Real-world Product Research Articles

Incorporating contracts with retailer into product line extension using Stackelberg game and nested bi-level genetic algorithms

Nowadays, product manufacturers commonly establish coordination with members of their distribution channels by making contracts with them. The contract may affect product design to a certain extent. Therefore, manufacturers should consider this in the product planning stage. Some analytical studies have investigated the effect of the contract scheme on the product line strategy. However, how contracts affect the real-world product line design in terms of product attribute settings and consumer choice behaviors is still unclear. Herein, a methodology of incorporating contracts for establishing coordination with distribution channel members into the product line extension is proposed. The Stackelberg game theory is adopted to formulate joint optimization models that involve a manufacturer and a retailer. Four common types of contracts are considered in the models, including wholesale-price (WP), revenue-sharing (RS), quantity-discount (QD), and retail-price-maintenance (RPM) contracts. Then, we adopt a nested bi-level genetic algorithm to identify the joint optimization for the product line extension. A case study of smartphone design is conducted to illustrate the applicability of the proposed methodology; thus, we identify the effects of different contract schemes and their parameter settings on the optimal product design, pricing, market shares, and both-side profits. The case study showed that if a QD parameter is relatively high, the manufacturer should add a lower-quality product in the product line. Meanwhile, an intermediate value of the QD parameter would maximize the manufacturer’s profits and the total profits. An RPM contract leads to a high-quality design for the new product to be launched than other contracts. The novelty of this study is that it incorporates retail contracts into the real-world product line design and builds a joint optimization model to solve it.

Calculation and design of mechanical transmissions with the help of various computer-aided design in conditions of educational process

The field of application of computer-aided design (CAD) is an integral part of the design of real-world products of construction, lifting-and-transportation, road vehicles, as well as the design of electronic products. Computer-aided design allow you to solve the most time-consuming and time-consuming tasks, carry out calculations, help you to represent the most accurate visualization of an object (3D visualization). The calculation and design of mechanical transmissions using CAD, which form the basis of most working machines, is an important component of the educational process in the training of future specialists in technical areas. In this work, some modules of computer-aided design have been analyzed to apply them to the educational process in the calculation of mechanical transmissions.

Intrinsic dose characteristics in electron beam irradiation

This paper addresses several dose characteristics typically encountered in product performance qualification for high energy electron beam processes. A computational approach, instead of an analytical is chosen, by using Monte Carlo modelling. Complex geometries, as they occur in real-world products, e.g. medical devices, are broken down into clear and elementary setups which are studied in detail using mathematical modelling. Electron scattering is found to be the dominant underlying mechanism which is responsible for many phenomena expressing themselves as dose gradients in the process load. Model variations in terms of geometry, density, and atomic composition deliver qualitative and quantitative results which can gain a better understanding of dosimetric results, help to guide dose mapping experiments, and interpret process critical output like minimum and maximum product dose. Experimental validation of some specific setups is presented which helps to gain confidence in the model results.

Exploring the Vastness of design space for greener solutions using a quality approach

Abstract Chemistry and biology constitute Vast and/or infinite design spaces which can be used by scientists and engineers to generate new and ever more complex chemical molecules and other structures having new emergent properties. A Vanishingly small proportion of those new molecules can generate even more complexity and new emergent properties by interacting with other molecules, bio-molecules, or complex collections of biomolecules. Large degrees of unpredictability reign throughout that Vastness, as well as in the Environment, and human society and behavior. Yet Life, the Earth’s ecosystem, consciousness, and civilized human societies evolved from simpler systems, despite the Vastness and unpredictability. Green Chemistry and Engineering emerged over the last few decades from an evolutionary search for new processes and products for the benefit of humans and the planet that are both economically viable and do less harm to the Environment. The author, who originally conceived, in 1984, the BHC Ibuprofen Process that won one of the very first Presidential Green Chemistry Awards, was inspired and guided in significant part by the “Quality” philosophy, teachings and methods of W. Edwards Deming. Deming’s Quality philosophy and methods adapt the empirical/reductionist perspectives and approaches of the Scientific Method, to readily integrate holistic, human, and ethical goals, ideas, and perspectives, in order to identify and solve Real-World problems. Deming’s methods include his “PDCA” circles, which, when continuously iterated, can guide scientists, engineers, and their interdisciplinary team-mates as to what to do toward the development and evolution of Real-World processes and products that create little waste, and therefore are both economically and ecologically beneficial, via a process of human guided and human accelerated evolution.

