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We introduce the basic concepts of deep learning. We use TensorFlow 2.x, the Google cloud service, and Google Drive interaction to make the concepts come alive with Python coding examples.

This paper investigates the effects of automation and offshoring on the dynamics of the occupational distribution of employment with a focus on Western Europe between 2000 and 2016. I use a general equilibrium model with three regions, three types of workers, ICT capital, trade in final goods and endogenous offshoring. Fed with exogenous measures of ICT-capital prices and trade costs, the model replicates key features of the data. It matches the observed dynamics of offshoring to Eastern Europe and Asian countries. It also reproduces accurately the observed polarization of the labor market: abstract and manual labor increase while routine labor falls. A counterfactual experiment reveals that automation is the main driver of polarization. Since it is also the only factor that drives individuals to become abstract (highskill) workers, it is welfare enhancing. The effects of falling trade costs on labor polarization are smaller, but imply welfare gains.

We introduce you to TensorFlow input pipelines with the tf.data API, which enables you to build complex input pipelines from simple, reusable pieces. Input pipelines are the lifeblood of any deep learning experiment because learning models expect data in a TensorFlow consumable form. It is very easy to create high-performance pipelines with the tf.data.Dataset abstraction (a component of the tf.data API) because it represents a sequence of elements from a dataset in a simple format.

In the previous chapter, we demonstrated how to work with TFDS objects. In this chapter, we work through two end-to-end deep learning experiments with large and complex TFDS objects. The Fashion-MNIST and beans datasets are small with simple images.

Feedforward neural nets are generally great for classification and regression problems. CNNs are great for complex image classification. But activations for feedforward nets and CNNs flow only in one direction, from the input layers to the output layer. Since signals flow in only one direction, feedforward and convolutional nets are not ideal if patterns in data change over time. So we need a different network architecture to work with data impacted by time.

We’ve already leveraged RNNs for NLP. In this chapter, we create experiments to forecast with time series data. We use the famous Weather dataset to demonstrate both a univariate and a multivariate example.

I study criminal street gangs using new data that describes the geospatial distribution of gang territory in Chicago and its evolution over a 15-year period. Using an event study design, I show that city blocks entered by gangs experience sharp increases in the number of reported batteries (6%), narcotics violations (18.5%), weapons violations (9.8%), incidents of prostitution (51.9%), and criminal trespassing (19.6%). I also find a sharp reduction in the number of reported robberies (-8%). The findings cannot be explained by pre-existing trends in crime, changes in police surveillance, crime displacement, exposure to public housing demolitions, reporting effects, or demographic trends. Taken together, the evidence suggests that gangs cause small increases in violence in highly localized areas as a result of conflict over illegal markets. I also find evidence that gangs cause reductions in median property values (-$8,436.9) and household income (-$1,866.8). Motivated by these findings, I explore the relationship between the industrial organization of the black market and the supply of criminal activity. I find that gangs that are more internally fractured or operate in more competitive environments tend to generate more crime. This finding is inconsistent with simple, market-based models of criminal behavior, suggesting an important role for behavioral factors and social interactions in the production of gang violence.

We guide you in the creation of augmented data experiments to increase the diversity of a training set by applying random (but realistic) transformations. Data augmentation is very useful for small datasets because deep learning models crave a lot of data to perform well.

Between 1909 and 1922 a private deposit insurance company coexisted with the state-sponsored deposit insurance program in Kansas. This paper documents its development using primary sources. In addition, it examines if affiliation with the private deposit insurance (i) had an effect on risk-taking and the probability of failure; (ii) increased confidence among depositors, and (iii) was influenced by a neighboring bank’s membership in the state’s deposit insurance. We find that affiliation with the private deposit insurance did not affect a bank’s likelihood of failure, although smaller national bank members did increase risk-taking. The evidence does not support the hypothesis that the company enhanced depositor confidence. Lastly, we do find strong evidence that a bank’s decision to join the private deposit insurance was influenced by neighboring banks’ affiliation with the Kansas deposit insurance program.

