| 1. |
- Khan, Adil, et al.
(author)
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Predictive modeling for depth of wear of concrete modified with fly ash : A comparative analysis of genetic programming-based algorithms
- 2024
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In: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 20
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Journal article (peer-reviewed)abstract
- There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.
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| 2. |
- Khan, Majid, et al.
(author)
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Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms
- 2023
-
In: Heliyon. - : Cell Press. - 2405-8440. ; 10
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Journal article (peer-reviewed)abstract
- Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.
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| 3. |
- Naghavi, Mohsen, et al.
(author)
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Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013
- 2015
-
In: The Lancet. - 1474-547X .- 0140-6736. ; 385:9963, s. 117-171
-
Journal article (peer-reviewed)abstract
- Background Up-to-date evidence on levels and trends for age-sex-specifi c all-cause and cause-specifi c mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specifi c all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specifi c causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65.3 years (UI 65.0-65.6) in 1990, to 71.5 years (UI 71.0-71.9) in 2013, while the number of deaths increased from 47.5 million (UI 46.8-48.2) to 54.9 million (UI 53.6-56.3) over the same interval. Global progress masked variation by age and sex: for children, average absolute diff erences between countries decreased but relative diff erences increased. For women aged 25-39 years and older than 75 years and for men aged 20-49 years and 65 years and older, both absolute and relative diff erences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10.7%, from 4.3 million deaths in 1990 to 4.8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specifi c mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.
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| 4. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(author)
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Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
-
In: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
-
Journal article (peer-reviewed)abstract
- Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.
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| 5. |
- Azam, Asad Muhammad, et al.
(author)
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Analysis of degradation in UHMWPE a comparative study among the various commercial and laboratory grades UHMWPE
- 2016
-
In: 14TH INTERNATIONAL SYMPOSIUM ON ADVANCED MATERIALS (ISAM 2015). - : IOP PUBLISHING LTD.
-
Conference paper (peer-reviewed)abstract
- Oxidative degradation of the ultra-high molecular weight polyethylene ( UHMWPE) limits the life of implants. This degradation can be monitored to estimate the service life of UHMWPE following the standard protocols as defined by American Standards for Testing Materials ( ASTM). In this work, a comparative study has been carried on two commercially available UHMWPE grades i. e. GUR 1020 and GUR 1050 and one laboratory grade UHMWPE which was purchased from Sigma Aldrich. These powder samples were pressed while using hot press with controlled heating and cooling setup in open air under 200 bar of external pressure. These sheets were then subjected to accelerated aging in an oven at 80 degrees C for three weeks. The degradation of the UHMWPE was monitored by ATR-FTIR techniquefor three weeks. The oxidation index ( OI) measurement showed that the commercial grade UHMWPE i. e. GUR-1020 and GUR-1050 degrade more as compared to laboratory grade UHMWPE. The values of OI after three weeks of accelerating aging were found 0.18, 0.14, and 0.09 for GUR-1020, GUR-1050, and Sigma Aldrich, respectively. In addition to this, it was found that commercial grades of UHMWPE suffer more structural alterations as compared to laboratory grade one. We hope that these results will be of particular and fundamental importance for the researchers and orthopaedic industry.
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| 6. |
- Huerta, E. A., et al.
(author)
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Enabling real-time multi-messenger astrophysics discoveries with deep learning
- 2019
-
In: Nature reviews physics. - : Springer Science and Business Media LLC. - 2522-5820. ; 1:10, s. 600-608
-
Research review (peer-reviewed)abstract
- Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics. A group of experts suggests ways in which deep learning can be used to enhance the potential for discovery in multi-messenger astrophysics.
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| 7. |
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| 8. |
- Khan, Wali Ullah, et al.
(author)
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Integration of NOMA with Reflecting Intelligent Surfaces : A Multi-cell Optimization with SIC Decoding Errors
- 2023
-
In: IEEE Transactions on Green Communications and Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 2473-2400. ; 7:3, s. 1554-1565
-
Journal article (peer-reviewed)abstract
- Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.
