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Vehicle-to-grid (V2G) technology enables bidirectional charging of electric vehicle (EV) and facilitates power grid ancillary services. However, battery pack in EV may develop in-cell dynamic variations over time. This is due to the structural complexity and electrochemical operations in the battery pack. These variations may arise in V2G systems due to: first, additional charging and discharging cycles to power grid; second, external shocks; and third, long exposures to high temperatures. A particular source of these variations is due to faulty sensors. Therefore, it can be argued that the battery packs in EV are highly reliant on the monitoring of these in-cell variations and their impact of propagation with each involved component. In this article, a prediction-based scheme to monitor the health of variation induced sensors is proposed. First, a propagation model is developed to predict the in-cell variations of a battery pack by calculating the covariance using a median-based expectation. Second, a hypothesis model is developed to detect and isolate each variation. This is obtained by deriving a conditional probability-based density function for the measurements. The proposed monitoring framework is evaluated using experimental measurements collected from Li-ion battery pack in EVs. The in-cell variation profiles have been verified using D-SAT Chroma 8000ATS hardware platform. The performance results of the proposed scheme show accurate analysis of these emerged variations.

The deployment of advanced sensors has strengthened the monitoring capability of power plants. In the context of the cogeneration process, the plant cooling is performed by the cooling towers using the condensation process on exhaust steam. However, the computer networks and industrial control systems built on this sensor-based digital layer may become vulnerable to cyberattacks. This may eventually raise a concern on the performance and security of these energy utilities. To resolve this issue, an inoculated subobserver-based fusion filter is proposed. It improves the resilience against malicious attacks in combined cycle power plants with desalination units, which are usually functioning in a closed-loop environment and infected with injected attacks. A time-delay-based state representation is considered for the system. To access latency in a closed-loop environment, a subsystem-based set of subobservers are introduced. Information from each sensor is then gathered using the interacting-multiple-model-based fusion process. The stability of the system has been proved and kept in-check at two different implementation levels of the procedure using functional equivalence and Lyapunov stability criteria, respectively. Performance evaluation is then conducted on a water-level system. Results show that the proposed scheme accurately extracted the system parameters from the contaminated measurements in the presence of multiple system disturbances and cyber intervention.

The exhaustion of natural energy and oil reserves has initiated the concept of renewable energy systems (RESs). This has expanded the vision of energy sector towards a diversified power grid while introducing the distributed energy resources (DERs) and distributed generation (DG). Though, this diversification is achieved by adding new energy generation sources and a two-way power flow, it opens the channel of production and trading with alternating current (AC) and direct current (DC) energy formats. But DC-based energy, due to its sporadic nature, can be further stored easily by energy storage devices. However, in recent years, a compelling need has arisen to understand the communication systems in distributed generation (DG) for better performance management, control and parallel power transfer. In this article, a bibliographic review on communication systems in distributed generations (DGs) is provided. The study identifies various communication technologies, standards, and protocols used in AC and DC-based DGs. Moreover, it contains the classification of different frameworks and methods involved. The methodology of different approaches and their likely combination are discussed for different types of communication networks. This study also represents useful information for readers, thereby demonstrate the complete life-cycle of digital data in sensors/actuators, transmitter, receiver, filter, decoder for control of DG elements and identifies future challenges as well. A comprehensive list of publications to date are compiled to provide a complete picture of different developments in this area.

The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks.

BackgroundThe lack of an objective method to evaluate the eardrum is a critical barrier to an accurate diagnosis. Eardrum images are classified into normal or abnormal categories with machine learning techniques. If the input is an otoscopy video, a traditional approach requires great effort and expertise to manually determine the representative frame(s).MethodsIn this paper, we propose a novel deep learning-based method, called OtoXNet, which automatically learns features for eardrum classification from otoscope video clips. We utilized multiple composite image generation methods to construct a highly representative version of otoscopy videos to diagnose three major eardrum diseases, i.e., otitis media with effusion, eardrum perforation, and tympanosclerosis versus normal (healthy). We compared the performance of OtoXNet against methods with that either use a single composite image or a keyframe selected by an experienced human. Our dataset consists of 394 otoscopy videos from 312 patients and 765 composite images before augmentation.ResultsOtoXNet with multiple composite images achieved 84.8% class-weighted accuracy with 3.8% standard deviation, whereas with the human-selected keyframes and single composite images, the accuracies were respectively, 81.8% ± 5.0% and 80.1% ± 4.8% on multi-class eardrum video classification task using an 8-fold cross-validation scheme. A paired t-test shows that there is a statistically significant difference (p-value of 1.3 × 10−2) between the performance values of OtoXNet (multiple composite images) and the human-selected keyframes. Contrarily, the difference in means of keyframe and single composites was not significant (p = 5.49 × 10−1). OtoXNet surpasses the baseline approaches in qualitative results.ConclusionThe use of multiple composite images in analyzing eardrum abnormalities is advantageous compared to using single composite images or manual keyframe selection.

Simulating the swimming of soft underwater robot remains challenging due to the absence of an efficient numerical framework that can effectively capture the geometrically nonlinear deformation of soft materials and structures when interacting with a liquid environment. Here, we address this by introducing a discrete differential geometry-based model that incorporates an implicit treatment of the elasticity of soft limbs and a fluid model with three different components: hydrodynamic drag, jetting, and virtual added mass. The physical engine can run faster than real-time on a single thread desktop processor. We experimentally validate this numerical simulation tool by performing tests using an untethered omni-directional star-shaped swimming soft robot that is capable of moving with multiple swimming gaits. Quantitative agreement between experiment and simulation indicates the potential application of such a numerical framework for robot design and for model-based control schemes.