Reinforcement learning (RL) is an area of machine learning that focuses on teaching intelligent agents how to take actions in an environment in order to maximize cumulative reward. Cumulative reward in RL is the sum of all rewards as a function of the number of training steps.

Autoencoders don’t typically work well with images unless they are very small. But convolutional and variational autoencoders work much better than feedforward dense ones with large color images.

With feedforward neural networks, we achieved good training performance with MNIST and Fashion-MNIST datasets. But images in these datasets are simple and centered within the input space that contains them. That is, they are centered within the pixel matrix that holds them. Input space is all the possible inputs to a model.

We work through a complete deep learning example with Python’s TensorFlow 2.x library in the Google Colab cloud service. We also demonstrate how to link your Google Drive with the Colab cloud service.

Technology can help to reduce inefficiencies and inequalities in developing countries. Although whether this happens in practice often depends on the social and geographic context. Deployment of solar home systems (SHS) across Nepal provides one such example. We examine a policy to promote SHS through a subsidy for adoption and its downstream benefits - children’s education, time allocated to agricultural and household work (both unpaid), and working for a wage. We overcome the challenges of the non-random assignment of subsidies by using geographic regression discontinuity design using (cost) distance as the assignment variable. This is based on the assumption that households cannot perfectly control the assignment variable because migrating to a different region, simply to acquire an additional solar subsidy of maximum NRs4000, is challenging and unlikely in the case of Nepal. Our results show that subsidy-eligible households are about 43% more likely to adopt SHS. We find a positive effect of the SHS adoption on the grade for age and educational z-score of children’s educations with the effect being higher on girls than boys. We also find that females are 14.2% more likely to participate and spend more time (about 19 extra days in a year) in agricultural practices by reducing their engagement in family work. However, there is no effect on the participation rates across the labour market. Results also show that males increase their contribution to household work.

Transfer learning is the process of creating new learning models by fine-tuning previously trained neural networks. Instead of training a network from scratch, we download a pre-trained open source learning model and fine-tune it for our own purpose. A pre-trained model is one that is created by someone else to solve a similar problem. We can use one of these instead of building our own model. A big advantage is that a pre-trained model has been crafted by experts, so we can be confident that it performs at a high level (in most cases). Another advantage is that we don’t have to have a lot of data to use a pre-trained model.

Generative modeling is an unsupervised learning technique that involves automatically discovering and learning the regularities (or patterns) in input data so that a trained model can generate new examples that plausibly could have been drawn from the original dataset. A popular type of generative model is a generative adversarial network. Generative adversarial networks (GANs) are generative models that create new data instances that resemble the training data.

We introduce you to Tensor Processing Units with code examples. A Tensor Processing Unit (TPU) is an application-specific integrated circuit (ASIC) designed to accelerate ML workloads. The TPUs available in TensorFlow are custom-developed from the ground up by the Google Brain team based on its plethora of experience and leadership in the ML community. Google Brain is a deep learning artificial intelligence (AI) research team at Google who research ways to make machines intelligent for the improvement of people’s lives.

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this paper was produced within the framework of the ESC Prevention of Cardiovascular Disease Programme which is led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP). The ESC Prevention of Cardiovascular Disease Programme is supported by unrestricted educational grants. The authors received no financial support for the research, authorship, and/or publication of this article. Rossello, X (corresponding author), Ctr Nacl Invest Cardiovasc CNIC Carlos III, Melchor Fernandez Almagro 3, Madrid 28029, Spain. fjrossello@cnic.es

Now that we have practiced tuning low-dimensional (or simple) data, we are ready to experiment tuning high-dimensional (or complex) data sets. Low-dimensional data consists of a limited number of features, whereas high-dimensional data consists of a very high number of features.

