ALBERT

Description: This article describes an aging study of a foam-vacuum insulation panel (VIP) composite insulation board installed on a test wall in a natural exposure test facility through a 30-month period. Silica-based VIPs with a polymeric barrier film were used in this study. The study results showed the effectiveness of a VIP-based insulation to reduce the heat gains and losses through a wall compared to regular rigid foam insulation of the same thickness. However, the long-term performance monitoring indicated a gradual decline in the thermal performance of the foam-VIP composite. In addition, one-dimensional numerical models were created to simulate the in situ behavior of the foam-VIP composite. One model utilized constant thermal conductivities of the test wall components and another utilized temperature-dependent thermal conductivities; the latter used measurements of conductivity over temperatures ranging from −15 to 55 °C. The results of the simulations emphasized the need to use both temperature and time-dependent material properties for accurately predicting the long-term performance of VIP-based insulation systems.

Description: The Internet-of-Things (IoT) framework has been considered as an enabler of the smart world where all devices will be deployed with extra-sensory power in order to sense the world as well as communicate with other sensor nodes. As a result, smart devices require more energy. Therefore, energy harvesting (EH) and wireless power transfer (WPT) emerge as a remedy for relieving the battery limitations of wireless devices. In this work, we consider a multi-user amplify-and-forward (AF)-assisted network, wherein multiple source nodes communicate with destination nodes with the help of a relay node. All the source nodes and the relay node have the capability of EH. In addition, to cope with a single point of failure i.e., failure of the relay node due to the lack of transmit power, we consider the WPT from the source nodes to the relay node. For WPT, a dedicated energy control channel is utilized by the source nodes. To maximize the sum rate using a deadline, we adopt a joint approach of power allocation and WPT and formulate an optimization problem under the constraints of the battery as well as energy causality. The formulated problem is non-convex and intractable. In order to make the problem solvable, we utilize a successive convex approximation method. Furthermore, an iterative algorithm based on the dual decomposition technique is investigated to get the optimal power allocation and transfer. Numerical examples are used to illustrate the performance of the proposed iterative algorithm.

Description: With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

Description: Alternative fuels for the transport sector are being emphasized due to energy security and environmental issues. Possible alternative fuel options need to be assessed to realize their potential to alleviate environmental burdens before policy formulations. Western Australia (WA) is dominated by private cars, accounting for around 72% vehicles with 87% of those using imported gasoline, and resulting in approximately 14% of greenhouse gas (GHG) emissions from the transport sector. There is an urgent need for WA to consider alternative transport fuels not only to reduce the environmental burden but also to avoid future energy security consequences. This study assesses the environmental life cycle assessment (ELCA) of transport fuel options suitable for WA. The study revealed that ethanol (E65), electric (EV) and plug-in electric vehicle (PHEV) options can decrease global warming potential (GWP) by 40%, 29% and 14%, respectively, when compared to gasoline. The EV and PHEV also performed better than gasoline in the fossil fuel depletion (FFD) and water consumption (WC) impact categories. Gasoline, however, demonstrated better environmental performance in all the impact categories compared to hydrogen and that was mainly due to the high electricity requirement during the production of hydrogen. The use of platinum in hydrogen fuel cells and carbon fibre in the hydrogen tank for hydrogen fuel cell vehicles (HFCV) and Li-ion battery for EVs are the most important sources of environmental impacts. The findings of the study would aid the energy planners and decision makers in carrying out a comparative environmental assessment of the locally-sourced alternative fuels for WA.

Description: Given the growing intent to prevent decay in environmental management, the present study seeks to unearth the impact of corporate environmental strategy on employees’ voluntary environmental behavior by regulating or facilitating their perceived psychological green climate. Research problems and research questions are built on the essence of multiple theories—goal-setting theory, social identity theory, and social learning theory for grounding the research model. A total of 294 replies were collected through a self-administered survey from diverse industrial panoramas. We used structural equation modeling (SEM) analytics via AMOS-version 20.0 for measuring the hypothesized results. The study revealed that the corporate environmental strategy is displaying an insignificant direct influence on voluntary environmental behavior. However, the corporate environmental strategy indirectly influences, via the mediation effect, voluntary environmental behavior of employees through their psychological green climate perception. Directions for future research are recommended based on insights from the implications and limitations of the study.