In this paper, we propose a pathological image compression framework to address the needs of Big Data image analysis in digital pathology. Big Data image analytics require analysis of large databases of high-resolution images using distributed storage and computing resources along with transmission of large amounts of data between the storage and computing nodes that can create a major processing bottleneck. The proposed image compression framework is based on the JPEG2000 Interactive Protocol and aims to minimize the amount of data transfer between the storage and computing nodes as well as to considerably reduce the computational demands of the decompression engine. The proposed framework was integrated into hotspot detection from images of breast biopsies, yielding considerable reduction of data and computing requirements.

A cue-driven retrospection (CDR) method is presented here that is developed for modeling the decision making process of naturalistic decision makers. The method is obtained from analysis of data obtained for nonroutine situations from fireground incident commanders and focuses on the use of cues instead of patterns. The method revolves around the tenet that in no routine situations, the decision makers think in a bottom-up fashion using the important, relevant and diagnostic cues intuitively (Shaikh, 2011); the method intends to capture the arguments that the firefighters give to term or not term a cue important, relevant and diagnostic. The thematic analysis of data gathered for Shaikh (2011) gave rise to this method. For testing the method, a total of 12 firefighters were asked to recall an incident that was of no routine nature and they were asked to fill the CDR template. The firefighters were provided a set of cues to assist in the activity and extract more mentally. The results shows that the firefighters were able to identify the relevance, importance and diagnosticity of cues. The CDR method can be used for naturalistic decision makers as well as for professionals such as design engineers, paramedics, and computer programmers etc. The method can be used for developing decision support systems, evaluating performance of decision makers, as certaining areas where more training is required, and for investigating the decision making process. This research in itself is one of its kind because fire brigade in Pakistan is mostly unexplored and how firefighters make decisions in the absence of even water and other basic resources needs to be investigated. Moreover this researcher found them highly non cooperative in this research endeavor.

The utilization of Multimedia Instruction (MI) in teaching and learning is growing rapidly. The combination of various media assists educational reform, and is important to the improvement of education outputs. The MIuse has been a challenge to educators especially in Jordan. This study aimed to re-calculate the reliability and validity of online individualized MI instrument in a new Online Individualized Multimedia Instruction (OIMI) framework for engineering communication skills. In this study, this model designates the multimedia instruction as one of the latent variables, to be measured by six observed variables, which are modality, contiguity, personalization, coherence, redundancy, and signaling. Data collected and tested from 166engineering learners. Confirmatory factor analysis using AMOS was conducted to obtain three best-fit measurement models. The results showed evidence of a five-dimension measurement model for MI except forcoherence. This result enlightens the model, which includes explanations of Mayer’s Cognitive Theory of MI and multimedia instructional in Practice.

Graphical abstract, Highlights • Studied eight (8) promising nanocarriers for anti-cancer drug delivery. • Studied up-to-date strategies for cancer site targeting used in SDDSs. • Various stimulus techniques utilized for drug release at targeted sites are mentioned. • Studied toxicity of various nanocarriers used in SDDSs. • Challenges and research scope of nanocarriers in cancer therapy also highlighted., Nonspecific distribution and uncontrollable release of drugs in conventional drug delivery systems (CDDSs) have led to the development of smart nanocarrier-based drug delivery systems, which are also known as Smart Drug Delivery Systems (SDDSs). SDDSs can deliver drugs to the target sites with reduced dosage frequency and in a spatially controlled manner to mitigate the side effects experienced in CDDSs. Chemotherapy is widely used to treat cancer, which is the second leading cause of death worldwide. Site-specific drug delivery led to a keen interest in the SDDSs as an alternative to chemotherapy. Smart nanocarriers, nanoparticles used to carry drugs, are at the focus of SDDSs. A smart drug delivery system consists of smart nanocarriers, targeting mechanisms, and stimulus techniques. This review highlights the recent development of SDDSs for a number of smart nanocarriers, including liposomes, micelles, dendrimers, meso-porous silica nanoparticles, gold nanoparticles, super paramagnetic iron-oxide nanoparticles, carbon nanotubes, and quantum dots. The nanocarriers are described in terms of their structures, classification, synthesis and degree of smartness. Even though SDDSs feature a number of advantages over chemotherapy, there are major concerns about the toxicity of smart nanocarriers; therefore, a substantial study on the toxicity and biocompatibility of the nanocarriers has been reported. Finally, the challenges and future research scope in the field of SDDSs are also presented. It is expected that this review will be widely useful for those who have been seeking new research directions in this field and for those who are about to start their studies in smart nanocarrier-based drug delivery.