Hybrid Plasma-Liquid Treatment of Carbon Nanotubes for Application in Direct Absorption Solar Thermal Collectors

Awareness of the environmental pollution caused by fossil fuels and the ever-increasing demand for energy has driven research focussed on zero-carbon renewable energy sources. Conscious of this, direct absorption solar thermal collectors are gaining prominence, both for domestic heating and steam generation. Carbon nanotubes (CNTs) have been investigated as an additive to take advantage of the near blackbody absorption and high thermal conductivity to improve system efficiency. Critical to the success of the additive nanoparticles is the stability in the fluid, however, due to their high surface energy CNTs tend to agglomerate which can limit the efficacy of their integration into real-world products. Covalent functionalization of the CNT sidewalls with oxygen-based or nitrogen-based functional groups is one of the most popular techniques to modify the surface energy of the CNTs.1 As an alternative to the harsh chemicals used by traditional chemical functionalisation, plasma-based surface treatments can be used with great efficacy and reduced reaction times.In this work, macroscopic ribbon-like assemblies of carbon nanotubes are functionalized using a simple direct current-based plasma-liquid system. This system utilizes the plasma-generated species in a fluid of 10 %vol ethanol in water, with or without a nitrogen precursor, for the oxygen and/or nitrogen functionalisation of the carbon nanotube assembly. For this treatment, a ribbon-like piece of CNT was used as the anode and a helium plasma discharge triggered by applying 10 mA and 1 - 1.6 kV to act as the cathode. The plasma-generated species are then expected to migrate towards the CNTs and functionalisation the sidewalls.The oxygen content is shown to be increased by between 50 % and 200 % when the treatment solution of 10 %vol ethanol is used in the ribbon electrode configuration. When ethylenediamine is added as a nitrogen precursor, the atomic concentration of nitrogen reaches 23 % in the ribbon electrode configuration, with amine groups, pyrrolic groups and graphitic nitrogen observed in the x-ray photoelectron spectra. This nitrogen content is unmatched in quantity when compared to either a simple soaking procedure or an electrolysis process with a platinum foil replacing the plasma as the counter-electrode. This demonstrates that the plasma either directly or indirectly, by means of plasma-generated species, facilitates and enhances the availability of nitrogen from the ethylenediamine precursor. The potential plasma-induced chemical pathways which lead to the functionalization of the CNTs are also investigated and discussed.In the application of a DASC system, it is found that the covalent functionalisation provided by the plasma-liquid system enhances the stability of the CNT-EG nanofluid, with the oxygen-functionalized samples producing the most stable nanofluids. This functionalization method generates interest for a broader range of applications including mechanically superior advanced composites,2 more sensitive gas sensors,3 enhanced energy storage materials4 or ultra-conductive fibres.5 References (1) Mallakpour, S.; Soltanian, S. Surface Functionalization of Carbon Nanotubes: Fabrication and Applications. RSC Adv. 2016, 6 (111), 109916–109935.(2) Williams, J.; Broughton, W.; Koukoulas, T.; Rahatekar, S. S. Plasma Treatment as a Method for Functionalising and Improving Dispersion of Carbon Nanotubes in Epoxy Resins. J. Mater. Sci. 2013, 48 (3), 1005–1013.(3) Ham, S. W.; Hong, H. P.; Kim, J. H.; Min, S. J.; Min, N. K. Effect of Oxygen Plasma Treatment on Carbon Nanotube-Based Sensors. J. Nanosci. Nanotechnol. 2014, 14 (11), 8476–8481.(4) Hulicova-Jurcakova, D.; Seredych, M.; Lu, G. Q.; Bandosz, T. J. Combined Effect of Nitrogen- and Oxygen-Containing Functional Groups of Microporous Activated Carbon on Its Electrochemical Performance in Supercapacitors. Adv. Funct. Mater. 2009, 19 (3), 438–447.(5) Li, L.; Liu, E.; Shen, H.; Yang, Y.; Huang, Z.; Xiang, X.; Tian, Y. Charge Storage Performance of Doped Carbons Prepared from Polyaniline for Supercapacitors. J. Solid State Electrochem. 2011, 15 (1), 175–182. Figure 1