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| 9. |
- Khan, Wali Ullah, et al.
(author)
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Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks
- 2023
-
In: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276. ; , s. 1-
-
Journal article (peer-reviewed)abstract
- Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.
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| 10. |
- Kleyko, Denis, et al.
(author)
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Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing
- 2017
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In: IEEE Transactions on Neural Networks and Learning Systems. - : IEEE. - 2162-237X .- 2162-2388. ; 28:6, s. 1250-1262
-
Journal article (peer-reviewed)abstract
- This article proposes the use of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli. The adoption of a Vector Symbolic representation ensures a one-layered design for the approach, while maintaining the previously reported properties and performance characteristics of Hierarchical Graph Neuron, and also improving the noise resistance of the architecture. The proposed architecture enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern.
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| 11. |
- Kleyko, Denis, et al.
(author)
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Imitation of honey bees’ concept learning processes using Vector Symbolic Architectures
- 2015
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In: Biologically Inspired Cognitive Architectures. - : Elsevier BV. - 2212-683X .- 2212-6848. ; 14, s. 57-72
-
Journal article (peer-reviewed)abstract
- This article presents a proof-of-concept validation of the use of Vector Symbolic Architectures as central component of an online learning architectures. It is demonstrated that Vector Symbolic Architectures enable the structured combination of features/relations that have been detected by a perceptual circuitry and allow such relations to be applied to novel structures without requiring the massive training needed for classical neural networks that depend on trainable connections.The system is showcased through the functional imitation of concept learning in honey bees. Data from real-world experiments with honey bees (Avarguès-Weber et al., 2012) are used for benchmarking. It is demonstrated that the proposed pipeline features a similar learning curve and accuracy of generalization to that observed for the living bees. The main claim of this article is that there is a class of simple artificial systems that reproduce the learning behaviors of certain living organisms without requiring the implementation of computationally intensive cognitive architectures. Consequently, it is possible in some cases to implement rather advanced cognitive behavior using simple techniques.
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| 12. |
- Maul, Kayleigh M., et al.
(author)
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Child abuse in Pakistan : A qualitative study of knowledge, attitudes and practice amongst health professionals
- 2019
-
In: International Journal of Child Abuse & Neglect. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0145-2134 .- 1873-7757. ; 88, s. 51-57
-
Journal article (peer-reviewed)abstract
- Child abuse is a global problem and pervades all cultures and socio-economic strata. The effects can be profound and life altering for victims. There is substantial literature from high income countries about signs of abuse, but a dearth of data from low and middle income countries like Pakistan. Healthcare professionals (HCP) are ideally placed to detect abuse, but, to inform interventions, an understanding of their experiences, training needs and cultural beliefs is needed. This study aimed to: (1) Explore the challenges that HCP face when managing cases of abuse; (2) Explore cultural beliefs and understand how these shape practice and (3) Identify training needs. A qualitative study using a phenomenological design was conducted. In-depth interviews were conducted with doctors, nurses and security staff in the emergency department of a large private hospital in Pakistan (n = 15). Interviews were undertaken in Urdu, translated into English and analysed using an inductive thematic approach. Multiple challenges were identified. The process of referral to legal services was poorly understood and further training and guidelines was suggested by participants. As the legal system in Pakistan does not allow HCP to keep potentially abused patients in their custody, they felt restricted in their ability to advocate and concerned about the safety of both the identified children and themselves. HCP have potential to detect abuse early; however, in Pakistan there are numerous challenges. HCP require support through training, as well as clear institutional frameworks and legal support to undertake this role.
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| 13. |
- Naeem, Rehan, et al.
