids of argali and domestic sheep. We detected genomic synteny between Chromosome 2 of sheep and two acrocentric chromosomes of argali. We revealed consistent satellite repeats around the chromosome breakpoints, which could have resulted in chromosome fusion. We observed many more hybrids with karyotype 2n = 54 than with 2n = 55, which could be explained by the selfish centromeres, the possible decreased rate of normal/balanced sperm, and the increased incidence of early pregnancy loss in the aneuploid ewes or rams. We identified genes and variants associated with important morphological and production traits (e.g., body weight, cannon circumference, hip height, and tail length) that show significant variations. We revealed a strong selective signature at the mutation (c.334C > A, p.G112W) in TBXT and confirmed its association with tail length among sheep populations of wide geographic and genetic origins. We produced an intercross population of 110 F2 offspring with varied number of vertebrae and validated the causal mutation by whole-genome association analysis. We verified its function using CRISPR-Cas9 genome editing. Our results provide insights into chromosomal speciation and phenotypic evolution and a foundation of genetic variants for the breeding of sheep and other animals.

The flexible robot has more advantages over the traditional machine in the cutting process for complex surface workpiece like the 3D printing part which has less residual material. During the process, the geometric smoothness of moving joints is critically vital for machining efficiency and accuracy. The tool orientation should also be limited in certain region considering the interfering requirements for a complex workpiece. Besides, the industrial robot always has six degrees of freedom (Dof) at least, and there are redundant Dofs for robot surface machining. Therefore, a smooth machining process under the tool orientation requirements can be optimized from these redundant Dofs. In this paper, the tool posture differential vector along the tool-tip path curve is optimized firstly. The whole trajectory is numerically integrated by the optimized differential vector step by step. In every calculating step, the minimum 2-norm of the joints' differential vector along the tool path curve is set as part of the optimization object to ensure the minimal change of joints along the machining process. At the same time, a state-related optimal object for the differential vector is established to keep the tool orientation move away from the boundary of the feasible region. Combining with the above two objects, the tool posture differential vector along the tool-tip path curve in every integral step is optimized and the whole machining process trajectory is obtained by numerical integrating method.

Transportation infrastructure foundations filled with calcareous sand are exposed to long-term cyclic loading under various frequencies. This study presents experimental results about the drained cyclic triaxial tests conducted on dense calcareous sand under different mean effective stresses ([Formula: see text]), cyclic stress ratios (ζ), and loading frequencies (f) to examine the effect of f on the long-term cyclic behavior. Results show that the increase in f causes the significant increase in the accumulated axial strain ([Formula: see text]). As ζ increases, [Formula: see text] becomes more sensitive to f, while the increase of p0 could limit the aggravating effect of f on [Formula: see text]. At a higher ζ, increasing f can even lead to a failure and larger dilated volumetric strain of dense calcareous sand specimens. The increase in f has a considerable impairing effect on the shakedown behavior, which can cause the stable shakedown behavior to transform into transitional or unstable shakedown behaviors. More accurate shakedown discriminant boundaries suitable for calcareous sand under different f were therefore proposed.

Blockchain radio access network (B-RAN) is a decentralized, trustworthy wireless networking paradigm spurred by distributed ledger technologies (DLTs). In B-RAN, even though blockchain builds trust in upper layers, the absence of trust between client devices still causes the problem with open access, or the so-called Rogue’s Dilemma, and degrades the network performance. Therefore, Hash Access was proposed for B-RAN to address the trust issue between clients and enforce client devices to obey the grant-free access rule. However, the characteristics and performance of Hash Access in B-RAN remain unclear. In this work, we dive deep into the Rogue’s Dilemma from a game-theoretic model to emphasize the necessity of Hash Access. We establish an analytical model to comprehensively evaluate the performance of B-RAN using Hash Access regarding transmission success probability, access delay, and network throughput. Based on the analytical model, we further optimize the Hash Access protocol for network throughput and provide useful practical guidelines. Simulation results are presented to validate our proposed model and insights.

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting with a posteriori channel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we design an iterative algorithm and present its convergence analysis. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

Massive multiple-input and multiple-output (MIMO) enables ultra-high throughput and low latency for tile-based adaptive virtual reality (VR) 360° video transmission in wireless network. In this paper, we consider a massive MIMO system where multiple users in a single-cell theater watch an identical VR 360° video. Based on tile prediction, base station (BS) deliveries the tiles in predicted field of view (FoV) to users. By introducing practical supplementary transmission for missing tiles and unacceptable VR sickness, we propose the first stable transmission scheme for VR video. we formulate an integer non-linear programming (INLP) problem to maximize users’ average quality of experience (QoE) score. Moreover, we derive the achievable spectral efficiency (SE) expression of predictive tile groups and the approximately achievable SE expression of missing tile groups, respectively. Analytically, the overall throughput is related to the number of tile groups and the length of pilot sequences. By exploiting the relationship between the structure of viewport tiles and SE expression, we propose a multi-lattice multi-stream grouping method aimed at improving the overall throughput for VR video transmission. Moreover, we analyze the relationship between QoE objective and number of predictive tile. We transform the original INLP problem into an integer linear programming problem by setting the predictive tiles groups as some constants. With variable relaxation and recovery, we obtain the optimal average QoE. Extensive simulation results validate that the proposed algorithm effectively improves QoE.

Gas barrier property and microstructure of reduced graphene oxide/polyethyleneimine multilayer films createdviarecast and layer-by-layer deposition processes.

Three novel actinomycete strains, designated TRM66264-DLMT, TRM88002T and TRM88003T, were isolated by using polyaspartic acid as a water-retaining agent for the enrichment in situ. The 16S rRNA gene sequence and phylogenetic analyses of three strains indicated that they belonged to the genus Actinoplanes . The phylogenetically closest strains of TRM66264-DLMT, TRM88002T and TRM88003T were Actinoplanes bogorensis LIPI11-2-Ac043T (98.4 %), Actinoplanes abujensis A4029T (98.0 %) and Actinoplanes ferrugineus IFO15555T (98.1 %), respectively. The major polar lipids of strains TRM66264-DLMT and TRM88002T were phosphatidylethanolamine and disphosphatidylglycerol, while strain TRM88003T only had phosphatidylethanolamine. The predominant menaquinones of strain TRM66264-DLMT were identified as MK-9(H4) and MK-9 (H6). Strains TRM88002T and TRM88003T had MK-9(H4). The cell-wall peptidoglycan of three strains contained meso-diaminopimelic acid. The whole-cell sugars of strain TRM66264-DLMT were identified as arabinose, glucose, galactose and xylose. Strains TRM88002T and TRM88003T mainly had arabinose and glucose. The DNA G+C content of strains TRM66264-DLMT, TRM88002T and TRM88003T were 70.48, 70.46 and 70.64 mol%, respectively. Genotypic and phenotypic analysis confirmed that all three strains sre new members of the genus Acinoplanes. Therefore, it is proposed that strains TRM66264-DLMT, TRM88002T and TRM88003T represent three novel species of the genus Actinoplanes , for which the names Actinoplanes polyasparticus sp. nov. (type strain TRM66264-DLMT=CCTCC AA 2021015T=LMG 32389T), Actinoplanes hotanensis sp. nov. (type strain TRM88002T=CCTCC AA 2021036T=LMG 32621T) and Actinoplanes aksuensis sp. nov. (type strain TRM88003T=CCTCC AA 2021037 T=LMG 32622T) are proposed.