Recent Submissions

  • Estimations of Integrated Information Based on Algorithmic Complexity and Dynamic Querying

    Hernández-Espinosa, Alberto; Zenil, Hector; Kiani, Narsis A.; Tegner, Jesper (World Scientific, 2021-08-22) [Book Chapter]
    Integrated information has been introduced as a metric to quantify the amount of information generated by a system beyond the information generated by its individual elements. While the metrics associated with the Greek letter ϕ require the calculation of the interaction of an exponential number of sub-divisions of the system, most of these numerical approaches related to the metric are based on the basics of classical information theory and perturbation analysis. Here we introduce and sketch alternative approaches to connect algorithmic complexity and integrated information based on the concept of algorithmic perturbation rooted in algorithmic information dynamics and its concept of programmability. We hypothesize that if an object is algorithmic random or algorithmic simple, algorithmic random perturbations will have little to no effect to the internal capabilities of a system to produce integrated information but when an object is more integrated the object will also display elements able to perturb the object and increase or decrease its algorithmic randomness. We sketch some of these ideas related to an object integrated information value and its algorithmic information content. We propose that such an algorithmic perturbation test quantifying compression sensitivity may provide a system with a means to extract explanations–causal accounts–of its own behavior hence making IIT and associated measure ϕ more explainable and interpretable. Our technique may reduce the number of calculations to arrive at some estimations with algorithmic perturbation guiding a more efficient search. Our work sets the stage for a systematic exploration and further investigation of the connections between algorithmic complexity and integrated information at the level of both theory and practice.
  • Pharmacometabolomics: A New Horizon in Personalized Medicine

    Emwas, AbdulHamid; Szczepski, Kacper; T. McKay, Ryan; Asfour, Hiba; Chang, Chungke; Lachowicz, Joanna; Jaremko, Mariusz (IntechOpen, 2021-08-09) [Book Chapter]
    Pharmacology is the predominant first-line treatment for most pathologies. However, various factors, such as genetics, gender, diet, and health status, significantly influence the efficacy of drugs in different patients, sometimes with fatal consequences. Personalized diagnosis substantially improves treatment efficacy but requires a more comprehensive process for health assessment. Pharmacometabolomics combines metabolomic, genomic, transcriptomic and proteomic approaches and therefore offers data that other analytical methods cannot provide. In this way, pharmacometabolomics more accurately guides medical professionals in predicting an individual’s response to selected drugs. In this chapter, we discuss the potentials and the advantages of metabolomics approaches for designing innovative and personalized drug treatments.
  • Towards a new model of human tissues for 5G and beyond

    Ben Saada, A.; Ben Mbarek, Sofiane; Choubani, F. (CRC Press, 2021-06-18) [Book Chapter]
    A planar multilayered model is investigated in this paper using a cylinder insertion simulating a hair shaft. This model has been proved sensible to the mean hair density and length of the human hair.
  • A protoplast-based bioassay to quantify strigolactone activity in arabidopsis using strigoquant

    Braguy, Justine; Samodelov, Sophia L.; Andres, Jennifer; Ochoa-Fernandez, Rocio; Al-Babili, Salim; Zurbriggen, Matias D. (Springer US, 2021-05-25) [Book Chapter, Protocol]
    Understanding the biological background of strigolactone (SL) structural diversity and the SL signaling pathway at molecular level requires quantitative and sensitive tools that precisely determine SL dynamics. Such biosensors may be also very helpful in screening for SL analogs and mimics with defined biological functions. Recently, the genetically encoded, ratiometric sensor StrigoQuant was developed and allowed the quantification of the activity of a wide concentration range of SLs. StrigoQuant can be used for studies on the biosynthesis, function and signal transduction of this hormone class. Here, we provide a comprehensive protocol for establishing the use of StrigoQuant in Arabidopsis protoplasts. We first describe the generation and transformation of the protoplasts with StrigoQuant and detail the application of the synthetic SL analogue GR24. We then show the recording of the luminescence signal and how the obtained data are processed and used to assess/determine SL perception.
  • Plant-Mediated Green Synthesis of Nanoparticles