Perceptions of Farm Size Heterogeneity and Demand for Group Index Insurance

Weather insurance is a financial instrument proposed to increase coverage of unprotected weather shocks in developing countries. Structuring sales as group-based products has been argued as a strategy to increase the attractiveness of index insurance, raising the question as to what impacts farmer demand for group insurance choices. We test if farmers prefer to purchase real-world insurance products as groups, and if groups of more similar individuals are more likely to demand group over individual index insurance for the upcoming season. We exogenously assign farmers into groups of similar versus dissimilar perceived farm size. We find that farmers, when offered, prefer group over individual insurance contracts, and that groups of farmers who perceive each other to be more similar in farm size are more likely to purchase in a group, but purchase less insurance on average.

An association-constrained LDA model for joint extraction of product aspects and opinions

The Latent Dirichlet Allocation (LDA) model, which is a document-level probabilistic model, has been widely used in topic modeling. However, an essential issue of the LDA is its shortage in identifying co-occurrence relationships (e.g., aspect-aspect, aspect-opinion, etc.) in sentences. To address the problem, we propose an association constrained LDA (AC-LDA) for effectively capturing the co-occurrence relationships. Specifically, based on the basic features of the syntactic structure in product reviews, we formalize three major types of word association combinations and then carefully design corresponding identifications. For reducing the influence of global aspect words on the local distribution, we apply an important constraint on global aspects. Finally, the constraint and related association combinations are merged into the LDA to guide the topic-words allocation in the learning process. Based on the experiments on real-world product review data, we demonstrate that our model can effectively capture the relationships hidden in local sentences and further increase the extraction rate of fine-grained aspects and opinion words. Our results confirm the superiority of the AC-LDA over the state-of-the-art methods in terms of the extraction accuracy. We also verify the strength of our method in identifying irregularly appeared terms, such as non-aspect opinions, low-frequency words, and secondary aspects.

What do Australian patients with inflammatory arthritis value in treatment? A discrete choice experiment

Background and objectivesThe purpose of this study was to develop an understanding of treatment preferences in patients with inflammatory arthritis (IA) [rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA)] focussing on treatment attributes that patients’ value, their relative importance, and the risk-benefit trade-offs that characterise patients’ choices around treatment.MethodsA discrete choice experiment (DCE) approach was used. Attributes of interest were clinical efficacy; slowing of disease progression; risk of mild-moderate side effects; risk of severe side effects; frequency of administration; real-world product evidence; management of related conditions; and availability of a patient support programme. Using data from the DCE component, a restricted latent class model (LCM) was estimated to determine discrete ‘classes’ of treatment preferences.ResultsIn this analysis, 206 participants were included (AS n = 59; PsA n = 62; RA n = 85). Two classes were identified. For ‘class 1’ (59.9%), the most important attributes (across all treatment modalities) were preventing disease progression, clinical efficacy and risk of mild-to-moderate side effects. For ‘class 2’ (40.1%), clinical and non-clinical attributes were important, and attribute importance depended on treatment modality. Patient demographic and treatment characteristics did not predict class membership.ConclusionFor most patients with IA, clinical efficacy, stopping disease progression and risks of mild-to-moderate side effects are important treatment attributes. Patients with prior biologic DMARD experience had greater preference for injection treatments. For a subset of patients, patient support programmes and the frequency of administration were important. Clinicians should be mindful of preferences when prescribing treatment to patients with IA.Key Points• Most patients consider clinical efficacy, stopping disease progression and the risk of mild-to-moderate side effects as important treatment attributes• Patients with prior biologic DMARD experience have greater preference for injection treatments.• For a subset of patients, patient support programmes, and the frequency of administration were important.• Clinicians should be mindful of preferences when prescribing treatment to patients with IA.

