FACTORS INFLUENCING PROJECT TEAM EFFECTIVENESS AS PERCEIVED BY PROJECT MANAGERS IN MALAYSIA – A PILOT STUDY

As more project teams are formed to help Malaysian organizations in achieving their objectives that individual efforts cannot achieve, there is a compelling reason to understand the critical factors that can influence project team effectiveness, because the effectiveness outcome can yield benefits to organizations. This study developed a research model underpinned on Cohen & Bailey’s (1997) Team Effectiveness Framework to empirically analyze some critical factors that influence project team effectiveness. Results show that project manager’s leadership roles are not directly influencing project team effectiveness, but they are directly influencing both team building & participation, and team shared mental models in which these two factors are directly and positively influencing project team effectivenessProject Team Effectiveness, Leadership Roles, Team Building & Participation, Team Shared Mental Models, Project Manager

A Smart Consumer-empowered Diabetes Education System (SCEDES): Integrating Human Wellbeing and Health Care in the Community Environment

Uncontrolled diabetes creates an increasing burden on individuals, their families, and the society in the UnitedStates. The expected total annual costs of diabetes, including both direct costs and the lost productivity, are expectedto be 20% of the Gross Domestic Products by 2016. In this paper, a community-based infrastructure is proposed toprovide consumers, both diabetics and pre-diabetics, with a Smart Consumer-empowered Diabetes EducationSystem (SCEDES) that will not only build consumer self-efficacy but also promote their self-management, selflearning,and support from peers and health care professionals. The system architecture and key implementationfeatures are addressed. Challenging issues and research directions are also discussed for further investigation

Scalability of Distributed Engineering Computation over Cloud of Virtual Machines

It is investigated to verify the scalability aspects of the distributed engineering computation on the cloud computing In the study a parallel virtual machine program distributed over a network of cloud computers is used in solving a finite difference version of a typical complicated engineering differential equation It is found that there exist a pseudo-optimal number of virtual machines which does not necessarily coincide with the number of tasks and the pseudooptimal number depends on various overheads over the network of virtual machines Increasing the number of machines in the cloud beyond certain threshold one does not improve computing performance due to the communication overhead between the task processes distributed over the networ

Infectious Inequalities; Epidemics, Trust, and Social Vulnerabilities in Cinema

This book explores societal vulnerabilities highlighted within cinema and develops an interpretive framework for understanding the depiction of societal responses to epidemic disease outbreaks across cinematic history.Drawing on a large database of twentieth- and twenty-first-century films depicting epidemics, the study looks into issues including trust, distrust, and mistrust; different epidemic experiences down the lines of expertise, gender, and wealth; and the difficulties in visualizing the invisible pathogen on screen. The authors argue that epidemics have long been presented in cinema as forming a point of cohesion for the communities portrayed, as individuals and groups “from below” represented as characters in these films find solidarity in battling a common enemy of elite institutions and authority figures. Throughout the book, a central question is also posed: “cohesion for whom?”, which sheds light on the fortunes of those characters that are excluded from these expressions of collective solidarity.This book is a valuable reference for scholars and students of film studies and visual studies as well as academic and general readers interested in topics of films and history, and disease and society

