Elaborate Case Study Research Articles

Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning

Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns during storage. On this basis, machine learning would help to process complex volatolomic data and predict pork microbial quality efficiently. This study focused on (1) investigating model generalizability based on different nested cross-validation settings, and (2) comparing the predictive power and feature importance of nine algorithms, including Artificial Neural Network (ANN), k-Nearest Neighbors, Support Vector Regression, Decision Tree, Partial Least Squares Regression, and four ensemble learning models. The datasets used contain 37 VOCs' concentrations (input) and total plate counts (TPC, output) of 350 pork samples with different storage times, including 225 pork loin samples stored under three high-O2 and three low-O2 conditions, and 125 commercially packaged products. An appropriate choice of cross-validation strategies resulted in trustworthy and relevant predictions. When trained on all possible selections of two high-O2 and two low-O2 conditions, ANNs produced satisfactory TPC predictions of unseen test scenarios (one high-O2 condition, one low-O2 condition, and the commercial products). ANN-based bagging outperformed other employed models, when TPC exceeded ca. 6 log CFU/g. VOCs including benzaldehyde, 3-methyl-1-butanol, ethanol and methyl mercaptan were identified with high feature importance. This elaborated case study illustrates great prospects of real-time detection techniques and machine learning in meat quality prediction. Further investigations on handling low VOC levels would enhance the model performance and decision making in commercial meat quality control.

Representing China in the British Museum: Do Museums Perpetuate Historical Biases?

Museums have never been neutral. Public museums and exhibitions that originated in colonial times are accused of passing on biases that feature Euro-centric interpretations of history. This paper investigates the nature of bias and the history of museums and their biases, and then focuses on the representation of China in the British Museum as an elaborate case study. The paper argues that from the British Museums virtual collections and public resources, the descriptive text and collection highlights in the China galleries reflect implicit and biased historical perspectives of China in the UK, exhibiting a contrast between the British appreciation of exotic Chinese art and contempt towards Chinas social status arising from colonial trade. This paper provides a specific statement and analysis of western biases for colonial discussions and could contribute to decolonization actions taken in museums.

Generating multi-level explanations for process outcome predictions

Process mining focuses on the analysis of event log data to build various process analytical capabilities. Predictive process analytics has emerged as one of such key capabilities and it uses machine learning techniques to construct process prediction models. In recent years, deep neural networks have gained increasing interest in process prediction since they can handle multi-dimensional sequential inputs with minimal information loss. However, they are considered black-box models and existing studies in explaining deep neural network-based process predictions rely on only event-level features for explanation. In this paper, we propose a new approach for generating explanations for process outcome predictions at multiple levels. The approach is underpinned by three different prediction models: a transparent model for generating global explanations based on case-level features, an attention-based deep neural network for generating local explanations based on event-level features, and a novel eXplainable Dual-learning Deep network (XD2-net) for generating local explanations based on case-level features. Using three publicly available datasets, we have tested the applicability of the approach and further examined the multi-level explanations generated by the approach through an elaborate case study. Unlike others, the design of our approach promotes the idea of leveraging the complementary capabilities of different models and utilizing their strengths, rather than focusing on model performance competition. This will contribute towards generating more comprehensive explanations that meet the needs of different end users and purposes in the future.

STATISTICAL SOFTWARE R IN CORPUS-DRIVEN RESEARCH AND MACHINE LEARNING

The rapid development of computer software and network technologies has facilitated the intensive application of specialized statistical software not only in the traditional information technology spheres (i.e., statistics, engineering, artificial intelligence) but also in linguistics. The statistical software R is one of the most popular analytical tools for statistical processing a huge array of digitalized language data, especially in quantitative corpus linguistic studies of Western Europe and North America. This article discusses the functionality of the software package R, focusing on its advantages in performing complex statistical analyses of linguistic data in corpus-driven studies and creating linguistic classifiers in machine learning. With this in mind, a three-stage strategy of computer-statistical analysis of linguistic corpus data is elaborated: 1) data processing and preparing to be subjected to a statistical procedure, 2) utilizing statistical hypothesis testing methods (MANOVA, ANOVA) and the Tukey post-hoc test, and 3) developing a model of a linguistic classifier and analyzing its effectiveness. The strategy is implemented on 11 000 tokens of English detached nonfinite constructions with an explicit subject extracted from the BNC-BYU corpus. The statistical analysis indicates significant differences in the realization of the factors of the parameter “Part of speech of the subject”. The analyzed linguistic data are employed to build a machine model for the classification of the given constructions. Particular attention is devoted to the methodological perspectives of interdisciplinary research in the fields of linguistics and computer studies. The potential application of the elaborated case study in training undergraduate, master, and postgraduate students of Applied Linguistics is indicated. The article provides all the statistical data and codes written in the R script with comprehensive descriptions and explanations. The concluding part of the article summarizes the obtained results and highlights the issues for further research connected with the popularization of the statistical software complex R and raising the awareness of specialists in this statistical analysis system.

