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The purpose of this paper is to study insulin pump therapy and accurate monitoring of glucose levels in diabetic patients, which are current research trends in diabetology. Both problems have a wide margin for improvement and promising applications in the control of parameters and levels involved.

The authors have registered data for the levels of glucose in diabetic patients throughout a day with a temporal resolution of 5 minutes, the amount and time of insulin administered and time of ingestion. The estimated quantity of carbohydrates is also monitored. A mathematical model for Type 1 patients was fitted piecewise to these data and the evolution of the parameters was analyzed.

They have found that the parameters for the model change abruptly throughout a day for the same patient, but this set of parameters account with precision for the evolution of the glucose levels in the test patients. This fitting technique could be used to personalize treatments for specific patients and predict the glucose-level variations in terms of hours or even shorter periods of time. This way more effective insulin pump therapies could be developed.

The proposed model could allow for the development of improved schedules on insulin pump therapies.

Guided by Bronfenbrenner’s Ecological System’s model, this study documented acculturation and parental involvement in low-income Chinese immigrant homes that serve as predictors of parental mediation. By surveying 165 parents of 3–13-year-old immigrant children, this study found that low-income Chinese parents enacted restrictive mediation the most and exhibited a slow acculturation process even after an average of seven years of emigration. Higher parental acculturation was related to a higher use of active and restrictive mediation. Additionally, different aspects of parental involvement also served as predictors of the three mediation strategies. Chinese cultural emphasis on academic excellence and success was used to help interpret the findings. Future research should consider implementing research-based adult media literacy programs for immigrant parents to help them practice their parental mediation skills in the host culture.

The purpose of this paper is to present a new service discovery protocol (SDP) suitable for wireless sensor networks (WSN). The constraints imposed by ultra low‐cost sensor and actuators devices (basic components of a WSN) are taken into account to minimize the overall footprint.

It is based on the lightweight WSN communication model used by picoObjects, a tiny implementation of the distributed object concept and the same design criteria are followed, aiming at minimal overhead for devices and communication protocols. In spite of its simplicity it is powerful enough to deploy a valuable set of services.

This approach provides a remote interface that client applications can use without knowing where the service is implemented (platform and location independence).

The future work is mainly focused on integrating third party services using different SDPs, making possible the real deployment of large heterogeneous pervasive environments.

Designers may change the underlying SDP model (e.g. centralized versus distributed) without affecting applications by just tweaking configuration settings.

Embedded devices can participate in the service discovering procedure providing their own services by means of standard distributed objects. Also, the protocol is suitable for any kind of dynamic networked system.

Big Data has become crucial for fulfilling the vision of smart cities; however, there are several challenges associated with its use. This study aims to provide a holistic view of challenges of using Big Data in smart cities and the solutions to deal with them by systematically selecting, reviewing and synthesizing the literature in this area.

A systematic literature review was conducted by following a rigorous search protocol that identified a total of 310 peer-reviewed academic research papers. These papers were subsequently filtered and finally 38 relevant papers were selected.

A number of major challenges (human dynamics, privacy, security, volume, velocity, variety, veracity and value) associated with Big Data in a smart city and the respective solutions to address them were identified. This study primarily contributes in providing the process of effectively managing Big Data in smart cities.

The study provides valuable information to the Big Data practitioners by illustrating the process of effective management of Big Data in smart cities. This study further provides the directions to the future researchers.

The study is original and is based on the existing literature and its interpretation.

Technology proliferation is on a movement to outpace an 18^th-century computing industry paradigm known as “Moore’s law.” This law establishes the rate of technological advancements. The premise of this edict is evident in our coupled workplace with the integration of an emerging technology known as Ambient Intelligence (Aml).

The modernization of the traditional office is designed to be collaborative and environment-friendly. Modernization is primarily due to ambient intelligence. “Opportunities for process and business improvements will derive from a “real-world Web” of smart objects and ambient intelligence, and from consumer-oriented trends such as Web business platforms, aesthetic design, and mobile robots as they move into the business world” (Fenn and Smith, 2005, para. 1). It is safe to reason that ambient intelligence is on a trendy trajectory in many business-oriented workplaces, worksites and workspaces. The business culture is inconspicuously changing before our eyes. Architects and designers are seamlessly incorporating this trend into their respective end-to-end processes of constructing new or retrofitting existing office spaces.