This paper addresses the nonlinear memory effects in the response of typical illumination light emitting diodes (LEDs), in order to enhance the performance of visible light communication (VLC) systems. These LEDs have a limited bandwidth of only several MHz. To reflect the physical mechanisms in the quantum well, we describe the LED transient response by a nonlinear dynamic differential equation. Three different mechanisms of the nonlinearity are relevant in the double hetero-structure LEDs, which result in dynamic nonlinearities, that is, a mixture of nonlinearities and memory effects. Hitherto, generic pre-distorter and non-linear equalizers have been studied for the LEDs. Yet this paper shows that recombination rates of photon generation can be translated into an equivalent discrete-time circuit that can be inverted. This allows us to develop a new pre-distorter with a simpler and more efficient structure than previously studied and overly generic approaches. The novel pre-distorter along with a parameter estimation can effectively overcome LED nonlinearity for high-speed VLC with amplitude-based single carrier modulations, including ON-OFF keying and pulse amplitude modulation-4 systems, and with the multi-carrier orthogonal frequency-division multiplexing. We report experimentally obtained eye-diagrams, first to justify our choice for the LED model on which our nonlinear pre-distorter have been based, and second to verify the effectiveness in enhancing the VLC link performance to the extent predicted by our model.

The COVID-19 pandemic has demonstrated the need for exploring different diagnostic and therapeutic modalities to tackle future viral threats. In this vein, we propose the idea of sentinel cells, cellular biosensors capable of detecting viral antigens and responding to them with customizable responses. Using SARS-CoV-2 as a test case, we developed a live cell sensor (SARSNotch) using a de novo-designed protein binder against the SARS-CoV-2 Spike protein. SARSNotch is capable of driving custom genetically-encoded payloads in immortalized cell lines or in primary T lymphocytes in response to purified SARS-CoV-2 Spike or in the presence of Spike-expressing cells. Furthermore, SARSNotch is functional in a cellular system used in directed evolution platforms for development of better binders or therapeutics. In keeping with the rapid dissemination of scientific knowledge that has characterized the incredible scientific response to the ongoing pandemic, we extend an open invitation for others to make use of and improve SARSNotch sentinel cells in the hopes of unlocking the potential of the next generation of smart antiviral therapeutics.

Drilling operation in Malaysia are typically from offshore, thus offshore weather condition does contributed to the success or delay of a drilling operation. Wait on Weather (WOW) especially during monsoon season in Malaysia has impacted Operator's drilling operation, thus incurring additional cost to Operator. Monsoon season in Malaysia is typically from November to February every year. This paper will discuss and share the statistics of actual WOW happening from 2008 to 2019 in Malaysia water especially for jack-up rig (JUR) and tender assisted drilling rig (TADR) which are two common rigs in Malaysia water. The data was collected from one of the drilling operator in Malaysia. These data will be of assistance to Operator in better planning and executing drilling operation with the actual statistics as the risk factor. WOW is considered as non-productive time (NPT), thus NPT data gathering from Operators in Malaysia water were conducted. Data was then filtered to achieve the WOW data. WOW data was segregated between region in Malaysia which are Peninsular Malaysia (PM), Sabah (SB) and Sarawak (SK) as well as rig type, which are JUR and TADR. Distribution analysis were made to calculate the average and observe the maximum numbers of actual WOW occurrence. Further analysis was made to zoom into monsoon season in Malaysia which typically in November to February. 11 years data is generally good coverage for the analysis since it covers the up and down of oil and gas industry. Analysis was also done for both mob/demob and operation stage where it can be observed that WOW for mob/demob stage during monsoon season is significantly higher compared to operation stage. At the end of the analysis, the average or maximum numbers of WOW will be shared, and it will be used as recommendation for future projects to consider these figures as WOW risk factor and embed in the planning stage. This paper will help not only Operators in Malaysia water but the host authority on understanding the WOW risk factor during monsoon season. As WOW is not something that can be predicted, utilizing the standard results from actual statistic data for the past 11 years will assist engineers to incorporate the WOW risk factor during planning and execution stage. Rig and project sequencing can be optimized with understanding of WOW impact thus reducing the value leakage during operation due to WOW.

Tympanic membrane (TM) diseases are among the most frequent pathologies, affecting the majority of the pediatric population. Video otoscopy is an effective tool for diagnosing TM diseases. However, access to Ear, Nose, and Throat (ENT) physicians is limited in many sparsely-populated regions worldwide. Moreover, high inter- and intra-reader variability impair accurate diagnosis. This study proposes a digital otoscopy video summarization and automated diagnostic label assignment model that benefits from the synergy of deep learning and natural language processing (NLP). Our main motivation is to obtain the key visual features of TM diseases from their short descriptive reports. Our video database consisted of 173 otoscopy records from three different TM diseases. To generate composite images, we utilized our previously developed semantic segmentation-based stitching framework, SelectStitch. An ENT expert reviewed these composite images and wrote short reports describing the TM's visual landmarks and the disease for each ear. Based on NLP and a bag-of-words (BoW) model, we determined the five most frequent words characterizing each TM diagnostic category. A neighborhood components analysis was used to predict the diagnostic label of the test instance. The proposed model provided an overall F1-score of 90.2%. This is the first study to utilize textual information in computerized ear diagnostics to the best of our knowledge. Our model has the potential to become a telemedicine application that can automatically make a diagnosis of the TM by analyzing its visual descriptions provided by a healthcare provider from a mobile device.

Can humans trust humanoid robots to perform social tasks? Trust is a multifactorial concept with several determinants that influence the interaction between humans and robots. These determinants include factors concerning human, robot, social, and context. To measure human trust in humanoid robots, interactive dialogues were used with subjective measures of general trust embedded in the dialogues. General trust was made up of three components: ability, benevolence, and integrity, each characterized by five attributes. These measures were mapped to objective measures of physiological trust comprising facial expressions, voiced speech, vision-based heart rate, and postural gestures. The purpose was to predict trust from a neural network of psychophysiological criteria. To validate the reliability of trust measures, a series of three experiments was conducted at three levels of interaction: human-human, human-robot and human-robot-human. While the methodology was used to measure human trust in robots, it has potential for measuring robot trust in humans.