Optimization of PV modules layout on high-rise building skins using a BIM-based generative design approach

Global economic growth is leading to a higher demand for energy. Considering the declining cost of technology and rising fossil fuel prices, the application of renewable energy technologies is promising. The worldwide use of photovoltaic electricity is growing rapidly by more than50% a year. In the urban environment, buildings are central to human activities. Therefore, to achieve sustainable urban development, buildings are of particular importance for distributed renewable energy generation. Of different types of buildings in the built environment, high-rise buildings are of particular interest because of their high potential for harvesting a considerable amount of photovoltaic (PV) energy on vertical and horizontal surfaces. Nevertheless, this high potential is seldom harnessed mainly because the deployment of PV modules on high-rise buildings involves consideration of a complex interplay between various factors that affect the installation of PV modules (e.g., urban canyons, self-shadowing effect, surface-specific PV modules, etc.). This renders the design of PV modules in high-rise buildings a complex optimization problem, one that requires a generative design approach. In recent years, and with the advent and rising popularity of Building Information Modeling (BIM) concept, the apparatus for the implementation of such a comprehensive generative design approach is becoming increasingly available. However, to the best of authors’ knowledge, there are currently no frameworks for the BIM-based generative design of PV modules for high-rise buildings. To this end, the present paper made a novel contribution to the body of knowledge by presenting a BIM-based generative design framework for PV module layout design on the whole exterior of tall buildings. This allows designers to consider the complex interaction between building surface types (e.g., windows, walls, etc.), type of PV module (e.g., opaque, semi-transparent, etc.), the efficiency of different PV modules, and the financial aspect of the PV system (i.e., revenue vs. cost at different study period). The results generated by the elaborate case study demonstrated that the generative design framework is capable of offering more favourable solutions (i.e., either or both of reduced costs and increased energy revenue) compared to baseline scenarios. It is observed that, in the majority of the studied scenarios, the optimum solutions favored a more consistent orientation of the panels (i.e., consistent pan and tilt angles across all the panels).

Using quantitative dynamic adaptive policy pathways to manage climate change-induced coastal erosion

Adaptation requires planning strategies that consider the combined effect of climatic and non-climatic drivers, which are deeply uncertain. This uncertainty arises from many sources, cascades and accumulates in risk estimates. A prominent trend to incorporate this uncertainty in adaptation planning is through adaptive approaches such as the dynamic adaptive policy pathways (DAPP). We present a quantitative DAPP application for coastal erosion management to increase its utilisation in this field. We adopt an approach in which adaptation objectives and actions have continuous quantitative metrics that evolve over time as conditions change. The approach hinges on an adaptation information system that comprises hazard and impact modelling and systematic monitoring to assess changing risks and adaptation signals in the light of adaptation pathway choices. Using an elaborated case study, we force a shoreline evolution model with waves and storm surges generated by means of stochastic modelling from 2010 to 2100, considering uncertainty in extreme weather events, climate variability and mean sea-level rise. We produce a new type of adaptation pathways map showing a set of 90-year probabilistic trajectories that link changing objectives (e.g., no adaptation, limit risk increase, avoid risk increase) and nourishment placement over time. This DAPP approach could be applied to other domains of climate change adaptation bringing a new perspective in adaptive planning under deep uncertainty.

A hybrid intelligence approach for sustainable service innovation of smart and connected product: A case study

Nowadays, smart and connected product (SCP) is gradually replacing the traditional functional products, which has attracted widespread attention from the industry and academia. Service innovation, as a crucial part of SCP iterative improvement, is a multi-criteria decision-making (DM) process facilitated by intelligent automation (IA) and cognitive technologies. However, product user’s intelligence (e.g. physiological feeling) that can intuitively reflect and evaluate the product service satisfaction is rarely considered in the process of service innovation. Hence, it is difficult to measure the product users’ preferences with precise numerical terms to make a strategic decision. In order to fill this gap, a hybrid intelligence approach is proposed to perform the service innovation for SCP. The product-user data (e.g. subjective data and physiological data) and product-sensor data are collected and used for the process of service innovation. A smart group spinning bicycle system is presented as an elaborated case study to illustrate the proposed architecture and approach. The service innovation of real-time and dynamic monitoring, user participation improvement and smart feedback manners are achieved. In addition, an ergonomic experiment is conducted to validate the effectiveness of the proposed approach in implementing the service innovation for SCP.

