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How does the viability of a product category shift over time? Studies abound on how categories emerge and become established, or fall out of use. Yet, extant research has often examined the evolution of categories one at a time, leaving open the question of how related categories affect a focal category's viability. In contrast, we consider both intra- and inter-category dynamics. Viewing categories as continuously shaped by actors' efforts to position their products, we argue that these efforts alter the coherence that products exhibit not only within a category (a category's heterogeneity), but also across related categories (a category's distinctiveness). We theorize how the interaction between a category's heterogeneity and distinctiveness shapes its subsequent viability. When a focal category's distinctiveness is low, the heterogeneity–viability relationship takes an inverted U-shape. However, as distinctiveness grows, the relationship flattens and eventually flips to a U-shape. We explain this by considering the trade-off between the "classification" and "valuation" benefits that a category affords. We find support for our argument by tracking 170 categories over an 11-year period on Kickstarter, one of the largest crowdfunding platforms. By providing a nuanced understanding of category dynamics, we shed new light onto the fluctuating viability of categories. [ABSTRACT FROM AUTHOR]

With the rise of the digital economy, many firms are enhancing product line flexibility (i.e., the ability to adjust product line length or depth) to better manage demand uncertainties brought about by disruptive technologies. Concurrently, the booming digital economy has widened the information gap in supply chains. Retailers/platforms can now gather more detailed data to predict demand, but such data remains unavailable to manufacturers. This article studies how a manufacturer's product line flexibility influences a retailer's incentive to voluntarily share private demand information. We show that upstream product line flexibility encourages a retailer to voluntarily share information when product substitutability is low and the product line extension fee is moderate. This insight challenges the conventional belief that retailers should keep demand information private to maintain an advantage over manufacturers. Our work is the first to suggest that upstream product line flexibility can significantly motivate retailer information sharing. Additionally, we show that, although a social planner might prefer to withhold demand information from the manufacturer in the absence of product line flexibility, sharing this information becomes preferable when the manufacturer can adjust the product line length. The retailer's decision on information sharing can align with the social planner's in such scenarios. Furthermore, we demonstrate that, given upstream product line flexibility, except when product substitutability is not sufficiently high and the product line extension fee is moderate, the retailer's information sharing decision is unaffected by whether the manufacturer determines the product line design before or after demand is realized and shared accordingly. [ABSTRACT FROM AUTHOR]

The automotive industry is experiencing a significant shift, transitioning from traditional hardware-centric systems to more advanced software-defined architectures. This change is enabling enhanced autonomy, connectivity, safety, and improved in-vehicle experiences. Service-oriented architecture is crucial for achieving software-defined vehicles and creating new business opportunities for original equipment manufacturers. A software update approach that is rich in variability and based on a Merkle tree approach is proposed for new vehicle architecture requirements. Given the complexity of software updates in vehicles, particularly when dealing with multiple distributed electronic control units, this software-centric approach can be optimized to handle various architectures and configurations, ensuring consistency across all platforms. In this paper, our software update approach is expanded to cover the solution space of the feature-based product line engineering, and we show how to combine our approach with product line engineering in creative and unique ways to form a software-defined vehicle modular architecture. Then, we offer insights into the design of the Merkle trees utilized in our approach, emphasizing the relationship among the software modules, with a focus on their impact on software update performance. This approach streamlines the software update process and ensures that the safety as well as the security of the vehicle are continuously maintained. [ABSTRACT FROM AUTHOR]

Livestreaming, now one of the most popular selling methods in e-commerce, affects consumers' shopping behaviors and firms' operations management. Compared with the traditional post-price strategy, livestreaming has many advantages such as strong interactivity, but it also has some defects, such as purchase time limits and the time cost of watching livestreams. This paper considers the characteristics of livestreaming and examines the strategic interaction with product line design. We find that the seller prefers to adopt livestreaming when the streamers' selling ability is high enough. In addition, the product line design can also affect the adoption of the livestream strategy. When the seller offers a single product, a high-quality product incentivizes the adoption of livestreaming; when the seller offers a product line, the upward-line extension can incentivize the adoption of livestreaming. We also test the robustness of our main results through numerical analysis and related extensions. [ABSTRACT FROM AUTHOR]

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions. [ABSTRACT FROM AUTHOR]