Magnetic nanoparticles (MNPs) have been a powerful tool in recent biomedical and environmental research. MNPs have widespread uses, ranging from DNA extraction using magnetic bioseparation to hypothermic killing of cancerous cells. Apart from their use in magnetic hyperthermia and magnetic bioseparation, MNPs can be used as drug delivery systems (DDSs). Conventional DDSs are prone to various challenges such as premature release of the drug, off- target delivery, low efficiency in drug loading, and interaction with the natural immune system of human body. Many of these issues can be ameliorated by incorporating MNPs into the DDS, which can be further developed into a magnetic nanocomposite (MNC). These MNCs have better targeting efficiency and higher drug-loading efficacy due to large surface area and are responsive to external magnetic stimuli. As well as improving drug delivery efficiency, these MNCs can be utilized as a theranostics platform to simultaneously diagnose and treat the diseased area. Unique magnetic properties such as low toxicity, high magnetic saturation, and stability in biological solutions make MNPs an excellent choice for targeted drug delivery vehicle and good agents for photodynamic therapy. At the forefront of modern genetic engineering, MNPs are being used in the revolutionary gene editing tool CRISPR-Cas9 system to increase the efficacy of gene editing via increasing gene delivery efficiency. In this chapter we will briefly discuss various aspects of MNPs and their synthesis as well as functionalization of the particles to incorporate them into different MNCs and their biomedical application. We will also discuss the use of MNPs in imaging modalities, which provide an efficient theranostics module against different types of diseases such as cancer.

ObjectivesWith the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video.MethodsFive Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image.ResultsThere were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate.ConclusionEqual to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training.Level of EvidenceLevel 3

Soft swimming robots are primarily composed of elastically deformable materials, which typically make up the robot’s body, limbs, and/or fins. Such robots can swim by moving their limbs, flapping their fins, or undulating their body in order to control thrust and direction. This chapter presents a technique to model these soft swimming robots using a computational framework based on the method of discrete elastic rods (DER). This approach to soft robot simulation draws inspiration from methods to simulate slender structures that are widely used in the computer graphics community. In this framework, the soft robot limbs or fins are treated as flexible rods that deflect in response to internal actuation and surface tractions from contacting bodies and the surrounding fluid. Here, we apply this model to the special case of a seastar-inspired robot composed of radiating limbs that produce motion through bending and hydrodynamic drag. We begin with an overview of the DER-based framework and then present simulation results for forward swimming and turning.

The complexity of an interconnected power system has grown exponentially with the increased load demands. These demands and stress conditions are the potential nodes in coupled system to challenge the control and stability of a multi-operational power system. Once these potential nodes are exploited, this could lead to developing stringent conditions and generation of power system oscillations which are required by the grid codes to be damped and controlled adequately. A poorly damped set of power oscillations may lead to system-wide breakups or considerably reduce the power transfer over critical channels and corridors. In recent years, a compelling need has arisen to understand the oscillation damping in the conventional and modern power grids for better performance management, control, and stability in power transfer. In this paper, a bibliographic review on the damping of power system oscillations is provided. The study identifies various control design techniques, technologies, and characteristics used in power systems. Moreover, it discusses various devices and tools to minimize damping during the era of conventional grids and renewable energy integration. The methodology of different approaches and their likely combination are discussed for different types of control design. This study also represents useful information for readers on the complete life-cycle and development phase of technology to minimize the instability variations in generating units, transmission lines, transformers, loads, and HVDC lines. A comprehensive list of publications to date in the open literature is compiled to portray a complete visual of different developments in this area.

Efficient compression of a huge number of medical images becomes necessary for storing and transmitting in telemedicine applications. In this paper, an algorithm is proposed for highly efficient compression of 2D medical images. The proposed algorithm used Legendre moments to extract the features from images and the whale optimization algorithm (WOA) to select which of these moments are the optimum to be used in the reconstruction process and in turn will produce the optimum reconstruction quality. The proposed algorithm aims to achieve higher compression ratios while maintaining the quality of the images. Medical images from different imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and X-ray images are used in testing the proposed algorithm. The mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized correlation coefficient (NCC) are quantitative measures used to evaluate the performance of the proposed algorithm and well-known existing medical image compression methods. The results showed that the quality of the reconstructed images using the proposed algorithm is much better than those of the conventional 2D compression algorithms in terms of MSE, PSNR, SSIM, and NCC.

Ear diseases are frequently occurring conditions affecting the majority of the pediatric population, potentially resulting in hearing loss and communication disabilities. The current standard of care in diagnosing ear diseases includes a visual examination of the tympanic membrane (TM) by a medical expert with a range of available otoscopes. However, visual examination is subjective and depends on various factors, including the experience of the expert. This work proposes a decision fusion mechanism to combine predictions obtained from digital otoscopy images and biophysical measurements (obtained through tympanometry) for the detection of eardrum abnormalities. Our database consisted of 73 tympanometry records along with digital otoscopy videos. For the tympanometry aspect, we trained a random forest classifier (RF) using raw tympanometry attributes. Additionally, we mimicked a clinician’s decision on tympanometry findings using the normal range of the tympanogram values provided by a clinical guide. Moreover, we re-trained Inception-ResNet-v2 to classify TM images selected from each otoscopic video. After obtaining predictions from each of three different sources, we performed a majority voting-based decision fusion technique to reach the final decision. Experimental results show that the proposed decision fusion method improved the classification accuracy, positive predictive value, and negative predictive value in comparison to the single classifiers. The results revealed that the accuracies are 64.4% for the clinical evaluations of tympanometry, 76.7% for the computerized analysis of tympanometry data, and 74.0% for the TM image analysis while our decision fusion methodology increases the classification accuracy to 84.9%. To the best of our knowledge, this is the first study to fuse the data from digital otoscopy and tympanometry. Preliminary results suggest that fusing information from different sources of sensors may provide complementary information for accurate and computerized diagnosis of TM-related abnormalities.

