The functional identification and evaluation of endophytic bacteria sourced from the roots of tolerant Achyranthes bidentata to overcome monoculture problems of Rehmannia glutinosa.

The isolation and identification of plant growth-promoting endophytic bacteria (PGPEB) from Achyranthes bidentata roots have profound theoretical and practical implications in ecological agriculture, particularly as bio-inoculants to address challenges associated with continuous monoculture. Our research revealed a significant increase in the abundance of these beneficial bacteria in A. bidentata rhizosphere soil under prolonged monoculture conditions, as shown by bioinformatics analysis. Subsequently, we isolated 563 strains of endophytic bacteria from A. bidentata roots. Functional characterization highlighted diverse plant growth-promoting traits among these bacteria, including the secretion of indole-3-acetic acid (IAA) ranging from 68.01 to 73.25 mg/L, phosphorus and potassium solubilization capacities, and antagonistic activity against pathogenic fungi (21.54%-50.81%). Through 16S rDNA sequencing, we identified nine strains exhibiting biocontrol and growth-promoting potential. Introduction of a synthetic microbial consortium (SMC) in pot experiments significantly increased root biomass by 48.19% in A. bidentata and 27.01% in replanted Rehmannia glutinosa. These findings provide innovative insights and strategies for addressing continuous cropping challenges, highlighting the practical promise of PGPEB from A. bidentata in ecological agriculture to overcome replanting obstacles for non-host plants like R. glutinosa, thereby promoting robust growth in medicinal plants.

26.5625 Gbaud PAM-4 short-reach transmission at 75°C using 850 nm VCSELs with strategic oxide guiding layer positioning.

This article presents an all-epitaxy approach to reduce the root mean square spectral width (ΔλR M S) of 850 nm oxide-confined vertical cavity surface-emitting lasers (VCSELs) with a large aperture of 7 µm through strategic optimization of the oxide guiding layer within the epitaxy structure. At 75°C, the VCSEL demonstrates a ΔλR M S of ∼0.3 nm at a bias current of 7.5 mA. Furthermore, the VCSEL achieves successful transmission of 26.5625 Gbaud PAM-4 modulation over a short-reach (SR) OM4 fiber link while maintaining a TDECQ budget below the 4.5 dB specified by 50G IEEE Ethernet standards.

Monolithically integrated 940†nm VCSELs on bulk Ge substrates.

This research successfully developed an independent Ge-based VCSEL epitaxy and fabrication technology route, which set the stage for integrating AlGaAs-based semiconductor devices on bulk Ge substrates. This is the second successful Ge-based VCSEL technology reported worldwide and the first Ge-based VCSEL technology with key details disclosed, including Ge substrate specification, transition layer structure and composition, and fabrication process. Compared with the GaAs counterparts, after epitaxy optimization, the Ge-based VCSEL wafer has a 40% lower surface root-mean-square roughness and 72% lower average bow-warp. After device fabrication, the Ge-based VCSEL has a 10% lower threshold current density and 19% higher maximum optical differential efficiency than the GaAs-based VCSEL.

25 Gb/s NRZ transmission at 85°C using a high-speed 940 nm AlGaAs oxide-confined VCSEL grown on a Ge substrate.

In this Letter, we present a comprehensive analysis of the high-speed performance of 940 nm oxide-confined AlGaAs vertical-cavity surface-emitting lasers (VCSELs) grown on Ge substrates. Our demonstration reveals a pronounced superiority of Ge-based VCSELs in terms of thermal stability. The presented Ge-VCSEL has a maximum modulation bandwidth of 16.1 GHz and successfully realizes a 25 Gb/s NRZ transmission at 85 ∘C. The experimental results underscore the significance and potential of Ge-VCSELs for applications requiring robust performance in high-temperature environments, laying the cornerstone for the future development of VCSEL devices.

Appropriately Reduced Nitrogen and Increased Phosphorus in Ratooning Rice Increased the Yield and Reduced the Greenhouse Gas Emissions in Southeast China.

