Purpose Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria. Design/methodology/approach A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field. Findings The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM. Originality/value This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.
Article Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach was published on August 1, 2021 in the journal Computer Law Review International (volume 22, issue 4).
Recent years have seen a reemergence of interest in artificial intelligence (AI) among both managers and academics. Driven by technological advances and public interest, AI is considered by some as an unprecedented revolutionary technology with the potential to transform humanity. But, at this stage, managers are left with little empirical advice on how to prepare and use AI in their firm’s operations. Based on case studies and the results of two global surveys among senior managers across industries, this article shows that AI is typically implemented and used with other advanced digital technologies in firms’ digital transformation projects. The digital transformation projects in which AI is deployed are mostly in support of firms’ existing businesses, thereby demystifying some of the transformative claims made about AI. This article then presents a framework for successfully implementing AI in the context of digital transformation, offering specific guidance in the areas of data, intelligence, being grounded, integrated, teaming, agility, and leadership.
We believe that a key to demystifying architecture products lies in the architecture decisions concept. We can make the architecture more transparent and clarify its rationale for all stakeholders by explicitly documenting major architecture decisions.
Previous articleNext article FreeDemystifying the Chinese Housing BoomHanming Fang, Quanlin Gu, Wei Xiong, and Li-An ZhouHanming FangUniversity of Pennsylvania and NBER Search for more articles by this author , Quanlin GuGuanghua, School of Management, Peking University Search for more articles by this author , Wei XiongPrinceton University and NBER Search for more articles by this author , and Li-An ZhouGuanghua, School of Management, Peking University Search for more articles by this author University of Pennsylvania and NBERGuanghua, School of Management, Peking UniversityPrinceton University and NBERGuanghua, School of Management, Peking UniversityPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreThere have been growing concerns across the global economic and policy communities regarding the decade-long housing market boom in China, which has the second largest economy in the world, and has been the major engine for global economic growth during the past decade. News in recent months seems to suggest that the housing boom might be slowing down. A main concern is that a housing market meltdown might severely damage the Chinese economy, which in turn might generate contagious effects across the world and slow down the fragile global economy that has just emerged from a series of crises that originated in the United States and Europe. In particular, critics are concerned that soaring housing prices and the enormous construction boom throughout the country might cause China to follow in the footsteps of Japan, which had an economic lost decade after its housing bubble burst in the early 1990s.How much have housing prices in different Chinese cities appreciated during the last decade? Did the soaring prices make housing out of the reach for typical households? How much financial burden did households face in buying homes? Addressing these questions is crucial for systematically assessing the risk to the Chinese economy presented by its housing market. We address these questions by taking advantage of a comprehensive data set of mortgage loans issued by a major Chinese commercial bank from 2003 to 2013. Specifically, we construct a set of housing price indices for 120 major cities in China, which allows us to evaluate housing price fluctuations across these cities, in conjunction with the growth of households’ purchasing power and stock price fluctuations. The detailed mortgage data also allow us to analyze the participation of low-income households in housing markets and the financial burdens faced by low-income home buyers.Due to the nascent nature of the Chinese housing market, there are relatively few repeat home sales available for building Case-Shiller type repeated sales housing indices. Instead, we take advantage of the large number of new housing developments in each city and build a housing price index for the city based on sales over time of new homes within the same developments, which share similar characteristics and amenities. Consistent with casual observations made by many commentators, our price indices confirm enormous housing price appreciation across China in 2003–2013. In first-tier cities, which include the four most populated and most economically important metropolitan areas in China—Beijing, Shanghai, Guangzhou, and Shenzhen—housing prices had an average annual real growth rate of 13.1% during this decade. Our sample also covers 31 second-tier cities, which are autonomous municipalities, provincial capitals, or vital industrial/commercial centers, and 85 other third-tier cities, which are important cities in their