How to Price a Product Line When Customer Preferences Change over Time Quality-differentiated products can help sellers increase their profits through market segmentation. However, in many business applications, such as online search and consumer lending, customer preferences evolve over time, making it difficult for sellers to use market segmentation. In their study "Selling Quality-Differentiated Products in a Markovian Market with Unknown Transition Probabilities," Keskin and Li analyze dynamic pricing of a vertically differentiated product line when customer preferences for quality can shift over time. Keskin and Li show that data-driven learning is essential when operating in a changing market with unknown customer heterogeneity. Keskin and Li also develop a bounded learning policy that implements near-optimal data-driven learning in a Markov-modulated demand environment. In this paper, we study a firm's dynamic pricing problem in the presence of unknown and time-varying heterogeneity in customers' preferences for quality. The firm offers a standard product as well as a premium product to deal with this heterogeneity. First, we consider a benchmark case in which the transition structure of customer heterogeneity is known. In this case, we analyze the firm's optimal pricing policy and characterize its key structural properties. Thereafter, we investigate the case of unknown market transition structure and design a simple and practically implementable policy, called the bounded learning policy, which is a combination of two policies that perform poorly in isolation. Measuring performance by regret (i.e., the revenue loss relative to a clairvoyant who knows the underlying changes in the market), we prove that our bounded learning policy achieves the fastest possible convergence rate of regret in terms of the frequency of market shifts. Thus, our policy performs well without relying on precise knowledge of the market transition structure. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.0316. [ABSTRACT FROM AUTHOR]

Frequently releasing a new product generation has become a common practice to sustain sales over time, thus accurately forecasting the sales trajectory of each product generation plays a vital role in the short-, medium-, and long-term planning of a firm. Classic multigeneration diffusion models do not incorporate within-generation repeat purchases, making them unusable for product lines with high rates of such purchases. Concentrating on technology products, we develop a multigeneration sales model to fill this void. We demonstrate that the new model can be used for predictive and prescriptive analytics. Our empirical results show that the new model estimates and forecasts sales more accurately than a state-of-the-art benchmark model that does not account for within-generation repeat purchases, underscoring the importance of incorporating repeat purchases. Furthermore, we use two different versions of our model to examine market entry timing under two main strategies, that is, (i) a phase-out transition strategy in which firms continue to sell the old generation after the release of the new generation, and (ii) a total transition strategy in which firms discontinue the old generation after the introduction of the new generation. Our results indicate that the repeat purchases rate determines whether it is optimal to expedite or delay the new product launch, underscoring the importance of incorporating repeat purchases in market entry strategies. [ABSTRACT FROM AUTHOR]

We prove that the countable product of lines contains a Haar-null Haar-meager Borel linear subspace L that cannot be covered by count-ably many closed Haar-meager sets. This example is applied to studying the interplay between various classes of "large" sets and Kuczma-Ger classes in the topological vector spaces ℝn for n ≤ w. [ABSTRACT FROM AUTHOR]

Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques. [ABSTRACT FROM AUTHOR]

This study aims to suggest variability mechanisms for software product line development and to explain the results of case study. Software product line engineering is an extension of software engineering and many organizations constantly engage in reengineering and refactoring to adopt the software product line engineering. Software product line engineering has two engineering processes: domain engineering process and application engineering process. Feature Identification and feature model are key success factor to construct variability model. Feature model describes the variable parts to be extended or replaced and common part to be reuse by themselves. Feature model gives the directions to the following architecture design and component implementation. However, feature model is not the design strategy and variability mechanism for product line implementation. Therefore, these are the obstacles for organizations that are unfamiliar with feature model and variability mechanism to adopt software product line engineering. Several variability mechanisms for software intensive software are suggested but these are not applicable for embedded software since it has different development process and structure. In this paper, variability elements in architecture design and component design of embedded software are identified as state variable, state transition information, and algorithm. Variability management mechanisms are defined for these elements. To provide the detail strategy and to evaluate the suggested variability management methods, process and results of real case study are described. [ABSTRACT FROM AUTHOR]

Existing software product line (SPL) engineering testing approaches generally provide positive testing that validates the SPL's functionality. Negative testing is commonly neglected. This research aims to unify behavioral and feature models of an SPL, enable testing before and after variability binding for domain-centric and product-centric testing, and combine positive and negative testing for a holistic testing view. This study suggests behavioral modeling with event sequence graphs (ESGs). This heterogeneous modeling strategy supports bottom-up domain testing and top-down product testing with the feature model. This new feature-oriented ESG test creation method generates shorter test sequences than the original ESG optimum test sequences. Statechart and original ESG test-generating methods are compared. Positive testing findings are similar. The Statechart technique generated 12 test cases with 59 events, whereas the ESG technique created six test cases with 60 events. The ESG technique generated 205 negative test cases with 858 events with the Test Suite Designer tool. However, the Conformiq Designer tool for the Statechart technique does not have a negative test case generation capability. It is shown that the proposed ESG-based holistic approach confirms not only the desirable (positive) properties but also the undesirable (negative) ones. As an additional research, the traditional ESG test-generating approach is compared to the new feature-oriented method on six SPLs of different sizes and features. Our case study results show that the traditional ESG test generation approach demonstrated higher positive test generation scores compare to the proposed feature-oriented test generation approach. However, our proposed feature-oriented test generation approach is capable of generating shorter test sequences, which could be beneficial for reducing the execution time of test cases compared to traditional ESG approach. Finally, our case study has also shown that regardless of the test generation approach, there has been found no significant difference between the Bottom-up and Top-down test strategies with respect to their positive test generation scores. [ABSTRACT FROM AUTHOR]

