Tramadol, Methadone and Buprenorphine-AuNPs Kit Based on Chemometrics Tools and Software

The SPR of the AuNPs, synthesized by the reduction of gold ion with citrate and modified with L-cysteine was used as a novel analytical method for the detection and determination of drugs and species based on aggregation of Au-nanoparticles. However, multivariate calibration modeling of the SPR absorbance data by chemometrics method resulted in more accurate results out-comes so that the relative prediction errors were almost lower than 5%. In comparison with the available analytical methods for highly selective and sensitive ternary determination of tramadol, methadone and buprenorphine the proposed method is in need of lower amounts of reagents. Through increasing the concentration of the Opiate and Fluent material, a visible color change of AuNPs from wine red to blue was observed so the obvious color change was easily noticeable by naked eyes. However, multivariate calibration modelling and 3-d way methods that used for the SPR absorbance data by PARAFAC resulted in more accurate outcomes so that the software, as well as the chemometrics and the solvent kit, with great assurance our kit is unique. PARAFAC is a multi-way method originating from psychometrics. Due to various reasons such as high computational power, awareness of the method and its possibilities, the increased complexity complication of the data dealt with in science and industry, is gaining more attention in chemometrics and associated areas.

Ontology Based Power Profiling for Internet of Things Deployed with Software Defined Data Center

Cost effective, secure and environment friendly data centers are crucial for the success of embracing the adoption of Internet of Things (IoT). Regardless of the potential benefits of IoT, there are still many hurdles to be overcome to leverage the wide growth of the IoT technology. One challenging vector is the efficient power profiling of the entities involved in an IoT ecosystem. Software-Defined Data Center (SDDC) virtualizes the key data center infrastructure such as compute, storage and network. IoT fabric with SDDC, due to virtualization and the reduction in the hardware footprint, an extent of reduced power consumption is achieved when compared to a non-virtualized traditional data center. Today, in the end-to-end IoT and IT fabric, deficiency of intelligent power profiling techniques merits the need to supervise power in such an ecosystem, especially from an OT/IT convergence point of view where SDDC represents IT (Information Technology) and IoT forms OT (Operational Technology). To that end, this paper looks at power profiling for IoT fabric with SDDC by modeling the IoT ecosystem using semantic approaches. Thus, we bring in a notion of power awareness to the software defined IoT fabric. Specifically, this paper models a power profile ontology for the IoT fabric with SDDC and monitor such an ecosystem at run time and derive useful value by bridging the OT/IT convergence gap with respect to end-to-end power profiling.

Utilizing Feminist Pedagogy to Foster Preservice Teachers' Critical Consciousness

This chapter describes a longitudinal case study investigating the use of feminist pedagogy to foster preservice teachers' critical consciousness in a Master of Arts in Teaching (MAT) program. Courses in the program were revised to intentionally employ the feminist pedagogical elements of personal experience, power awareness, community-building, identity awareness, and intentional reflection. Coursework, focus groups, and interviews from three cohorts of teacher candidates were analyzed for dimensions of critical consciousness according to Freirean principles. Across cohorts, students demonstrated a deep awareness of racialized systems of power that affect educational processes. Most students were able to decode educational rhetoric and challenge the assumptions and biases embedded within. While all students expressed a deep commitment to inclusive teaching and could articulate multicultural teaching methods, most admitted to not yet having the depth of knowledge necessary and/or the emotional fortitude to combat systems of inequity beyond their classrooms.

Power Profiling of Context Aware Systems: A Contemporary Analysis and Framework for Power Conservation

With the advent of smart, inexpensive devices and a highly connected world, a need for smart service discovery, delivery, and adaptation has appeared. This interconnection is composed of sensors within devices or placed externally in the surrounding environment. Our research addresses this need through a context aware system, which adapts to the users’ context. Given that the devices are mobile and battery operated, the main challenge in a context awareness approach is power conservation. The devices are composed of small sensors that consume power in the order of a few mW. However, their consumptions increase manifold during data processing. There is a need to conserve power while delivering the requisite functionality of the context aware system. Therefore, this feature is termed as ‘power awareness.’ In this paper, we describe different power awareness techniques and compare them in terms of their conservation effectiveness. In addition, based on the investigations and comparison of the results, a power aware framework is proposed for a context aware system.

OperaBLE: An IoT-Based Wearable to Improve Efficiency and Smart Worker Care Services in Industry 4.0

Industry 4.0 is leading the Fourth Industrial Revolution transforming traditional factories into smart factories governed by the Internet of Things (IoT). In order to assist smart factory employees, this paper introduces OperaBLE, a Bluetooth Low Energy (BLE) wearable proposal which is aimed at enhancing working conditions and efficiency in Industry 4.0 scenarios. We have developed two innovative algorithms for OperaBLE focused on power awareness as the key-enabling attribute towards success: Low-Frequency Movement Characterisation Algorithm (LoMoCA) and Adaptive Heart Rate Algorithm (AHRA). Novel experiments have been carried out using OperaBLE to determine its operability, reliability, and lifespan. Results obtained during experimentation demonstrate how OperaBLE empowers human-machine collaboration embedding workers in closed-loop performance and ensuring nonharmful working conditions by means of power-aware algorithms. OperaBLE is due to bring digitalisation into smart factories, playing an essential role in the emerging wearable revolution to arise in the following years towards smart production systems.

Qualitative Study of Profit Margin Determinants and Pricing Practices: An Islamic Banking Perspective

The purpose of this study is to investigate and validate the determinants of profit margins and pricing practices of Islamic banking institutions in Malaysia. The key determinants and pricing approaches were extracted using a verbal protocol followed by probing of respondents. Focus group interviews were conducted with Islamic banking experts from Bank Muamalat Malaysia Berhad, CIMB Islamic Bank Berhad, Bank Islam Malaysia Berhad and Maybank Islamic Berhad. Preliminary analysis suggested some distinct outcomes in the identification of determinants and practices in pricing and bank margins for future research. This study has provided important insights for Islamic banks to improve their bank margins and profit by considering significant variables affecting profitability. Generally, the themes which emerged from the interviews concurred with findings from literature. However, the determinants that have yet to be captured in the literature are marketing strategies which include product risk, financing terms and conditions; and discretionary power. Awareness of Islamic banking products and cost of fund strategies are other important determinants of bank margins and profitability. This paper also has the potential to enhance bank margin determinants, concept, theory and practice.