Researching Pure Digital Entrepreneurship – A Multimethod Insider Action Research approach
تحقیق در مورد کارآفرینی دیجیتالی خالص - یک رویکرد تحقیقات خود عملی چند منظوره -2019
Knowledge production in Pure Digital Entrepreneurship (PDE) needs to reflect the non-linear nature of a journey defined by digital artifact and platform creation. Accordingly, this paper proposes and offers practical guidance on the use of Multimethod Insider Action Research (MIAR) as a suitable research design for studying the entrepreneurial journey in this context. It argues for integrating first-person Reflective Practice, second-person Collaborative Inquiry and Design Research for third-person knowledge production that balances rigour and relevance. While calls for such forms of longitudinal process inquiry have largely gone unanswered due to identified challenges, this paper uses a case narrative to illustrate the feasibility of conducting them in a PDE context.
Keywords: Digital entrepreneurship | Multimethod | Insider Action Research | Design research
Evidence-based clinical engineering: Machine learning algorithms for prediction of defibrillator performance
مهندسی بالینی مبتنی بر شواهد: الگوریتم های یادگیری ماشین برای پیش بینی عملکرد دفیبریلاتور-2019
tPoorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performanceaccuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treat-ments. Even with the increase of technological sophistication of devices, incidents involving defibrillatormalfunction are unfortunately not rare.To address this, we have developed an automated system based on machine learning algorithms thatcan predict performance of defibrillators and possible performance failures of the device which can affectperformance. To develop an automated system, with high accuracy, overall dataset containing safety andperformance measurements data was acquired from periodical safety and performance inspections of1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public health-care institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number ofsamples, 974 of them were used during system development and 247 samples were used for subsequentvalidation of system performance. During system development, 5 different machine learning algorithmswere used, and resulting systems were compared by obtained performance.The results of this study demonstrate that clinical engineering and health technology managementbenefit from application of machine learning in terms of cost optimization and medical device manage-ment. Automated systems, based on machine learning algorithms, can predict defibrillator performancewith high accuracy. Systems based on Random Forest classifier with Genetic Algorithm feature selectionyielded highest accuracy among other machine learning systems. Adoption of such systems will help inovercoming challenges of adapting maintenance and medical device supervision mechanism protocolsto rapid technological development of these devices. Due to increased complexity of healthcare institu-tion environment and increased technological complexity of medical devices, performing maintenancestrategies in traditional manner is causing a lot of difficulties.
Keywords:Automated system | Machine learning | Medical device | Maintenance | Managemen | tPrediction | Performance | Inspection | Evidence-based
Droplet vitrification versus straw cryopreservation for spermatozoa banking in Persian sturgeon (Acipenser persicus) from metabolite point of view
انجماد قطره ای در مقابل انجماد کاه برای بانکی اسپرماتوزوا در ماهیان خاویاری فارسی (Acipenser persicus) از دیدگاه متابولیت-2019
Persian sturgeon (Acipenser persicus), a commercially valuable and critically endangered fish species has been suffering considerable declines in populations in the nature due to over-fishing, habitat destruction and marine pollution during past decades. Since there were no achievements in artificial reproduction programs, genetic resource banking such as gametes and embryo cryopreservation can be a good strategy however, reported resulting gamete qualities were considerably low. In the present study, the metabolome content of Persian sturgeon spermatozoa was investigated during common straw cryopreservation and novel droplet vitrification by the use of 1H NMR (Nuclear Magnetic Resonance) spectroscopy. Univariate (ANOVA) and multivariate (PCA) analysis showed significant differences in the metabolic profiles between cryopreserved and fresh spermatozoa samples. Adenine, creatine, creatine phosphate, glucose, guanidoacetate, lactate, N, N-dimethylglycine, and glycine levels showed no significant differences between these two cryopreservation techniques suggesting these metabolites and their corresponding enzymes and chemical pathways are so vulnerable to the temperature changes and even higher cooling rate in droplet vitrification could not conserve them. However, significant differences were found in acetate, creatinine, betaine, b-alanine and trimethylamine N-oxide suggesting better efficiency of droplet vitrification in protection of some metabolites associated to spermatozoa energetics, redox balance and hypoxia compensation compared to straw cryopreservation.
