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

High-density parking for autonomous vehicles.

In a common parking lot, much of the space is devoted to lanes. Lanes must not be blocked for one simple reason: a blocked car might need to leave before the car that blocks it. However, the advent of autonomous vehicles gives us an opportunity to overcome this constraint, and to achieve a higher storage capacity of cars. Taking advantage of self-parking and intelligent communication systems of autonomous vehicles, we propose puzzle-based parking, a high-density design for a parking lot. We introduce a novel method of vehicle parking, which leads to maximum parking density. We then propose a heuristic method to solve larger problems, and mathematically prove that the method produces near-optimal results. To improve layout designs reducing vehicular movements, we propose a use of a meta-heuristic algorithm integrated with a deep reinforcement learning method. Finally, to take advantage of these puzzle-based designs in large-scale, we propose a modular layout design. This design process consists of two steps: i) design of a high-density modular lot, which we call sub-lot, and ii) integration of these sub-lots into a large parking lot. We have conducted a set of experiments to determine which sub-lot size provide the best performance in terms of density and retrieval time

SURVIVAL IMPACT OF SKELETAL METASTASIS ON BONE SCINTIGRAPHY IN PATIENTS WITH GERM CELL TUMOURS

Objective: Our aim was to determine the frequency of skeletal metastasis in germ cell tumours (GCT) at baseline and relapse on conventional technetium-99m methylene diphosphonate (Tc-99m MDP) whole body bone scan (bone scan) and to evaluate the effect of bone metastases on survival. Materials and Methods: Electronic medical records of histologically proven GCT over 64 months were retrospectively analysed. Basic demographic and histologic information were correlated with the presence of osseous and visceral metastases. 5-year disease-free survival (DFS) and overall survival (OS) were calculated in presence, the absence of bone metastases at baseline and at relapse. Results: A total of 130 gonadal and extragonadal GCT patients underwent Tc-99m MDP bone scans; four with insuf cient data were excluded from the study. 47% were females and 53% were males with the age range of 1 month – 72 years. 105 (83%) were under 18 years of age. Osseous metastasis was detected in 12 (9.5%). Two (17%) had solitary and 10 (83%) had multifocal skeletal metastases. Clinically, 83% had localised bone pain. Osseous metastases were more frequently associated with mixed GCT and yolk sac tumour. 50% of mediastinal GCT developed bone metastases. 42% died within 4–18 months. There was a statistically signi cant impact of visceral metastases on DFS and OS. OS at 5 years in patients without bone metastases, with bone metastases at baseline and bone metastases at relapse, was 77%, 38% and 75%, respectively. 5-year DFS for the same cohort groups was 63%, 38% and 20%, respectively. Conclusion: Osseous involvement was found in 9.5% of GCT patients undergoing diagnostic Tc-99m MDP bone scan. Baseline skeletal evaluation for metastases should be done, particularly in the case of bone pains or known systemic metastases. Although skeletal relapses are rare, they have a grim outcome. Key words: Bone scintigraphy, germ cell tumours, skeletal metastases

Establishment of a continuous sonocrystallization process for lactose in an oscillatory baffled crystallizer

Crystallization at production scale (>10 kg) is typically a poorly understood unit operation with limited application of first-principles understanding of crystallization to routine design, optimization, and control. In this study, a systematic approach has been established to transfer an existing batch process enabling the implementation of a continuous process in an oscillatory baffled crystallizer (OBC) using ultrasound. Process analytical technology (PAT) was used to understand and monitor the process. Kinetic and thermodynamic parameters have been investigated for lactose sonocrystallization using focused beam reflectance measurement (FBRM) (Mettler Toledo) and mid-infrared spectroscopy (mid-IR) (ABB) in a multiorifice batch oscillatory baffled crystallizer (Batch-OBC). This platform provides an ideal mimic of the mixing, hydrodynamics and operating conditions of the continuous oscillatory flow crystallizer (COBC) while requiring only limited material. Full characterization of the hydrodynamics of the COBC was carried out to identify conditions that deliver plug-flow behavior with residence times of 1–5 h. The results show that continuous crystallization offers significant advantages in terms of process outcomes and operability, including particle size distribution (mean particle size <1500 μm) of alpha lactose monohydrate (ALM), as well as reduced cycle time (4 h compared to the 13–20 h in a batch process). Continuous sonocrystallization was performed for the first time at a throughput of 356 g·h–1 for 12–16 h. During the run at near plug flow, with supersaturation and controlled nucleation using sonication, no issues with fouling or agglomeration were observed. This approach has demonstrated the capability to provide close control of particle attributes at an industrially relevant scale

Air quality in Mecca and surrounding holy places in Saudi Arabia during Hajj: initial survey.