respective regions. Housing prices in second-tier cities had an average annual real growth rate of 10.5%; third-tier cities had an average annual real growth rate of 7.9%. These growth rates easily surpass the housing price appreciation during the US housing bubble in the first decade of the twenty-first century and are comparable to that during the Japanese housing bubble in the 1980s.Despite the enormous price appreciation, the Chinese housing boom is different in nature from the housing bubbles in the United States and Japan. Our analysis offers several important observations that are useful for understanding the Chinese housing boom. First, as banks in China imposed down payments of over 30% on all mortgage loans, banks are protected from mortgage borrowers’ default risk even in the event of a sizable housing market meltdown of 30%. This makes a USstyle subprime credit crisis less likely in China.Second, while the rapid housing price appreciation has been often highlighted as a concern for the Chinese housing market, the price appreciation was accompanied by equally spectacular growth in households’ disposable income—an average annual real growth rate of about 9.0% throughout the country during the decade, with the exception of a lower average growth rate of 6.6% in the first-tier cities. This joint presence of enormous housing price appreciation and income growth contrasts the experiences during the US and Japanese housing bubble. Even during the Japanese housing bubble in late 1980s, the Japanese economy was growing at a more modest rate than that of China. The enormous income growth rate across Chinese cities thus provides some assurance to the housing boom and, together with the aforementioned high mortgage down-payment ratios, renders the housing market an unlikely trigger for an imminent financial crisis in China.Third, despite the enormous housing price appreciation over the decade, the participation of low-income households in the housing market remained stable. Specifically, we analyze the financial status of mortgage borrowers with incomes in the bottom 10% of all mortgage borrowers in each city for each year. By mapping the incomes of these marginal home buyers into the income distribution of the urban population in the city, we find that they came from the low-income fraction of the population, roughly around the 25th percentile of the distribution in the first-tier cities and around the 30th percentile in the second-tier cities.Fourth, while these low-income home buyers were not excluded from the housing market, they did endure enormous financial burdens in buying homes at price-to-income ratios of around eight in second- and third-tier cities and, in some years, even over 10 in first-tier cities. In concrete terms, this means that a household paid eight times its annual disposable income to buy a home. In order to obtain a mortgage loan, it had to make a down payment of at least 30%, and more typically 40%, of the home price, which was equivalent to 2.4 times to 3.2 times the household’s annual income. Suppose that the household made a down payment of 40% and took a mortgage loan for the other 60% of the home price, which would be 4.8 times its annual income. A modest mortgage rate of 6%, which is low relative to the actual rate observed during the decade, would require the household to use nearly 30% of its annual income to pay for the interest on the mortgage loan. Furthermore, paying the mortgage would consume another 16% of its annual income using a linear amortization, even if the mortgage had a maximum maturity of 30 years. Together, buying the home entailed saving 3.2 times the annual household income to make the down payment and another 45% of its annual income to service the mortgage loan.To explain the willingness of households to endure such severe financial burdens for a home, it is important to take into account the households’ expectations. To the extent that urban household income in China has been rising steadily during the studied period, as well as in the previous two decades, many households may expect their income to continue growing at this rate. At a 10% nominal income growth rate, a household’s income in five years would grow to 1.6 times of its initial income and the ratio of current housing price to its future income in five years would drop to five. Thus, a high expected income growth rate renders the aforementioned financial burdens temporary.Such high income growth expectations might have resulted from extrapolative behavior as emphasized by Barberis, Shleifer, and Vishny (1998) and Shiller (2000), or from contagious social dynamics between households as modeled by Burnside, Eichenbaum, and Rebelo (2013). Recently, Pritchett and Summers (2014) examined historical data on growth rates and demonstrated that regression to the mean is the single-most robust and empirically relevant fact about cross-country growth rates. Thus, they argue that while China might continue to grow for another two decades at a 9, or even a 7 or 6% rate, such continued rapid growth rate would be an extraordinary event, given the powerful force of regression to the mean, which had averaged 2% in the cross-country data with a standard deviation of 2%. If so, the high expectation of future income growth, which might have been a key driver of the observed enormous price-to-income ratios, may