Search results for: microcosm experiments.

Authors: Siyamak Ziyaei, Siti Khalijah Mazlan, Petros Lappas

Abstract:

Compressed natural gas (CNG) is primarily composed of methane (CH4), and has a lower carbon to hydrogen ratio than other hydrocarbon fuels such as gasoline (C8H18) and diesel (C12H23). Consequently, it has the potential to reduce CO2 emissions compared to conventional fuels. Although Natural Gas (NG) has environmental advantages compared to other hydrocarbon fuels, its main component, CH4, burns at a slower rate compared to the conventional fuels. A higher pressure and leaner cylinder environment will unravel the slow burn characteristic of CH4. Lean combustion and high compression ratios are well-known methods for increasing the efficiency of internal combustion engines. In order to achieve successful a CNG lean combustion in Spark Ignition (SI) engines, a strong ignition system is essential to avoid engine misfires, especially in ultra-lean conditions. Turbulent Jet Ignition (TJI) is an ignition system that employs a pre-combustion chamber to ignite the lean fuel mixture in the main combustion chamber using a fraction of the total fuel per cycle. TJI enables ultra-lean combustion by providing distributed ignition sites through orifices. The fast burn rate provided by TJI enables the ordinary SI engine to be comparable to other combustion systems such as Homogeneous Charge Compression Ignition (HCCI) or Controlled Auto-Ignition (CAI) in terms of thermal efficiency, through the increased levels of dilution without the need of sophisticated control systems. Due to the physical geometry of TJI, which contains small orifices that connect the pre-chamber to the main chamber, providing the right mixture of fuel and air has been identified as a key challenge due to the insufficient amount of air that is pushed into the pre-chamber during each compression stroke. There is also the problem of scavenging which contributed to the factors that reduces the TJI performance. Combustion residual gases such as CO2, CO and NOx from the previous combustion cycle dilute the pre-chamber fuel-air mixture preventing rapid combustion in the pre-chamber. An air-controlled active TJI is presented in this paper in order to address these issues. By supplying air into the pre-chamber at a sufficient pressure, residual gases are exhausted, and the air-fuel ratio is controlled within the pre-chamber, thereby improving the quality of the combustion. An investigation of the 3D combustion characteristics of a CNG-fueled SI engine using a direct injection fuelling strategy employing an air channel in the prechamber is presented in this paper. Experiments and simulations were performed at the Worldwide Mapping Point (WWMP) at 1500 revolutions per minute (rpm), 3.3 bar Indicated Mean Effective Pressure (IMEP), using only conventional spark plugs as a baseline. With a validated baseline engine simulation, the settings were set for all simulation scenarios at λ=1. Following that, the pre-chambers with and without an auxiliary fuel supply were simulated. In the study of (DI-CNG) SI engine, active TJI was observed to perform better than passive TJI and conventional  spark plug ignition. In conclusion, the active pre-chamber with an air channel demonstrated an improved thermal efficiency (ηth) over other counterparts and conventional spark ignition systems.

Keywords: Turbulent Jet Ignition, Active Air Control Turbulent Jet Ignition, Pre-chamber ignition system, Active and Passive Pre-chamber, thermal efficiency, methane combustion, internal combustion engine combustion emissions.

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Authors: Zofija Tupikovskaja-Omovie, David Tyler, Sam Dhanapala, Steve Hayes

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The UK is leading in online retail and mobile adoption. However, there is a dearth of information relating to mobile apparel retail, and developing an understanding about consumer browsing and purchase behaviour in m-retail channel would provide apparel marketers, mobile website and app developers with the necessary understanding of consumers’ needs. Despite the rapid growth of mobile retail businesses, no published study has examined shopping behaviour on fashion mobile apps and websites. A mixed method approach helped to understand why fashion consumers prefer websites on smartphones, when diverse mobile apps are also available. The following research methods were employed: survey, eye-tracking experiments, observation, and interview with retrospective think aloud. The mobile gaze tracking device by SensoMotoric Instruments was used to understand frustrations in navigation and other issues facing consumers in mobile channel. This method helped to validate and compliment other traditional user-testing approaches in order to optimize user experience and enhance the development of mobile retail channel. The study involved eight participants - females aged 18 to 35 years old, who are existing mobile shoppers. The participants used the Topshop mobile app and website on a smart phone to complete a task according to a specified scenario leading to a purchase. The comparative study was based on: duration and time spent at different stages of the shopping journey, number of steps involved and product pages visited, search approaches used, layout and visual clues, as well as consumer perceptions and expectations. The results from the data analysis show significant differences in consumer behaviour when using a mobile app or website on a smart phone. Moreover, two types of problems were identified, namely technical issues and human errors. Having a mobile app does not guarantee success in satisfying mobile fashion consumers. The differences in the layout and visual clues seem to influence the overall shopping experience on a smart phone. The layout of search results on the website was different from the mobile app. Therefore, participants, in most cases, behaved differently on different platforms. The number of product pages visited on the mobile app was triple the number visited on the website due to a limited visibility of products in the search results. Although, the data on traffic trends held by retailers to date, including retail sector breakdowns for visits and views, data on device splits and duration, might seem a valuable source of information, it cannot explain why consumers visit many product pages, stay longer on the website or mobile app, or abandon the basket. A comprehensive list of pros and cons was developed by highlighting issues for website and mobile app, and recommendations provided. The findings suggest that fashion retailers need to be aware of actual consumers’ behaviour on the mobile channel and their expectations in order to offer a seamless shopping experience. Added to which is the challenge of retaining existing and acquiring new customers. There seem to be differences in the way fashion consumers search and shop on mobile, which need to be explored in further studies.