Partial encryption of feature-based product models for collaborative development

Abstract Feature-based product systems have been predominantly adopted for product development. To facilitate product development collaboration, product models need to be effectively shared among co-developers. However, in a shared model, it is challenging to preserving confidential features pertaining to intellectual property originating from the initial developer. To address the challenge, this paper presents a novel approach of partially encrypting feature-based product models to support effective collaboration. In the approach, collaborative features to support co-development, and confidential features to specify intellectual property information, are defined in a shared model. An innovative Encryption Transformation Matrix (ETM) is then designed to encode confidential features parametrically and synchronized with collaborative features as a partially encrypted product model. Finally, the approach is validated via complex case studies to demonstrate its effectiveness in industrial applications. The novelty of the approach is based on the innovative ETM design, which makes encryption processes parametric, randomized and self-adaptive. In detail, zooming and transformation variables are devised in ETM to facilitate users to manage encryption processes in a parametric means. Random probability is embedded into ETM to achieve security effectiveness in encrypted models. A self-adaptive mechanism is integrated into ETM to ensure the geometrical validity of encrypted models. The research in this paper, which supports collaborative design on real-world product models, presents a disruptive innovative potential for industrial applications.

RotateView: A Video Composition System for Interactive Product Display

Product display in e-commerce commonly uses static images or videos. In this paper, we design a novel video composition system “RotateView” which displays real-world products videos on the mobile phone in an interactive and 3D-like way. The system estimates the rotation direction of products using optical flow, segments products using an improved version of online adaptive video object segmentation, and adjusts motion and color for composition. The rotation direction estimation methods are evaluated on a video dataset of 1500 rotated videos that are uploaded by sellers, and on our collected real-world video object segmentation (RVOS) dataset. On our RVOS dataset, experimental results show an improvement in segmentation over the state of the art. Moreover, the proposed algorithms for motion and color adjustment are evaluated using extensive user studies. The RVOS dataset can be downloaded via https://realvos.github.io/ for further studies and code will be made available.

Understanding Persuasion Cascades in Online Product Rating Systems

Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google play store and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called “messagebased persuasion” lead to “biased” product ratings in a cascading manner (we call this the persuasion cascade). This paper investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights? We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on the data from Amazon and TripAdvisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.

How Does Biologically Inspired Design Cope with Multi-Functionality?

AbstractAs catalysts for product innovation and product development, different approaches for biologically inspired design (BID) are exciting options. However, while general BID theory require a focus on single functions, real world products are characterized by performing multiple functions. The development of an anterior eye-chamber model is used to showcase the issue.In a systematic literature review (SLR), state-of-the-art methodologies, methods and tools BID practice are discovered and the current state of multi-functionality in BID are assessed.The SLR revealed 18 contributions with 8 BID methodologies and 12 stage-specific BID tools (of which 50% addressed the solution search phase) in addition to 5 papers addressing multi-functionality in BID. At present multi-functionality in BID is only treated in a limited set of papers. While designers interested in BID are advised to discover multi-functional analogies, the present approach to handling multi-functional problems in BID suggest functional decomposition and multiple BID efforts. Therefore, the development of design support for handling multi-functional problems, including tools for problem analysis are needed.