not be sustainable and thus presents an important source of risk to the housing market. When China’s growth rate eventually regresses to the mean, and especially when China experiences a sudden stop, households’ expectations may crash. In such a case, the large price-to-income ratios have substantial room to contract, which in turn could act as an amplifier of the initial shock that triggers the economic slowdown.Frictions in the Chinese financial system might also have contributed to the high housing prices across Chinese cities, as reflected by the large price-to-income ratios endured by households. It is well known that the spectacular economic growth in China since the 1980s has been accompanied by a high savings rate (e.g., Yang, Zhang, and Zhou 2013). Due to stringent capital controls, savers cannot invest their savings in international capital markets and, instead, have only a few domestic investment vehicles. Bank deposit accounts have remained the predominant investment vehicle, with assets totaling near 100 trillion RMB in 2013, despite the fact that the real one-year deposit rate averaged only 0.01% in 2003–2013. While the Chinese stock market experienced dramatic growth during this decade, it was still relatively small, with a capitalization of slightly less than 20 trillion RMB in 2013. The size of bond markets was even smaller. Facing this largely constrained investment set, it has been common for households to treat housing as an alternative investment vehicle, which also helps explain their willingness to pay dearly for housing.From an investment perspective, it is interesting to note a tale of two markets at the time of the world economic crisis in 2008–2009. During this period, the Chinese economy faced tremendous pressure. Nevertheless, the housing market in China remained strong. Housing prices in first-tier cities suffered a modest drop of about 10%, and recovered more than the loss shortly after the crisis. Housing prices in second- and third-tier cities continued to rise throughout the period after 2008. This experience was in sharp contrast to the dramatic decline of over 60% in the Chinese stock market in 2008—which has not recovered, even to date. To understand this puzzling contrast, we argue that the frequent policy interventions by the central government and the heavy reliance of local governments on land sales revenue for their fiscal budget might have emboldened many households to believe that the housing market is too important to fall and that the central government would institute policies to support the housing market if necessary.There are divergent views about the Chinese housing boom. Chow and Niu (2014) use a simultaneous equations framework to analyze the demand and supply of residential housing in urban China in 1987–2012 and find that the rapid housing price growth can be well explained by the force of demand and supply, with income determining demand and construction costs affecting supply. Deng, Gyourko, and Wu (2014a) are far more concerned by the risk in the Chinese housing market. In particular, they present evidence of a rapid increase in housing supply and housing inventory held by developers in various major cities in recent years. Different from these studies, we provide an informed account of the demand side by thoroughly analyzing characteristics of mortgage borrowers. Our analysis leads us to take a more balanced stand between these two contrasting views. On the comforting side, the rapid income growth, which accompanied the enormous housing price appreciation, helped support the steady participation by low-income households in the housing market. On the concerning side, high expectations about future income growth might have motivated low-income households to buy homes by undertaking substantial financial burdens, causing them to be particularly vulnerable to future sudden stops in the Chinese economy.This paper is organized as follows. Section I briefly describes some institutional background. We introduce the housing price indices in Section II and then discuss the housing price boom across three tiers of cities in Section III. Section IV summarizes characteristics of mortgage borrowers, and Section V discusses housing as an investment vehicle. Section VI provides some conceptual discussion. We summarize the role of government in Section VII and discuss several sources of risk in Section VIII.I. Institutional BackgroundThe development of housing markets in mainland China is a relatively new phenomenon. From the 1949 founding of the People’s Republic of China to 1978, all land was publicly owned and the Chinese constitution prohibited any organization or individual from buying, selling, leasing, or transferring land. Housing was allocated through a working unit-employee linkage as a form of in-kind compensation, with the size and location of homes depending on the length of employment and the size of the household, among other factors. In 1978, per capita residential area in urban areas was 3.6 square meters, which was even lower than that in 1949.To reform (and to a large extent privatize) the state-owned enterprises in the mid-1980s, it was considered necessary to introduce an alternative housing system that would delink home allocation from employment. An important milestone occurred in 1988 when the Chinese constitution was amended to allow for