Reducing greenhouse gas emissions while improving productivity is the core of sustainable agriculture development. In recent years, rice ratooning has developed rapidly in China and other Asian countries, becoming an effective measure to increase rice production and reduce greenhouse gas emissions in these regions. However, the lower yield of ratooning rice caused by the application of a single nitrogen fertilizer in the ratooning season has become one of the main reasons limiting the further development of rice ratooning. The combined application of nitrogen and phosphorus plays a crucial role in increasing crop yield and reducing greenhouse gas emissions. The effects of combined nitrogen and phosphorus application on ratooning rice remain unclear. Therefore, this paper aimed to investigate the effect of combined nitrogen and phosphorus application on ratooning rice. Two hybrid rice varieties, 'Luyou 1831' and 'Yongyou 1540', were used as experimental materials. A control treatment of nitrogen-only fertilization (187.50 kg·ha-1 N) was set, and six treatments were established by reducing nitrogen fertilizer by 10% (N1) and 20% (N2), and applying three levels of phosphorus fertilizer: N1P1 (168.75 kg·ha-1 N; 13.50 kg·ha-1 P), N1P2 (168.75 kg·ha-1 N; 27.00 kg·ha-1 P), N1P3 (168.75 kg·ha-1 N; 40.50 kg·ha-1 P), N2P1 (150.00 kg·ha-1 N; 13.50 kg·ha-1 P), N2P2 (150.00 kg·ha-1 N; 27.00 kg·ha-1 P), and N2P3 (150.00 kg·ha-1 N; 40.50 kg·ha-1 P). The effects of reduced nitrogen and increased phosphorus treatments in ratooning rice on the yield, the greenhouse gas emissions, and the community structure of rhizosphere soil microbes were examined. The results showed that the yield of ratooning rice in different treatments followed the sequence N1P2 > N1P1 > N1P3 > N2P3 > N2P2 > N2P1 > N. Specifically, under the N1P2 treatment, the average two-year yields of 'Luyou 1831' and 'Yongyou 1540' reached 8520.55 kg·ha-1 and 9184.90 kg·ha-1, respectively, representing increases of 74.30% and 25.79% compared to the N treatment. Different nitrogen and phosphorus application combinations also reduced methane emissions during the ratooning season. Appropriately combined nitrogen and phosphorus application reduced the relative contribution of stochastic processes in microbial community assembly, broadened the niche breadth of microbial communities, enhanced the abundance of functional genes related to methane-oxidizing bacteria and soil ammonia-oxidizing bacteria in the rhizosphere, and decreased the abundance of functional genes related to methanogenic and denitrifying bacteria, thereby reducing greenhouse gas emissions in the ratooning season. The carbon footprint of ratooning rice for 'Luyou 1831' and 'Yongyou 1540' decreased by 25.82% and 38.99%, respectively, under the N1P2 treatment compared to the N treatment. This study offered a new fertilization pattern for the green sustainable development of rice ratooning.

Zone-addressable 20 × 20 940 nm VCSEL array with a 5-bit binary number pattern.

This article presents a monolithically zone-addressable 20 × 20 940 nm vertical-cavity surface-emitting laser (VCSEL) array with a binary number pattern design for sensing applications. The emitters in this VCSEL array have a uniquely designed binary pattern design, with each row representing a 5-bit pattern designed to aid pattern-matching algorithms to deduce the shape and depth information efficiently. Approximately 200 VCSELs are arranged in four individually addressable light-emitting zones, with ∼50 emitters in each zone. Each zone generates laser pulses up to 7.2 W in peak power.

WiTransformer: A Novel Robust Gesture Recognition Sensing Model with WiFi.

The past decade has demonstrated the potential of human activity recognition (HAR) with WiFi signals owing to non-invasiveness and ubiquity. Previous research has largely concentrated on enhancing precision through sophisticated models. However, the complexity of recognition tasks has been largely neglected. Thus, the performance of the HAR system is markedly diminished when tasked with increasing complexities, such as a larger classification number, the confusion of similar actions, and signal distortion To address this issue, we eliminated conventional convolutional and recurrent backbones and proposed WiTransformer, a novel tactic based on pure Transformers. Nevertheless, Transformer-like models are typically suited to large-scale datasets as pretraining models, according to the experience of the Vision Transformer. Therefore, we adopted the Body-coordinate Velocity Profile, a cross-domain WiFi signal feature derived from the channel state information, to reduce the threshold of the Transformers. Based on this, we propose two modified transformer architectures, united spatiotemporal Transformer (UST) and separated spatiotemporal Transformer (SST) to realize WiFi-based human gesture recognition models with task robustness. SST intuitively extracts spatial and temporal data features using two encoders, respectively. By contrast, UST can extract the same three-dimensional features with only a one-dimensional encoder, owing to its well-designed structure. We evaluated SST and UST on four designed task datasets (TDSs) with varying task complexities. The experimental results demonstrate that UST has achieved recognition accuracy of 86.16% on the most complex task dataset TDSs-22, outperforming the other popular backbones. Simultaneously, the accuracy decreases by at most 3.18% when the task complexity increases from TDSs-6 to TDSs-22, which is 0.14-0.2 times that of others. However, as predicted and analyzed, SST fails because of excessive lack of inductive bias and the limited scale of the training data.

UAV's Status Is Worth Considering: A Fusion Representations Matching Method for Geo-Localization.

Visual geo-localization plays a crucial role in positioning and navigation for unmanned aerial vehicles, whose goal is to match the same geographic target from different views. This is a challenging task due to the drastic variations in different viewpoints and appearances. Previous methods have been focused on mining features inside the images. However, they underestimated the influence of external elements and the interaction of various representations. Inspired by multimodal and bilinear pooling, we proposed a pioneering feature fusion network (MBF) to address these inherent differences between drone and satellite views. We observe that UAV's status, such as flight height, leads to changes in the size of image field of view. In addition, local parts of the target scene act a role of importance in extracting discriminative features. Therefore, we present two approaches to exploit those priors. The first module is to add status information to network by transforming them into word embeddings. Note that they concatenate with image embeddings in Transformer block to learn status-aware features. Then, global and local part feature maps from the same viewpoint are correlated and reinforced by hierarchical bilinear pooling (HBP) to improve the robustness of feature representation. By the above approaches, we achieve more discriminative deep representations facilitating the geo-localization more effectively. Our experiments on existing benchmark datasets show significant performance boosting, reaching the new state-of-the-art result. Remarkably, the recall@1 accuracy achieves 89.05% in drone localization task and 93.15% in drone navigation task in University-1652, and shows strong robustness at different flight heights in the SUES-200 dataset.