    Munir, Hira; Bilal, Muhammad; Mulla, Sikandar I.; Abbas Khan, Hassnain; Iqbal, Hafiz M.N. (Springer International Publishing, 2021-05-19) [Book Chapter]
    Nanoparticles are an inspiring group of nanostructured materials with broad-spectrum applications in different fields such as catalysis, antimicrobial treatment, drug delivery, nanomedicine, environmental remediation, electronics, and chemical sensors. Nevertheless, the techniques used for preparation are environmentally unfriendly. Aiming to promote the greener synthesis of nanoparticles, this chapter spotlights plant-mediated eco-friendly and sustainable development of nanoparticles. Naturally occurring plant extracts are enriched with a plethora of various biologically active biomolecules and secondary metabolites, including alkaloids, terpenoids, flavonoids, enzymes, and phenolic substances. These bioactive compounds can catalyze the reduction of metal ions into biogenic nanoparticles in an eco-sustainable single-step biosynthetic process. Additionally, the utilization of plant extracts and their derived compounds circumvents the necessity for capping and stabilizing agents and generates bioactive size and shape-dependent green nanoparticles. Herein, we have made an effort that describes the synthesis of a wide range of metal-based nanoparticles (platinum, gold, zinc oxide, silver, and titanium dioxide nanoparticles) by using plant extract as a green synthesis matrix. In addition, different parts of plants that have widely been utilized for the biosynthesis of these NPs with several sizes and shapes by biological methodologies are briefly described. In conclusion, the greener synthesis approaches are safer and easier to exploit the massive preparation of nanostructured particles.
  • Fundamental Aspects and Applications of Ultrasonically Induced Cavitation in Heavy Fuel Oil with a Focus on Deasphalting, Emulsions, and Oxidative Desulfurization

    Guida, Paolo; Jameel, Abdul Gani Abdul; Saxena, Saumitra; Roberts, William L. (American Chemical Society, 2021-04-29) [Book Chapter]
    The combustion of hydrocarbons will continue to feed the planet’s growing demand for mobility and power generation over the next several decades, shifting to lower-value, more-difficult-to-burn fuels while at the same time meeting more stringent emissions regulations. These lower-value fuels include heavy fuel oils and vacuum residuals, which are difficult to burn cleanly due to the presence of asphaltenes, the insoluble fractions with exceptionally high molecular weight that are found in high concentrations in crude oils. In particular, heavy fuel oils (HFO) are widely used in marine and power-generation sectors, and the International Maritime Organization’s (IMO2020) promulgation has redistributed the HFO demand and pushed the world’s economy into a new paradigm. We seek solutions for such a complex oil industry paradigm by utilizing some state-of-the-art technologies like ultrasonically induced cavitation (UIC). In the current chapter, we have discussed a roadmap for use of “bottom-of-barrel fuel” with high asphaltene content via UIC-based fuel upgrading, desulfurization, and direct use (emulsions). We expect that a strategy of using UIC for asphaltene modification and water-in-HFO-enabled microexplosions will significantly impact the combustion of HFO. Furthermore, ultrasonic-assisted oxidative desulfurization can be utilized to remove undesired sulfur to meet marine or power sector requirements. Deasphalting, emulsions, and desulfurization solutions could be applied in a multiplicity of combustion-driven energy conversion platforms, including compression ignition engines, gas turbines, and boilers.
  • Meaningful physical education in an individual pursuits unit

    Vasily, Andrew (Routledge, 2021-02-18) [Book Chapter]
    In this chapter, I provide an in-depth look at how I explicitly planned an individual pursuits unit (cycling, skateboarding, and racquet sports) to not only consider the features of the Meaningful Physical Education (Meaningful PE) approach, but also how I unpacked each of these features with students throughout the unit. I show how the specific feature of challenge was discussed with students in order to help them better understand what were their own entry points to learning in these units. Included in this chapter are practical assessment examples that connect to the features of Meaningful PE and student reflections on their experiences in these units.
  • Recent Advancement of Interdigital Sensor for Nitrate Monitoring in Water