Disruptive innovation, business model and sharing economy: the bike-sharing cases in China

PurposeThe purpose of this study is to connect disruptive innovation and sharing economy by exploring the underlying mechanisms of how a disruptive innovation–based business project creates, delivers and captures value in sharing economy through analyzing the case of bike sharing in China.Design/methodology/approachAn elaborate case study is used to unfold the process as well as the underlying mechanism and relationships among disruptive innovation, business model, bike-sharing business and value creation in sharing economy.FindingsBike sharing case fits well in disruptive innovation theory. Its low price and great convenience have led to rapid development in China. However, failures to improve their products and services and build an effective business model which can create, deliver and capture value have caused the failure of many bike-sharing companies. Other factors such as strategic decision-making, internal management problems, external conflicts as well as uncivilized consumer behaviors have also inhibited the sustainable development of bike-sharing companies.Originality/valueThe theoretical contributions of this study include the following: to explore how a disruptive innovation–based business creates, delivers and captures value successfully in sharing economy. This study contributes to both research and practice on disruptive innovation and sharing economy.

The urban-rural discrepancy of generational housing pathways: A new source of intergenerational inequality in urban China?

In China's past decades of marketization reforms, original urban dwellers and early settlers in the home ownership sector enjoyed a drastic improvement in living conditions and family wealth stored in properties. Despite their significant contribution to the urban economy, rural migrants benefited far less from the housing market growth. Institutional discrimination, such as duality in welfare state provisions due to hukou segregation is often seen as main the mechanism behind this urban-rural discrepancy. With three elaborated case studies of generational housing pathways from Chongqing, this paper contributes to the debate of urban-rural housing inequality from a longitudinal perspective, focusing on the role of intergenerational transfers of wealth. We find that the original hukou, the original place of living and the housing positions of the parents all have great influence on the younger generation's housing experiences. The gaps between the urban locals and the migrants in housing resulted from generational accumulations of unbalanced opportunities in accessing housing.

Hybrid data-driven vigilance model in traffic control center using eye-tracking data and context data

Vigilance decrement of traffic controllers would greatly threaten public safety. Hence, extensive studies have been conducted to establish the physiological data-based vigilance model for objectively monitoring or detecting vigilance decrement. Nevertheless, most of them using intrusive devices to collect physiological data and failed to consider context information. Consequently, these models can be used in a laboratory environment while cannot adapt to dynamic working conditions of traffic controllers. The goal of this research is to develop an adaptive vigilance model for monitoring vigilance objectively and non-intrusively. In recent years, with advanced information and communication technology, a massive amount of data can be collected from connected daily use items. Hence, we proposed a hybrid data-driven approach based on connected objects for establishing vigilance model in the traffic control center and provide an elaborated case study to illustrate the method. Specifically, eye movements are selected as the primary inputs of the proposed vigilance model; Bagged trees technique is adapted to generate the vigilance model. The results of case study indicated that (1) eye metrics would be correlated with the vigilance performance subjected to the mental fatigue levels, (2) the bagged trees with the fusion features as inputs achieved a relatively stable performance under the condition of data loss, (3) the proposed method could achieve better performance than the other classic machine learning methods.

A structural service innovation approach for designing smart product service systems: Case study of smart beauty service

Smart Product-service systems (PSS) are the emerging type of PSS that offer market value and dynamic intelligence combining products and services as solutions to consumers based on digital technology. To design a smart PSS with an effective way, a structural design approach is required. Nevertheless, only a few existing researchers discussed this topic. Aiming to bridge this gap, an integrated way is proposed for smart PSS design. This study refines a generic approach for structural service innovation approach which integrates the advantage of PSS engineering and service engineering for designing smart product service systems. The structural design approach is based on the Theory of Inventive Problem Solving (TRIZ) incorporating with service blueprint named PRR method. Three design phases are demonstrated as verification of the PRR method by an elaborated case study of the smart beauty service. Based on PRR, three key design phases are integrated, i.e., (1) problem definition, (2) resolution generation, and (3) resolution design. Empirical results and implications are collected and discussed to obtain valuable insights for value creation.