Automatic identification of tissue structures in the analysis of digital tissue biopsies remains an ongoing problem in digital pathology. Common barriers include lack of reliable ground truth due to inter- and intra- reader variability, class imbalances, and inflexibility of discriminative models. To overcome these barriers, we are developing a framework that benefits from a reliable immunohistochemistry ground truth during labeling, overcomes class imbalances through single task learning, and accommodates any number of classes through a minimally supervised, modular model-per-class paradigm. This study explores an initial application of this framework, based on conditional generative adversarial networks, to automatically identify tumor from non-tumor regions in colorectal H&E slides. The average precision, sensitivity, and F1 score during validation was 95.13 ± 4.44%, 93.05 ± 3.46%, and 94.02 ± 3.23% and for an external test dataset was 98.75 ± 2.43%, 88.53 ± 5.39%, and 93.31 ± 3.07%, respectively. With accurate identification of tumor regions, we plan to further develop our framework to establish a tumor front, from which tumor buds can be detected in a restricted region. This model will be integrated into a larger system which will quantitatively determine the prognostic significance of tumor budding.

In this paper, an algorithm is proposed for efficient compression of bio-signals based on discrete Tchebichef moments and Artificial Bee Colony (ABC). The Tchebichef moments are used to extract features of the bio-signals, then, the ABC algorithm is used to select of the optimum features which achieve the best bio-signal quality for a specific compression ratio (CR). The proposed algorithm has been tested by using different datasets of Electrocardiogram (ECG), Electroencephalogram (EEG), and Electromyogram (EMG). The optimum feature selection using ABC significantly improve the quality of the reconstructed bio-signals. Different numerical experiments are performed to compress different records of ECG, EEG and EMG bio-signals by using the proposed algorithm and the most recent existing methods. The performance of the proposed algorithm and the other existing methods are evaluated using different metrics such as CR, PRD, and peak signal to noise ratio (PSNR). The comparison has shown that, at the same CR, the proposed compression algorithm yields the best quality of the reconstructed signals over the other existing methods.

Rare earth oxides (REOs) have gathered interest in recent years due to their multifarious applications in the biomedical field. A focused review depicting their applicability along with their prospects and associated challenges in this specific field is absent in the literature. This review attempts to specifically report the applications of six (6) REOs in the biomedical field to properly represent the advancement and state-of-the-art of the sector. While the applications can be divided into antimicrobial, tissue engineering, drug delivery, bio-imaging, cancer treatment, cell tracking and labeling, biosensor, reduction of oxidative stress, theranostic, and miscellaneous applications, it is found that the bio-imaging aspect is the most widely applied and holds the most promising ground from a biomedical perspective. Specifically, REOs have shown successful implementation in real water and sewage samples as antimicrobial agents, in bone tissue regeneration as biologically active and healing material, in anti-cancer therapeutic maneuvers by providing substantial binding sites for multifarious functional groups, in dual-modal and multi-modal MRI imaging by providing excellent or increased contrasting capabilities, in biosensing aspects by providing fast and parameter-dependent sensing, and so on. As per their prospects, it is predicted that several REOs will rival and/or replace currently available commercial bio-imaging agents, due to superior doping flexibility, healing mechanism in biological systems, and economic features in terms of bio-imaging and sensing. Furthermore, this study extends the findings with regards to the prospects and desired cautions in their applications, suggesting that while they are promising in multiple aspects, their cytotoxicity in particular cell lines should not be overlooked. This study will essentially invoke multiple studies to investigate and improve the utilization of REOs in the biomedical field.

In this article a vast literary and ample historic review has been examined to provide in detail introduction, knowledge and comparison of night vision imaging techniques. It unveils night vision enhancement methods described in the recent times such as contrast enhancement; color transfer based clustering, fast color contrast enhancement and pseudo-color fusion algorithm for self-adaptive enhancement system along many others. Furthermore, the scientific and mathematical details are elaborated along with the mechanisms used image fusion techniques, color mapping, histogram matching and statistical evaluation. Conclusively, the channel based color fusion technique stood out through statistical and perceptual analysis.

False Data Injection (FDI) is one of the most dangerous attacks on cyber-physical systems as it could lead to disastrous consequences in the operation of the power grids. In this paper, a comprehensive investigation of the (FDI) attacks in smart grids is presented. A detection algorithm is utilized in analyzing the FDI attacks in real-time environment based on Principle Component Analysis (PCA). It provides an adequate solution to the FDI problem for its ability to extract information about correlation of the collected measurements. This provides a more accurate and sensitive response than the previous FDI detection techniques. Furthermore, the light computations associated with this algorithm make it a very good candidate for real-time environment testing. The results concluded in the paper illustrate a very promising future for the PCA-based realtime FDI attack detection schemes.

