This study addresses the event-triggered transient performance control problem for switched singular systems (SSSs). Different from the classical transient performance control method, a novel transient performance framework for SSSs is established, where multiple Lyapunov functions (MLFs) and multiple barrier functions (MBFs) are used as upper and lower performance boundaries to constrain the states. And a more accurate consistency projector is developed to better capture the state jump characteristic inherent in SSSs. To enhance sampling efficiency and reduce control costs, a new hybrid event-triggering mechanism (ETM) is designed by combining the advantages of static ETM and dynamic ETM. Based on this ETM, the average dwell time method (ADT) and the MLFs method, sufficient conditions expressed as linear matrix inequalities (LMIs) are established to guarantee that the closed-loop system is regular, impulse-free, globally uniformly asymptotically stable (GUAS), and satisfies transient performance requirements. Moreover, the theoretical analysis further proves that there is no Zeno phenomenon. Finally, comparative numerical simulations and a DC motor drives loads application validate the efficacy of the proposed approach.

Modern genomics faces challenges from “reference bias” in linear genomes, prompting the adoption of pangenomic graphs to integrate multi-allelic variations. Sequence-to-graph alignment is a fundermental procedure in many pangenomic analyses. However, the alignment in complex topologies like cyclic graphs and highly polymorphic regions remains difficult due to path branch explosion and computational complexity. In this paper, we propose GESA, a sequence-to-graph alignment framework for sequences in highly divergent genomic regions. GESA adopts a hybrid strategy integrating haplotype-guided path linearization to organize topological information, thereby reducing information loss and potential path branch explosion. It employs a Transformer-CNN contrastive learning strategy to further capture global and local genomic features, enabling the identification of genetic characteristics in complex regions across the entire genome. Finally, a hierarchical vector-space retrieval technique is used to simplify the complex graph alignment computation into linear alignments on multiple sequences through vector similarity retrieval algorithms. GESA achieves an alignment ratio of 0.79 in cyclic graphs within the complex MHC region, outperforming Minigraph and GraphAligner by$4.3 \times$and$3.3 \times$, respectively. GESA lays a foundation for the future development of deep learning model applications in the field of pangenome graph alignment. The GESA code is available at https://github.com/nudt-bioinfo/GESA.

BACKGROUND: The advent of Single Molecule Real-Time (SMRT) sequencing has overcome many limitations of second-generation sequencing, such as limited read lengths, PCR amplification biases. However, longer reads increase data volume exponentially and high error rates make many existing alignment tools inapplicable. Additionally, a single CPU's performance bottleneck restricts the effectiveness of alignment algorithms for SMRT sequencing. RESULTS: To address these challenges, we introduce ParaHAT, a parallel alignment algorithm for noisy long reads. ParaHAT utilizes vector-level, thread-level, process-level, and heterogeneous parallelism. We redesign the dynamic programming matrices layouts to eliminate data dependency in the base-level alignment, enabling effective vectorization. We further enhance computational speed through heterogeneous parallel technology and implement the algorithm for multi-node computing using MPI, overcoming the computational limits of a single node. CONCLUSIONS: Performance evaluations show that ParaHAT got a 10.03x speedup in base-level alignment, with a parallel acceleration ratio and weak scalability metric of 94.61 and 98.98% on 128 nodes, respectively.