We combine the Anaconda distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised machine learning algorithms supplemented with unsupervised learning algorithms where appropriate. With clear examples, all written in Python, we demonstrate how these algorithms work to solve machine learning problems.

Classification from complex data is handled exactly as with simple data. Data is loaded into feature set X and target y. X data is composed of a matrix of vectors where each vector represents a data element and y data is composed of a vector of targets. However, complex data is composed of a high number of features (hundreds to thousands). Such a data set is commonly referred to as one with a high-dimensional feature space. Text data is also complex because each document must be converted into vectors of numerical values suitable for machine learning algorithms.

Summary Background Myocardial injury after non-cardiac surgery (MINS) increases the risk of cardiovascular events and deaths, which anticoagulation therapy could prevent. Dabigatran prevents perioperative venous thromboembolism, but whether this drug can prevent a broader range of vascular complications in patients with MINS is unknown. The MANAGE trial assessed the potential of dabigatran to prevent major vascular complications among such patients. Methods In this international, randomised, placebo-controlled trial, we recruited patients from 84 hospitals in 19 countries. Eligible patients were aged at least 45 years, had undergone non-cardiac surgery, and were within 35 days of MINS. Patients were randomly assigned (1:1) to receive dabigatran 110 mg orally twice daily or matched placebo for a maximum of 2 years or until termination of the trial and, using a partial 2-by-2 factorial design, patients not taking a proton-pump inhibitor were also randomly assigned (1:1) to omeprazole 20 mg once daily, for which results will be reported elsewhere, or matched placebo to measure its effect on major upper gastrointestinal complications. Research personnel randomised patients through a central 24 h computerised randomisation system using block randomisation, stratified by centre. Patients, health-care providers, data collectors, and outcome adjudicators were masked to treatment allocation. The primary efficacy outcome was the occurrence of a major vascular complication, a composite of vascular mortality and non-fatal myocardial infarction, non-haemorrhagic stroke, peripheral arterial thrombosis, amputation, and symptomatic venous thromboembolism. The primary safety outcome was a composite of life-threatening, major, and critical organ bleeding. Analyses were done according to the intention-to-treat principle. This trial is registered with ClinicalTrials.gov, number NCT01661101. Findings Between Jan 10, 2013, and July 17, 2017, we randomly assigned 1754 patients to receive dabigatran (n=877) or placebo (n=877); 556 patients were also randomised in the omeprazole partial factorial component. Study drug was permanently discontinued in 401 (46%) of 877 patients allocated to dabigatran and 380 (43%) of 877 patients allocated to placebo. The composite primary efficacy outcome occurred in fewer patients randomised to dabigatran than placebo (97 [11%] of 877 patients assigned to dabigatran vs 133 [15%] of 877 patients assigned to placebo; hazard ratio [HR] 0·72, 95% CI 0·55–0·93; p=0·0115). The primary safety composite outcome occurred in 29 patients (3%) randomised to dabigatran and 31 patients (4%) randomised to placebo (HR 0·92, 95% CI 0·55–1·53; p=0·76). Interpretation Among patients who had MINS, dabigatran 110 mg twice daily lowered the risk of major vascular complications, with no significant increase in major bleeding. Patients with MINS have a poor prognosis; dabigatran 110 mg twice daily has the potential to help many of the 8 million adults globally who have MINS to reduce their risk of a major vascular complication. Funding Boehringer Ingelheim and Canadian Institutes of Health Research.

Faced with limited resources, nurse educators are challenged with transforming nursing education while preparing enough qualified nurses to meet future demand; therefore, innovative approaches to teaching are needed. In this article, we describe the development of an innovative teaching activity. Baby Boy Jones is a Web-delivered, case-based learning activity focused on neonatal infection. It was created using e-learning authoring software and delivered through a learning management system.