    Alahi, Md Eshrat E.; Hui, Yun; Tina, Fahmida Wazed; Akhter, Fowzia; Nag, Anindya; Wu, Tianzhun; Mukhopadhyay, S. C. (Springer Nature, 2021-02-16) [Book Chapter]
    Water contamination is a significant problem in all over the world, and it is crucial to monitor the contaminating nutrients regularly for keeping the groundwater or drinking water safe. The nitrate ion has a remarkable impact on human health and the environment, and excessive use of this ion might damage the ecological system and the natural environments. Nitrate ions can be detected through various laboratory-based methods or in-situ sensor-based methods to develop a monitoring system. But for the last few years, the Interdigital sensor is used to detect the nitrate ions due to their reasonable fabrication costs and secure sensing mechanism. Some such sensors have high sensitivity with a reasonable limit of detection (LOD). Others might have a reasonable sensitivity with the reduced cost, where the proposed detection method uses a sensor-based portable sensing system. This chapter discusses the different working principles and fabrication methods of the Interdigital sensor for nitrate ions detection in water.
  • Fabrication of Interdigitated Sensors: Issues and Resolution

    Nag, Anindya; Mukhopadhyay, S. C.; Gooneratne, C. P. (Springer Nature, 2021-02-16) [Book Chapter]
    The design and implementation of interdigital sensors in the sensing world have been pivotal in the last few decades. Due to the advantages imparted by these designs, researchers all over the world have practiced developing prototypes based on them. The variation in the physical and material specificities of the interdigital sensors varies with their individual applications. In order to develop a range of sensing prototypes for biomedical, industrial and environmental applications, the optimization of the fabrication of these interdigitated electrodes has been done over the years. This chapter highlights some of the research works done on their design, development and implementation, based on a range of fabrication processes. Some of the issues faced by each of these fabrication techniques, as well as the formed prototypes, have also been discussed in the chapter. Finally, some of the possible remedies to address the existing issues have been elucidated, along with a brief market survey that has been mentioned at the end of the chapter.
  • Theory and practice of orbital angular momentum and beyond

    Trichili, Abderrahmen; Cox, Mitchell A.; Perez-Garcia, Benjamin; Ooi, Boon S.; Alouini, Mohamed-Slim (Accepted by Wiley, 2021-02) [Book Chapter]
    Nearly three decades since its discovery, orbital angular momentum (OAM) has proven to be highly versatile for a wide range of applications. It is an indispensable tool in quantum optics, has made a significant impact in optical tweezing, enabled higher contrast and more detailed imaging, and offers a convenient way to harness the space degree of freedom in telecommunications. In this paper, we present a review of a wide range of applications of OAM as well as describing the creation and detection of OAM modes, with a focus on the use of OAM in communications. In addition, we detail various similar higher-order optical modes, such as vector vortex modes, and provide an introduction to the use of OAM in quantum optics, pitched for readers new to the field.
  • RISCuer: a reliable multi-UAV search and rescue testbed

    Abdelkader, Mohamed; Fiaz, Usman A.; Toumi, Noureddine; Mabrok, Mohamed A.; Shamma, Jeff S. (Elsevier BV, 2021-01-29) [Book Chapter]
    We present the Robotics Intelligent Systems & Control (RISC) Lab multiagent testbed for reliable search and rescue and aerial transport in outdoor environments. The system consists of a team of three multirotor unmanned aerial vehicles (UAVs), which are capable of autonomously searching, picking up, and transporting randomly distributed objects in an outdoor field. The method involves vision-based object detection and localization, passive aerial grasping with our novel design, GPS-based UAV navigation, and safe release of the objects at the drop zone. Our cooperative strategy ensures safe spatial separation between UAVs at all times and we prevent any conflicts at the drop zone using communication-enabled consensus. All computation is performed onboard each UAV. We describe the complete software and hardware architecture for the system and demonstrate its reliable performance using comprehensive outdoor experiments and by comparing our results with some recent, similar works.
  • Co-creation of Pediatric Physical Therapy Environments: Humanistic Co-design Process