Keywords : Cryopreservation | Vitrification | Persian sturgeon | Spermatozoa | 1H NMR spectroscopy
Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models
تجزیه و تحلیل جامع مدلهای یادگیری ماشین برای پیش بینی ورم پستان تحت بالینی: یادگیری عمیق و رشد شیب درختان نسبت به سایر مدلها-2019
Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore, early detection of infected cows is of great importance. The Somatic Cell Count (SCC) day-test used for mastitis surveillance, gives data that fluctuate widely between days, creating questions about its reliability and early prediction power. The recent identification of risk parameters of sub-clinical mastitis based on milking parameters by machine learning models is emerging as a promising new tool to enhance early prediction of mastitis occurance. To develop the optimal approach for early sub-clinical mastitis prediction, we implemented 2 steps: (1) Finding the best statistical models to accurately link patterns of risk factors to sub-clinical mastitis, and (2) Extending this application from the farms tested to new farms (method generalization). Herein, we applied various machine learning-based prediction systems on a big milking dataset to uncover the best predictive models of sub-clinical mastitis. Data from 364,249 milking instances were collected by an electronic automated in-line monitoring system where milk volume, lactose concentration, electrical conductivity (EC), protein concentration, peak flow and milking time for each sample were measured. To provide a platform for the application of the models developed to other farms, the Z transformation approach was employed. Following this, various prediction systems [Deep Learning (DL), Naïve Bayes, Generalized Liner Model, Logistic Regression, Decision Tree, Gradient-Boosted Tree (GBT) and Random Forest] were applied to the non-transformed milking dataset and to a Z-standardized dataset. ROC (Receiver Operating Characteristics Curve), AUC (Area Under The Curve), and high accuracy demonstrated the high sensitivity of GBT and DL in detecting sub-clinical mastitis. GBT was the most accurate model (accuracy of 84.9%) in prediction of sub-clinical bovine mastitis. These data demonstrate how these models could be applied for prediction of subclinical mastitis in multiple bovine herds regardless of the size and sampling techniques.
“Lets preserve the achieved – And ask for more!” - Transformations of gender politics in Croatia
"بیایید برجام را حفظ کنیم - و بیشتر بخواهید!" - تحولات سیاست جنسیتی در کرواسی-2019
Research on gender and transformation in Eastern Europe has often emphasized the great changes that took place after 1989, without assessing the socialist period. This article analyses the transformation processes of gender-(relevant) policies in Croatia over 30 years by engaging both with socialism and subsequent transformation periods of democratization and EU accession. To disclose normative/discursive changes in gender-(relevant) policies, the article uses feminist historical institutionalism and policy framing. The article provides two main findings. First, the high positioning of gender-(relevant) policies on the political agenda indicates their embeddedness into wider economic-led goals. Second, when encountered with changed economic circumstances because of this embeddedness gender equality is often sacrificed. The article finally suggests that gender policies require an approach that goes beyond the economic sphere, includes the spheres of labour organization, intimacy and citizenship, discursively acknowledges differences, and considers the specificities of a local context.
Keywords: Gender policy | Feminist historical institutionalism | Policy framing | (post)socialism | Transformation | Social reproduction
Improving the Performance of Manufacturing Technologies for Advanced Material Processing Using a Big Data and Machine Learning Framework
بهبود عملکرد فن آوری های ساخت برای پردازش مواد پیشرفته با استفاده از یک چارچوب یادگیری ماشین و داده های بزرگ-2019
The paper offers a new approach to improving the performance of the materials knowledge analysis based on Big Data processing and machine learning. We consider a framework in which thread functioning of five machine learning mechanisms intended for solving the classification problem is realized. Classifier operation results are exposed to majority voting. The experimental assessment of performance and accuracy of framework operation is made on the data set containing technological data of the production line. Assessment showed that the offered framework provides a scoring on productivity of materials knowledge processing by 7.4 times.