Background and Purpose: No scales measure self-efficacy in women with Type 2 diabetes. A scale was developed and tested. Methods: Items generated, content validity index (CVI) assessed by experts, the 2-part Diabetes Self-Efficacy Scale (DSLF-I and DSLF-II) was piloted with 62 women, administered to 208 women, and then readministered to 30 women to determine initial reliability. Factor analysis was conducted for construct validity. Discriminant, convergent, and predictive validity was examined. Results: The CVI index was 98%. Cronbach's alphas were 0.88 (DSLF-I) and 0.82 (DSLF-II; pilot) and 0.87 and 0.86, respectively (main study); test-retest correlation was .60 (DSLF-I) and .69 (DSLF-II). There were 3 factors that emerged: diabetes knowledge of self-care activity, diabetes diet self-care, and diabetes medication self-care. Conclusions: The Diabetes Self-Efficacy Scale demonstrates good initial reliability and validity.Keywords: instrument development; self-efficacy; diabetes mellitus Type 2; reliability; validity; factor analysisDiabetes mellitus affects 8.3% of the population in the United States, or 25.8 million people. More than 7 million people are estimated to be undiagnosed and unaware they have the disease (Center for Disease Control and Prevention [CDC], 2011). Approximately 90%-95% of persons with diabetes have Type 2 diabetes mellitus (T2DM). The San Antonio Heart Study revealed a rising incidence of T2DM, with age, ethnicity, and neighborhood as significant predictors of T2DM (Burke et al., 1999).Although T2DM affects all ethnic and gender groups, there is a disparate effect. For example, African Americans are 1.6 times more likely to have diabetes than Whites and are more likely to go undiagnosed American Diabetes Association (ADA, 2005a). Complications from T2DM are also more prevalent in minority populations (Carter, Pugh, & Monterosa, 1996). In addition, diabetic women, compared to diabetic men, have a significantly higher prevalence of cardiovascular disease and suffer greater adverse outcomes (D'Arrigo, 1999; Gu, Cowie, & Harris, 1999; Howard et al., 1998; Smitherman & Reis, 1997). Further, Sowers (1998) reported that the gender protectiveness for coronary heart disease (CHD) declines in women between the ages of 50 and 59 years, and that the loss of gender protectiveness increases the risk ratio for CHD to 3.5 for women with diabetes compared to 2.4 for men with diabetes (p. 618). Koerbel and Korytkowski (2003) stated that women with diabetes not only have greater risk for cardiovascular disease but they also suffer greater adverse outcomes. They cite several factors, which increase this risk in women: poorer glycemic control, elevated blood pressure and lipids, central obesity, increased depression and lower socioeconomic status. These factors contribute to increased morbidity and health care costs for women with diabetes. These data support the need for more research into prevention, detection, and cost-effective methods to effectively manage T2DM and prevent its complications in women.Maintenance of health and prevention of diabetic complications require significant changes in lifestyle management and adherence to a therapeutic diabetic regimen. Behavior change and adherence are not only difficult but are also often inadequate to maintain recommended glycemic control (Anderson, Fitzgerald, & Oh, 1993; Skelly, Marshall, Haughey, Davis, & Dunford, 1995). The importance of participation in diabetic self-care behavior in achieving glycemic control has been demonstrated by the Diabetes Control and Complications Trial Research Group (1993) for individuals with Type 1 diabetes mellitus (T1DM) and by the United Kingdom Prospective Diabetes Study Group (1998) for individuals with T2DM. This goal is accomplished through a therapeutic alliance among clients, family, and health care providers to promote self-care behaviors and therapeutic modalities (ADA, 2005b). Self-care behaviors include a combination of changing or modifying eating habits, maintaining weight or achieving weight loss, participating in a physical exercise program, self-monitoring of blood glucose, and/or taking oral or injectable medication. …