land transactions, which set the legal stage for the privatization of housing in China.1Comprehensive housing reform was initiated in 1994 when employees in the state sector were allowed to purchase full or partial property rights to their current apartment units at subsidized prices. Nascent markets for homes, known as “commodity houses,” emerged in some large cities in the early 1990s, but they grew rapidly only after 1998 when the central government completely abolished the traditional model of housing allocation as an in-kind benefit and privatized housing properties of all urban residents.Also in 1998, partly as a response to the adverse effects of the 1997 Asian Financial Crisis, the Chinese government established the real estate sector as a new engine of economic growth. As an important impetus to the development of private housing markets, China’s central bank, the People’s Bank of China (PBC), outlined the procedures for home buyers to obtain residential mortgages at subsidized interest rates in 1998.2 Moreover, between 1998 and 2002, the PBC lowered the mortgage interest rate five times to encourage home purchases. By 2005, China had become the largest residential mortgage market in Asia. According to a PBC report published in 2013, financial institutions made a total of 8.1 trillion RMB in mortgage loans in 2012, accounting for 16% of all bank loans in that year. At the same time, the PBC also developed policies to encourage housing development, including broadening the scope of development loans and allowing presales by developers.These policies were effective in stimulating both the demand and supply of residential housing. During this period, home sales maintained about 15% of annual growth on average, and areas of residential housing under construction grew even faster, reaching about 18% of annual growth. Figure 1 provides a rough estimate of the supply of newly completed residential housing from 2002 to 2013 by city tier, measured by completed areas in each city and each year divided by the city’s urban population in 2012.Fig. 1. Per capita area of newly built residential housingNote: For each tier of cities, we divide its annual flow of newly constructed residential housing, measured in square meters, by its urban population in 2012. The National Bureau of Statistics provides annual city-level data on space of newly constructed residential housing from 2002 to 2013 and resident population from 2005 to 2012, for 35 large cities only. These cities include all four tier-one cities: Beijing, Shanghai, Guangzhou, and Shenzhen. The other 31 cities all belong to the tier-two cities defined in the paper. We use the aggregate of these 31 cities to compute the per capita area built for tier-two cities. We then subtract these 35 cities from the national aggregates on newly constructed urban housing and urban population to get measures for tier-three (and other) cities. Resident population includes all people residing six months or more in the area governed by the city in the current year (in contrast to the hukou population). We assume all resident populations in tier-one and tier-two cities are urban, which leads to a slight overestimation of urban population in tier-one and tier-two cities and, consequently, a slight underestimation of urban population in tier-three (and other) cities. In 2012, China had a total population of 13.5 billion, out of which 7.2 billion are urban and 6.3 billion are rural. Out of those 7.2 billion who live in cities, 0.7 billion reside in tier-one cities, 2.4 billion in tier-two cities, and 4.1 billion in tier-three cities by our baseline calculation.View Large ImageDownload PowerPointIt is common in China to separate cities into three tiers. The first tier includes the four cities with the largest population and economic importance in China—Beijing, Shanghai, Guangzhou, and Shenzhen. Our data cover all of these first-tier cities. The second tier is comprised of Tianjing and Chongqing (the two autonomous municipalities other than Beijing and Shanghai) and capital cities of the 24 provinces3 and nine other cities, which are typically vital industrial or commercial centers. Our data cover 31 of these 35 second-tier cities. There is not a commonly used list for third-tier cities. Instead, we group 85 other cities in our sample as the third tier. Appendix B provides a list of all cities in our sample.The construction boom of residential housing in first-tier cities started in the late 1990s, followed by that of second- and third-tier cities early in the twenty-first century. In figure 1, new construction of residential housing showed a similar growth rate across the three tiers of cities in 2002–2005. From 2005, the new construction in first-tier cities had slowed down substantially due to the shortage of land supply in these cities, while the supply in second- and third-tier cities continued to grow at similar rates as before. The growth rate in third-tier cities was especially strong. Some estimates suggest that investment in residential housing accounted for 25% of total fixed-asset investment and contributed to roughly one-sixth of China’s gross domestic product (GDP) growth (Barth, Lea, and Li 2012).The development of the housing market was also accompanied by an urbanization process throughout China with rural migrants moving into cities, especially into first- and