The spatial representativeness error of in situ soil moisture (SM) is recognized as a major source of uncertainty when validating satellite SM products with a spatial resolution of tens of kilometers. Site underrepresentation is primarily caused by environmental heterogeneity, but their relationship remains poorly understood. Here, we assessed the spatial representativeness of in situ SM from 322 strictly screened stations worldwide relative to coarse-resolution (~0.25°) satellite footprint based on the extended triple collocation (ETC) method. We then evaluated the influence of the heterogeneity of four environmental factors (soil texture, land cover types, elevation, and vegetation coverage) on site representativeness. Moreover, we calculated SM variability within the satellite footprint based on 1-km SM data to explore its relationship with environmental heterogeneity. Results indicate that about 63% of the sites have relatively good spatial representativeness (ETC-derived correlation coefficient$\ge 0.7$). Soil texture and land cover exhibit greater heterogeneity across the mid and high latitudes of the Northern Hemisphere. The larger heterogeneity in elevation and vegetation coverage is primarily found in regions with significant ridges and dense vegetation, respectively. Land cover is the major factor influencing the spatial representativeness of SM sites, and the increase in the heterogeneity of land cover enhances SM variability, which negatively impacts site representativeness. The in situ SM can be more representative when the proportion of the land cover type where the site is located is higher or when there are fewer land cover types within the satellite footprint. Moreover, it is found that the newly proposed metric of the similar area ratio of sites, as a measure of land cover heterogeneity, can effectively reflect SM variability. This metric can also serve as a supplementary criterion for selecting representative sites, particularly in situations where sites are sparse and the ETC method is inapplicable. These findings provide useful references for robust evaluation of satellite SM products based on in situ measurements (e.g., in situ SM upscaling and SM site deployment).

Sequence alignment is the core of many bioinformatics tasks such as read mapping, genome assembly, variant detection and so on. With the advent of the third generation sequencing, classical dynamic programming-based alignment algorithms face challenges in efficiently handling these long reads. To address this issue, we present WFA-vect, a SIMD-based fast sequence alignment algorithm based on WFA. In WFA-vect, we introduce load synchronous and mask-based branch strategies to make the algorithm more suitable for vectorization. The load synchronous equalizes the load across different vector units to facilitate vectorization. The mask-based branch uses branch masking to bypass branch, avoiding pipeline hazards. To avoid binding the SIMD algorithm to specific hardware, we design a universal vectorization framework, which allows researchers to quickly port WFA-vect to other platforms without needing to understand the details of the algorithm. WFA-vect attains a peak speedup of 3.87× and 3.98× for data with error rates of 1% and 20%, respectively, compared to the scalar algorithm, while maintaining the alignment result consistent. The code and documentation of WFA-vect are publicly available at https://github.com/nudt-bioinfo/WFA-vect.

The uncertainty inherent in validating satellite-derived soil moisture (SM) products is significantly attributed to the spatial underrepresentation of in situ SM measurements. The main reason for this phenomenon is the varying environmental conditions (called as environmental heterogeneity) within the satellite footprint. To better understand this issue, we assessed the spatial representativeness of in situ SM from 383 strictly screened stations worldwide relative to the coarse-resolution (~0.25°) satellite footprint and analyzed the effects of four environmental factors (i.e., soil texture, land cover, elevation, and vegetation coverage) using the extended triple collocation (ETC) technique. Results show about 63% of the sites have satisfactory levels of spatial representativeness (ETC derived correlation coefficient ⩾0.7). Land cover is the foremost factor affecting the spatial representativeness of SM sites. The in situ SM can better represent the true variability of SM when the proportion of land cover types where the site is located is higher or there are fewer land cover types within the satellite pixels.