Electronic-commerce has become an important channel for conducting business. Researchers as well as market executives are trying to better understand online consumer behavior. One model used by researchers to understand behavior in the information systems field in general is the technology acceptance model (TAM). The TAM variables are perceived usefulness, perceived ease of use, and intentions. In this study, we suggest the extension of the TAM for its application in the E-commerce field. The original TAM will be extended, by adding four predictor variables. The four predictor variables are process satisfaction, outcome satisfaction, expectations, and E-commerce use. In addition, the TAM will be extended by measuring actual behavior as opposed to measuring intentions as a substitute for actual behavior in previous TAM application studies. We suggest measuring actual use variable in terms of four criterion variables, namely, purchase, access number, access total time, and access average time. The extended TAM is expected to better explain actual behavior in E-commerce environments than the original TAM.

Resettlement can be an uncertain time for refugees as they have often suffered life-threatening circumstances prior to flight from their countries, yet few resettlement programs screen for mental health. The purpose of this study was to pilot the Refugee Health Screener-15 (RHS-15) to assess mental health and the Pathways to Wellness intervention to identify internal and structural barriers affecting resettlement with a refugee women's sewing group. Community collaborations that create healthy social and physical environments through access to resources, economic opportunities, and social support promote a holistic approach to health and can improve quality of life for this vulnerable population.

Hypotension is type of secondary insult and it is related to poor outcome. The ability to predict adverse hypotensive events, where a patient’s arterial blood pressure drops to abnormally low levels, would be of major benefit to the fields of primary and secondary health care. The aim of the paper is to present the novel method for predicting of acute hypotensive episodes, based on ECG analysis by the complex system theory approach. 45 patients (in four neurointensive care facilities throughout Europe) data were selected for the analysis. 11 patients had EUSIG-defined hypotensive events. The method includes determining of time varying biomarkers corresponding to plurality of physiological processes in patient’s organism as a non-linear dynamic complex system and generating an acute hypotension prediction classifier. The calculations of biomarkers are based on complex system approach and algebraic matrix analysis of ECG parameters. The classifier is based on the comparison of biomarkers behaviour in 3D images. It is demonstrated that the presented method allows us to predict arterial hypotension events 40-50 minutes ahead with a sensitivity of 81 %, specificity 94 %. This result was obtained from prospective real-time data collection in a live clinical intensive care environment.

PurposeThe purpose of this study is to explore the attitudes and beliefs of front‐line employees regarding how customer‐company interactions might be improved. Since front‐line employees are closely connected to customers, the resultant experiences and relationships with customers are highly dependent on their actions. However, little is written from the perspective of front‐line employees.Design/methodology/approachUsing a qualitative approach and employing an adaptation of the grounded theory method, this study uses front‐line employee participants with data collection occurring over a six‐month period. A variety of data sources were used including open‐ended reciprocal interviews, member‐checking interviews, observation, and collection of participant and researcher artifacts.FindingsFindings suggest that organizational changes initiated by management create strategically constructed silos that force implementation through people and systems to control and standardize the service interface and resultant customer experience(s). This reactive and inwardly focused construction of silos often serves company rather than customer needs and can result in deteriorating service levels based on the type of customer‐company interaction occurring.Originality/valueContrary to how most customer service processes are designed by management, not all customer‐company interactions are alike and vary both in regard to the degree of knowledge needed by front‐line employees to fully serve customers, and the routine or non‐routine nature of the interaction. A two‐by‐two matrix is presented to highlight the impacts of silos occurring for various interaction types.