second-tier cities. As shown by figure 2, the total population of the four first-tier cities, the vast majority of which lived inside the city proper, grew from 48 million in 2004 to almost 70 million in 2012. The total population of the second-tier cities, which is distributed roughly half inside the city proper and half outside, grew from 220 million in 2004 to about 260 million in 2012. The total population of third-tier cities remained stable in this period at around 370 million, among which only 100 million lived inside the city proper.Fig. 2. Population in three tiers of citiesNote: There are two lines in each panel. The solid line depicts the total population within the jurisdiction of each tier of cities, while the dashed line depicts the population within the city proper of each tier.View Large ImageDownload PowerPointII. Constructing a Chinese Housing Price IndexTo systematically examine the housing market boom, it is important to construct an accurate housing price index for major cities in China. The difficulty in constructing a housing price index arises because a good price index requires that we compare the prices of the same (or at least comparable) houses over time. To the extent that the set of homes involved in the transactions in different periods of time is likely to be different, a price index constructed by simply comparing the mean or median sale prices per square meter likely measures not only the changes in the prices of similar homes, but also the changes in the composition of transacted homes. This problem is likely to be more severe in emerging housing markets than in mature ones because in emerging housing markets, homes in more central locations are likely to be built and transacted earlier than homes in the outer rings of cities.A. Standard MethodologiesThere are two standard methodologies that are widely used to construct housing price indices. These methods, which we review briefly below, are aimed at finding a suitable way to compare the prices of similar homes.One prominent approach for constructing housing prices is to use hedonic price regressions, which goes back to Kain and Quigley (1970). In this approach, the sales price is regressed on a set of variables that characterize the housing unit—number of rooms, square feet of interior space, lot size, quality of construction, condition, and so forth. The regression coefficients can be interpreted as prices for implicit attributes. This hedonic approach can then be used to construct a price index in two and Shiller The first way to construct a price index is to separate on data from each time The equations are then used to the of a standard in each period, which is in turn used to construct the housing price index for the standard A second way is to a regression on the data from sales in all time of a time for the period of the sale allows the to over time, in for hedonic price can price on well the data the actual characteristics and quality of the and characteristics that are by the market but not in the data can to estimates of the housing price This is a in China. Due to the rapid of Chinese cities, new housing units have been constructed on land near the urban According to the China by of Housing and the total size of developed urban area at the national from square in 2003 to square in 2013 a dramatic of urban residential land that characteristics as transacted homes from locations to city to locations in city is likely to to housing price and Shiller another using repeated This approach originated with and who a a regression the of the is the of the price of the at its second sale the of its price on its first sale date. The variables of only for each time period in the for the first (the period for the The coefficients are then as the price This initial on a that the of the is across As this is likely to on the time between and Shiller a with a to this repeat sales approach not require the of it only requires that the quality of individual units in the sample over time. it is well that this repeated sales a large fraction of transactions data because repeated sales may to only a fraction of all housing
Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API. In providing an in-depth characterization of membership privacy risks against machine learning models, this paper presents a comprehensive study towards demystifying membership inference attacks from two complimentary perspectives. First, we provide a generalized formulation of the development of a black-box membership inference attack model. Second, we characterize the importance of model choice on model vulnerability through a systematic evaluation of a variety of machine learning models and model combinations using multiple datasets. Through formal analysis and empirical evidence from extensive experimentation, we characterize under what conditions a model may be vulnerable to such black-box membership inference attacks. We show that membership inference vulnerability is data-driven and corresponding attack models are largely transferable. Though different model types display different vulnerabilities to membership inference, so do different datasets. Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning exposes vulnerabilities to membership inference risks when the adversary is a participant. We also discuss countermeasure and mitigation strategies.
