Contextual diversity and anchoring: Null effects on learning word forms and opposing effects on learning word meanings

Words that appear in many contexts/topics are recognised faster than those occurring in fewer contexts (Nation, 2017). However, contextual diversity benefits are less clear in word learning studies. Mak et al. (2021) proposed that diversity benefits might be enhanced if new word meanings are anchored before introducing diversity. In our study, adults (N = 288) learned meanings for eight pseudowords, four experienced in six topics (high diversity) and four in one topic (low diversity). All items were first experienced five times in one topic (anchoring phase), and results were compared to Norman et al. (2022) which used a similar paradigm without an anchoring phase. An old-new decision post-test (did you learn this word?) showed null effects of contextual diversity on written form recognition accuracy and response time, mirroring Norman et al.. A cloze task involved choosing which pseudoword completed a sentence. For sentences situated in a previously experienced context, accuracy was significantly higher for pseudowords learned in the low diversity condition, whereas for sentences situated in a new context, accuracy was non-significantly higher for pseudowords learned in the high diversity condition. Anchoring modulated these effects. Low diversity item accuracy was unaffected by anchoring. However, for high diversity items, accuracy in familiar contexts was better in the current experiment (anchoring) than in Norman et al. (non-anchoring), but accuracy in new contexts did not differ between the two experiments. These results suggest that anchoring facilitates meaning use in familiar contexts, but not generalisation to new contexts, nor word recognition in isolation.</p

Characterization of the surface-active exopolysaccharide produced by Halomonas sp TGOS-10: understanding its role in the formation of marine oil snow

In this study, we characterize the exopolymer produced by Halomonas sp. strain TGOS-10 –one of the organisms found enriched in sea surface oil slicks during the Deepwater Horizon oil spill. The polymer was produced during the early stationary phase of growth in Zobell’s 2216 marine medium amended with glucose. Chemical and proton NMR analysis showed it to be a relatively monodisperse, high-molecular-mass (6,440,000 g/mol) glycoprotein composed largely of protein (46.6% of total dry weight of polymer). The monosaccharide composition of the polymer is typical to that of other marine bacterial exopolymers which are generally rich in hexoses, with the notable exception that it contained mannose (commonly found in yeast) as a major monosaccharide. The polymer was found to act as an oil dispersant based on its ability to effectively emulsify pure and complex oils into stable oil emulsions—a function we suspect to be conferred by the high protein content and high ratio of total hydrophobic nonpolar to polar amino acids (52.7:11.2) of the polymer. The polymer’s chemical composition, which is akin to that of other marine exopolymers also having a high protein-to-carbohydrate (P/C) content, and which have been shown to effect the rapid and non-ionic aggregation of marine gels, appears indicative of effecting marine oil snow (MOS) formation. We previously reported the strain capable of utilising aromatic hydrocarbons when supplied as single carbon sources. However, here we did not detect biodegradation of these chemicals within a complex (surrogate Macondo) oil, suggesting that the observed enrichment of this organism during the Deepwater Horizon spill may be explained by factors related to substrate availability and competition within the complex and dynamic microbial communities that were continuously evolving during that spill

A mmWave Leaky-Wave Antenna for Efficiency Enhanced Near-Field Wireless Power Transfer and Communication

A mmWave near-field-focused and beam steerable circularly polarized (CP) leaky-wave antenna (LWA) for wirelesses power transfer (WPT) with multiple users and communication is presented. The proposed design is achieved by etching uniform fan-shaped slots in a rectangular substrate integrated waveguide (SIW). The slots are elaborately designed and positioned for beam focusing at a desired focal distance of 0.5 m. Compared to the conventional LWA, the power density of the proposed design is enhanced by 1.4 dB. It allows a dual beam scanning propriety with a wide angular coverage of −43.18◦ and −33◦ in near field and far field, respectively. The proposed design benefits WPT systems by eliminating the beam forming network and offering wide power coverage. Moreover, it offers high realized gain no less than 12.62 dBi in the bandwidth of 22.5 GHz to 26 GHz. Hence, the LWA proposed is a promising transmitter candidate for integrated WPT and communication

Quantification of pulse train instabilities in mode-locked quantum-dot laser diodes

We have quantified pulse train instabilities in mode-locked laser diodes, using a highly-sensitive dispersion-scan setup with the self-calibrating retrieval algorithm. We investigated the influence of operating bias conditions on pulse instabilities from quantum-dot lasers.</p

Beyond the hurdles:Exploring policy obstacles in the path to circular economy adoption

By redefining growth, focusing on positive society-wide benefits, and emphasizing the restoration and regeneration of natural systems, CE models present a transformative approach to achieving environmental sustainability and economic resilience. However, the adoption of CE models is often hindered by policy-related barriers, a challenge that has been underscored by numerous anecdotal evidences. Against this backdrop, the present study aims to identifying and analyzing the policy-related barriers that obstruct the adoption of CE models, particularly in the context of developing economies. This research adopts a three-step methodology to comprehensively understand these barriers. Initially, an extensive literature review was conducted to identify a preliminary set of policy-related barriers. This was followed by a survey, which gathered insights from experts in the field of CE from three Asian countries: India, Pakistan, and Bangladesh. These countries were selected due to their increasing environmental challenges, and their nascent but growing interest in adopting CE principles. In the final stage, the AHP method was employed to quantify and prioritize these barriers. The AHP analysis revealed several key insights. Notably, it identified alack of clear regulatory frameworks, insufficient economic incentives, and limited awareness and knowledge about CE principles as the most significant barriers. These barriers, along with others, were found to vary in their degree of influence and interconnectivity, providing a better understanding of the policy landscape surrounding CE adoption