    Alomrani, Hadeel; Aljabr, Rana; Almansoury, Rneem; Alsinan, Abduallah (Springer Nature, 2021-01-26) [Book Chapter]
    In this paper, we describe the co-design process for creating interactive pediatric physical therapy (PT) spaces by applying gamification techniques, with a focus on helping children with gross motor delay which impact their mobility, independence, quality of movement, and balance coordination. The co-design of activities for age-appropriate gross motor skills was conducted through iterative cycles of co-creation with physical therapists and occupational therapists. Consequently, we propose exergaming interactive user interfaces, designed in a way to support improving a child’s gross motor performance. The design involves personalized illustrative graphic characters drawn on the room’s surfaces. The efficacy of such immersive space will be examined in experimental user acceptance studies and the design implications will be discussed.
  • Backflash Light as a Security Vulnerability in Quantum Key Distribution Systems

    Vybornyi, Ivan; Trichili, Abderrahmen; Alouini, Mohamed-Slim (Springer Nature, 2021-01-25) [Book Chapter]
    Based on the fundamental rules of quantum mechanics, two communicating parties can generate and share a secret random key that can be used to encrypt and decrypt messages sent over an insecure channel. This process is known as quantum key distribution (QKD). Contrary to classical encryption schemes, the security of a QKD system does not depend on the computational complexity of specific mathematical problems. However, QKD systems can be subject to different kinds of attacks, exploiting engineering, and technical imperfections of the components forming the systems. Here, we review the security vulnerabilities of QKD. We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks. We equally highlight the method for quantifying backflash emission and the different ways to mitigate this effect.
  • Energy Geoscience and Engineering

    Santamarina, Carlos; Rached, Rached (Springer Nature, 2021-01-15) [Book Chapter]
    Quality of life is strongly correlated with power consumption. The geo-disciplines have a crucial role to play in the energy challenge by contributing solutions to all kind of energy resources from resource recovery to energy and waste storage. Energy geoengineering requires a broad understanding of physical processes (sediments, fractured rocks and complex multiphase fluids), coupled phenomena, constitutive models for extreme conditions, and wide-ranging spatial and time scales. Numerical methods are critical for the analysis, design, and optimal operation of energy geosystems under both short and long-term conditions. Furthermore, they allow “numerical experiments” at temporal and spatial scales that are unattainable in the laboratory. Yet, computer power can provide a false sense of reality and unjustified confidence; simulations face uncertainties related to the validation of complex multi-physics codes, limited data, excessive numbers of degrees of freedom, ill-conditioning, and uncertain model parameters. Dimensional analyses help identify the governing processes and allow for simpler and more reliable simulations. Educational programs must evolve to address the knowledge needs in energy geoscience and engineering.
  • Nanocomposite sensors for smart textile composites

    Nauman, Saad; Lubineau, Gilles (Elsevier BV, 2021) [Book Chapter]
    Textile composites are an emerging class of materials pulled by the emerging demand in wearable electronics. These composites use some aspect of textile technologies in their fabrication, ranging from fiber placement techniques such as weaving and knitting to various coating practices such as printing or dying. Contrary to most of structural composites that use fibers in various forms and types as reinforcement, smart textile composites mainly take advantage of exceptional properties of nanofillers, both carbonaceous and noncarbonaceous. These fillers can be used to create reinforcement network with exceptional properties even at very low filler concentrations. These fillers have not only been used to improve mechanical properties but also been incorporated for their exceptional electrical and thermal conductivities. These latter attributes have been successfully exploited for the development of smart composites capable of sensing changes in their environment.
  • Conclusion and further research directions

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Developing efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.
  • Linear latent variable regression (LVR)-based process monitoring