(author)
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Impact of DC Grid Topology on Transient Stability of HVDC-Segmented Power System
- 2016
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In: 2016 International Conference on Intelligent Systems Engineering, ICISE 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467387538 ; , s. 51-54
-
Conference paper (peer-reviewed)abstract
- This paper compares the effects of ring, radial and interconnected DC grid topologies on the transient stability of a segmented 15 - bus electric grid for different HVDC schemes. For this purpose, three HVDC convertor technologies and four HVDC station configurations are considered. A three phase AC bus fault is applied and the rotor angle disturbance is analyzed. The results show that the transient stability is dependent on the topology for bipolar configuration of line commutated current source and capacitor commutated convertor technologies. For rest of the HVDC schemes, transient stability is independent of the topology used.
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| 14. |
- Nawaz, Muhammad Ul Saqlain, et al.
(author)
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Efficient resource prediction framework for software-defined heterogeneous radio environmental infrastructures
- 2023
-
In: Advanced Engineering Informatics. - : Elsevier Ltd.. - 1474-0346. ; 56
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Journal article (peer-reviewed)abstract
- Artificial Intelligence (AI) is defining the future of next-generation infrastructures as proactive and data-driven systems. AI-empowered radio systems are replacing the existing command and control radio networks due to their intelligence and capabilities to adapt to the radio environmental infrastructures that include intelligent networks, smart cities and AV/VR enabled factory premises or localities. An efficient resource prediction framework (ERPF) is proposed to provide proactive knowledge about the availability of radio resources in such software-defined heterogeneous radio environmental infrastructures (SD-HREIs). That prior information enables the coexistence of radio users in SD-HREIs. In a proposed framework, the radio activity is measured in both the unlicensed bands that include 2.4 and 5 GHz, respectively. The clustering algorithms k- means and DBSCAN are implemented to segregate the already measured radioactivity as signal (radio occupancy) and noise (radio opportunity). Machine learning techniques CNN and LRN are then trained and tested using the segregated data to predict the radio occupancy and radio opportunity in SD-HREIs. Finally, the performance of CNN and LRN is validated using the cross-validation metrics.
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| 15. |
- Osipov, Evgeny, et al.
(author)
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Holographic Graph Neuron
- 2014
-
In: International Conference on Computer and Information Sciences. - : IEEE Communications Society. - 9781479943913 ; , s. 1-6
-
Conference paper (peer-reviewed)abstract
- This article proposes the usage of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron. The adoption of VSA representation maintains previously reported properties and performance characteristics of HGN and further makes it suitable for implementation in distributed wireless sensor networks of tiny devices.
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| 16. |
- Patil, Ajinkya H., et al.
(author)
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Constraining the epoch of reionization with the variance statistic : simulations of the LOFAR case
- 2014
-
In: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 443:2, s. 1113-1124
-
Journal article (peer-reviewed)abstract
- Several experiments are underway to detect the cosmic-redshifted 21-cm signal from neutral hydrogen from the Epoch of Reionization (EoR). Due to their very low signal-to-noise ratio, these observations aim for a statistical detection of the signal by measuring its power spectrum. We investigate the extraction of the variance of the signal as a first step towards detecting and constraining the global history of the EoR. Signal variance is the integral of the signal's power spectrum, and it is expected to be measured with a high significance. We demonstrate this through results from a simulation and parameter estimation pipeline developed for the Low-Frequency Array (LOFAR)-EoR experiment. We show that LOFAR should be able to detect the EoR in 600 h of integration using the variance statistic. Additionally, the redshift (z(r)) and duration (Delta z) of reionization can be constrained assuming a parametrization. We use an EoR simulation of z(r) = 7.68 and Delta(z) = 0.43 to test the pipeline. We are able to detect the simulated signal with a significance of four standard deviations and extract the EoR parameters as z(r) = 7.72(-0.18)(+0.37) and Delta z = 0.53(-0.23)(+0.12) in 600 h, assuming that systematic errors can be adequately controlled. We further show that the significance of detection and constraints on EoR parameters can be improved by measuring the cross-variance of the signal by cross-correlating consecutive redshift bins.
