Consumer‐diet discrimination of δ13C and δ15N: Source‐ and feeding‐oriented patterns based on gut content analysis in a large subtropical river of China

Understanding the trophic discrimination (?¹³C and ?¹⁵N) between consumers and diets in fluvial systems remains difficult because of the variable food sources and complex predator–prey interactions from headwaters to the estuaries. Here, stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotopes in fish and invertebrates from a large subtropical river in southern China were determined to explore trophic discrimination in conjunction with a gut content analysis. The ?¹³C values showed significant differences (p < .05) among functional feeding groups, with fish, shrimp, and insect scrapers presenting higher ?¹³C values (1.20 ± 0.23‰ to 1.51 ± 0.31‰) than other groups. The ?¹⁵N values varied significantly between invertebrates (0.64 ± 0.17‰ of insect collector‐gatherers to 1.63 ± 0.36‰ of shrimp predators) and fish (1.98 ± 0.19‰ of detritivores to 2.71 ± 0.43‰ of crustaceavores) and exhibited an increasing tendency from primary to secondary consumers. A linear regression analysis revealed that the longitudinal changes in ?¹³C and ?¹⁵N were closely associated with the δ¹³C of periphyton, the δ¹⁵N of particulate organic matter (POM) in water, and the relative contribution (%) of periphyton and organic detritus to the diet composition of consumers. These results indicated that discrimination factors might not only be influenced by the isotope signatures of basal food sources but also downstream shifts in dominant food items utilized by consumers. In particular, trophic discrimination between periphyton– and detritus–based food chains, such as “epilithic diatoms–shrimp scrapers–crustaceavorous fish” and “POM–bivalves–molluscivorous fish,” displayed regionally specific patterns. When back‐calculating to the diet assimilation and trophic position in subtropical streams and rivers, we suggest using the basin‐scale ?¹³C value of 0.96 ± 0.26‰ for all consumers and ?¹⁵N values of 1.07 ± 0.32‰ for invertebrates and 2.38 ± 0.37‰ for fish.     

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand-alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables.     

铜锌铝合金自催化生长硅纳米结构的研究

在常温常压下，以Cu-Zn—Al合金为基体经过简单的化学处理，在基体上成功地合成了直径为几十纳米，长度在微米量级的Si-维纳米结构。利用TEM，HREM，EDS等测试手段对Si-维纳米结构的形貌和微观结构进行了表征。观察到Cu-Zn-Al合金中存在着运动的“非线性振荡胞区”，并且发现Si-维纳米结构的生长和形貌与这些胞区的运动有着密切的关系。另外，对于Si-维纳米结构生长机制进行了初步探讨。     

Preparation and electrocatalytic properties of tungsten carbide electrocatalysts

1　ＩＮＴＲＯＤＵＣＴＩＯＮ ｉｎ 1 5 0℃ .Ｍａａｓ[1 0 ] ａｌｓｏｍａｎｕｆａｃｔｕｒｅｄｔｈｅＨ2 Ｏ2 ｆｕｅｌｃｅｌｌｓｔａｃｋｓｗｉｔｈｔｈｅｏｕｔｐｕｔｐｏｗｅｒｕｐｔｏ 1ｋＷ ,ａｎｄｔｈｅｐｒｏｐｅｒｔｉｅｓｏｆｔｈｅａｓ ｐｒｅｐａｒｅｄｆｕｅｌ     

Constructing online switching barriers: examining the effects of switching costs and alternative attractiveness on e-store loyalty in online pure-play retailers

Developing switching barriers to retain customers has become a critical marketing strategy for online retailers. However, research on the role of switching barriers in e-retailing is still limited. Recent trends show that when competitors are just one click away, it is questionable if customer loyalty can be achieved at all in online environments. This leads to the research question on whether switching barriers have any impact on e-loyalty in pure-play retailers. The paper examines the influence of switching barriers on customer retention (i.e., e-store loyalty) and further investigates the moderating effects of switching costs and alternative attractiveness. Data were gathered via a survey of 590 shoppers of online pure-play retailers in the UK. Findings show that customer satisfaction and the two dimensions of switching barriers (perceived switching costs and perceived attractiveness of alternatives) significantly influence customer loyalty. Contrary to findings in earlier studies, it was found that switching costs did not moderate the relationships between satisfaction and loyalty nor between perceived attractiveness of alternatives and loyalty. The paper makes imperative recommendations to develop switching barriers and to foster loyalty along with suggestions for future research.     

