Towson University

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.

The authors reviewed the application of consensual qualitative research (CQR) in 27 studies published since the method’s introduction to the field in 1997 by C. E. Hill, B. J. Thompson, and E. N. Williams (1997). After first describing the core components and the philosophical underpinnings of CQR, the authors examined how it has been applied in terms of the consensus process, biases, research teams, data collection, data analysis, and writing up the results and discussion sections of articles. On the basis of problems that have arisen in each of these areas, the authors made recommendations for modifications of the method. The authors concluded that CQR is a viable qualitative method and suggest several ideas for research on the method itself. In the early 1990s, when we wanted to conduct qualitative research, we explored several different approaches. Although the existing qualitative approaches had a number of valuable features, we were frustrated because the descriptions seemed vague, diffi-cult to comprehend, and equally difficult to implement. Hence, based on our experiences, we (Hill, Thompson, & Williams, 1997) developed consensual qualitative research (CQR), which we hoped

Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user's experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications.

In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values.

The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge”, near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

This study introduces and validates a measure of country institutional profile for entrepreneurship consisting of regulatory, cognitive, and normative dimensions. Subscales based on data from six c...

We survey the determinants of earnings and propose a framework for understanding labor market success. We suggest that the advantages of the children of successful parents go considerably beyond the benefits of superior education, the inheritance of wealth, or the genetic inheritance of cognitive ability. We suggest that noncognitive personality variables, such as attitudes towards risk, ability to adapt to new economic conditions, hard work, and the rate of time preference affect both earning and the transmission of economic status across generations.

Atomically thin molybdenum disulfide (MoS2) offers potential for advanced devices and an alternative to graphene due to its unique electronic and optical properties. The temperature-dependent Raman spectra of exfoliated, monolayer MoS2 in the range of 100-320 K are reported and analyzed. The linear temperature coefficients of the in-plane E2g 1 and the out-of-plane A1g modes for both suspended and substrate-supported monolayer MoS2 are measured. These data, when combined with the first-order coefficients from laser power-dependent studies, enable the thermal conductivity to be extracted. The resulting thermal conductivity κ = (34.5(4) W/mK at room temperature agrees well with the first principles lattice dynamics simulations. However, this value is significantly lower than that of graphene. The results from this work provide important input for the design of MoS2-based devices where thermal management is critical.

Journal Article Mass Media and American Politics, by Doris A. Graber Mass Media and American Politics by Doris A. Graber. Washington, D.C., Congressional Quarterly Press, 1980. 304 pp. $6.95. Martha Joynt Kumar Martha Joynt Kumar Towson State College Search for other works by this author on: Oxford Academic Google Scholar Political Science Quarterly, Volume 95, Issue 4, Winter 1980, Pages 701–702, https://doi.org/10.2307/2150629 Published: 15 December 1980

BACKGROUND: Obesity and its cardiovascular complications are extremely common medical problems, but evidence on how to accomplish weight loss in clinical practice is sparse. METHODS: We conducted a randomized, controlled trial to examine the effects of two behavioral weight-loss interventions in 415 obese patients with at least one cardiovascular risk factor. Participants were recruited from six primary care practices; 63.6% were women, 41.0% were black, and the mean age was 54.0 years. One intervention provided patients with weight-loss support remotely--through the telephone, a study-specific Web site, and e-mail. The other intervention provided in-person support during group and individual sessions, along with the three remote means of support. There was also a control group in which weight loss was self-directed. Outcomes were compared between each intervention group and the control group and between the two intervention groups. For both interventions, primary care providers reinforced participation at routinely scheduled visits. The trial duration was 24 months. RESULTS: At baseline, the mean body-mass index (the weight in kilograms divided by the square of the height in meters) for all participants was 36.6, and the mean weight was 103.8 kg. At 24 months, the mean change in weight from baseline was -0.8 kg in the control group, -4.6 kg in the group receiving remote support only (P<0.001 for the comparison with the control group), and -5.1 kg in the group receiving in-person support (P<0.001 for the comparison with the control group). The percentage of participants who lost 5% or more of their initial weight was 18.8% in the control group, 38.2% in the group receiving remote support only, and 41.4% in the group receiving in-person support. The change in weight from baseline did not differ significantly between the two intervention groups. CONCLUSIONS: In two behavioral interventions, one delivered with in-person support and the other delivered remotely, without face-to-face contact between participants and weight-loss coaches, obese patients achieved and sustained clinically significant weight loss over a period of 24 months. (Funded by the National Heart, Lung, and Blood Institute and others; ClinicalTrials.gov number, NCT00783315.).