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| 17. |
- Sadrollah, Ghazaleh Pour, et al.
(author)
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A Distributed Framework for Supporting 3D Swarming Applications
- 2014
-
Conference paper (peer-reviewed)abstract
- In-flight wireless sensor networks (WSN) are ofincreased interest owing to efficiency gains in weight and operationallifetime of IP-enabled computers. High impact 3Dswarming applications for such systems include autonomousmapping, surveying, servicing, environmental monitoring anddisaster site management. For distributed robotic applications,such as quad copter swarms, it is critical that the robots are ableto localise themselves autonomously with respect to other robotsand to share information. The importance of fast and reliabledissemination of localised information in these elastic threedimensionalnetworks provides us sufficient reason to presenta distributed framework and hardware settings for passing thisinformation pervasively through the swarm. The research field ofInternet of Things (IoT) have for several years been addressingissues around low-power, low-bandwidth wireless communication.By applying IoT technologies to the challenges around swarming,new opportunities are created. However, since IoT have beenprimarily used with stationary devices, the introduction of flyingsensors will add more challenges to address.
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| 18. |
- Sandin, Fredrik, et al.
(author)
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Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?
- 2014
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In: Journal of Software Engineering and Applications. - : Scientific Research Publishing, Inc.. - 1945-3116 .- 1945-3124. ; 7:5, s. 387-395
-
Journal article (peer-reviewed)abstract
- Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.
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| 19. |
- Smith, Gregory N., et al.
(author)
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The effects of counterion exchange on charge stabilization for anionic surfactants in nonpolar solvents
- 2016
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In: Journal of Colloid and Interface Science. - : Elsevier BV. - 1095-7103 .- 0021-9797. ; 465, s. 316-322
-
Journal article (peer-reviewed)abstract
- Hypothesis: Sodium dioctylsulfosuccinate (Aerosol OT or NaAOT) is a well-studied charging agent for model poly(methyl methacrylate) (PMMA) latexes dispersed in nonpolar alkane solvents. Despite this,few controlled variations have been made to the molecular structure. A series of counterion exchanged analogs of NaAOT with other alkali metals (lithium, potassium, rubidium, and cesium) were prepared, and it was expected that this should influence the stabilization of charge on PMMA latexes and the properties of the inverse micelles. Experiments: The electrophoretic mobilities of PMMA latexes were measured for all the counterion exchanged AOT analogs, and these values were used to calculate the electrokinetic or f potentials. This enabled a comparison of the efficacy of the different surfactants as charging agents. Small-angle scattering measurements (using neutrons and X-rays) were performed to determine the structure of the inverse micelles, and electrical conductivity measurements were performed to determine the ionized fractions and Debye lengths.
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| 20. |
- Zahid, Nida, et al.
(author)
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Psychosocial factors influencing quality of life in patients with primary brain tumors in Pakistan : an analytical cross-sectional study
- 2023
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In: BMC Research Notes. - : Springer Nature. - 1756-0500. ; 16:1
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Journal article (peer-reviewed)abstract
- ObjectiveDespite quality of life (QoL) being recognized as an important outcome in neuro-oncology, there is a lack of research from Pakistan where sociocultural differences may influence QoL. This study aimed to measure the QoL in patients with primary brain tumors (PBTs) and assess its association with mental health outcomes and social support.ResultsOur study included a total of 250 patients, with a median age of 42 years (range 33-54 years). The commonest brain tumors were glioma (46.8%) and meningioma (21.2). The mean global QoL of the sample was 75.73 +/- 14.9. The majority of patients had high social support (97.6%) and were not depressed (90%) or anxious (91.6%).On multivariable linear regression, global QoL was inversely associated with no or low income (beta coefficients: -8.75 to -11.84), having hypertension (-5.53), currently using a urine catheter (-13.55), having low social support (-28.16) suffering from mild (-15.31) or symptomatic (-23.84) depression, or mild anxiety (-13.22).
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