PurposeA paucity of literature considers a growing trend within the retail space whereby franchise companies and their franchisees market and sell products and services across multiple channels, including company‐owned retail stores. This case study aims to explore the processes used to support the customer experience, the control mechanisms that are in place, and the channels by which these customer‐company interactions occur.Design/methodology/approachA qualitative approach employing an adaptation of the grounded theory method for data collection, coding, and analysis was used and this study specifically focused on an international van‐based service franchise during the integration of the franchise company's service into the retail brick‐and‐mortar locations of the parent company. Participants included retail employees of the parent company, franchise company support staff, franchisees, and third‐party call center agents working for the parent company.FindingsFindings suggest a relationship exists between the alignment of the internal factors of the customer relationship management (CRM) experience (e.g. people, processes, and technology) and the relative strength or weakness of each external factor (e.g. customer, company, and competition). Moreover, it is postulated that weaker customer‐centric service results in greater misalignment of internal factors and leads to larger service variability, or sub‐optimized CRM.Originality/valueThe unique contribution of this research is the juxtaposition of the disparate marketing approaches of the parent company and franchisees and the subsequent impact on CRM efforts of the company. A conceptual model of internal and external factors of the CRM experience is presented.

Purpose – A paucity of research on the motivations and goals of vendors who develop electronic customer relationship management (eCRM) tools and systems exists in the literature. This is an important gap in the literature because many eCRM systems are products of outsourcing (to vendors). This paper aims to discuss these issues.Design/methodology/approach – This exploratory case study focuses on an international van‐based service franchise and the experiences of the vendor, company, and franchisees as they design, develop, and implement certain eCRM tools. Participants include the president and founder of the eCRM vendor company, the chief executive officer, chief financial officer, and marketing director from the franchisor company, and six franchisees. This paper is triangulated via personal interviews, member‐checking interviews, and relevant literature from information systems (IS) and marketing.Findings – Specifically, this paper sheds light on the role and influence of the vendor in the design, deve...

(2009). Customer Relationship Management (CRM): An Approach for Transforming the ’Myth’ of CRM Success into Dual-Creation of Value. Journal of Information Technology Case and Application Research: Vol. 11, No. 3, pp. 1-8.

Introduction In many ways, the Internet has changed the way we conduct our daily activities. Online shopping, social networking, and 'googling' for information are just a few examples of a new door that has been opened through the use of the Internet as the electronic portal to the world. For customers, easier availability of new products, access to more options, and the greatly expanded competitive landscape brought about by the Internet can all work together in a positive manner to provide greater choice, value, and pricing flexibility in many cases. For merchants, this same access to a world of new customers is of great potential benefit, but with it comes a world of new competitors. To survive and prosper in such a competitive environment, many marketers have recognized that, while vast opportunity may exist to attract new customers to their offerings, real value rests in keeping existing customers loyal to a brand. For many, the idea of "get there first and don't give them a reason to switch" is as true today as it was in the 1960s when more sophisticated models of marketing and brand management began to emerge. The idea that the value of existing customers is greater than that of new customers is not new since it is well understood and recognized that the cost of acquiring new customers far exceeds the cost of keeping existing ones (Berry, 1995; Crafton, 2002; Cuthbertson & Bridson, 2006; Gronoos, 1994; Gummesson, 1994; Kennedy, 2006; Li, Browne, & Wetherbe, 2007). Over the past decade or so, the idea of managing customer relationships has been a top priority for marketers worldwide. Customer relationship management (CRM), the idea of fine-tuning the ways in which firms interact with customers, has required energy, focus, and investment in new tools and software to aid in the development of CRM systems. With the advent of the Internet, this idea has been extended into a Web-based business model and relies on e-business technologies to interact with, communicate to, and collect information from customers. This new evolved model is aptly termed electronic customer relationship management (eCRM). While eCRM is considered by many to hold even greater promise for business due to its digital foundation that enables greater data mining potential, it has yet to consistently deliver on its promise of facilitating better understanding of customer behavior (Adamson, Jones, & Tapp, 2005; Bentum & Stone, 2005; Q. Chen & Chen, 2004; Fjermestad & Romano, 2003; Jang, Hu, & Bai, 2006; Li et al., 2007; Lin & Huang, 2007). The very nature of eCRM, with its many stakeholders, helps to illuminate the layers of complexity and their meaning with regard to what is intended by the idea of "informing" (Cohen, 2009). This complexity can be best understood by comparing eCRM to more "routine" informing systems which "tend to be driven by the skills of the designer" (Gill, 2009, p. 743). In contrast, the eCRM system central to this exploratory case study appears to be ever-changing as the interactions and needs of the varying stakeholder groups emerge. For example, "eCRM is too complex a concept to be comprehensively researched and efficiently classified merely by usual procedure and simple criteria" (Kevork & Vrechopoulos, 2009, p. 62). Yet, research in this area rarely considers the views of all stakeholders involved in the phenomenon thereby rendering the complexity of eCRM into a seemingly routine construct. To effectively bridge this research divide requires consideration for all players involved in the phenomenon. It is the intent to inform all players that guides this research. Therefore, we embarked on an exploratory case study focused on a van-based service franchise and motivated by the need to understand the perceptions and attitudes of all key stakeholders regarding the objectives of and motivations for implementing an eCRM program. The results of this study reveal important implications for the informing sciences including the need to balance human and computer interaction, issues related to control, the impact that "electronic" systems may have on marketing relationships, and the importance of balancing the interests and expectations of all stakeholders. …