Keywords: material processing | big data | machine learning | principal component analysis | classifier
Legal mobilization in medicine: Nurses, rape kits, and the emergence of forensic nursing in the United States since the 1970s
بسیج حقوقی در پزشکی: پرستاران ، کیت های تجاوز جنسی و ظهور پرستاری پزشکی قانونی در ایالات متحده از دهه 1970-2019
Routine administration of the sexual assault medical forensic exam (commonly known as the “rape kit”) is one of the most significant healthcare reforms advanced by the U.S. anti-rape movement since the 1970s. To promote reform, nurses acted as practitioner-activists in emergency medicine and created the new specialty of forensic nursing to administer the medical forensic exam independent of physicians. Their efforts suggest a new way of conceptualizing the interface of law and medicine: the proactive invocation of criminal law in clinical medicine for the purpose of institutional reform in healthcare organizations, or what I term legal mobilization in medicine. Using the framework of legal mobilization in medicine, I ask: (1) how did nurses mobilize criminal law and rights to health in emergency medicine to facilitate reform? and (2) what were the effects on clinical practice and knowledge production? To chart this history, I draw on a review of published writings by early forensic nurses, interviews with leaders in the field, and ethnographic observation at the 20th anniversary International Association of Forensic Nurses conference in 2012, commemorating the founders and origins of this new specialty. Bringing together scholarship on law and social movements in socio-legal studies and scholarship on health and social movements in science, technology, and medicine studies, I argue that nurses forged a porous boundary between the overlapping institutional spheres of medicine and law in order to align the objectives of medical care and criminal investigation and, thereby, seek rights to healthcare and rights to justice for patientvictims through the enactment of new medical routines. I demonstrate the historical emergence of a novel, hybrid form of professional jurisdiction and medical practice, and I explore its benefits as well as its unintended consequences. I conclude by discussing the ethical implications of this case for the use of medical evidence to corroborate rape.
Keywords: United States | Forensic nursing | Rape kit | Sexual assault medical forensic exam | Legal mobilization in medicine
Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media
بدون نظارت با هر نام دیگری: لایه های پنهان تولید دانش در هوش مصنوعی در رسانه های اجتماعی-2019
Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.
Keywords: Artificial intelligence | machine learning | classification | social media| Facebook | discrimination | bias
Accounting for crime in the neoliberal world
حسابداری برای جرم و جنایت در جهان نئولیبرالی-2019
This paper examines the recent European public sector accounting reform which introduces controversial calculative practices for the recognition of criminal activities in national accounts. Namely, accounting for unlawful drug production and drug trafficking, and accounting for prostitution. Challenging the presumption of accounting neutrality, this study analyses this “accounting for crime” policy from a semantic and an epistemological view point as a cognitive system of creation of meaning and formation of knowledge. The analysis reveals the polyhedrality of neoliberalism, and the way it exerts its influence on society through its circuitous discursive process of social construction and transfiguration of reality which flows crosswise its multiple dimensions. At the macro level this policy operates as a ‘hegemonic project’: It bonds together the economic and political interests of different ‘historical blocs’, making the implementation of these practices a matter of ‘common sense’. At the micro level this policy functions as an ‘apparatus of governmentality’: It encapsulates the cognition of crime within a panoptic logic of economic rationality, transforming its outcome into a contributory value of a countrys prosperity. In this context, this study outlines the centrality of accounting practice as a pivotal tool of the neoliberal ideology: It permits extending the realm of calculative methodologies to the commodification of human weaknesses, addictions, and sexuality, in a rational process of accounting to balance the supply and demand of sex and drugs, between prostitutes and clients, pushers and addicts.
Keywords: Government accounting | Crime | Accounting neutrality | Ideology | Neoliberalism and accounting | Neoliberal discourse | Hegemony | Governmentality
Determinants of the competitive advantage of dairy supply chains: Evidence from the Chinese dairy industry
عوامل تعیین کننده مزیت رقابتی در زنجیره های تامین فراورده های لبنی: شواهدی از صنعت لبنی چینی-2019
In this study, we use an evidence-based approach to examine the factors that determine the competitive advantage of dairy supply chains using evidence from the Chinese dairy industry. We focus on the quality assurance of dairy products, which is considered one of the fundamental influential factors. We investigate interrelationships among the identified determinants, which include dairy production behavior, dairy cow culture model, government regulations, corporate social responsibility, and quality assurance, and examine how these determinants influence the competitive advantage of dairy supply chains. We employ the structural equation modeling approach in which grouped observable variables that represent the identified determinants are extrapolated from primary data collected through a questionnaire survey. Our key findings show that by mediating the effects of dairy production behavior and the dairy cow culture model, government regulation and corporate social responsibility significantly affect the quality assurance of dairy products. In turn, dairy production behavior and the dairy cow culture model significantly affect the competitive advantage of the dairy supply chain via the fully mediated effects of the quality assurance of dairy products. Specifically, the dairy cow culture model helps ensure the safety and quality of milk supply, allowing core dairy firms to control product quality throughout the dairy supply chain. Our empirical study shows that the identified determinants interact to assure the quality of dairy products and enhance the competitive advantage of the dairy supply chain in China.
Keywords: Dairy | Supply chain | Competitive advantage | Quality assurance | Structural equation model