The shore line is the limit that separates the public water domain from the property of individuals, the criterion for its delimitation is stated by law, both in Argentina and in other countries of the world. But its practical analysis remains a matter of current discussion. In this paper, a geomorphological interpretation of the established criterion is proposed as well as a methodology for its delimitation. To achieve this, different international, national, provincial and papers on shore line works were analyzed, which allowed to establish a criterion adjusted to the natural form of the channel and to cartographically define the shore line by using products of Unmanned Aerial Vehicles (UAVs). If the waterfront is delimited on the basis of natural facts, its indubitable mark on the ground will be close to what actually belongs to the public water domain. [ABSTRACT FROM AUTHOR]

This study investigated cadaverine as a spoilage indicator in commercial beef products stored under conditions favourable for the growth of lactic acid bacteria. Samples included vacuum-skin-packed entrecotes (EB) aged up to 42 days and modified-atmosphere-packed (70% O2 + 30% CO2) minced beef (MB) stored at 5 °C. Two MB product lines were analysed: one stored aerobically two days post-slaughter before mincing and another stored for 14 days in vacuum packaging prior to mincing. Sensory assessment/evaluation and microbial analysis were performed throughout the shelf life of the products and compared to cadaverine levels measured using LC-MS/MS. Cadaverine concentrations in EB reached approximately 40,000 µg/kg on the "best before" date, while remaining below 50 µg/kg in both MB products on the corresponding date. While cadaverine concentrations in EB displayed a consistent increase, suggesting its potential as a spoilage indicator post-ageing, the low concentrations in MB, did not correlate with sensory assessments, revealing its limitations as a universal spoilage marker. In conclusion, it is necessary to conduct product-specific studies to evaluate the applicability of cadaverine as a spoilage indicator for beef products. [ABSTRACT FROM AUTHOR]

Today, customer retention and growth is the first goal. Companies have taken a heavy toll on each customer, and competitors are constantly seeking to steal them. Satisfied customers have many benefits for the company, they are less sensitive to prices, will be a customer for a long period of time, and give a favorable view of the company and its products and services to others. Paying attention to the factors that are less important from the customer's point of view and evaluating these factors leads to spending resources in areas that have less impact on customer behavior, so understanding the factors that have the greatest impact on customer satisfaction to the organization to move in this will help. In this study, we intend to use the operational data of the lines and customer satisfaction scales to evaluate the relative efficiency of product lines using the network data envelopment analysis method. In order to achieve this goal and quantify the data obtained from the questionnaire, another linear programming method called multi-criteria satisfaction analysis will be used. Another innovation of this research is the application of customer expectations in the data envelopment analysis mole because most researchers that focus on quality do not consider this component in their evaluation model. The results indicate that the distribution system is the most effective factor and resources and supply is the most effective factor affecting customer satisfaction. [ABSTRACT FROM AUTHOR]

In this paper, we study a sufficient condition for subelliptic estimates in the weak Z(k) domain with C³ boundary in an n-dimentionsl Stein manifold X. Consequently, the compactness of the ∂̅-Neumann operator N on M is obtained and the closedness ranges of ∂̅ and ∂̅* are presented. The L²-setting and the Sobolev estimates of N on M are proved. We study the ∂̅ problem with support conditions in M for Ξ-valued (p, k) forms, where Ξ is the m-times tensor product of holomorphic line bundle Ξ⊗m for positive integer m. Moreover, the compactness of the weighted ∂̅-Neumann operator is studied on an annular domain in a Stein manifold M = M1\M2̅, between two smooth bounded domains M1 and M2 satisfy M2̅ ∈ M1, M1 is weak Z(k), M2 is weak Z(n-1-k), 1 ≤ k ≤ n - 2 with n ≥ 3. In all cases, the closedness of ∂̅ and ∂̅*, global boundary regularity for ∂̅ and ∂̅b are studied. [ABSTRACT FROM AUTHOR]

