Internet-based Framework to Support Integration of Customer in the Design of Customizable Products

A necessary element to design and produce customer-centric products is the integration of customers in the design process. Challenges faced during customer integration into the design process include generating models of the customized product, performing analysis of these to determine feasibility, and optimizing to increase the performance. These tasks have to be performed relatively quickly, if not in real time, to provide feedback to the customer. The focus of this article is to present a framework that utilizes CAD, finite element analysis (FEA), and optimization to integrate the customer into the design process via the Internet for delivering user customized products. The design analysis, evaluation, and optimization need to be automated and enhanced to enable operation over the Internet. A product family CAD/FEA template has been developed to perform analysis, along with a general formulation to optimize the customized product. The CAD/FEA template generalizes the geometry building and analysis of each configuration developed using a product platform approach. The proposed setup is demonstrated through the use of a bicycle frame family. In this study, the focus is on the application of optimization and FEA to facilitate the design of customer-centric products.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

The chickpea book : a technical guide to chickpea production

The area of chickpea production in Australia has expanded rapidly in recent years especially in south-western Australia. This has been partly brought about by the keen interest of farmers and a concerted research effort and industry development by Agriculture Western Australia, The Centre for Legumes in Mediterranean Agriculture (CLIMA) and other institutions, in partnership with the Grains Research and Development Corporation and other industry funding bodies. Private consultants, grain traders and other industry groups have also contributed to the expansion of the industry. Much of the local knowledge generated by these research and development projects has been published in various experimental summaries, Tech notes, Farm notes, magazine articles, \u27On the Pulse\u27 newsletters, conference and workshop proceedings, and scientific papers. This book collates much of this research and development in the one document, together with experience and knowledge from the eastern states and overseas. It is a comprehensive publication, much more than simply how to grow the crop in Western Australia. It describes much of the scientific data behind our recommendations and highlights the role of chickpea production in maximising whole farm profits.https://researchlibrary.agric.wa.gov.au/bulletins/1212/thumbnail.jp

Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema

Task-oriented dialog systems empower users to accomplish their goals by facilitating intuitive and expressive natural language interactions. State-of-the-art approaches in task-oriented dialog systems formulate the problem as a conditional sequence generation task and fine-tune pre-trained causal language models in the supervised setting. This requires labeled training data for each new domain or task, and acquiring such data is prohibitively laborious and expensive, thus making it a bottleneck for scaling systems to a wide range of domains. To overcome this challenge, we introduce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD, that leverages domain schemas to allow for robust generalization to unseen domains and exploits effective summarization of the dialog history. We employ GPT-2 as a backbone model and introduce a two-step training process where the goal of the first step is to learn the general structure of the dialog data and the second step optimizes the response generation as well as intermediate outputs, such as dialog state and system actions. As opposed to state-of-the-art systems that are trained to fulfill certain intents in the given domains and memorize task-specific conversational patterns, ZS-ToD learns generic task-completion skills by comprehending domain semantics via domain schemas and generalizing to unseen domains seamlessly. We conduct an extensive experimental evaluation on SGD and SGD-X datasets that span up to 20 unique domains and ZS-ToD outperforms state-of-the-art systems on key metrics, with an improvement of +17% on joint goal accuracy and +5 on inform. Additionally, we present a detailed ablation study to demonstrate the effectiveness of the proposed components and training mechanis

Case studies of six CBFM-2 water bodies

The case studies report on how CBFM-2 interventions have affected aquatic productivity, income, employment and livelihoods in six case study sites, Beelbhora beel cluster (Kishoreganj), Sholuar beel (Narail), Chapundaha beel (Rangpur), Hamil beel (Tangail), Kutir beel (Kishoreganj) and Dikshi beel (Pabna).

Terpenes from Zingiber montanum and Their Screening against Multi-Drug Resistant and Methicillin Resistant Staphylococcus aureus

Bioassay directed isolation of secondary metabolites from the rhizomes of Zingiber montanum (Fam. ‎Zingiberaceae) led to the isolation of mono-, sesqui-, and di-terpenes. The compounds were characterized as (E)-8(17),12-labdadiene-15,16-dial (1), zerumbol (2), zerumbone (3), buddledone A (4), furanodienone (5), germacrone (6), borneol (7), and camphor (8) by analysing one-dimensional (1D) (¹H and ¹³C) and two-dimensional (2D) (COSY, HSQC, HMBC, and NOESY) NMR data and mass spectra. Among these terpenes, compounds 1 and 2 revealed potential antibacterial activity (minimum inhibitory concentrations (MIC) values 32–128 µg/mL; 0.145–0.291 mM)) against a series of clinical isolates of multi-drug resistant (MDR) and Methicillin resistant Staphylococcus aureus (MRSA)

Earth Pipe Cooling Strategy in Buildings: A Sustainable Approach

Abundant energy supply is one of the preconditions of economic growth, however, the economic growth in turn leads to higher energy consumption to support higher living standard. The energy demand is increasing at an alarming rate throughout the world, which may lead to scarcity of energy in near future. Most of this energy is used in buildings for heating and cooling. Therefore, it is important to adopt a system to save energy in buildings without using any habitual mechanical devices. Passive air cooling is such a system assists us to save energy in passive process. Earth pipe cooling strategy is one of them, which can cool a space with minimal energy. In this strategy, air comes through a pipe inlet and passes underground via buried pipes, transfers heat to the earth (soil), gets cooler and goes to the room through pipe outlet. This paper reviews the earth pipe cooling performance in different climates by an intensive literature survey. The performance was also compared with other common passive air cooling strategies used in buildings. The findings of the study recommend an optimum passive air cooling guidelines, and passive air cooling products to the occupants of the buildings. Keywords: Cooling Performance; Passive Air Cooling; Energy Consumption

Personalizing Task-oriented Dialog Systems via Zero-shot Generalizable Reward Function

Task-oriented dialog systems enable users to accomplish tasks using natural language. State-of-the-art systems respond to users in the same way regardless of their personalities, although personalizing dialogues can lead to higher levels of adoption and better user experiences. Building personalized dialog systems is an important, yet challenging endeavor and only a handful of works took on the challenge. Most existing works rely on supervised learning approaches and require laborious and expensive labeled training data for each user profile. Additionally, collecting and labeling data for each user profile is virtually impossible. In this work, we propose a novel framework, P-ToD, to personalize task-oriented dialog systems capable of adapting to a wide range of user profiles in an unsupervised fashion using a zero-shot generalizable reward function. P-ToD uses a pre-trained GPT-2 as a backbone model and works in three phases. Phase one performs task-specific training. Phase two kicks off unsupervised personalization by leveraging the proximal policy optimization algorithm that performs policy gradients guided by the zero-shot generalizable reward function. Our novel reward function can quantify the quality of the generated responses even for unseen profiles. The optional final phase fine-tunes the personalized model using a few labeled training examples. We conduct extensive experimental analysis using the personalized bAbI dialogue benchmark for five tasks and up to 180 diverse user profiles. The experimental results demonstrate that P-ToD, even when it had access to zero labeled examples, outperforms state-of-the-art supervised personalization models and achieves competitive performance on BLEU and ROUGE metrics when compared to a strong fully-supervised GPT-2 baselineComment: 11 pages, 4 tables, 31st ACM International Conference on Information and Knowledge Management (CIKM'22