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With its conceptual roots in fields including psychology and organisational behaviour, the engagement concept is emerging in the marketing literature, with preliminary research indicating that engaged consumers might exhibit greater loyalty to focal brands. Despite these advancements, the engagement concept remains underexplored to date. This paper addresses this gap by reviewing literature in other disciplines and marketing, and developing a tripartite customer brand-engagement conceptualisation comprising activation, identification, and absorption dimensions. Further, a conceptual model illustrating the conceptually distinct nature of and relationships between customer brand engagement and other marketing constructs is proposed, followed by a customer engagement/loyalty-based segmentation analysis. Enhanced insights into customer engagement are expected to be valuable for marketing scholars and practitioners seeking to enhance customer relationships, retention, and loyalty. The paper thus concludes with a set of research and practitioner implications, which may be used to guide future developments in this promising area.
The reduced-form approach to the income–environment relationship has been a useful first step towards answering the question of how economic growth affects the environment. However, without an explicit consideration of the underlying determinants of environmental quality, the scope for policy intervention is unduly circumscribed. In this paper a modest attempt is made to incorporate explicit policy considerations into the income–environment relationship and to explore its determinants as a step towards a better understanding of this relationship and its potential as a policy tool. The role of the rate of economic growth and population density is also explored. A main finding is that at least in the case of ambient SO 2 levels, policies and institutions can significantly reduce environmental degradation at low income levels and speed up improvements at higher income levels, thereby flattening the EKC and reducing the environmental price of economic growth.
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<b>Objective.</b> In the course of daily teaching responsibilities, pharmacy educators collect rich data that can provide valuable insight into student learning. This article describes the qualitative data analysis method of content analysis, which can be useful to pharmacy educators because of its application in the investigation of a wide variety of data sources, including textual, visual, and audio files. <b>Findings.</b> Both manifest and latent content analysis approaches are described, with several examples used to illustrate the processes. This article also offers insights into the variety of relevant terms and visualizations found in the content analysis literature. Finally, common threats to the reliability and validity of content analysis are discussed, along with suitable strategies to mitigate these risks during analysis. <b>Summary.</b> This review of content analysis as a qualitative data analysis method will provide clarity and actionable instruction for both novice and experienced pharmacy education researchers.
Critical-thinking scholarship is in a mystified state. No single definition of critical thinking is widely accepted, but stakeholders in higher education often enter conversation about critical thinking with the premise that their individual definitions are uniformly shared. With an increasing emphasis on academic accountability, we need to work toward a better understanding of the varying ways the term critical thinking is used and explore the implications of the variation for effective pedagogy. In this article, I describe the confusion about critical thinking in psychology and offer suggestions for demystifying the concept. I provide a framework for organizing critical-thinking scholarship into meaningful dimensions. Finally, I describe a psychology curriculum using performance-based assessment practices as one context in which critical thinking in psychology can be made clearer for students and faculty.
This article provides a practical introduction to the design trade-offs of the currently available 3D IC technology options. It begins with an overview of techniques, such as wire bonding, microbumps, through vias, and contactless interconnection, comparing them in terms of vertical density and practical limits to their use. We then present a high-level discussion of the pros and cons of 3D technologies, with an analysis relating the number of transistors on a chip to the vertical interconnect density using estimates based on Rent's rule. Next, we provide a more detailed design example of inductively coupled interconnects, with measured results of a system fabricated in a 0.35-μm technology and an analysis of misalignment and crosstalk tolerances. Lastly, we present a case study of a fast Fourier transform (FFT) placed and routed in a 0.18-μm through-via silicon-on-insulator (SOI) technology, comparing the 3D design to a traditional 2D approach in terms of wire length and critical-path delay.
The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements' data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.
Numerous proteins, including cytokines and chemokines, enzymes and enzyme inhibitors, extracellular matrix proteins, and membrane receptors, bind heparin. Although they are traditionally classified as heparin-binding proteins, under normal physiological conditions these proteins actually interact with the heparan sulfate chains of one or more membrane or extracellular proteoglycans. Thus, they are more appropriately classified as heparan sulfate-binding proteins (HSBPs). This review provides an overview of the various modes of interaction between heparan sulfate and HSBPs, emphasizing biochemical and structural insights that improve our understanding of the many biological functions of heparan sulfate.