Product line testing is significant because any faults in a product line platform can lead to widespread impacts on multiple products configured from that platform within a product line. Due to the shared platform, certain testing can be repeatedly performed across different products, leading to unnecessary costs. To enhance quality and reduce costs in product line testing, it is essential to minimize redundant testing of the products in a product line. Because test coverage provides a way to explicitly state the extent to which a software item has been tested, having a clear understanding of test coverage helps avoid unnecessary repetition of tests and ensures that the testing efforts are focused on areas that require attention, ultimately leading to more efficient and effective product line testing. It is necessary to define appropriate test coverage metrics of product line testing that enable testers to identify redundancies in their testing efforts. Path-based integration testing has been proven to be an effective approach to product line integration testing. This paper defines coverage metrics for path-based product line integration testing and demonstrates their effectiveness in preventing redundant testing between platform testing and testing for individual products, while also effectively detecting faults. The experiment results highlight the coverage metrics' effectiveness in avoiding redundant testing, reducing costs, and covering interfacing across different modules. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This document provides information about various authors and their research interests in the field of marketing. The authors come from diverse backgrounds and institutions, including Duke University, Rice University, Johns Hopkins University, University of Chicago, Cornell University, and more. Their research covers a wide range of topics such as pricing strategies, e-commerce, online platforms, advertising, consumer behavior, and public policy. The authors have published their work in reputable journals and have received recognition and awards for their contributions to the field. [Extracted from the article]

Danfoss Power Solutions Aps has a product line focusing on hydraulic steering units for heavy-duty machines. The focus of this paper is on the end-stop torque encountered by the operator for a new asymmetrical hydraulic steering unit, referred to as sSteer. This hydraulically asymmetric concept increases the steering responsiveness between the steering wheel input and the output. However, compared to traditional hydraulic steering units, the asymmetrical design has a drawback regarding the level of end-stop torque felt by the operator when reaching the left-side end stop. This paper investigates three different concepts for improving/increasing the end-stop torque, namely, including a bleed orifice, removing a set of suction valves, and a solution with pre-tensioned suction valves and tank line. During the investigations, these concepts were compared and benchmarked using experimental data to identify advantages and disadvantages. Based on the investigations, it is concluded that the concept with pre-tensioned suction valves and a pressurized tank line ensures the best compromise between the different design requirements and the establishment of a firm end-stop feeling for the operator. [ABSTRACT FROM AUTHOR]

Weak contact metric structures on a smooth manifold, introduced by V. Rovenski and R. Wolak in 2022, have provided new insight into the theory of classical structures. In this paper, we define new structures of this kind (called weak nearly Sasakian and weak nearly cosymplectic and nearly Kähler structures), study their geometry and give applications to Killing vector fields. We introduce weak nearly Kähler manifolds (generalizing nearly Kähler manifolds), characterize weak nearly Sasakian and weak nearly cosymplectic hypersurfaces in such Riemannian manifolds and prove that a weak nearly cosymplectic manifold with parallel Reeb vector field is locally the Riemannian product of a real line and a weak nearly Kähler manifold. [ABSTRACT FROM AUTHOR]

The burgeoning digital economy has expanded the information gap in supply chains. Supply chain information transparency has thus attracted increasing attention from both industry and academia. Meanwhile, as consumers pay more attention to corporate social responsibility (CSR), firms have made efforts to audit their suppliers to avoid CSR violations. This paper explores the strategic role of CSR auditing in supply chain information transparency. We examine how CSR auditing interacts with supply chain information transparency by considering a scenario where a firm strategically sells either a single product or a product line through an online retail platform. Our results show that when the product line strategy is uncertain, the firm's audit commitment strengthens the platform's incentive to voluntarily disclose information. Our results also show that when the length of the product line is unchangeable, the platform prefers to share the information even if the firm does not make audit commitments. Additionally, when the platform's information sharing decision and the firm's product line design are made simultaneously, the platform may choose to privately withhold the information if the product line extension cost is in the moderate range and the demand loss from CSR-concerned consumers is relatively minor. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The export competitiveness has only calculated on only two aspects either comparatively advantageous or comparatively disadvantageous products for India or China. There is not any thorough study that has been undertaken for Indian manufacturing sector at a segregated level along with that of China. So, in the light of these shortcomings, the purpose of this study is to analyse the dynamics of export competitiveness of indian manufacturing sector vis-à-vis its emerging counterpart, china in the global market. Design/methodology/approach: A modified revealed comparative advantage index has been used in two different phases of 2001–08 and 2010–18 to find the dynamic pattern of manufacturing exports of India and China in the world market. Findings: The study revealed that India has shown a positive response in increasing its competitive positioned products from low-technology to medium-technology products during the study period. There has been a decline in the competitive positioned products of China and simultaneously China's threatened product lines have shown an immense increment over the years. Moreover, Indian exports are concentrated to few low-technology and resource-intensive products, that share more than 50% of total exported value for its manufacturing in the global market, whereas, China is much diversified and the exported value is more scattered over its manufactured items. Research limitations/implications: The study does not include the factors that impacted the export competitiveness of the sample economies and thus adds a limitation to this study. Originality/value: As there is very limited research on dynamics of export competitiveness of Indian manufacturing exports at harmonised system 6-digit level with China, this study fulfils the gap. [ABSTRACT FROM AUTHOR]