Soft robots are primarily composed of soft materials that can allow for mechanically robust maneuvers that are not typically possible with conventional rigid robotic systems. However, owing to the current limitations in simulation, design and control of soft robots often involve a painstaking trial. With the ultimate goal of a computational framework for soft robotic engineering, here we introduce a numerical simulation tool for limbed soft robots that draws inspiration from discrete differential geometry based simulation of slender structures. The simulation incorporates an implicit treatment of the elasticity of the limbs, inelastic collision between a soft body and rigid surface, and unilateral contact and Coulombic friction with an uneven surface. The computational efficiency of the numerical method enables it to run faster than real-time on a desktop processor. Our experiments and simulations show quantitative agreement and indicate the potential role of predictive simulations for soft robot design., To advance the design of soft robots, novel computational frameworks that accurately model the dynamics of soft material systems are required. Here, the authors report a numerical framework for studying locomotion in limbed soft robots that is based on the discrete elastic rods algorithm.

Electric vehicles are a vital addition to the intelligent transportation systems. They bring features which could provide enhancements to decision making while improving on information and support systems. The critical nature of control is one of the main concerns for their advanced operations. In this work, the vehicle has been considered as a constrained system, where parameter optimization is required during the transition between different set-points of degree of freedom. Firstly, dynamics of the vehicle have been derived. Each vehicle dynamic has been monitored by a modeled sensor. Secondly, constrained optimization-based cost function has been set. This cost function is considered here to be the main building block for deriving augmented Lagrangian-based optimization to provide optimal control to the vehicle. Thirdly, co-states of the derived constraints are determined to maintain convergence. Numerical evaluations were made on a vehicle model. Results showed an ensured performance and accuracy of the proposed scheme.

The battery packs are one of the primary sources of energy for the electric vehicles (EVs), In order to achieve the maximum power, the structural complexity of battery has to be raised with arrays of thousands of cells. However, this has also raised the cost of battery packs which is around half the cost of an EV. This has given the concept of inductive wireless charging (IWC), which could instantly charge the running EVs, while decreasing the size and thereby cost of the battery packs to a considerable extent. In this paper, the focus is to analyze an electric vehicle self-charging system (SCS), where such a system has been considered as a constrained optimization problem. The SCS contains a set of power transmitters which provide the facility of self-charging, where optimization is required during transition between different set-points of these power transmitters. This optimization will be achieved here by deriving an Augmented Lagrangian-based cost function. Moreover, the proposition is built on a condition where there are no traffic interactions. Performance evaluations under different constraints ensure accuracy of the proposed system.

In recent years, there has been a growing interest in intelligent and adaptive vehicles. They have installed information and support systems, which may involve several applications and gadgets. A foremost reason on such an initiative could be for an adequate prevention of highway crashes and fatal accidents. Literature shows most of these accidents happen when the vehicle fail to estimate the sharp turns with steering angle. In this work, the focus is on an adequate estimation of the steering angle to minimize these accidents. Firstly, dynamics of vehicle have been derived. Secondly, an Unscented Kalman smoother (UKS) has been built to estimate the nonlinear parameters. Numerical evaluations were made on a vehicle model. Results ensure a better performance and accuracy of the proposed scheme.

In recent era, the quadcopters have become popular particularly due to their extension as unmanned aerial vehicles and remotely piloted aerial systems. The critical nature of its flight control is one of the main concerns for its advanced operations. These advanced operations involve on-board stabilization, trajectory tracking, unpredictable changes in the environment, and resilience to noisy data from small sensor systems. In this work, the quadcopter has been considered as a constrained problem, where optimization is required during transition between different set-points of advanced flight control, operations in action and reaction pairs. Firstly, dynamics of quadcopter along with its translational motion have been derived. Secondly, constrained optimization cost function has been set, which is the main building block for deriving the augmented Lagrangian optimization. Thirdly, co-states of the derived constraints are determined to maintain convergence. Performance evaluation showed accuracy of the proposed scheme.

Complex traffic flow is a huge concern towards achieving an efficient transportation system. It involves handling all the operational issues and effects while regulating the traffic system. These operational issues also have situation of uncertainties and vagueness. In this paper, the uncertainties in the form of perturbation have been the focus of study in the theory of traffic. To achieve that, the moving-block train model has been utilized for the perturbation analysis. Firstly, a delay-dependent moving-block model is represented. This model considered the dynamics of position and velocity in the perturbation modeling of system. These dynamics have been extracted from a measurement sensor. Secondly, an augmented Lyapunov- Krasoviskii function (ALKF) is built on this model for the realization of feasible region of stability. Numerical evaluations were made on a moving-block model. Results showed an effective implementation of the proposed scheme.

Automated facial expression recognition schemes have been a subject of interest ever since the inception of its idea. The initial efforts required supervised and controlled environments to get realistic and convincing experimental results. Various improved methods have been suggested to detect the facial expressions of a human. Some of the popularly used methods are automatic expression recognition system (AERS), graph-preserving sparse nonnegative matrix factorization (GSNMF) algorithm, two-phase test sample representation (TPTSR) technique and temporal template method. The current state-of-the-art techniques are able to detect facial expressions in difficult and obscured environments. Advancements in this technique have helped researchers to use video sequences as images to detect expressions. A single image is extracted from a video sequence, and elaborate techniques are applied to detect the expression. This article endeavors to discuss these intricate techniques and critically analyzes them. It helps the reader to understand the contemporary problems in facial recognition systems and how various researchers have employed different models to overcome these challenges. In this paper, the performance of various techniques such as AERS, GSNMF algorithm, TPTSR, performance-based character animation, temporal template method, feature extractions using Gabor filter and image sequencing-based methods has been scrutinized in terms of their efficiency, accuracy and effectiveness. The efficiency and accuracy of the techniques have been compared using various benchmarks such as leave one out, cross-validation and receiver operating characteristics. Each technique bears its own advantages and disadvantages in terms of accuracy and efficiency. The highest accuracy rate is exhibited by the technique using canny edge detection algorithm and chamfer image method.