Keywords: Consumer behaviour, eye-tracking technology, fashion retail, mobile app, m-retail, smart phones, Topshop, user experience, website.

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Authors: Ajay Kumar Pandey, Mayank Kumar

Abstract:

White grub (Coleoptera: Scarabaeidae) is a major destructive pest in western Himalayan region of Uttarakhand. Beetles feed on apple, apricot, plum, walnut etc. during night while, second and third instar grubs feed on live roots of cultivated as well as non-cultivated crops. Collection and identification of scarab beetles through light trap was carried out at Crop Research Centre, Govind Ballab Pant University Pantnagar, Udham Singh Nagar (Uttarakhand) during 2018. Field trials were also conducted in 2018 to evaluate pre and post sown application of different insecticides against the white grub infesting soybean. The insecticides like Carbofuran 3 Granule (G) (750 g a.i./ha), Clothianidin 50 Water Dispersal Granule (WG) (120 g a.i./ha), Fipronil 0.3 G (50 g a.i./ha), Thiamethoxam 25 WG (80 g a.i./ha), Imidacloprid 70 WG (300 g a.i./ha), Chlorantraniliprole 0.4% G(100 g a.i./ha) and mixture of Fipronil 40% and Imidacloprid 40% WG (300 g a.i./ha) were applied at the time of sowing in pre sown experiment while same dosage of insecticides were applied in standing soybean crop during (first fortnight of July). Commutative plant mortality data were recorded after 20, 40, 60 days intervals and compared with untreated control. Total 23 species of white grub beetles recorded on the light trap and Holotrichia serrata Fabricious (Coleoptera: Melolonthinae) was found to be predominant species by recording 20.6% relative abundance out of the total light trap catch (i.e. 1316 beetles) followed by Phyllognathus sp. (14.6% relative abundance). H. rosettae and Heteronychus lioderus occupied third and fourth rank with 11.85% and 9.65% relative abundance, respectively. The emergence of beetles of predominant species started from 15th March, 2018. In April, average light trap catch was 382 white grub beetles, however, peak emergence of most of the white grub species was observed from June to July, 2018 i.e. 336 beetles in June followed by 303 beetles in the July. On the basis of the emergence pattern of white grub beetles, it may be concluded that the Peak Emergence Period (PEP) for the beetles of H. serrata was second fortnight of April for the total period of 15 days. In May, June and July relatively low population of H. serrata was observed. A decreasing trend in light trap catch was observed and went on till September during the study. No single beetle of H. serrata was observed on light trap from September onwards. The cumulative plant mortality data in both the experiments revealed that all the insecticidal treatments were significantly superior in protection-wise (6.49-16.82% cumulative plant mortality) over untreated control where highest plant mortality was 17.28 to 39.65% during study. The mixture of Fipronil 40% and Imidacloprid 40% WG applied at the rate of 300 g a.i. per ha proved to be most effective having lowest plant mortality i.e. 9.29 and 10.94% in pre and post sown crop, followed by Clothianidin 50 WG (120 g a.i. per ha) where the plant mortality was 10.57 and 11.93% in pre and post sown treatments, respectively. Both treatments were found significantly at par among each other. Production-wise, all the insecticidal treatments were found statistically superior (15.00-24.66 q per ha grain yields) over untreated control where the grain yield was 8.25 & 9.13 q per ha. Treatment Fipronil 40% + Imidacloprid 40% WG applied at the rate of 300 g a.i. per ha proved to be most effective and significantly superior over Imidacloprid 70WG applied at the rate of 300 g a.i. per ha.

Keywords: Bio efficacy, insecticide, Holotrichia, soybean, white grub.

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

Abstract:

We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: Autonomous surveillance, Bayesian reasoning, decision-support, interventions, patterns-of-life, predictive analytics, predictive insights.

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