Latent-space variational bayes

Variational Bayesian Expectation-Maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has been a standard technique for practical Bayesian inference. In this paper, we introduce a more general approximate inference framework for conjugate-exponential family models, which we call Latent-Space Variational Bayes (LSVB). In this approach, we integrate out model parameters in an exact way, leaving only the latent variables. It can be shown that the LSVB approach gives better estimates of the model evidence as well as the distribution over latent variables than the VBEM approach, but in practice, the distribution over latent variables has to be approximated. As a practical implementation, we present a First-order LSVB (FoLSVB) algorithm to approximate this distribution over latent variables. From this approximate distribution, one can estimate the model evidence and the posterior over model parameters. The FoLSVB algorithm is directly comparable to the VBEM algorithm and has the same computational complexity. We discuss how LSVB generalizes the recently proposed collapsed variational methods [20] to general conjugate-exponential families. Examples based on mixtures of Gaussians and mixtures of Bernoullis with synthetic and real-world data sets are used to illustrate some advantages of our method over VBEM.     

Layered Diffusion-based Coverage Control in Wireless Sensor Networks

Coverage is an important performance metric for many applications, such as surveillance in wireless sensor networks. Coverage control is used to select as few active nodes as possible from all deployed sensor nodes, such that sufficient coverage of the monitored area can be guaranteed while reducing the energy consumption of each individual sensor node to prolong the network lifetime. This paper classifies three types of coverage control protocols based on the available information about nodes’ distances or locations, and reviews several representative protocols for each type. We also propose a new distributed and localized coverage control protocol, called Layered Diffusion-based Coverage Control (LDCC). The LDCC protocol does not require information about the node location coordinates when selecting active nodes. Instead, it exploits hop count information, which is easily obtained in a WSN, to select active sensor nodes. Furthermore, the LDCC protocol is very simple and does not require any sophisticated computation such as distance or covered area computation. Our simulation results show that the LDCC protocol achieves a high coverage ratio while incurring very low message overhead compared with other existing protocols. Furthermore, simulation results suggest that in a large-scale sensor network with medium to large localization errors, LDCC performs even better than location-based coverage control protocols.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

融合隐向量对齐和Swin Transformer的OCTA血管分割

目的 光学相干断层扫描血管造影（optical coherence tomography angiography,OCTA）是一种非侵入式的新兴技术,越来越多地应用于视网膜血管成像。与传统眼底彩照相比,OCTA技术能够显示黄斑周围的微血管信息,在视网膜血管成像邻域具有显著优势。临床实践中,医生可以通过OCTA图像观察不同层的血管结构,并通过分析血管结构的变化来判断是否存在相关疾病。大量研究表明,血管结构的任何异常变化通常都意味着存在某种眼科疾病。因此,对OCTA图像中的视网膜血管结构进行自动分割提取,对众多眼部相关疾病量化分析和临床决策具有重大意义。然而,OCTA图像存在视网膜血管结构复杂、图像整体对比度低等问题,给自动分割带来极大挑战。为此,提出了一种新颖的融合隐向量对齐和Swin Transformer的视网膜血管结构的分割方法,能够实现血管结构的精准分割。方法 以ResU-Net为主干网络,通过Swin Transformer编码器获取丰富的血管特征信息。此外,设计了一种基于隐向量的特征对齐损失函数,能够在隐空间层次对网络进行优化,提升分割性能。结果 在3个OCTA图像数据集上的...     

Implementation of mmWave long-range backhaul for UAV-BS

Uncrewed aerial vehicles (UAVs) have become a vital element in nonterrestrial networks, especially with respect to 5G communication systems and beyond. The use of UAVs in support of 4G/5G base station (uncrewed aerial vehicle base station [UAV-BS]) has proven to be a practical solution for extending cellular network services to areas where conventional infrastructures are unavailable. In this study, we introduce a UAV-BS system that utilizes a high-capacity wireless backhaul operating in millimeter-wave frequency bands. This system can achieve a maximum throughput of 1.3 Gbps while delivering data at a rate of 300 Mbps, even at distances of 10 km. We also present the details of our testbed implementation alongside the performance results obtained from field tests.