The purpose of the study was to evaluate the physical and mechanical properties of various textile materials based on polyester and cotton for shoe uppers and lining. For that purpose, the influence of the ratio of a mixture of cotton and polyester fibers on the quality of the fabric was investigated. As a result, fabrics with the same composition but different yarn numbers differed from each other in specified properties. With an increase in the content of polyester, all specified properties increase except for vapor permeability, which makes it possible to correctly select the composition of fabrics for shoes. [ABSTRACT FROM AUTHOR]

Currently, software development is more associated with families of configurable software than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking. Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment's time and financial constraints, these features are rendered unfeasible. Thus, testing subsets of configured products is one approach to solving this issue. To reduce the testing effort and obtain better results, alternative methods for testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately, the CIT method produces unscalable solutions for large SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, should also be considered. In this article, we proposed a search-based software engineering solution using multi-objective evolutionary algorithms (MOEAs). In particular, the research was applied to different types of MOEA method: the Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-dominant Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms were examined in the context of distinct objectives and two quality indicators. The results revealed how the feature model attributes, implementation context, and number of objectives affected the performances of the algorithms. [ABSTRACT FROM AUTHOR]

This article addresses aspects related to publicity in media and strategies that promotes subjectivation processes. For analysis, we took the speeches propagated in play publicities in Natura Ekos line products to critically talk about how beauty activates references related to nature, especially concerning the Amazon and people that live in this area. We created our empirical corpus by using seven demonstrative magazines from products that correspond to the cycles 10 to 16, which were current between May and September of 2021. We ensured all play publicities analyzed formed a plot discursive that works as a trap to seduce us and teach us how we should see and count ourselves inside a regularities matrix. [ABSTRACT FROM AUTHOR]

Agile Product Line Engineering (APLE), a relatively new approach combining the two successful methods of Agile Software Development (ASD) and Software Product Lines (SPLs), makes product lines more responsive to ever-changing customer needs or market changes. However, SPLs often fail to keep up with market demand due to high coordination costs, slow development processes, and long release cycles in the case of frequent changes in business requirements; in agile software projects, the lack of a unified specification for describing requirements leads to high coordination costs and inconvenient requirement management. Some studies in the literature have proposed optimized approaches to integrate ASD and SPLs, but they still have not covered all aspects of APLE's characteristics, and software resource reuse is rarely considered in these approaches during product line development. In view of this, we propose a collaborative framework of agile product line engineering for software resource reuse, namely ScrumOntoSPL. The ScrumOntoSPL approach efficiently merges ASD and SPL based on the agile method Scrum, SPL architecture, and ontology technology. In ScrumOntoSPL, uniform requirement specification is constructed by utilizing ontology, and the Matching Requirement with Component (MRC) process is designed to match product new requirements and software resources stored in a resource pool. In addition, we evaluated the proposed framework and approach with CMMI. In the end, a case study of a software development tool called IMC-Tool based on ScrumOntoSPL for a universal Instrument Microcontroller Chip (IMC) is discussed. The IMC-Tool case illustrates that the ScrumOntoSPL has the advantages of dynamically managing demand changes, enhancing software resource reuse, reducing coordination costs, and reducing time to market. [ABSTRACT FROM AUTHOR]

In the era of big data, data-driven product configuration improvement and product line restructuring are two important and interrelated problems, and joint decision-making regarding these two problems needs to be tackled. There are two difficulties to achieve the joint decision-making. One is to obtain consumer choice probabilities related to improved product configurations, and the other is to obtain the best product configuration portfolio. In this study, a framework combining text mining and multitask learning is developed to deal with the difficulties. In the framework, improved product configurations are generated using online reviews and transaction data of the target product and its competitors. A one-to-many mapping from customer requirements to improved product configurations is realized by using a multitask support vector machine to obtain consumer choice probabilities for the improved product configurations. The profit maximization models considering the customer choice probabilities are then developed to obtain the best product configuration portfolio. A case study of Huawei P20 series smartphones is used to illustrate the effectiveness of the proposed methods. The results indicate that the multitask support vector machine obtained a higher prediction accuracy than two single-task learning and two other multi-task learning methods, and the proposed framework has the ability to increase the profits produced by the best product configuration portfolio. [ABSTRACT FROM AUTHOR]