Thermoelectric devices that utilize the Seebeck effect convert heat flow into electrical energy and are highly desirable for the development of portable, solid state, passively powered electronic systems. The conversion efficiencies of such devices are quantified by the dimensionless thermoelectric figure of merit (ZT), which is proportional to the ratio of a device's electrical conductance to its thermal conductance. In this paper, a recently fabricated two-dimensional (2D) semiconductor called phosphorene (monolayer black phosphorus) is assessed for its thermoelectric capabilities. First-principles and model calculations reveal not only that phosphorene possesses a spatially anisotropic electrical conductance, but that its lattice thermal conductance exhibits a pronounced spatial-anisotropy as well. The prominent electrical and thermal conducting directions are orthogonal to one another, enhancing the ratio of these conductances. As a result, ZT may reach the criterion for commercial deployment along the armchair direction of phosphorene at T = 500 K and is close to 1 even at room temperature given moderate doping (∼2 × 10(16) m(-2) or 2 × 10(12) cm(-2)). Ultimately, phosphorene hopefully stands out as an environmentally sound thermoelectric material with unprecedented qualities. Intrinsically, it is a mechanically flexible material that converts heat energy with high efficiency at low temperatures (∼300 K), one whose performance does not require any sophisticated engineering techniques.

Human cooperation is highly unusual. We live in large groups composed mostly of non-relatives. Evolutionists have proposed a number of explanations for this pattern, including cultural group selection and extensions of more general processes such as reciprocity, kin selection, and multi-level selection acting on genes. Evolutionary processes are consilient; they affect several different empirical domains, such as patterns of behavior and the proximal drivers of that behavior. In this target article, we sketch the evidence from five domains that bear on the explanatory adequacy of cultural group selection and competing hypotheses to explain human cooperation. Does cultural transmission constitute an inheritance system that can evolve in a Darwinian fashion? Are the norms that underpin institutions among the cultural traits so transmitted? Do we observe sufficient variation at the level of groups of considerable size for group selection to be a plausible process? Do human groups compete, and do success and failure in competition depend upon cultural variation? Do we observe adaptations for cooperation in humans that most plausibly arose by cultural group selection? If the answer to one of these questions is "no," then we must look to other hypotheses. We present evidence, including quantitative evidence, that the answer to all of the questions is "yes" and argue that we must take the cultural group selection hypothesis seriously. If culturally transmitted systems of rules (institutions) that limit individual deviance organize cooperation in human societies, then it is not clear that any extant alternative to cultural group selection can be a complete explanation.

OBJECTIVE: To measure long-term changes in resting metabolic rate (RMR) and body composition in participants of "The Biggest Loser" competition. METHODS: Body composition was measured by dual energy X-ray absorptiometry, and RMR was determined by indirect calorimetry at baseline, at the end of the 30-week competition and 6 years later. Metabolic adaptation was defined as the residual RMR after adjusting for changes in body composition and age. RESULTS: Of the 16 "Biggest Loser" competitors originally investigated, 14 participated in this follow-up study. Weight loss at the end of the competition was (mean ± SD) 58.3 ± 24.9 kg (P < 0.0001), and RMR decreased by 610 ± 483 kcal/day (P = 0.0004). After 6 years, 41.0 ± 31.3 kg of the lost weight was regained (P = 0.0002), while RMR was 704 ± 427 kcal/day below baseline (P < 0.0001) and metabolic adaptation was -499 ± 207 kcal/day (P < 0.0001). Weight regain was not significantly correlated with metabolic adaptation at the competition's end (r = -0.1, P = 0.75), but those subjects maintaining greater weight loss at 6 years also experienced greater concurrent metabolic slowing (r = 0.59, P = 0.025). CONCLUSIONS: Metabolic adaptation persists over time and is likely a proportional, but incomplete, response to contemporaneous efforts to reduce body weight.

Interactivity is a key feature of Web sites. This article identifies the determinants that enhance user perceptions of interactivity in a communication scenario in which consumers send instant messages to an e-store. Two conceptualizations of interactivity—telepresence theory and interactivity theory—predict that different antecedents (e.g., the number of clicks, response time, message type) are important. The results of Experiment 1 indicate that message type (i.e., how personal a particular message is) is the strongest predictor of interactivity perceptions. Furthermore, the findings suggest that the effects of message type on perceived interactivity and Web site effectiveness are greater when consumers are complaining than when they are inquiring about services. The results of Experiment 2 show that as the level of message personalization increases, interactivity perceptions and site effectiveness are enhanced (linear relationship). The authors discuss the implications of the findings for theory and practice and provide directions for measuring and manipulating interactivity in further research.

The relationship between a reported history of trauma and dissociative symptoms has been explained in 2 conflicting ways. Pathological dissociation has been conceptualized as a response to antecedent traumatic stress and/or severe psychological adversity. Others have proposed that dissociation makes individuals prone to fantasy, thereby engendering confabulated memories of trauma. We examine data related to a series of 8 contrasting predictions based on the trauma model and the fantasy model of dissociation. In keeping with the trauma model, the relationship between trauma and dissociation was consistent and moderate in strength, and remained significant when objective measures of trauma were used. Dissociation was temporally related to trauma and trauma treatment, and was predictive of trauma history when fantasy proneness was controlled. Dissociation was not reliably associated with suggestibility, nor was there evidence for the fantasy model prediction of greater inaccuracy of recovered memory. Instead, dissociation was positively related to a history of trauma memory recovery and negatively related to the more general measures of narrative cohesion. Research also supports the trauma theory of dissociation as a regulatory response to fear or other extreme emotion with measurable biological correlates. We conclude, on the basis of evidence related to these 8 predictions, that there is strong empirical support for the hypothesis that trauma causes dissociation, and that dissociation remains related to trauma history when fantasy proneness is controlled. We find little support for the hypothesis that the dissociation-trauma relationship is due to fantasy proneness or confabulated memories of trauma.

Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products remain obscure. In contrast, researchers across disciplines have been incorporating deep learning into their research to solve problems that could not have been approached before. In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems. Furthermore, we hope to outline recent key advancements in the technology, and provide insight into areas, in which deep learning can improve investigation, as well as highlight new areas of research that have yet to see the application of deep learning, but could nonetheless benefit immensely. We hope this survey provides a valuable reference for new deep learning practitioners, as well as those seeking to innovate in the application of deep learning.

Recent observation of intrinsic ferromagnetism in two-dimensional (2D) ${\mathrm{CrI}}_{3}$ is associated with the large magnetic anisotropy due to strong spin-orbit coupling of I. Magnetic anisotropy energy (MAE) defines the stability of magnetization in a specific direction with respect to the crystal lattice and is an important parameter for nanoscale applications. In this work we apply the density functional theory to study the strain dependence of MAE in 2D monolayer chromium trihalides $\mathrm{Cr}{X}_{3}$ (with $X$ = Cl, Br, and I). Detailed calculations of their energetics, atomic structures, and electronic structures under the influence of a biaxial strain $\ensuremath{\varepsilon}$ have been carried out. It is found that all three compounds exhibit ferromagnetic ordering at the ground state (with $\ensuremath{\varepsilon}=0$), and upon applying a compressive strain, phase transition to the antiferromagnetic state occurs. Unlike in ${\mathrm{CrCl}}_{3}$ and ${\mathrm{CrBr}}_{3}$, the electronic band gap in ${\mathrm{CrI}}_{3}$ increases when a tensile strain is applied. The MAE also exhibits a strain dependence in the chromium trihalides: it increases when a compressive strain is applied in ${\mathrm{CrI}}_{3}$, while an opposite trend is observed in the other two compounds. In particular, the MAE of ${\mathrm{CrI}}_{3}$ can be increased by 47% with a compressive strain of $\ensuremath{\varepsilon}$ = 5%.

Breaking from traditional studies of women that focus on how women differ from men or use psychotherapy clients as research subjects, this book examines how normal women forge their individual identities. Presenting the stories of ordinary women who were interviewed first as college students and then 12 years later, living with the choices they made, the author shows the subtle yet significant differences in ways women form relationships, raise children, pursue careers and develop values. The author describes four divergent pathways women follow and outlines the choices, challenges and struggles each path presents.

The vision of Industry 4.0, otherwise known as the fourth industrial revolution, is the integration of massively deployed smart computing and network technologies in industrial production and manufacturing settings for the purposes of automation, reliability, and control, implicating the development of an Industrial Internet of Things (I-IoT). Specifically, I-IoT is devoted to adopting the Internet of Things (IoT) to enable the interconnection of anything, anywhere, and at anytime in the manufacturing system context to improve the productivity, efficiency, safety and intelligence. As an emerging technology, I-IoT has distinct properties and requirements that distinguish it from consumer IoT, including the unique types of smart devices incorporated, network technologies and quality of service requirements, and strict needs of command and control. To more clearly understand the complexities of I-IoT and its distinct needs, and to present a unified assessment of the technology from a systems perspective, in this paper we comprehensively survey the body of existing research on I-IoT. Particularly, we first present the I-IoT architecture, I-IoT applications (i.e., factory automation (FA) and process automation (PA)) and their characteristics. We then consider existing research efforts from the three key systems aspects of control, networking and computing. Regarding control, we first categorize industrial control systems and then present recent and relevant research efforts. Next, considering networking, we propose a three-dimensional framework to explore the existing research space, and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine (M2M) communication, and software defined networking (SDN). Similarly, concerning computing, we again propose a second three-dimensional framework that explores the problem space of computing in I-IoT, and investigate the cloud, edge, and hybrid cloud and edge computing platforms. Finally, we outline particular challenges and future research needs in control, networking, and computing systems, as well as for the adoption of machine learning, in an I-IoT context.

Oral human papillomavirus (HPV) infection is a cause of oropharyngeal cancer. We investigated whether sexual behaviors that elevated the odds of oropharyngeal cancer developing in a case-control study similarly elevated the odds of oral HPV infection developing among control patients. HPV infection was detected in 4.8% of 332 control patients from an outpatient clinic and in 2.9% of 210 college-aged men (age range, 18-23 years). Among control patients, the odds of infection developing independently increased with increases in the lifetime number of oral (P = .007, for trend) or vaginal sex partners (P = .003, for trend). Among college-aged men, the odds of oral HPV infection developing increased with increases in the number of recent oral sex partners (P = .046, for trend) or open-mouthed kissing partners (P = .023, for trend) but not vaginal sex partners. Oral sex and open-mouthed kissing are associated with the development of oral HPV infection.