(2008). Change Management: A Sensible Approach for Information Technology Researchers. Journal of Information Technology Case and Application Research: Vol. 10, No. 3, pp. 1-8.

Membranous nephropathy is a common cause of nephrotic syndrome in adults. Although some patients with membranous nephropathy achieve a spontaneous remission, renal function continues to deteriorate in others. We conducted a prospective randomized trial evaluating monotherapy with tacrolimus to achieve complete or partial remission in patients with biopsy-proven membranous nephropathy. Twenty-five patients received tacrolimus (0.05mg/kg/day) over 12 months with a 6-month taper, whereas 23 patients were in the control group. The probability of remission in the treatment group was 58, 82, and 94% after 6, 12, and 18 months but only 10, 24, and 35%, respectively in the control group. The decrease in proteinuria was significantly greater in the treatment group. Notably, six patients in the control group and only one in the treatment group reached the secondary end point of a 50% increase in their serum creatinine. No patient in the tacrolimus group showed a relapse during the taper period. Nephrotic syndrome reappeared in almost half of the patients who were in remission by the 18th month after tacrolimus withdrawal. We conclude that tacrolimus is a very useful therapeutic option for patients with membranous nephropathy and preserved renal function. The majority of patients experienced remission with a significant reduction in the risk for deteriorating renal function.

Summary Recently we demonstrated that in inflammatory bowel disease (IBD) macrophage-oxidative burst activity is increased and NADPH oxidase mRNA is induced. The herbal phenylethanoid acteoside isolated from Plantago lanceolata L. was shown to exhibit anti-oxidative potential. Using the dextran sulphate sodium (DSS)-induced colitis model, in this study we have assessed whether systemic application of acteoside affects colitis. Colitis was induced by DSS in Balb/c mice. Treatment with acteoside (120, 600 µg/mouse/day) was performed intraperitoneally. The colon lengths were determined. Colonic tissue was scored histologically (max. score 8) by a blinded investigator. T cells isolated from mesenteric lymph nodes (MLN) were stimulated with anti-CD3 antibody in the presence of interleukin (IL)-2 (final concentration 10 U/ml). After incubation for 24 h, IL-1β, IL-6, IL-12 tumour necrosis factor (TNF)-α and interferon (IFN)-γ levels in supernatants were analysed by the beadlyte® cytokine detection system. Histological scoring of colonic tissue revealed that application of acteoside was followed by a significantly improved histological score. In acute colitis the histological score was 3·2 with acteoside versus 5·2 with phosphate-buffered saline (PBS) (P

The case study describes a small travel company that conducts business exclusively on the Internet. The case elucidates the many problems that the company faced as it tried to survive in a turbulent environment. Survival hinged on the company’s ability to strategically and tactically execute a business plan based on the technical acumen, loyalty, and hard work of its small group of talented people. The technical alternatives are described and explained within the context of a travel company seeking to use and develop new technology to improve productivity and increase profits. Additionally, the strategic need for market flexibility, adaptability, and diversification, especially in times of unexpected events and or problems, are examined. Finally, the company’s process for controlling costs are identified and explored.