A precise solution of a mathematical model of a mass connected to an elastic wire is being given in this work. The Optimal Homotopy Asymptotic Method is applied to solve this conventional model. Also, comparison with other numerical methodologies and its exact solution will be given for distinct amplitude of oscillations and compliance can be observed. Results suggest that this technique is useful for solving non-linear oscillatory system quite easily. The solution procedure confirm that this method can be easily extended to other kinds of non-linear oscillators.

In this paper, a numerical technique is applied to a five variable giving up smoking fractional mathematical model. This model is based on five types of smokers, i.e. potential, occasional, heavy, temporary quitters and permanent quitters. Efficacy of Perturbation Iteration Algorithm on fractional system of differential equations is shown graphically between standard Runge-Kutta method and PIA.

Troesch’s problem arose while the investigation of the confinement of a plasma column by applying radiation pressure was being conducted. It is an unstable problem in itself, with a two-point boundary value. The application of the Optimal Homotopy Asymptotic Method (OHAM) is used to attain an approximate solution for the nonlinear differential equation which provides and apt description of Troesch’s problem. Opposing the other reported results, through the variational iteration method, Laplace Transform Decomposition Method, Homotopy Analysis Method, and the Homotopy Perturbation Method, the accuracy of the current solution is commendable for a remarkably wide range of values of Troesch’s parameter. A conducted error analysis clearly admits the efficiency of OHAM.

A decision making model for building decision support system is reported here that is developed through working with fireground incident commanders of the United Kingdom. The model is suitable for decision making in non-routine situations. A detailed description of the development of the model is available in Shaikh (2011). There is a need to develop a working prototype decision support systems based on this model particularly for training and helping FGCs working in countries such as Pakistan, Sri Lanka, Indonesia etc.Editorial

An all new technique has been devised to solve non-linear LaneEmden type equations. This novel technique is based on the Perturbation Iteration Algorithm. In this paper, a few examples are presented for the illustration of the power and wide usability of the proposed method. Moreover, a compare and contrast with the actual solution is provided. It has been evaluated that by employment of this method, the construction of perturbation solutions converging swiftly to the true solutions usually becomes easy, by giving us room to exactly demonstrate how -terms influence linearized equations. This swift convergence of the method gives solutions that are accurate quantitatively through relatively little iteration.

Background and ObjectiveThe aim of this study is to develop and validate an automated image segmentation-based frame selection and stitching framework to create enhanced composite images from otoscope videos. The proposed framework, called SelectStitch, is useful for classifying eardrum abnormalities using a single composite image instead of the entire raw otoscope video dataset.MethodsSelectStitch consists of a convolutional neural network (CNN) based semantic segmentation approach to detect the eardrum in each frame of the otoscope video, and a stitching engine to generate a high-quality composite image from the detected eardrum regions. In this study, we utilize two separate datasets: the first one has 36 otoscope videos that were used to train a semantic segmentation model, and the second one, containing 100 videos, which was used to test the proposed method. Cases from both adult and pediatric patients were used in this study. A configuration of 4-levels depth U-Net architecture was trained to automatically find eardrum regions in each otoscope video frame from the first dataset. After the segmentation, we automatically selected meaningful frames from otoscope videos by using a pre-defined threshold, i.e., it should contain at least an eardrum region of 20% of a frame size. We have generated 100 composite images from the test dataset. Three ear, nose, and throat (ENT) specialists (ENT-I, ENT-II, ENT-III) compared in two rounds the composite images produced by SelectStitch against the composite images that were generated by the base processes, i.e., stitching all the frames from the same video data, in terms of their diagnostic capabilities.ResultsIn the first round of the study, ENT-I, ENT-II, ENT-III graded improvement for 58, 57, and 71 composite images out of 100, respectively, for SelectStitch over the base composite, reflecting greater diagnostic capabilities. In the repeat assessment, these numbers were 56, 56, and 64, respectively. We observed that only 6%, 3%, and 3% of the cases received a lesser score than the base composite images, respectively, for ENT-I, ENT-II, and ENT-III in Round-1, and 4%, 0%, and 2% of the cases in Round-2.ConclusionsFrame selection improves the diagnostic quality of composite images from otoscope video clips.

Massive Open Online Course (MOOC) is an online course accessible through the internet to deliver online learning content to anyone who is interested to gain knowledge in the various fields offered. Some of the accessible materials are video clips, downloadable and plenary discussion in social media activity. In addition, there are interactive forums that build a community of educators and students. This paper reviews the ongoing research trends from ongoing research in various disciplines at MOOC. Its background reviews are structured descriptively and chronologically from the information collected. This paper then focuses on the role of Self-Regulated Learning (SRL) as a strong support to MOOC. The advantages of MOOC are very beneficial, it enables more people to gain knowledge at no cost, without space or temporal constraints, in online courses over the web. The findings indicate the SRL has a great impact in the MOOC achievement. Although there are obstacles in providing solutions for the implementation of MOOCs to achieve the best results. Future studies should look at more complex and challenging current situations for the implementation of MOOCs in keeping with the latest modern learning.