The comprehensive and robust assessment of diverse global-scale satellite-based soil moisture products from various satellite data sources (e.g., different frequencies and incidence angles) and retrieval algorithms is essential for the refinements as well as applications of these products. To date, soil moisture retrieval algorithms and products are rapidly evolving and their updated iterations are ongoing. In support of the validation activities of recently developed/reprocessed satellite soil moisture products, the study firstly assessed eight commonly-employed satellite soil moisture datasets comprising SMAP (DCA, IB, and MTDCA), SMOS-IC, AMSR2 (LPRM and JAXA), FY-3C, and ESA CCI on a global scale using three different strategies, i.e., ERA5 reanalysis soil moisture dataset with similar spatial resolution to satellite products,in situmeasurements from densely-instrumented networks worldwide with mitigated spatial mismatch between ground site and satellite pixel, and the Extended Triple Collocation (ETC) method that can obtain error indicators relative to ground truth. The skills of these products under a broad range of vegetation density, land cover and climate types, and surface heterogeneity (heterogeneity in terrain, land cover, soil texture, and vegetation coverage) were also examined. The results indicate: (1) different soil moisture products show overall consistency in skill ranking under three different evaluation strategies, except for SMAP DCA, SMAP-IB, and SMAP MTDCA in terms ofRvalue; (2) ESA CCI, SMAP-IB, SMAP DCA products generally perform better than the others under three strategies, and SMOS-IC and SMAP MTDCA also show satisfactory performance concerning ubRMSD andRvalues; (3) vegetation density exerts visible influences on satellite soil moisture datasets. Specifically, the C/X-band (AMSR2 and FY-3C) and L-band (SMAP and SMOS) products display the optimal skills under sparse and moderate vegetation coverage respectively, and the impacts of vegetation density on C/X-band products are evidently stronger than those on L-band datasets. The errors of satellite soil moisture data also increase as the increase of heterogeneity in terrain, land cover, and vegetation coverage, while the effect of heterogeneity in soil texture on the skill of satellite soil moisture products is insignificant; (4) the skills of L-band products are more stable than those of C/X-band datasets under different ground conditions.

Soil moisture (SM) is influenced by changes in meteorological elements and vegetation, as well as by environmental heterogeneity. Due to the prevalence of extreme events in the past two decades, this complexity is growing in the 21st century. In this study, the global spatiotemporal trend of SM and its possible influencing factors were investigated by using the satellite-based ESA CCI SM from 2000-2021. The results reveal global SM generally declines at a rate of -1×10-4m3m-3yr-1, dominated by a drying trend in the southern hemisphere. From a global perspective, the driving force of precipitation and vegetation on SM fluctuation is stronger than that of temperature. Different environmental variables (e.g., land cover, soil texture, and terrain) have different regulatory effects on SM changes. In contrast to other types, there are general wetting trends of SM in croplands, savannas, forests, loam soils, and high elevations (> 1000 m). The response of SM to temperature and vegetation is greatly limited under barren and forests, respectively. The impact of temperature on SM is enhanced with increasing clay content or decreasing sand content. The high positive correlation between precipitation and SM is rarely influenced by environmental factors compared to temperature and vegetation.

In this article, we show how to obtain all of the Pareto optimal decision vectors and solutions for the finite horizon indefinite mean-field stochastic cooperative linear-quadratic (LQ) difference game. First, the equivalence between the solvability of the introduced N coupled generalized difference Riccati equations (GDREs) and the solvability of the multiobjective optimization problem is established. However, it is difficult to obtain Pareto optimal decision vectors based on the N coupled GDREs because the optimal joint strategy adopted by all players to optimize the performance criterion of some players in the game is different from the strategies of other players, which rely on the weighted matrices of cost functionals that may be different among players. Second, a necessary and sufficient condition is developed to guarantee the convexity of the costs, which makes the weighting technique not only sufficient but also necessary for searching Pareto optimal decision vectors. It is then shown that the mean-field Pareto optimality algorithm (MF-POA) is presented to identify, in principle, all of the Pareto optimal decision vectors and solutions via the solutions to the weighted coupled GDREs and the weighted coupled generalized difference Lyapunov equations (GDLEs), respectively. Finally, a cooperative network security game is reported to illustrate the results presented. Simulation results validate the solvability, correctness, and efficiency of the proposed algorithm.

This article derives necessary and sufficient conditions for Pareto optimal solutions in infinite horizon cooperative difference games of autonomous systems with exponentially discounted performances. First, the$\mathcal {N}$constrained optimal control problems are converted into an unconstrained characterization with mixed endpoint constraints by introducing appropriate auxiliary states. Second, the infinite horizon cooperative difference games are transformed equivalently into an augmented and truncated finite horizon optimization problem by defining two real-valued functions and the necessary conditions are derived based on the maximum principle (MP) of discrete-time type. Moreover, we present sufficient conditions for general nonautonomous systems. Finally, the results developed are employed to address the linear quadratic (LQ) cooperative difference games for fixed as well as arbitrary initial states.