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Fast-paced developments in data acquisition, instrumentation technology and the era of the Internet-of-Things have resulted in large amounts of data produced by modern industrial processes. The ability to extract useful information from these multivariate datasets has vital benefits that could be utilized in process monitoring. In the absence of a physics-based process model, data-driven approaches such as latent variable modeling have proved to be practical for process monitoring over the past four decades. The aim of this chapter is to review and show the challenges in multivariate process monitoring based on linear models. Specifically, after presenting the limitations of the full-rank regression model, we provide a brief overview of linear latent variable models such as principal component analysis, principal component regression, and partial least squares regression. To deal with dynamic systems, we present dynamic extensions of these methods that capture both static and dynamic features in multivariate processes. We then provide a brief overview of univariate monitoring schemes, such as exponentially-weighted moving average and cumulative sum and generalized likelihood ratio monitoring schemes and their multivariate counterparts. To apply such tools to multivariate data, we employ appropriate multivariate dimension-reduction techniques according to the features of a process, and we use monitoring schemes to monitor more informative variables in a lower dimension. Next, we aim to identify which process variables contribute to abnormal change; conventional contribution plots and radial visualization tool are briefed. Lastly, the effectiveness of the presented inferential modeling techniques is assessed using simulated data. We also present a study on monitoring influent measurements at a water resource recovery facility. Finally, we discuss limitations of the presented monitoring approaches and give some possible directions to rectify these limitations.
  • Unsupervised deep learning-based process monitoring methods

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    In this chapter, first we provide an overview of some of the shallow-machine learning approaches used in anomaly detection and outlier detection in data mining, namely data clustering techniques. Then, we give a brief description of two frequently used unsupervised machine learning algorithms for one-class classification or detection, namely one-class SVM and support vector data description (SVDD). Particular attention is paid to deep learning models. We present the commonly used deep learning models based on autoencoders (Variational Autoencoder, Denoising Autoencoder, and Contrastive Autoencoder), probabilistic models (Boltzmann Machine and Restricted Boltzmann Machine) and deep neural models (Deep Belief Network and Deep Boltzmann Machine), and we show their capacity and limitations. Finally, we merge the desirable properties of shallow learning approaches, such as one-class support vector machine and k-nearest neighbors and unsupervised deep-learning approaches to develop more sophisticated and efficient monitoring techniques.
  • Unsupervised recurrent deep learning scheme for process monitoring

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Precisely detecting anomalies in process monitoring is beneficial to enhance the operation of the monitored process by avoiding catastrophic failures and reducing maintenance costs. Unsupervised deep learning techniques are increasingly popular because of their capacity to uncover relevant information from large and complex datasets without using labeled data. In this chapter, we review and evaluate the detection performance of recurrent neural networks (RNNs)-based approaches based on a multivariate time series. RNNs are a powerful tool to appropriately model temporal dependencies in multivariate time series data. We first offer a brief overview of RNNs, from the simplest RNNs with no memory states, to sophisticated architectures with several gates and memory components. Particularly, we focus on those that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a gated recurrent unit (GRU). We then present hybrid deep learning models that integrate the desirable features of RNNs and LSTM, which are capable of approximating complex distributions of deep belief networks and restricted Boltzmann machines. We then apply these models with numerous clustering algorithms for uncovering anomalies. We finally demonstrate these methods on real measurements of effluents from a coastal municipal wastewater treatment plant in Saudi Arabia.
  • Modeling air pollution by atmospheric desert

    Lelieveld, Jos; Abdelkader, Mohamed; Astitha, Marina; Karydis, Vlassis A.; Klingmüller, Klaus (Elsevier BV, 2021) [Book Chapter]
    High concentrations of aeolian dust affect the air quality and climate in large regions across Northern Africa, the Middle East, and parts of Asia. To assess the environmental impacts, numerical models have been developed that include mineral dust emissions, atmospheric transport and chemistry, and deposition processes. Since the dust can disperse across continents and oceans, there is a need to model a large geographical area. Here we present a state-of-the-art global atmospheric chemistry–climate model, with detailed representations of these processes. One unique model feature is the chemical interaction of dust with air pollution (chemical aging), which alters the microphysics of particles relevant for their atmospheric lifetime, e.g., the hygroscopic growth behavior, optical properties, and aerosol–cloud interactions, thus influencing the hydrologic cycle and climate. Based on recent developments and published results, we present a comparison of model calculations with satellite and ground-based remote sensing data as well as surface observations of dust concentrations and deposition. The model results are used to evaluate the consequences of aeolian dust for climate and public health.

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