In many settings, consumer surplus directly impacts a firm or organization's objective, and the profit‐only objective becomes inadequate. Our paper is the first to consider the dual objective of profit and consumer surplus in multi‐product pricing under the multinomial logit demand, where the prices are continuously set. We define a firm's marginal consumer surplus as its marginal contribution to the expected customer utility transformed into monetary unit. Although the profit is concave in the choice probability space, the marginal consumer surplus is convex, complicating the analysis. We identify the optimal monopoly pricing solution and develop solution approaches for the equilibrium solutions of both price‐competition and quantity‐competition oligopolies. In the monopolistic setting, we solve the optimal solution in a near‐closed‐form expression, and show that the firm's markup and profit decline as its emphasis for marginal consumer surplus increases and eventually drops to zero when the firm turns into a social welfare maximizer. Therefore, social welfare is maximized only when the monopolist is willing to endure zero profit. In competitive settings, if one firm's emphasis on marginal consumer surplus increases, then the improvement in marginal consumer surplus can magnify through competitive forces, reflected in reduced equilibrium prices for all firms. Moreover, we find that, while the overall marginal consumer surplus always increases with any firm's weight on marginal consumer surplus in its objective (referred to as the CS weight), a firm's marginal consumer surplus increases in its own CS weight but decreases in other firms' CS weight. Finally, we prove that, all else equal, a firm with a higher CS weight can earn a higher profit than its profit‐maximizing competitors, which is counterintuitive. In the quantity competition, a firm may even increase its absolute profit level by increasing its CS weight. [ABSTRACT FROM AUTHOR]

As the sharing economy develops in sustainable transportation, car-sharing platforms improve the allocation efficiency of idle cars to customers. However, as the travel demand increases, and the vehicle type becomes various, the sharing market faces a new challenge to satisfy the customers' diverse needs for quality-differentiated cars. Each customer would like to choose the right car type, while the manufacturing firm and the e-commerce platform should set the right car price for profit maximization. How to match the platform's product line with the customer's choice behavior becomes a problem for all the stakeholders in the sharing economy. From the perspective of customer heterogeneity, we establish a game-theoretic framework to study product line optimization in business-to-consumer (B2C) and consumer-to-consumer (C2C) sharing. We solve the optimal quality decision and the optimal pricing strategy for the manufacturing firm and the e-commerce platform in the sharing economy. In numerical experiments, we investigate how the sensitivity of the quality and the cost can jointly influence the profits of sustainable transportation. Our findings show that within the product line, the firm's selling profit in the C2C sharing is negatively influenced by the sharer's valuation, while the platform's sharing profit in the B2C sharing is independent of either buyer's valuation. We give some policy implications for scholars, practitioners, and policymakers in the sharing economy and point out some limitations and recommendations for the product line design. [ABSTRACT FROM AUTHOR]

Parenteral products appear to be sensitive to process conditions in bioprocessing steps, such as interfacial stress and shear stress. The combination of these elements is widely believed and proven to influence product stability, but the defined roles of these players in the product damage process have not yet been identified. The present work addresses a current industrial problem, by focusing on the analysis of shear stress on protein-based therapeutics flowing in tubing by means of Computational Fluid Dynamics simulations. The purpose of this article is not to pinpoint the mechanism triggering the damage of the product, but it represents the first step towards wider experimental investigations and introduces a new strategy to quantify the average shear stress. The field of scale-down approaches, used to scale the commercial process down to the laboratory level, is also explored. Since quality control is critical in the pharmaceutical realm, it is essential that the scale-down approach preserves the same stress exposure as the commercial scale, which in the present work is considered to be that resulting from shear effects. Therefore, a new approach for scaling down the commercial process is proposed, which has been compared with traditional approaches and shown to provide greater representativeness between the two scales. [ABSTRACT FROM AUTHOR]

Producing a large family of resource-constrained multi-processing systems on chips (MPSoC) is challenging, and the existing techniques are generally geared toward a single product. When they are leveraged for a variety of products, they are expensive and complex. Further in the industry, a considerable lack of analysis support at the architectural level induces a strong dependency on the experiences and preferences of the designer. This paper proposes a formal foundation and analysis of MPSoC product lines based on a featured transition system (FTS) to express the variety of products. First, features diagrams are selected to model MPSoC product lines, which facilitate capturing its semantics as FTS. To this end, the probabilistic model checker verifies the resulting FTS that is decorated with tasks characteristics and processors' failure probability. The experimental results indicate that the formal approach offers quantitative results on the relevant product that optimizes resource usage when exploring the product family. [ABSTRACT FROM AUTHOR]

