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  1. Reliable statistical inference is central to forest ecology and management, much of which seeks to estimate population parameters for forest attributes and ecological indicators for biodiversity, functions and services in forest ecosystems. Many populations in nature such as plants or animals are characterized by aggregation of tendencies, introducing a big challenge to sampling. Regardless, a biased or imprecise inference would mislead analysis, hence the conclusion and policymaking. Systematic adaptive cluster sampling (SACS) is designunbiased and particularly efficient for inventorying spatially clustered populations. However, (1) oversampling is common for nonrare variables, making SACS a difficult choice for inventorying common forest attributesmore »or ecological indicators; (2) a SACS sample is not completely specified until the field campaign is completed, making advance budgeting and logistics difficult; (3) even for rare variables, uncertainty regarding the final sample still persists; and (4) a SACS sample may be variable-specific as its formation can be adapted to a particular attribute or indicator, thus risking imbalance or non-representativeness for other jointly observed variables. Consequently, to solve these challenges, we aim to develop a generalized SACS (GSACS) with respect to the design and estimators, and to illustrate its connections with systematic sampling (SS) as has been widely employed by national forest inventories and ecological observation networks around the world. In addition to theoretical derivations, empirical sampling distributions were validated and compared for GSACS and SS using sampling simulations that incorporated a comprehensive set of forest populations exhibiting different spatial patterns. Five conclusions are relevant: (1) in contrast to SACS, GSACS explicitly supports inventorying forest attributes and ecological indicators that are nonrare, and solved SACS problems of oversampling, uncertain sample form, and sample imbalance for alternative attributes or indicators; (2) we demonstrated that SS is a special case of GSACS; (3) even with fewer sample plots, GSACS gives estimates identical to SS; (4) GSACS outperforms SS with respect to inventorying clustered populations and for making domain-specific estimates; and (5) the precision in design-based inference is negatively correlated with the prevalence of a spatial pattern, the range of spatial autocorrelation, and the sample plot size, in a descending order.« less
  2. Self-healing triboelectric nanogenerators (SH-TENGs) with fast self-healing, high output performance, and wearing comfort have wide and promising applications in wearable electronic devices. This work presents a high-performance hydrogel-based SH-TENG, which consists of a high dielectric triboelectric layer (HDTL), a self-healing hydrogel electrode layer (SHEL), and a physical cross-linking layer (PCLL). Carbon nanotubes (CNTs), obtained by a chemical vapor deposition (CVD) method, were added into polydimethylsiloxane (PDMS) to produce the HDTL. Compared with pure PDMS, the short-circuit transferred charge (44 nC) and the open circuit voltage (132 V) are doubled for PDMS with 0.01 wt% CNTs. Glycerin, polydopamine particles (PDAP) andmore »graphene were added to poly (vinyl alcohol) (PVA) to prepare the self-healing hydrogel electrode layer. SHEL can physically self-heal in ~1 min when exposed to air. The self-healing efficiency reaches up to 98%. The PCLL is made of poly(methylhydrosiloxane) (PMHS) and PDMS. It forms a good physical bond between the hydrophilic hydrogel and hydrophobic PDMS layers. The electric output performance of the SH-TENG can reach 94% of the undamaged one in 1 min. The SH-TENG (6 × 6 cm2) exhibits good stability and superior electrical performance, enabling it to power 37 LEDs simultaneously.« less
  3. Nature does nothing in vain. Through millions of years of revolution, living organisms have evolved hierarchical and anisotropic structures to maximize their survival in complex and dynamic environments. Many of these structures are intrinsically heterogeneous and often with functional gradient distributions. Understanding the convergent and divergent gradient designs in the natural material systems may lead to a new paradigm shift in the development of next-generation high-performance bio-/nano-materials and devices that are critically needed in energy, environmental remediation, and biomedical fields. Herein, we review the basic design principles and highlight some of the prominent examples of gradient biological materials/structures discovered overmore »the past few decades. Interestingly, despite the anisotropic features in one direction (i.e., in terms of gradient compositions and properties), these natural structures retain certain levels of symmetry, including point symmetry, axial symmetry, mirror symmetry, and 3D symmetry. We further demonstrate the state-of-the-art fabrication techniques and procedures in making the biomimetic counterparts. Some prototypes showcase optimized properties surpassing those seen in the biological model systems. Finally, we summarize the latest applications of these synthetic functional gradient materials and structures in robotics, biomedical, energy, and environmental fields, along with their future perspectives. This review may stimulate scientists, engineers, and inventors to explore this emerging and disruptive research methodology and endeavors.« less