Further Empowering Variant Tables for Mass Customization

Tables are a standard form of data representation in business. A variant table lists valid or excluded combi-nations of product features where each table column refers to a product property and each table row denotes a combination of product features. A table cell defines a feature, e.g. Color = Red, as an assignment of its value to the column's property. As technology and consumer demand drive ever increasing product choices, the number of feature combinations that can be offered for a product increases exponentially and can easily exceed the limits of a traditional table. However, variant tables can often be compressed in a way that scales both in size and query performance while retaining the tabular paradigm in a manner useful for a business. The basic idea is to partition the table rows into unconstrained slices, where each slice consists of all possible combinations of the product features it references. Such a slice can be represented as a c-tuple and readily stored in a spreadsheet. C-tuple representation is already supported in some product configurators. We give examples of products where it is feasible to efficiently represent all valid variants in one overall table using c-tuple compression. For cases where c-tuples do not suffice, the stronger compression to a variant decom-position diagram (VDD), a form of decision diagram, can be used. We propose complexity measures for a product based on the compressibility of its variants and discuss their usefulness to the business. We illustrate these ideas with examples and present some results on dealing with variant tables from real-world product models. We show that compression empowers variant tables by enabling enormous tables to be functionally used in a way like regular tables.

Introducing ergonomics requirements in the eco-design of energy-related products from users’ behaviour approach

Ergonomics has been a very important activity in the design process. However, ergonomics rarely includes the environmental requirements into the design of products. The article proposes and presents the Eco-Ergo model through its application to a real-world product, a washing machine, to allow designers and ergonomists to establish product design requirements in order to minimise environmental impacts related to user–product interaction during the use stage. This model uses a visual language of representation, Blueprinting-based, that helps designers explore problems they have not previously considered during the market research when a wide variety of products with different interaction elements is analysed. The application of this model allows direct efforts and attention on the user analysis phase in the most influential user’s actions on the environmental performance of energy-related products during use, establishing ergonomics requirements related to users behaviour at the initial design phase.Practitioner summary: This study provides a proposal to incorporate ergonomics into the practice of eco-design through the use of human factors in the establishment of initial eco-design requirements. This blueprint-based model combines an empirical and theoretical approach, based on the product test developed by designers, ergonomists and environmentalists.Abbreviations: CO2: carbon dioxide; DBIM: design behaviour intervention model; DfSB: design for sustainable behaviour; DwI Method: design with intent method; LCA: life cycle assessment; LCD: liquid cristal display; MJ: megajoule; MTM: methods-time measurement; PSS: product-service system; RPM: revolutions per minute

Structure-Aware Deep Learning for Product Image Classification

Automatic product image classification is a task of crucial importance with respect to the management of online retailers. Motivated by recent advancements of deep Convolutional Neural Networks (CNN) on image classification, in this work we revisit the problem in the context of product images with the existence of a predefined categorical hierarchy and attributes, aiming to leverage the hierarchy and attributes to improve classification accuracy. With these structure-aware clues, we argue that more advanced deep models could be developed beyond the flat one-versus-all classification performed by conventional CNNs. To this end, novel efforts of this work include a salient-sensitive CNN that gazes into the product foreground by inserting a dedicated spatial attention module; a multiclass regression-based refinement that is expected to predict more accurately by merging prediction scores from multiple preceding CNNs, each corresponding to a distinct classifier in the hierarchy; and a multitask deep learning architecture that effectively explores correlations among categories and attributes for categorical label prediction. Experimental results on nearly 1 million real-world product images basically validate the effectiveness of the proposed efforts individually and jointly, from which performance gains are observed.

Observational Study of Real-World Factor Utilization and Health Outcomes in Patients with Hemophilia in Canada

Observational Study of Real-World Factor Utilization and Health Outcomes in Patients with Hemophilia in Canada