Test case generation, selection and coverage from natural language

In Model-based Testing (MBT), test cases are automatically generated from a formal model of the system. A disadvantage of MBT is that developers must deal with formal notations. This limitation is addressed in this paper, where use cases are used to model the system. In previous work, we have proposed an automatic strategy for generating test cases from use cases written in a Controlled Natural Language (CNL), which is an English textual notation with a well-defined grammar. Due to its precise syntax, it can be processed and translated into a formal representation for the purpose of automatic test case generation. This paper extends our previous work by proposing a state-based CNL for describing use case control flows enriched with state and data operations. We translate state-based use case descriptions into CSP processes from which test cases can be automatically generated. In addition, we show how a similar notation can be used to specify test selection via the definition of state-based test purposes, which are also translated into CSP processes. Test generation and selection are mechanised by refinement checking using the CSP tool FDR. Despite the fact that we work at a purely process algebraic level to define a test generation strategy, we are able to address model coverage criteria. Particularly, by using FDR, it is possible to have access to the underlying LTS models; we then implemented algorithms for covering events or transitions, possibly combined with selection using test purposes. We also discuss several ways of improving the efficiency of the test generation strategy. As far as we are aware, this integration between an algebraic approach to test case generation with an operational approach for coverage criteria is an original and promising insight. All steps of the strategy are integrated into a tool that provides a GUI for authoring use cases and test purposes described in the proposed CNL, so the formal CSP notation is completely hidden from the test designer. We illustrate our tool and techniques with a running example and a more elaborate case study taken from an industrial setting. • A process algebraic approach to test generation from use cases in natural language. • Integration of coverage criteria into a purely algebraic test generation approach. • Test generation considering coverage of events or transitions of the model. • Complexity analysis and a provably sound optimisation of the test generation approach. • An industrial case study with metrics on the efficiency of the test generation strategy.

Selection in Border Areas: Profiling Immigrants or Crimmigrants?

AbstractResearch on profiling in law enforcement focuses mostly on traffic stops and stop and searches in an Anglo‐American context. This article aims to expand this scope by studying profiling practices in migration checks in the Netherlands, aiming to see how migration officers select potential immigrants in the absence of a requirement of reasonable suspicion and how they deal with the legal complexity of the role of race and nationality in the selection process. It does so by employing an elaborate case study of the Royal Netherlands Marechaussee (RNLM). Findings show that officers are not looking for immigrants in general but focus on a subset of criminal immigrants based on stereotypical perceptions of specific nationalities. While these perceptions could be attributed to the discriminatory beliefs of the individual officers, the present study moves beyond that by documenting how unclear legislation and organisational factors are important to understand how these beliefs come to be and are maintained.

A systematic design approach for service innovation of smart product-service systems

Recent development and implementation of advanced information and communication technologies (ICT) (e.g. Internet-of-Things (IoT), Cyber-Physical System (CPS)) has triggered a promising market of smart, connected product (SCP), and hence, enabled the prevailing tendency of manufacturing value proposition towards a service-oriented manner, viz. Smart product-service systems (PSS). Smart PSS, as a unique type of PSS, aims to achieve individual customer satisfaction with less environment impact by utilizing SCP as the media and tool, to generate various e-services as a bundle. Its service innovation is enabled by a platform-based approach and generated in a data-driven manner. Moreover, the digitalization of users, things, manufacturers and service providers in the cloud-based environment also provides potential service innovation opportunities. Nevertheless, only few existing works discuss about the Smart PSS, let alone an effective manner with valid cases to support its service innovation. Aiming to fill this gap, this research provides a novel definition of Smart PSS, and proposes a systematic design framework for its service innovation with an elaborated case study of a personalized smart wearable. Meanwhile, other potential industrial applications are discussed as well to offer valuable insights to manufacturers in their servitization for value creation.

Negotiating Uncertain Claims

Recent developments in the relations between politicians and journalists in the US have (among other things) created a situation where journalists often have to deal with information that is very difficult, even impossible, to verify, yet which has potential societal significance that cannot be ignored. This has, we argue, affected how journalists and journalistic outlets relate to each other within what we tentatively term an inferential community. To argue this, we analyze journalistic demonstrations of authority in attempts to establish and connect “facts” related to uncertain claims in two cases of the coverage of the nascent Trump administration. This is, however, not a fully elaborated case study through which we can conclude something broader about contemporary journalism. The paper should rather be seen as a preliminary empirical probe allowing us to focus on a specific issue while proposing a tentative conceptual and analytical frame through which this may be studied in a more sustained and detailed way.