BladeView:Toward Automatic Wind Turbine Inspection with Unmanned Aerial Vehicle

This paper presents a fully automatic method, BladeView, for drone-based wind turbine blade inspection using an Unmanned Aerial Vehicle (UAV). With the need for highly efficient blade inspection coupled with the rapid increase of wind turbines, existing methods provide limited automation on wind turbine parameter estimation, full blade coverage, and safety control. We introduce an Automatic Parameter Calculation (APC) algorithm and an Automatic Flight System (AFS) in BladeView to compute wind turbine parameters and inspection paths, respectively. Leveraging triangulation and linear fitting integration techniques, the APC automatically calculates the wind turbine parameters and estimates the relative angle and position between a drone and the turbine. Furthermore, with dynamic path finding and B-spline optimization, the AFS plans a path covering 3 blades within specified flight corridors, in compliance with the turbine parameters obtained from APC. Thus, the proposed BladeView can properly ensure an inspection's automation, coverage, safety, and smoothness. The efficiency and usability of BladeView are validated through 100,000 flight simulations in the Gazebo simulation environment and 9,239 field runs at various wind farms, including offshore, near-shore, deserts, mountainous areas, farmlands, and suburbs. Note to Practitioners - The proposed BladeView is distinguished in three aspects: (1) It automatically adapts wind turbines with varying geometric properties and physical locations relative to the take-off point. (2) It dramatically improves the quality of collected data with optimal UAV speed and flight corridors. (3) It thoroughly covers all three blades of a Horizontal-Axis Wind Turbine (HAWT), including regions where defects frequently occur. Thus, BladeView is more efficient and robust than existing UAV-based methods for blade inspection, with only around 25 minutes per HAWT. Moreover, it does not require experienced pilots to fly the UAV and manual interventions are rarely needed. Extensive simulation and real-world experiments demonstrate the efficiency and usability of BladeView in various on-and offshore wind farms.</p

Secure Artificial Intelligence for Precise Vehicle Behavior Prediction in 6G Consumer Electronics

In the context of Secure Artificial Intelligence for 6G Consumer Electronics, accurately predicting vehicle behavior in dynamic traffic scenarios is a significant challenge in intelligent transportation. To avoid sending all raw data to a centralized cloud server, this study presents an artificial intelligence (AI) based distributed machine learning framework (AICEML) that can run on local edge devices. This method protects user privacy while minimizing transmission and processing delays. Accurate predictions are maintained despite the presence of many cars because to AICEML's use of the model on edge devices, which incorporates edge-enhanced attention and graph convolutional neural network features to swiftly collect and transmit vehicle interaction information. Each edge device can adapt its neural network type and scale based on its computing capabilities, accommodating various application scenarios. Experimental results using the NGGSIM dataset demonstrate AICEML's superiority, achieving precision, recall, and F1 scores of 0.9391, 0.9557, and 0.9473, respectively. With a 1-second prediction horizon, it maintains 91.21% accuracy and low time complexity even as the number of vehicles increases. This framework holds promise for enhancing intelligent transportation systems in the 6G era while prioritizing security and efficiency.</p

Double-Loop Importance Sampling for McKean–Vlasov Stochastic Differential Equation

This paper investigates Monte Carlo (MC) methods to estimate probabilities of rare events associated with the solution to the d-dimensional McKean–Vlasov stochastic differential equation (MV-SDE). MV-SDEs are usually approximated using a stochastic interacting P-particle system, which is a set of P coupled d-dimensional stochastic differential equations (SDEs). Importance sampling (IS) is a common technique for reducing high relative variance of MC estimators of rare-event probabilities. We first derive a zero-variance IS change of measure for the quantity of interest by using stochastic optimal control theory. However, when this change of measure is applied to stochastic particle systems, it yields a P×d-dimensional partial differential control equation (PDE), which is computationally expensive to solve. To address this issue, we use the decoupling approach introduced in (dos Reis et al. 2023), generating a d-dimensional control PDE for a zero-variance estimator of the decoupled SDE. Based on this approach, we develop a computationally efficient double loop MC (DLMC) estimator. We conduct a comprehensive numerical error and work analysis of the DLMC estimator. As a result, we show optimal complexity of OTOLr-4 with a significantly reduced constant to achieve a prescribed relative error tolerance TOLr. Subsequently, we propose an adaptive DLMC method combined with IS to numerically estimate rare-event probabilities, substantially reducing relative variance and computational runtimes required to achieve a given TOLr compared with standard MC estimators in the absence of IS. Numerical experiments are performed on the Kuramoto model from statistical physics