Assessment of topsoil removal as an effective method for vegetation restoration in farmed peatlands

Peatland areas have dramatically declined in the past century because of the demand for agriculture. Therefore, it is necessary to develop suitable techniques to preserve these unique ecosystems. We studied the effects of topsoil removal on vegetation restoration in silt- and sand-amended peatlands in Changbai Mountain, China. We observed that topsoil removal effectively improved soil nutrient levels and water holding capacity in the silt-amended peatland but exhibited no significant effect on the sand-amended peatland. Topsoil removal decreased the species richness in both silt- and sand-amended peatlands but did not have any effect on the plant cover and biomass in the sand-amended peatland. The coverage, density, and aboveground biomass of dominant species, namely, Carex schmidtii, significantly increased after topsoil removal in the silt-amended peatland. The target Carex species was absent from the sand-amended peatland. Redundancy analysis identified that the soil water content, soil organic carbon, total nitrogen, and total phosphorus explained the most variance in vegetation composition in the silt-amended peatland. Our results demonstrated that topsoil removal is necessary to reduce the weed seeds and promote the recolonization of peatland species, particularly the tussock-forming Carex, in the silt-amended peatland during restoration

One-step preparation of optically transparent Ni-Fe oxide film electrocatalyst for oxygen evolution reaction

Optically transparent cocatalyst film materials is very desirable for improved photoelectrochemical (PEC) oxygen evolution reaction (OER) over light harvesting photoelectrodes which require the exciting light to irradiate through the cocatalyst side, i.e., front-side illumination. In view of the reaction overpotential at electrode/electrolyte interface, the OER electrocatalysts have been extensively used as cocatalysts for PEC water oxidation on photoanode. In this work, the feasibility of a one-step fabrication of the transparent thin film catalyst for efficient electrochemical OER is investigated. The Ni-Fe bimetal oxide films, 200 nm in thickness, are used for study. Using a reactive magnetron co-sputtering technique, transparent (> 50% in wavelength range 500-2000 nm) Ni-Fe oxide films with high electrocatalytic activities were successfully prepared at room temperature. Upon optimization, the as-prepared bimetal oxide film with atomic ratio of Fe/Ni = 3:7 demonstrates the lowest overpotential for the OER in aqueous KOH solution, as low as 329 mV at current density of 2 mA cm 2, which is 135 and 108 mV lower than that of as-sputtered FeOx and NiOx thin films, respectively. It appears that this fabrication strategy is very promising to deposit optically transparent cocatalyst films on photoabsorbers for efficient PEC water splitting.This work was financially supported by the National Natural Science Foundation of China (No. 21090340), 973 National Basic Research Program of the Ministry of Science and Technology (No. 2014CB239400) and Solar Energy Action Plan of Chinese Academy of Sciences (KGCX2-YW-399+7-3)

FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space

This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals through self-supervised training. Existing multimodal contrastive frameworks mostly rely on the shared information between sensory modalities, but do not explicitly consider the exclusive modality information that could be critical to understanding the underlying sensing physics. Besides, contrastive frameworks for time series have not handled the temporal information locality appropriately. FOCAL solves these challenges by making the following contributions: First, given multimodal time series, it encodes each modality into a factorized latent space consisting of shared features and private features that are orthogonal to each other. The shared space emphasizes feature patterns consistent across sensory modalities through a modal-matching objective. In contrast, the private space extracts modality-exclusive information through a transformation-invariant objective. Second, we propose a temporal structural constraint for modality features, such that the average distance between temporally neighboring samples is no larger than that of temporally distant samples. Extensive evaluations are performed on four multimodal sensing datasets with two backbone encoders and two classifiers to demonstrate the superiority of FOCAL. It consistently outperforms the state-of-the-art baselines in downstream tasks with a clear margin, under different ratios of available labels. The code and self-collected dataset are available at https://github.com/tomoyoshki/focal.Comment: Code available at: [github](https://github.com/tomoyoshki/focal