Impact of Bio-Based Plastics on Current Recycling of Plastics

Bio-based plastics are increasingly appearing in a range of consumption products, and after use they often end up in technical recycling chains. Bio-based plastics are different from fossil-based ones and could disturb the current recycling of plastics and hence inhibit the closure of plastic cycles, which is undesirable given the current focus on a transition towards a circular economy. In this paper, this risk has been assessed via three elaborated case studies using data and information retrieved through an extended literature search. No overall risks were revealed for bio-based plastics as a group; rather, every bio-based plastic is to be considered as a potential separate source of contamination in current recycling practices. For PLA (polylactic acid), a severe incompatibility with PET (polyethylene terephthalate) recycling is known; hence, future risks are assessed by measuring amounts of PLA ending up in PET waste streams. For PHA (polyhydroxy alkanoate) there is no risk currently, but it will be crucial to monitor future application development. For PEF (polyethylene furanoate), a particular approach for contamination-related issues has been included in the upcoming market introduction. With respect to developing policy, it is important that any introduction of novel plastics is well guided from a system perspective and with a particular eye on incompatibilities with current and upcoming practices in the recycling of plastics.

The ANANKE relative energy gradient (REG) method to automate IQA analysis over configurational change

Much chemical insight ultimately comes down to finding out which fragment of a total system behaves like the total system, in terms of an energy profile. A simple example is that of the water dimer, where this system is regarded as held together by a hydrogen bond. The hydrogen bond consists of two atoms (H···O), which energetically behave similarly to the total system (H2O)2. However, from a quantum mechanical point of view, each atom in the total system interacts with any other atom. Thus, the view that the hydrogen bond by itself governs the energetic stability of the water dimer needs rigorous justification. In this work, we propose a method that provides such a justification, in general, but only illustrated on the water dimer here. This method is based on the topological energy partitioning method called interacting quantum atoms (IQA). The method is implemented in the program ANANKE, which calculates correlations between the energy profile of the total system and those of subsystems (or fragments). ANANKE acts on the IQA energy contributions obtained for a sequence of full-system geometries controlled by a coordinate of interest (e.g. the O···H distance in the water dimer). Although applied only for the water dimer in this work, the method is general and able to explain the gauche effect, the torsional barrier in biphenyl, the arrow-pushing scheme of an enzymatic reaction (peptide hydrolysis in the HIV-1 Protease active site), and halogen-alkane nucleophilic substitution (SN2) reactions. Those applications will appear elsewhere as separate and elaborated case studies; here we focus on the details of the ANANKE method and its justification, using the water dimer as a concrete case.

Modelling Decision-Making Processes in the Management Support of the Manufacturing Element in the Logistic Supply Chain

This paper highlights the problems of mathematical modelling for a specific element of the logistic supply chain, that is, the manufacturing system. The complex manufacturing system consisting of a determined number of parallel subsystems is modelled. The fact that the same manufacturing procedure can be carried out in various locations is emphasised. Control algorithms as well as manufacturing strategies are explained. The equations of state are introduced. The two-stage criterion lets us use the result data generated by the simulator of the production system as the initial data for further processing; however, the main goal remains to minimise the time of the course of production. The precisely elaborated case study implements initial data obtained by preceding simulation procedures carried out in manufacturing systems consisting of three, four, and five subsystems.

Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.

Motivation: As ‘omics’ biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.Results: In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcriptome dynamics through the analysis of aggregated cell–cell statistical distances within biomolecular pathways. Cell–cell statistical distances are calculated from pathway mRNA fold changes between two cells. Within an elaborate case study of circulating tumor cells derived from prostate cancer patients, we develop analytic methods of aggregated distances to identify five differentially expressed pathways associated to therapeutic resistance. Our aggregation analyses perform comparably with Gene Set Enrichment Analysis and better than differentially expressed genes followed by gene set enrichment. However, these methods were not designed to inform on differential pathway expression for a single cell. As such, our framework culminates with the novel aggregation method, cell-centric statistics (CCS). CCS quantifies the effect size and significance of differentially expressed pathways for a single cell of interest. Improved rose plots of differentially expressed pathways in each cell highlight the utility of CCS for therapeutic decision-making.Availability and implementation: http://www.lussierlab.org/publications/CCS/Contact: yves@email.arizona.edu or piegorsch@math.arizona.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Theorising Decentralisation – Comparative Evidence from Sub‐national SwitzerlandMueller, SeanColchester/UK, ECPR Press (2015), 296p., ISBN 9781785521294

Theorising Decentralisation – Comparative Evidence from Sub‐national SwitzerlandMueller, SeanColchester/UK, ECPR Press (2015), 296<i>p., ISBN 9781785521294</i>