Internet sites are sources of information for the detection of events, a special mention of traffic activity and accidental accidents or earthquake detection system. Because of the rapid growth of the last 20 years, there have been frequent traffic congestions in cities around the world. The increase in vehicles has caused a greater number of traffic events and, as a result, there are no common resources. We present a methodology for the acquisition, processing and classification of public Tweets with Natural Language Processing (NLP) techniques using the Vector Machine Support (SVM) algorithm, using text classification using social network data to detect incidents. Our view can detect tweets related to traffic, with an accuracy of 88.27%. In this document, we focus on a real-time monitoring system to detect traffic, for Twitter streams analysis by ranking of Twitter posts. We cannot even distinguish if an outdoor event throws traffic or not, multiplying the classification problem and correcting it by point 88.89%.

The development of whole slide scanners has revolutionized the field of digital pathology. Unfortunately, whole slide scanners often produce images with out-of-focus/blurry areas that limit the amount of tissue available for a pathologist to make accurate diagnosis/prognosis. Moreover, these artifacts hamper the performance of computerized image analysis systems. These areas are typically identified by visual inspection, which leads to a subjective evaluation causing high intra- and inter-observer variability. Moreover, this process is both tedious, and time-consuming. The aim of this study is to develop a deep learning based software called, DeepFocus, which can automatically detect and segment blurry areas in digital whole slide images to address these problems. DeepFocus is built on TensorFlow, an open source library that exploits data flow graphs for efficient numerical computation. DeepFocus was trained by using 16 different H&E and IHC-stained slides that were systematically scanned on nine different focal planes, generating 216,000 samples with varying amounts of blurriness. When trained and tested on two independent datasets, DeepFocus resulted in an average accuracy of 93.2% (± 9.6%), which is a 23.8% improvement over an existing method. DeepFocus has the potential to be integrated with whole slide scanners to automatically re-scan problematic areas, hence improving the overall image quality for pathologists and image analysis algorithms.

Ergonomics improvements in the workplace are gaining impetus in industry. As a means of fitting the workplace to workers, ergonomics improvements are increasingly applied with the aim of improving safety, health and productivity at work. A broad range of ergonomics measures can be applied through the initiative of workers. To support ergonomics application in several types of workplaces, a new manual specifying Ergonomic Checkpoints for indoor and outdoor workplaces was developed by the Southeast Asian Network of Ergonomics Societies (SEANES). This paper presents SEANES Ergonomic Checkpoints for Indoor and Outdoor Workplaces, with an illustration from Process Control room activities. The goal was to motivate workers to recognize hazards in the work environment and take initiatives that minimize the impact of unsafe acts on workers. The initiatives are published in the form of a self-guided manual. The manual makes use of a Workplace Checklist comprising low-cost actions in six technical areas: materials handling, workstation design, machine and tool safety, physical environment, welfare facilities, communication and work organization. Generic issues shared by 12 workplaces were identified in order to standardize and modify existing checkpoints.

The measurement of human trust in humanoid robots in human-robot interaction requires novel approaches that can predict trust effectively. We present a method that mapped subjective measures (i.e. general trust, psychological) to objective measures (i.e. physiological) to predict trust. We designed interactive dialogs that represent real world service scenarios of Business, Disaster, and Healthcare. The dialogs embedded fifteen trust attributes of Ability, Benevolence and Integrity (ABI) in the communication dialogs. The ABI measures were mapped to physiological measures of facial expressions, voiced speech and camera-based heart rate. Forty-eight subjects comprising 24 males and 24 females aged between 18 to 36 years participated in the experiment. Half of the subjects were Malays and half were Chinese. Three humanoid robots represented full bodied, partial bodied and virtual agents. The experimental design was a within-subjects design. Each subject was tested on all robots in all scenarios. Subjects scored trust on an online scale that ranged from 0 to 7 points. The subjective data was analyzed using Univariate and Oneway MANOVA. The results found the humanoids to be trustworthy in different service tasks. The attributes of ‘Integrity’ and ‘Ability’ trust components are important in Business and Disaster scenarios. The estimation of trust was about 83% accurate when using this creative approach. In conclusion, humanoid robots can interact with humans using dialogs that are representative of real world communication.

Trust is crucial when integrating virtual agents in human teams. Our study investigated the combined use of psychological and physiological measures in predicting human trust of agents undertaking social tasks. The psychological measures comprised trust scores on ability, benevolence and integrity. The physiological measures included facial expressions, voice, heart rate and gestural postures. Subjects interacted with two avatars. A neuro-fuzzy algorithm extracted rules from the psychophysiological data to predict trust. Results revealed that trust can be predicted with 88% accuracy.

High availability is always expected for the continuation of operations in a plant. However, this objective is unable to attain due to the unplanned breakdowns, which could lead to unusual variations and faults. This paper works on improving the availability of system by accurately handling the fault information. This was made possible by proposing a subsystem-based robust control. The overlapping decomposition was employed to tackle the variations independently for each state. The stability of the system was proved further using a Lyapunov stability criteria. Performance evaluation was then conducted on an interconnected continuous time stirred tank reactor (CSTR) with recycle. The proposed scheme effectively controlled the faults to avoid costly failures.