A Recurrent Neural Network Survival Model: Predicting Web User Return Time

The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl

Diagnosis, synthesis and analysis of probabilistic models

This dissertation considers three important aspects of model checking Markov models:\ud diagnosis — generating counterexamples, synthesis — providing valid parameter\ud values and analysis — verifying linear real-time properties. The three aspects are relatively\ud independent while all contribute to developing new theory and algorithms in the\ud research field of probabilistic model checking.\ud We start by introducing a formal definition of counterexamples in the setting of\ud probabilistic model checking. We transform the problem of finding informative counterexamples\ud to shortest path problems. A framework is explored and provided for\ud generating such counterexamples. We then investigate a more compact representation\ud of counterexamples by regular expressions. Heuristic based algorithms are applied to\ud obtain short regular expression counterexamples. In the end of this part, we extend\ud the definition and counterexample generation algorithms to various combinations of\ud probabilistic models and logics.\ud We move on to the problem of synthesizing values for parametric continuous-time\ud Markov chains (pCTMCs) wrt. time-bounded reachability specifications. The rates\ud in the pCTMCs are expressed by polynomials over reals with parameters and the\ud main question is to find all the parameter values (forming a synthesis region) with\ud which the specification is satisfied. We first present a symbolic approach where the\ud intersection points are computed by solving polynomial equations and then connected\ud to approximate the synthesis region. An alternative non-symbolic approach based on\ud interval arithmetic is investigated, where pCTMCs are instantiated. The error bound,\ud time complexity as well as some experimental results have been provided, followed by\ud a detailed comparison of the two approaches.\ud In the last part, we focus on verifying CTMCs against linear real-time properties\ud specified by deterministic timed automata (DTAs). The model checking problem aims\ud at computing the probability of the set of paths in CTMC C that can be accepted\ud by DTA A, denoted PathsC(A). We consider DTAs with reachability (finite, DTA♦)\ud and Muller (infinite, DTAω) acceptance conditions, respectively. It is shown that\ud PathsC(A) is measurable and computing its probability for DTA♦ can be reduced to\ud computing the reachability probability in a piecewise deterministic Markov process\ud (PDP). The reachability probability is characterized as the least solution of a system\ud of integral equations and is shown to be approximated by solving a system of PDEs.\ud Furthermore, we show that the special case of single-clock DTA♦ can be simplified to\ud solving a system of linear equations. We also deal with DTAω specifications, where the\ud problem is proven to be reducible to the reachability problem as in the DTA♦ case

ASSESSING THE EFFECTIVENESS OF MEDICAL/DRUG-RELATED APPS IN PATIENT HEALTHCARE MANAGEMENT

Abstract In recent years, the availability and usage of medical and drug-related mobile applications (apps) have rapidly increased, promising to revolutionize patient healthcare management. This research paper aims to evaluate the effectiveness of medical/drug-related apps in enhancing patient outcomes and healthcare management. The study will employ a mixed-methods approach, starting with a comprehensive review of existing literature on medical/drug-related apps, their functionalities, and their impact on patient care. A systematic analysis of various app categories, including medication management, symptom trackers, appointment reminders, and health education, will be conducted. Quantitative data will be collected through surveys and usage analytics to assess the adoption rates, user satisfaction, and perceived effectiveness of medical/drug-related apps among patients. Additionally, qualitative data will be gathered through interviews or focus groups to explore users' experiences, challenges, and perceptions related to these apps. Key performance indicators such as medication adherence, self-care behaviors, patient empowerment, and health outcomes will be assessed to determine the impact of medical/drug-related apps on patient healthcare management. Statistical analysis and thematic coding techniques will be applied to analyze the data and identify patterns and themes. The research will also investigate the factors influencing app adoption and the barriers or challenges faced by patients in utilizing these apps effectively. Privacy and security concerns, user interface design, and healthcare professional recommendations will be considered in the evaluation process

Chemical characterization of water-soluble organic carbon aerosols at a rural site in the Pearl River Delta, China, in the summer of 2006

Online measurements of water-soluble organic carbon (WSOC) aerosols were made using a particle-into-liquid sampler (PILS) combined with a total organic carbon (TOC) analyzer at a rural site in the Pearl River Delta region, China, in July 2006. A macroporous nonionic (DAX-8) resin was used to quantify hydrophilic and hydrophobic WSOC, which are defined as the fractions of WSOC that penetrated through and retained on the DAX-8 column, respectively. Laboratory calibrations showed that hydrophilic WSOC (WSOCHPI) included low-molecular aliphatic dicarboxylic acids and carbonyls, saccharides, and amines, while hydrophobic WSOC (WSOCHPO) included longer-chain aliphatic dicarboxylic acids and carbonyls, aromatic acids, phenols, organic nitrates, cyclic acids, and fulvic acids. On average, total WSOC (TWSOC) accounted for 60% of OC, and WSOCHPO accounted for 60% of TWSOC. Both WSOC HIP and WSOCHPO increased with photochemical aging determined from the NOx/NOy ratio. In particular, the average WSOCHPO mass was found to increase by a factor of five within a timescale of ∼10 hours, which was substantially larger than that of WSOCHPI (by a factor of 2-3). The total increase in OC mass with photochemical aging was associated with the large increase in WSOCHPO mass. These results, combined with the laboratory calibrations, suggest that significant amounts of hydrophobic organic compounds (likely containing large carbon numbers) were produced by photochemical processing. By contrast, water-insoluble OC (WIOC) mass did not exhibit significant changes with photochemical aging, suggesting that chemical transformation of WIOC to WSOC was not a dominant process for the production of WSOC during the study period. Copyright 2009 by the American Geophysical Union