  4. Abstract Quantum chromodynamics, the theory of the strong force, describes interactions of coloured quarks and gluons and the formation of hadronic matter. Conventional hadronic matter consists of baryons and mesons made of three quarks and quark-antiquark pairs, respectively. Particles with an alternative quark content are known as exotic states. Here a study is reported of an exotic narrow state in the D 0 D 0 π + mass spectrum just below the D *+ D 0 mass threshold produced in proton-proton collisions collected with the LHCb detector at the Large Hadron Collider. The state is consistent with the ground isoscalarmore »$${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + tetraquark with a quark content of $${{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}\overline{{{{{{\rm{u}}}}}}}\overline{{{{{{\rm{d}}}}}}}$$ c c u ¯ d ¯ and spin-parity quantum numbers J P  = 1 + . Study of the DD mass spectra disfavours interpretation of the resonance as the isovector state. The decay structure via intermediate off-shell D *+ mesons is consistent with the observed D 0 π + mass distribution. To analyse the mass of the resonance and its coupling to the D * D system, a dedicated model is developed under the assumption of an isoscalar axial-vector $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + state decaying to the D * D channel. Using this model, resonance parameters including the pole position, scattering length, effective range and compositeness are determined to reveal important information about the nature of the $${{{{{{\rm{T}}}}}}}_{{{{{{\rm{c}}}}}}{{{{{\rm{c}}}}}}}^{+}$$ T c c + state. In addition, an unexpected dependence of the production rate on track multiplicity is observed.« less
    Free, publicly-accessible full text available December 1, 2023
  5. In this work, we revisit the classical stochastic jump-diffusion process and develop an effective variant for estimating visibility statuses of objects while tracking them in videos. Dealing with partial or full occlusions is a long standing problem in computer vision but largely remains unsolved. In this work, we cast the above problem as a Markov Decision Process and develop a policy-based jump-diffusion method to jointly track object locations in videos and estimate their visibility statuses. Our method employs a set of jump dynamics to change object’s visibility statuses and a set of diffusion dynamics to track objects in videos. Differentmore »from traditional jump-diffusion process that stochastically generates dynamics, we utilize deep policy functions to determine the best dynamic for the present state and learn the optimal policies using reinforcement learning methods. Our method is capable of tracking objects with full or partial occlusions in crowded scenes. We evaluate the proposed method over challenging video sequences and compare it to alternative tracking methods. Significant improvements are made particularly for videos with frequent interactions or occlusions.« less
  6. Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address the problem of how to evaluate the evaluation metrics. We start with a few necessary conditions for metrics to produce meaningful scores, such as distinguishing real from generated samples, identifying mode dropping and mode collapsing, and detecting overfitting. With a series of carefully designed experiments, we comprehensively investigate existing sample-based metrics and identify their strengths and limitations in practical settings. Based on these results, we observe that kernel Maximum Mean Discrepancy (MMD) and the 1-Nearest- Neighbor (1-NN) two-samplemore »test seem to satisfy most of the desirable properties, provided that the distances between samples are computed in a suitable feature space. Our experiments also unveil interesting properties about the behavior of several popular GAN models, such as whether they are memorizing training samples, and how far they are from learning the target distribution.« less
  7. Free, publicly-accessible full text available May 1, 2023
  8. Free, publicly-accessible full text available June 1, 2023
  9. Free, publicly-accessible full text available April 1, 2023