Test out our new Bento Search

The maximum sustainable yield (MSY) and status of 54 fishery resources exploited by the Gujarat fishing fleet in the northeastern Arabian Sea were assessed using the data-limited CMSY method. The prioritized fishery resources in this study make a significant contribution to the total marine catch in the region and play a vital role in trophic interactions. The Gujarat marine fisheries recorded an annual percentage growth rate of 5.04 per year from 1985 to 2018. The MSY was computed at 8.73 × 105 t across all resources. The biomass indicators (B/BMSY) indicated alarming overexploitation for commercially exploited resources like Bregmaceros mcclellandi, Tenualosa ilisha, Coilia dussumieri, Euthynnus affinis, Thunnus tonggol, Scomberoides spp., Psettodes erumei, Pampus argenteus, etc. Stock assessment using biological reference points (B/BMSY, F/FMSY) classified 39 resources were found either healthy or slightly overfished (B/BMSY>0.8), while 13 showed varying degrees of overexploitation, including 2 severely depleted species (unicorn cod, silverbellies). The rampant increase of fishing efforts and target exploitation can cause the collapse or overfishing of resources, which eventually affects the integrity of the ecosystem. The fishery and biomass indicators computed in the current study suggest periodic monitoring of overexploited stocks and a framework of rebuilding strategies for grossly overfished and overfished resources.Clinical Trial Number: Not applicable.

We explore the effect of lottery presentation formats on elicitation of risk preferences using a popular probability varying task (Holt & Laury. The American Economic Review92 (5), 1644–1655. 2002) and a payoff-varying task (Drichoutis & Lusk. Journal of Risk and Uncertainty53 (2), 89–106. 2016). The presentation formats use horizontal bars that vary either the width or height of the bars (or both at the same time) to help subjects in judging how large or small probabilities and monetary amounts are in a given choice set. These graphical formats are compared to a text only format. We complement our choice data with eye tracking data that enriches our structural models with additional information regarding how visual attention varies with the presented information. While we find no statistically significant effects of presentation formats on elicited parameters for risk preferences, we find that eye tracking information not only is associated with preference parameters, but it also changes the inferences with respect to which decision theory better fits the choice data.

Even though peer review enjoys sound theoretical support, classroom research has not always provided a clear picture of its effectiveness, particularly when it comes to form-focused editing. In addition to yielding conflicting findings with respect to the number of corrections students make based on peer feedback (Connor & Asenavage, 1994; Mendonça & Johnson, 1994; Paulus, 1999; Villamil & de Guerrero, 1998), several gaps in the literature remain unaddressed, particularly in terms of error detection rates. This study sought to determine to what extent learners are able to (a) detect errors during peer editing, (b) provide each other with correct information, and (c) incorporate peer-provided suggestions in subsequent individual writing tasks. Participants were 20 undergraduate students enrolled in an intermediate Spanish course. They individually wrote a story based on pictures, exchanged papers with a partner, and provided each other with feedback on lexical, grammatical, and orthographic issues. The learners' audio-recorded interactions and the narratives produced by the learners during and after the peer-editing session were analyzed. Results indicate that even though learners detect a small proportion of errors, the majority of corrections were accurate and were incorporated in writing tasks completed immediately after the peer editing session, as well as two weeks later.

Present artificial muscles have been suffering from poor actuation step precision and the need of energy input to maintain actuated states due to weak interactions between guest and host materials or the unstable structural changes. Herein, these challenges are addressed by deploying a mechanism of reversible faradaic insertion and extraction reactions between tetrachloroaluminate ions and collapsed carbon nanotubes. This mechanism allows tetrachloroaluminate ions as a strong but dynamic “locker” to achieve an energy-free high-tension catch state and programmable stepwise actuation in the yarn muscle. When powered off, the muscle nearly 100% maintained any achieved contractile strokes even under loads up to 96,000 times the muscle weight. The actuation mechanism allowed the programmable control of stroke steps down to 1% during reversible actuation. The isometric stress generated by the yarn muscle (14.6 MPa in maximum, 40 times that of skeletal muscles) was also energy freely lockable and step controllable with high precision. Importantly, when fully charged, the muscle stored energy with a high capacity of 102 mAh g–1, allowing the muscle as a battery to power secondary muscles or other devices.

Quantum mechanical electron tunneling is explored as the mediator of chemical bonding. Three topics are addressed. First, a baseball game-of-catch metaphor is employed to elucidate the physics of chemical bonding. According to this metaphor, a baseball player, a baseball, and the act of throwing a baseball are analogous to an atom, an electron, and the act of electron tunneling. Using this framework, the tunneling physics of covalent, polar covalent, ionic, and hydrogen bonding is clarified. Second, gas-state energies associated with frontier orbital positioning are established and are used in the chemical bond tunneling assessment of four homopolar bonds, H–H, NN, OO, and F–F, and four heteropolar bonds, H–F, H–Cl, H–Br, and H–I. Third, a new procedure for estimating solid-state tunneling parameters is introduced, involving quantum confinement of gas-state energy frontier orbitals.

In Cyber-Physical Systems (CPS), sensor data integrity is crucial since acting on malicious sensor data can cause serious consequences, given the tight coupling between cyber components and physical systems. While extensive works focus on sensor attack detection, attack diagnosis that aims to find out when the attack starts has not been well studied yet. This temporal sensor attack diagnosis problem is equally important because many recovery methods rely on the accurate determination of trustworthy historical data. To address this problem, we propose a lightweight data-driven solution to achieve real-time sensor attack diagnosis. Our novel solution consists of five modules, with the attention and diagnosis ones as the core. The attention module not only helps accurately predict future sensor measurements but also computes statistical attention scores for the diagnosis module. Based on our unique observation that the score fluctuates sharply once an attack launches, the diagnosis module determines the onset of an attack through monitoring the fluctuation. Evaluated on high-dimensional high-fidelity simulators and a testbed, our solution demonstrates robust and accurate temporal diagnosis results while incurring millisecond-level computational overhead on Raspberry Pi.