Description: The next few years will be exciting as prototype universal quantum processors emerge, enabling the implementation of a wider variety of algorithms. Of particular interest are quantum heuristics, which require experimentation on quantum hardware for their evaluation and which have the potential to significantly expand the breadth of applications for which quantum computers have an established advantage. A leading candidate is Farhi et al.’s quantum approximate optimization algorithm, which alternates between applying a cost function based Hamiltonian and a mixing Hamiltonian. Here, we extend this framework to allow alternation between more general families of operators. The essence of this extension, the quantum alternating operator ansatz, is the consideration of general parameterized families of unitaries rather than only those corresponding to the time evolution under a fixed local Hamiltonian for a time specified by the parameter. This ansatz supports the representation of a larger, and potentially more useful, set of states than the original formulation, with potential long-term impact on a broad array of application areas. For cases that call for mixing only within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficiently implementable mixers than was possible in the original framework. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace, and soft constraints whose violation we wish to minimize. More efficient implementation enables earlier experimental exploration of an alternating operator approach, in the spirit of the quantum approximate optimization algorithm, to a wide variety of approximate optimization, exact optimization, and sampling problems. In addition to introducing the quantum alternating operator ansatz, we lay out design criteria for mixing operators, detail mappings for eight problems, and provide a compendium with brief descriptions of mappings for a diverse array of problems.

Description: Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

Description: A novel biochar alginate composite adsorbent was synthesized and applied for removal of Zn2+ ions from aqueous solution. Kinetics, equilibrium and thermodynamic studies showed the suitability of the adsorbent. From a Langmuir isotherm study, the maximum monolayer adsorption capacity of the composite adsorbent was found to be 120 mg/g. To investigate the effect of process variables like initial Zn2+ concentration (25–100 mg/L), adsorbent dose (0.4–8 g/L) and temperature (298–318 K) on Zn2+ adsorption, response surface methodology (RSM) based on a three independent variables central composite design of experiments was employed. A quadratic model equation was developed to predict the relationship between the independent variables and response for maximum Zn2+ removal. The optimization study reveals that the initial Zn2+ concentration and adsorbent dose were the most effective parameters for removal of Zn2+ due to higher magnitude of F-statistic value which effects to a large extent of Zn2+ removal. The optimum physicochemical condition for maximum removal of Zn2+ was determined from the RSM study. The optimum conditions are 43.18 mg/L initial metal ion concentration, 0.062 g adsorbent dose and a system temperature of 313.5 K. At this particular condition, the removal efficiency of Zn2+ was obtained as 85%.

Description: In this US-based study of the National Cancer Database (NCDB), we examined 8550 patients diagnosed with non-metastatic, invasive inflammatory breast cancer (IBC) who received surgery from 2004–2013. Patients were grouped into four biologic subtypes (HR+/HER2−, HR+/HER2+, HR−/HER2+, HR−/HER2−). On average, women were 56 years of age at diagnosis and were followed for a median of 3.7 years. The majority were white (80%), had private health insurance (50%), and presented with poorly differentiated tumors (57%). Approximately 46% of the cancers were 〉5 cm. Most patients underwent mastectomy (94%) and received radiotherapy (71%). Differences by biologic subtypes were observed for grade, lymph node invasion, race, and tumor size (p 〈 0.0001). Patients experiencing pathologic complete response (pCR, 12%) vs. non-pCR had superior 5-year overall survival (OS) (77% vs. 54%) (p 〈 0.0001). Survival was poor for triple-negative (TN) tumors (37%) vs. other biologic subtypes (60%) (p 〈 0.0001). On multivariable analysis, TN-IBC, positive margins, and not receiving either chemotherapy, hormonal therapy or radiotherapy were independently associated with poor 5-year survival (p 〈 0.0001). In this analysis of IBC, categorized by biologic subtypes, we observed significant differential tumor, patient and treatment characteristics, and OS.

Description: While traditional soil water sensors measure soil water content (SWC) at point scale, the actively heated fiber-optics (AHFO) sensor measures the SWC at field scale. This study compared the performance of a distributed (e.g., AHFO) and a point-based sensor on closing the field water balance and estimating the evapotranspiration (ET). Both sensors failed to close the water balance and produced larger errors in estimated ET (ETε), particularly for longer time periods with 〉60 mm change in soil water storage (ΔSWS), and this was attributed to a lack of SWC measurements from deeper layers (〉0.24 m). Performance of the two sensors was different when only the periods of ˂60 mm ΔSWS were considered; significantly lower residual of the water balance (Re) and ETε of the distributed sensor showed that it could capture the small-scale spatial variability of SWC that the point-based sensor missed during wet (70–104 mm SWS) periods of ˂60 mm ΔSWS. Overall, this study showed the potential of the distributed sensor to provide a more accurate value of SWS at field scale and to reduce the errors in water balance for shorter wet periods. It is suggested to include SWC measurements from deeper layers to better evaluate the performance of the distributed sensor, especially for longer time periods of 〉60 mm ΔSWS, in future studies.