Workflow research is commonly concerned with optimization, modeling, and dependency. In this research, we however address a more fundamental issue. By modeling humans and machines as agents and making use of a theoretical computer and statecharts, we prove that many workflow problems do not have computer-based solutions. We also demonstrate a sufficient condition under which computers are able to solve these problems. We end by discussing the relationships between our research and Petri Nets, the multi-agent framework in the literature, linear programming and workflow verification.

in: III WOCMAP Congress on Medicinal and Aromatic Plants - Volume 4: Targeted Screening of Medicinal and Aromatic Plants, Economics and Law

This case study documents an organizational change intervention concerning the implementation of a novel information technology at a university-owned research foundation (URF). It evidences the disparate expectations and reactions by key actors toward the change event, marking a mismatch between a new paradigm required by the new technology and existing information technology practices. Drawing upon change management and management information systems (MIS) literature, we discuss the perceived change management issues hindering the change process at URF. Our discussion is tempered by a theoretical lens that attempts to integrate the literature bases drawn upon in this research. In particular, resistance from in-house IT specialists was observed as the strongest force obstructing the novel IT implementation. This study offers a forum to stimulate both researchers and practitioners to rethink the necessary elements required to enact change, especially with respect to novel IT implementations.

In this article, we explore the organizational process change dynamic through a theoretical lens of business process reengineering (BPR) models. We review relevant literature related to such models to devise a synthesized model of BPR. The synthesized model facilitates the identification of success factors for BPR. Results from in-depth case-study research add explanatory power to our model. It is hoped that our model and subsequent success factors will offer insights to help organizations effectively manage change and transformation.

The commercial potential for voice technology innovations currently being developed by the U.S. Navy is immense.

This study investigated the fit between contingency variables (e.g. task uncertainty and decentralization) in accounting information systems (AIS); a good fit is assumed to improve user satisfaction. We divided task uncertainty into two dimensions: variability and analyzability. We chose information scope, timeliness, and aggregation as the AIS characteristics to be studied within each dimension. User satisfaction was adopted as a surrogate to measure the performance of AIS. The responses of 126 managers were drawn from a cross-industry sample in Taiwan. Multiple regression models were employed to test the hypotheses. The results of the empirical test led us to two results: (1) broad-scope information (external environment or future-oriented information) promotes user satisfaction in high task variability situations, and (2) in a highly decentralized organization, broad-scope, timely, and aggregated information will also facilitate user satisfaction.

The failure of dot-coms is a staggering 75% in the first two years. Many of these failures were small and medium-sized enterprises (SMEs). One of the most commonly cited reasons for failure has been lack of a workable strategic business model to guide e-commerce (electronic commerce) efforts. We began this study because we wanted to explore if successful e-commerce SMEs use a guiding strategic process model, and if they do, ask them to articulate the model components. We chose a qualitative research method to obtain a rich description of the e-commerce process. The interview pool consisted of 15 interviews with established SME e-commerce entrepreneurs. From deep analysis of the data, a set of e-commerce process components and interrelationships between those components emerged from the data. We then synthesized (from the rich description) a model of the e-commerce process.

In this paper, we model business workflows using Harel’s statecharts. Mapping to statecharts allows us to systematically identify potential workflow problems. It also allows us to investigate specific properties inherent in actual business workflows. Our research focuses on three desirable properties of active database systems — termination, confluence, and observable determinism. For termination and confluence, we develop algorithms to provide a theoretical lens linking desirable active database system properties to workflow management systems problems. We initially validate our algorithms by mapping business workflows from a case study. Our research thus builds preliminary theory by developing a systematic method for identifying workflow problems.