When outcomes are missing for reasons beyond an investigator’s control, there are two different ways to adjust a parameter estimate for covariates that may be related both to the outcome and to missingness. One approach is to model the relationships between the covariates and the outcome and use those relationships to predict the missing values. Another is to model the probabilities of missingness given the covariates and incorporate them into a weighted or stratified estimate. Doubly robust (DR) procedures apply both types of model simultaneously and produce a consistent estimate of the parameter if either of the two models has been correctly specified. In this article, we show that DR estimates can be constructed in many ways. We compare the performance of various DR and non-DR estimates of a population mean in a simulated example where both models are incorrect but neither is grossly misspecified. Methods that use inverse-probabilities as weights, whether they are DR or not, are sensitive to misspecification of the propensity model when some estimated propensities are small. Many DR methods perform better than simple inverse-probability weighting. None of the DR methods we tried, however, improved upon the performance of simple regression-based prediction of the missing values. This study does not represent every missing-data problem that will arise in practice. But it does demonstrate that, in at least some settings, two wrong models are not better than one.
Demystifying Legal Reasoning defends the proposition that there are no special forms of reasoning peculiar to law. Legal decision makers engage in the same modes of reasoning that all actors use in deciding what to do: open-ended moral reasoning, empirical reasoning, and deduction from authoritative rules. This book addresses common law reasoning when prior judicial decisions determine the law, and interpretation of texts. In both areas, the popular view that legal decision makers practise special forms of reasoning is false.
The role and value of theory in improvement work in healthcare has been seriously underrecognised. We join others in proposing that more informed use of theory can strengthen improvement programmes and facilitate the evaluation of their effectiveness. Many professionals, including improvement practitioners, are unfortunately mystified-and alienated-by theory, which discourages them from using it in their work. In an effort to demystify theory we make the point in this paper that, far from being discretionary or superfluous, theory ('reason-giving'), both informal and formal, is intimately woven into virtually all human endeavour. We explore the special characteristics of grand, mid-range and programme theory; consider the consequences of misusing theory or failing to use it; review the process of developing and applying programme theory; examine some emerging criteria of 'good' theory; and emphasise the value, as well as the challenge, of combining informal experience-based theory with formal, publicly developed theory. We conclude that although informal theory is always at work in improvement, practitioners are often not aware of it or do not make it explicit. The germane issue for improvement practitioners, therefore, is not whether they use theory but whether they make explicit the particular theory or theories, informal and formal, they actually use.
Networks of randomized clinical trials can be evaluated in the context of a network meta-analysis, a procedure that permits inferences into the comparative effectiveness of interventions that may or may not have been evaluated directly against each other. This approach is quickly gaining popularity among clinicians and guideline decision makers. However, certain methodological aspects are poorly understood. Here, we explain the geometry of a network, statistical and conceptual heterogeneity and incoherence, and challenges in the application and interpretation of data synthesis. These concepts are essential to make sense of a network meta-analysis.
There is consensus that health promotion programs should be culturally sensitive (CS). Yet, despite the ubiquitous nature of CS within public health research and practice, there has been surprisingly little attention given to defining CS or delineating a framework for developing culturally sensitive programs and practitioners. This paper describes a model for understanding CS from a public health perspective; describes a process for applying this model in the development of health promotion and disease prevention interventions; and highlights research priorities. Cultural sensitivity is defined by two dimensions: surface and deep structures. Surface structure involves matching intervention materials and messages to observable, "superficial" characteristics of a target population. This may involve using people, places, language, music, food, locations, and clothing familiar to, and preferred by, the target audience. Surface structure refers to how well interventions fit within a specific culture. Deep structure involves incorporating the cultural, social, historical, environmental and psychological forces that influence the target health behavior in the proposed target population. Whereas surface structure generally increases the "receptivity" or "acceptance" of messages, deep structure conveys salience. Techniques, borrowed from social marketing and health communication theory, for developing culturally sensitive interventions are described. Research is needed to determine the effectiveness of culturally sensitive programs.