Its unnoticeably embedded adoption is in conference rooms, doorways, elevators, escalators, lighting, meeting rooms, phone displays, and walkways. As ambient technology naturally collides with the functional way an office professional interactively operates through a usual workday, its adaptation becomes seemingly smart and swift. The interesting facet of this technology is that one would not know it unless it was pointed out.

Although there are equipment and devices that offer a singular approach of being convenient and hands-free, there exist common misconceptions and unassuming annoyances that are in place as inherent issues. Once the work environment impedes productivity or natural flow of movement, we realize something is different. These differences align to the surrounding tangible and intangible cues. The information presented in this chapter will disclose the underlying issues at a practical level.

The purpose of this paper is to review the literature on maintenance optimization models and associated case studies. For these optimization models critical observations are made.

The paper systematically classifies the published literature using different techniques, and also identifies the possible gaps.

The paper outlines important techniques used in various maintenance optimization models including the analytical hierarchy process, the Bayesian approach, the Galbraith information processing model and genetic algorithms. There is an emerging trend towards uses of simulation for maintenance optimization which has changed the maintenance view.

A limited literature is available on the classification of maintenance optimization models and on its associated case studies. The paper classifies the literature on maintenance optimization models on different optimization techniques and based on emerging trends it outlines the directions for future research in the area of maintenance optimization.

The paper provides many references and case studies on maintenance optimization models and techniques. It gives useful references for maintenance management professionals and researchers working on maintenance optimization.

Effective leaders have emerged as the cornerstone of project success. The major purpose of this paper is to propose a framework to categorize and prioritize leadership competencies for project managers in megaprojects.

In the first stage, this study utilizes PMBOK 6th Edition, IPMA ICB 4.0 frameworks to develop a hierarchy-based four clusters of leadership competencies. In the second stage, a Fuzzy-AHP (Analytic Hierarchy Process) approach was employed to prioritize the leadership competencies for an organization dealing in megaprojects. Finally, using ordinal priority approach (OPA), the results of Fuzzy-AHP method are validated.

Based on PMBOK, IPMA and literature, the proposed framework deduced twenty-four leadership competencies and grouped them in four clusters. The Fuzzy-AHP results indicate that among clusters, people competencies cluster is ranked most important, followed by perspective, practice, and innovation competencies. Considering the sub-categories and global weights, culture/values, governance, interpersonal skills, and development and growth emerged as the most important leadership competencies. The results from OPA corroborate the findings of Fuzzy-AHP method.

Megaprojects are characterized by massive investments and extensive economic and social impact. The proposed framework would be an important aid for policymakers to develop suitable strategies and programs to inculcate leadership competencies that would lead to successful project managers and improved project performance.

The need for this research stems from the need to integrate popular project management frameworks in leadership competencies development in project based organizations. The proposed integrated framework, based on PMBOK 6th Edition and IPMA ICB 4.0 competency frameworks, is an original contribution to understand and prioritize leadership competencies for megaproject success.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

The purpose of this paper is to present an analytical drain current model for output characteristics of strained‐Si/SiGe bulk MOSFET.

A physics‐based model for current output characteristics and transconductance of strained‐Si/SiGe bulk devices has been developed incorporating the impact of strain (in terms of equivalent Ge mole fraction), strained silicon thin film thickness, gate work function, channel length and other device parameters. The accuracy of the results obtained using this model is verified by comparing them with 2D device simulations.

This model correctly predicts the output characteristics, I_DS−V_GS characteristics, transconductance and output conductance of the strained‐Si/SiGe MOSFET and demonstrates a significant enhancement in the drain current of the MOSFET with increasing strain in the strained‐Si thin film, i.e. with increasing equivalent Ge concentration in the SiGe bulk.

Can be implemented in a SPICE like simulator for studying circuit behaviour containing strained‐Si/SiGe bulk MOSFETs.

The model discussed in this paper can be easily implemented in a circuit simulator and used for the characterization of strained silicon devices. This complements the recent trend of investigation of new materials and device structures to maintain the rate of advancement in VLSI technology.

This paper presents, for the first time, a compact surface potential‐based analytical model for strained‐Si/SiGe MOSFETs which predicts the device characteristics reasonably well over their range of operation.