The product line approach enables the rapid building of software products by reusing core assets. How to manage the commonality and variability of product family is the core problem of product line engineering. The feature modeling approach is one of the main approaches to solve this problem. However, this approach does not define the internal functions of features. Some researchers use formal descriptions to define the function of features, but they do not consider the connection between feature specification and feature model. These product family modeling approaches result in product family models that suffer from inconsistent descriptions, which are difficult to resolve manually. To this end, this paper proposes a computer-aided approach to construct an internally consistent product family model. The method performs an inconsistency check during the formalization of each feature in the given initial feature model, pushing the evolution of the feature model and feature specification. The creation and evolution of feature models are achieved through a template-based feature modeling approach. The method is implemented by means of templates, each of which contains two parts of knowledge: the elements required for a class of feature model, and a way to convert the given elements into a formal description. An inconsistency check is achieved through a rule-based detection approach. Each rule contains two parts: a class of conditions that the feature model and the feature specification satisfy, and the actions to resolve inconsistencies once the conditions are satisfied. We have developed a tool. The tool guides the user to build an internally consistent product family model that contains the feature model and feature specifications. Experiments have shown that our approach can provide effective automation support for the process of formal product family modeling. [ABSTRACT FROM AUTHOR]

The misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine‐grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri‐variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain. [ABSTRACT FROM AUTHOR]

The cam-linkage mechanism is a typical transmission mechanism in mechanical science and is widely used in various automated production equipment. However, conventional modeling methods mainly focus on the design and dimensional synthesis of the cam-linkage mechanism in the slow-speed scenario. The influence of component dimensions is not taken into consideration. As a result, the model accuracy dramatically falls when analyzing large-size cam-linkage mechanisms, especially in high-speed environments. The kinematic aspects of cam design have been investigated, but there are few studies discussing the motion characteristic and accuracy analysis models of the large-size cam-linkage mechanism under high-speed scenarios. To handle such issues, this paper proposes a parameter optimization methodology for the design analysis of the large-size high-speed cam-linkage mechanism considering kinematic performance. Firstly, the mathematical model of the cam five-bar mechanism is presented. The cam curve and motion parameters are solved forward with linkage length and output speed. Then, a particle swarm-based multi-objective optimization method is developed to find the optimal structure parameters and output speed curve to minimize cam pressure angle and roller acceleration and maximize linkage mechanism drive angle. A Monte Carlo-based framework is put forward for the reliability and sensitivity analysis of kinematic accuracy. Finally, a transverse device of a sanitary product production line is provided to demonstrate the applicability of the proposed method. With the parameter optimization, the productivity of the transverse device is doubled, from 600 pieces per minute (PPM) to 1200 PPM. [ABSTRACT FROM AUTHOR]

Software ecosystems are considered the natural evolution of software product lines. A software ecosystem provides a (software) product within a particular business and organizational context that supports the exchange of activities and services within a domain. However, the increasing degree of autonomy demanded by software ecosystems is elevating the system response to end users, while the existing software ecosystem architectures are not well prepared to deal with the dynamicity of context changes and autonomous behavior needs. In order to provide a transition toward an increased level of autonomy, in this article, we introduce the notion of autonomous dynamic ecosystems as representative of those software ecosystems able to support dynamic, smart, and autonomous features demanded by modern software systems. In this work, we further investigate and provide evidence of four industrial examples that have fully embodied the principles of autonomous dynamic ecosystems, and we characterize the main features and technology requirements of this kind of new ecosystems. [ABSTRACT FROM AUTHOR]

Motivated by the rising environmental protection awareness and the importance of green product design, in this article we develop two models to investigate the quality and greenness decisions and product line choices of a green manufacturer as well as the corresponding impact of product line length on environmental performance. In our models, both the manufacturer and consumers show environmental responsibility behaviors. We find that the manufacturer extends the product line when the unit fit cost, the green cost, the quality cost, the market scale of low-segment consumers, and the environmental responsibility level (or unit environmental cost) are low. In the presence of two products with different market positioning, our analysis reveals that: first, owing to the consumer's self-selection behavior, product quality and greenness may be distorted from the efficient quality and greenness; second, product line extension may improve both the environmental performance and the manufacturer's utility, i.e., achieving a win–win outcome; and third, a higher unit environmental cost (environmental responsibility level or a longer product line) will improve the environmental performance if the unit environmental cost is high. In the continuous green valuation setting, interestingly, we find different results that the manufacturer with a high environmental responsibility level may extend its product line, and the environmental performance increases with the unit environmental cost in the single-product case. [ABSTRACT FROM AUTHOR]