The Arabian Peninsula experiences severe air pollution, the extent and sources of which are poorly documented. Each year in Saudi Arabia this situation is intensified during Hajj, the Holy Pilgrimage of Islam that draws millions of pilgrims to Mecca. An initial study of air quality in Mecca and surrounding holy sites during the 2012 Hajj (October 24-27) revealed strongly elevated levels of the combustion tracer carbon monoxide (CO, up to 57 ppmv) and volatile organic compounds (VOCs) along the pilgrimage route-especially in the tunnels of Mecca-that are a concern for human health. The most abundant VOC was the gasoline evaporation tracer i-pentane, which exceeded 1200 ppbv in the tunnels. Even though VOC concentrations were generally lower during a follow-up non-Hajj sampling period (April 2013), many were still comparable to other large cities suffering from poor air quality. Major VOC sources during the 2012 Hajj study included vehicular exhaust, gasoline evaporation, liquefied petroleum gas, and air conditioners. Of the measured compounds, reactive alkenes and CO showed the strongest potential to form ground-level ozone. Because the number of pilgrims is expected to increase in the future, we present emission reduction strategies to target both combustive and evaporative fossil fuel sources

Keynote: The Small World of Material Handling Rearch

Keynote Addres

The Small World of Material Handling Research

Using data from 88 journals over an 8 year period, we investigate the relationships among researchers in material handling. We apply social network analysis to measure many attributes of the network, including papers published each year, papers published per author, number of collaborators per author, strength of collaboration between authors, and how influential an author is in the network. We observe that collaboration patterns in material handling follow a scale-free structure in the presence of some hub-like researchers. According to social network theory, these hub researchers facilitate rapid dissemination of knowledge in the network. We conclude that the scientific community in material handling indeed forms a “small world,” yet the level of connectedness is lower than in other scientific networks. We hope these findings will inspire new and increasing levels of collaboration in the discipline

Development and characterisation of a cascade of moving baffle oscillatory crystallisers (CMBOC)

A novel four stage Cascade of Moving Baffle Oscillatory Crystallisers (CMBOC) is developed, characterised and implemented for continuous crystallisation of pharmaceuticals. The platform was fully automated with pressure controlled slurry transfer and process analytical tools (PAT) to support process monitoring and control. Model predictive control was used to achieve precise temperature control during operation of crystallisations. Mixing and flow characterisation for liquids and slurries was performed confirming near-ideal mixing performance for mean residence times in the range 20 – 90 min. Heat transfer characteristics were determined and shown to be well suited to the demands of cooling crystallisation processes. Heat transfer efficiency increased with increasing oscillatory Reynolds number (Reo). This cascade is shown to provide the advantages of more uniform mixing and efficient heat transfer performance compared to a traditional cascade of stirred tank crystallisers. Continuous crystallisations of both alpha lactose monohydrate (ALM) and paracetamol (PCM) were carried out in which the target size, form, agglomeration and encrustation were controlled. For ALM, the products showed a narrow particle size distribution (PSD) with dv50 = 65 ± 5 μm and a span of 1.4 ± 0.2, and achieved a yield of 70%. The continuous crystallisation of paracetamol in the CMBOC produced non-agglomerated product with dv50 = 398 ± 20μm with a span of 1.5 ± 0.2 and achieved an 85% yield. No fouling or encrustation in the vessels or transfer lines were observed during the processes. The flexible configuration and operation of the platform coupled with well characterised shear rate distribution, residence time distributions and heat transfer shows that this platform is well suited to a range of crystallisation modes including seeded, antisolvent, cooling or reactive processes, where careful control of crystal attributes is required

Improved two-stream model for human action recognition

This paper addresses the recognitions of human actions in videos. Human action recognition can be seen as the automatic labeling of a video according to the actions occurring in it. It has become one of the most challenging and attractive problems in the pattern recognition and video classification fields. The problem itself is difficult to solve by traditional video processing methods because of several challenges such as the background noise, sizes of subjects in different videos, and the speed of actions. Derived from the progress of deep learning methods, several directions are developed to recognize a human action from a video, such as the long-short-term memory (LSTM)-based model, two-stream convolutional neural network (CNN) model, and the convolutional 3D model.In this paper, we focus on the two-stream structure. The traditional two-stream CNN network solves the problem that CNNs do not have satisfactory performance on temporal features. By training a temporal stream, which uses the optical flow as the input, a CNN can have the ability to extract temporal features. However, the optical flow only contains limited temporal information because it only records the movements of pixels on the x-axis and the y-axis. Therefore, we attempt to design and implement a new two-stream model by using an LSTM-based model in its spatial stream to extract both spatial and temporal features in RGB frames. In addition, we implement a DenseNet in the temporal stream to improve the recognition accuracy. This is in-contrast to traditional approaches which typically utilize the spatial stream for extracting only spatial features. The quantitative evaluation and experiments are conducted on the UCF-101 dataset, which is a well-developed public video dataset. For the temporal stream, we choose the optical flow of UCF-101. Images in the optical flow are provided by the Graz University of Technology. The experimental result shows that the proposed method outperforms the traditional two-stream CNN method with an accuracy of at least 3%. For both spatial and temporal streams, the proposed model also achieves higher recognition accuracies. In addition, compared with the state of the art methods, the new model can still have the best recognition performance