Design for mobile mental health: an exploratory review

A large number of mobile mental health apps are available to the public but current knowledge about requirements of designing such solutions is scarce, especially from sociotechnical and user centred points of view. Due to the significant role of mobile apps in the mental health service models, identifying the design requirements of mobile mental health solutions is crucial. Some of those requirements have been addressed individually in the literature, but there are few research studies that show a comprehensive picture of this domain. This exploratory review aims to facilitate such holistic understanding. The main search keywords of the review were identified in a cross-disciplinary requirements workshop. The search was started by finding some core references in the healthcare databases. A wider range of references then has been explored using a snowball method. Findings showed that there is a good understanding of individual design requirements in current literature but there are few examples of implementing a combination of different design requirements in real world products. The design processes specifically developed for mobile mental health apps are also rare. Most studies on operational mobile mental health apps address major mental health issues while prevention and wellbeing areas are underdeveloped. In conclusion, the main recommendations for designing future mobile mental health solutions include: moving towards sociotechnical and open design strategies, understanding and creating shared value, recognizing all dimensions of efficacy, bridging design and medical research and development, and considering an ecosystem perspective.

Upconversion Nanoprobes: Recent Advances in Sensing Applications.

Rare earth ions doped upconversion nanoparticles (UCNPs) are capable of stepwisely converting two or more lower energy near-infrared (NIR) excitation photons into a higher energy visible and ultraviolet emission photon, resulting in a large anti-Stokes shift of several hundred nanometers. Because the f-electron configurations of lanthanide ions have abundant energy levels, and many of them possess long lifetimes, UCNPs exhibit unique optical properties including optical tunability over emission wavelengths and lifetimes, better photo-stability and improved monochromatic color-purity and spatial resolution. However, the low luminescent efficiency, largely owing to their substantially weak and narrow band absorptions, has greatly hampered the translation of UCNPs-based technologies from the academic stage to real world products. Recently, increasing efforts have been paid on boosting the upconversion luminescence brightness and efficiency, including inert shells coating, high concentration of activators dop...

A branch-and-price-and-cut algorithm for a pickup and delivery problem in retailing

We investigate a real-world product distribution problem faced by a fast fashion retailer in Singapore. It is a generalization of the multi-commodity pickup and delivery traveling salesman problem (m-PDTSP) proposed by Hernández-Pérez and Salazar-González [15]. Each weekend the retailer forecasts the demand of each product for the next week. To ensure the inventory level equal to the forecast, the retailer schedules a fleet of vehicles to pick up or deliver products from/to each store at the beginning of each week. Shipping products from a store in surplus to another store in shortage is encouraged. For products that cannot be balanced among the stores, they are either picked up or delivered from/to the warehouse, which incurs a handling cost for each unit. The objective is to minimize the total travel distance as well as the total handling cost at the warehouse. To solve this problem, we propose a branch-and-price-and-cut algorithm based on a strong set-partitioning model, where an ad-hoc label-setting algorithm is designed to solve the pricing problem. The algorithm is tested on a set of instances generated according to a real-world database from the retailer and the m-PDTSP benchmark instances. Computational results show that our algorithm can optimally solve instances with 20 stores and over 100 products in one hour computational time. Moreover, it successfully solves several open m-PDTSP benchmark instances to optimality.

Embedding QR codes onto B-spline surfaces for 3D printing

Recent advance of Additive Manufacturing technologies allows us to manufacture various parts used in real-world products. Consequently, product tracking of such 3D printed parts is an important issue. Quick Response (QR) code which is a two-dimensional matrix barcode invented by Denso, a Japanese automotive industry, in 1994, can be used for this purpose. It can store more data than the 1D barcode in a smaller space, and using a smartphone as a scanner, one can directly visit a website where all the information of the parts is stored. However, QR codes require secondary procedures to add them to products and are also vulnerable to wear and tear. Moreover, QR codes cannot be added to freeform surfaces, but only to developable surfaces. In this paper we propose a novel technique to embed QR codes onto CAD models consisting of freeform surfaces represented by B-spline surfaces, which produces 3D QR codes. 3D QR codes work similar to 2D QR codes and can be read by existing QR scanners, but are designed by grooving the surface to obtain light and dark regions caused by ambient occlusion. Unlike conventional QR codes, 3D QR codes do not fall off from the part and can even be painted if necessary. Furthermore, we do not need to prepare dark-colored and light-colored materials for 3D printing as the dark color is provided by the grooving. We demonstrate the effectiveness of our technique with various examples.