The specifications of a certain domain are addressed by a portfolio of software products, known as Software Product Line (SPL). Feature Model (FM) supports domain engineering by modeling domain knowledge along with variability among SPL. The quality of FM is one of the significant factors for the successful SPL in order to attain high quality software products. However, the benefits of SPL can be reduced due to defects in FM. Dead Feature (DF) is one of such defects. Several approaches exist in the literature to detect defects due to DF in FMs. But only a few can handle their sources and solutions which are cumbersome and difficult to understand by humans. An ontological knowledge-based method for handling defects due to DF in FMs is described in this paper. It specifies FM in the form of ontology-based knowledge representation. The rules based on first-order logic are created and implemented using Prolog to detect defects due to DF with sources as well as suggest solutions to resolve these defects. A case study of the product line available on SPLOT repository is utilized for illustrating the proposed work. The experiments are performed with real-world FMs of varied sizes from SPLOT and FMs created with the FeatureIDE tool. The results prove the efficiency, scalability (up to model with 32,000 features) and accuracy of the presented method. Therefore, reusability of DFs free knowledge enables deriving defect free products from SPL and eventually enhances the quality of SPL. [ABSTRACT FROM AUTHOR]

This paper studies two fundamental questions regarding probabilistic selling in vertically differentiated markets: When is it profitable and how does one design it optimally? For the first question, we identify an important but overlooked economic mechanism driving probabilistic selling in vertically differentiated markets: the convexity of consumer preferences. In stark contrast to the literature finding that probabilistic selling is never profitable except in the presence of certain capacity constraint or consumer bounded rationality, we find that with many alternative utility functions capable of representing convex preference, probabilistic selling is always profitable. For the second question, we study the optimal strategy of probabilistic selling, including the design and price of the probabilistic good and the prices of the component goods. We show that under some technical conditions, the optimal price of the high‐quality component good increases while the optimal price of the low‐quality component good decreases upon the introduction of probabilistic selling, thereby increasing the market coverage and the economic efficiency without launching an actual new product line. To further illustrate the design of probabilistic selling, we use an example based on the canonical utility function, which is widely used in the economics literature on vertical product differentiation. We derive a closed‐form solution to the problem of optimal probabilistic selling. We also take advantage of the analytical tractability of the canonical utility to further explore the design of multiple probabilistic goods. [ABSTRACT FROM AUTHOR]

Context: Dynamic software product lines (DSPLs) have considerably increased their adoption for variability management for self-adaptive systems. The most widely used models for managing the variability of DSPLs are the MAPE-K control loop and context-aware feature models (CFMs). Aim: In this paper, we review and synthesize evidence of using variability constraint approaches, methodologies, and challenges for DSPL. Method: We conducted a systematic mapping, including three research questions. This study included 84 papers published from 2010 to 2021. Results: The main results show that open-dynamic variability shows a presence in 57.1% of the selected papers, and on the other hand, closed-dynamic variability appears in 38.1%. The most commonly used methodology for managing a DSPL environment is based on proprietary architectures (60.7%), where the use of CFMs predominates. For open-dynamic variability approaches, the MAPE-K control loop is mainly used. The main challenges in DSPL management are based on techniques (28.6%) and open variation (21.4%). Conclusions: Open-dynamic variability has prevailed over the years as the primary approach to managing variability in DSPL, where its primary methodology is the MAPE-K control loop. Response RQ3 requires further review. [ABSTRACT FROM AUTHOR]

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Purpose: This paper studies how multichannel retailers choose the product quality level and decide which attribute to make prominent in their physical store in a competitive environment. Design/methodology/approach: This paper develops a game theoretic model, in which multichannel retailers' decisions are made in three stages. Using prominent experiential attributes (e.g. functionality) in their offline store and product quality decisions, multichannel retailers are capable of transferring the sales between different channels. Findings: This analysis shows that making different attributes prominent in their physical store may be an equilibrium, and each multichannel retailer chooses the highest quality level for the prominent attribute. However, the prominent attribute of the highest quality level is not always optimal. Under certain conditions, multichannel retailers may make the experiential attribute prominent in their respective physical stores, which can result in equilibrium. Practical implications: The results indicate that multichannel retailers should avoid blindly highlighting high-quality attributes in a competitive environment, or falling into price completion. Originality/value: From the perspective of prominent attributes, this study designs the optimal product line based on channel characteristics. The results of the research can provide practical implications for multichannel retailers to increase sales. [ABSTRACT FROM AUTHOR]