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The five papers in this special section focus on collaboration and innovation dynamics in software ecosystems. Over the past years, there has been an exponential growth in the number of articles mentioning "ecosystem" as their topic, and among them, ecosystems in the context of information and communication technologies have received particular attention. Much of this research is motivated by the intuition that ecosystems facilitate innovation. Yet, despite this burgeoning research activity accompanied by a flourish of reviews, actual empirical investigations of the collaboration and innovation dynamics in an ecosystem in general and in software ecosystems specifically remain few and far between. [ABSTRACT FROM AUTHOR]

This paper is devoted to electric arc furnace (EAF) modeling using a random differential equation based on the power balance equation. The proposed approach broadens and improves the model through the introduction of stochastic processes in place of existing coefficients. The paper presents a method which enables the estimation of EAF model coefficients with the help of measurement data - voltage and current waveforms recorded during the melting stage of an EAF work cycle. The estimation process is conducted with a Monte Carlo method and genetic algorithm, which is applied iteratively to each of the defined frames of the input signal. The estimated coefficients have been analyzed with respect to their time variability as well as the probability distributions of their values and increments. The results have been extensively visualized. Next, the identification of the stochastic processes representing the model coefficients has been carried out. Based on the previous results and autocorrelation functions, the density functions and parameters of discrete-time stochastic processes were identified. The paper presents solutions validated with statistical tests. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the human body shape using a parametric-based template that lacks the surface details. As such resulting body shape appears to be without clothing. In this paper, we propose a novel learning-based framework that combines the robustness of the parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information. Our method can restore detailed human body shapes with complete textures beyond skinned models. Experiments demonstrate that our method has outperformed previous state-of-the-art approaches, achieving better accuracy in terms of both 2D IoU number and 3D metric distance. [ABSTRACT FROM AUTHOR]

This paper presents a command governor (CG) based adaptive cruise controller (ACC) that is applied in simulation to normal driving scenarios and emergency stopping scenarios. The vehicle-following case study used in this paper involves a heavy-duty ego vehicle and a light-duty non-connected lead vehicle (i.e., the ego vehicle does not communicate with the lead vehicle and can only infer the lead vehicles’ position and velocity states through its own sensors). Typically, to ensure constraints in the presence of disturbances, receding horizon based ACCs will assume some known worst-case behavior of the lead vehicle. In the presence of a stochastic, non-connected lead vehicle, however, achieving such a guarantee requires a worst-case assumption on the behavior of the lead vehicle for all future time. In this work, the CG assumes a lead vehicle velocity profile that will be achieved with a prescribed level of certainty, based on a stochastic characterization of lead vehicle behavior that has been informed by actual on-road data. The CG ensures safe following distance under this probabilistic lead vehicle assumption. Here, “safe following distance” is based on the ego vehicle’s ability to come to a stop without collision if the lead vehicle were to suddenly brake at maximum deceleration after proceeding at a velocity profile that is prescribed based on a statistical lower bound on lead vehicle velocity. Ultimately, the CG ensures that the worst-case safe following distance is satisfied with a prescribed probability, thereby paralleling chance-constrained CG formulations. Simulation results for a heavy-duty truck indicate that the CG-based ACC outperforms a PID-ACC in terms of fuel economy and drivability. Additionally, the CG-ACC approach was able to ensure rear-end collision avoidance in emergency stopping simulations. [ABSTRACT FROM AUTHOR]

Periodicity is a frequently happening phenomenon for social interactions in temporal networks. Mining periodic communities are essential to understanding periodic group behaviors in temporal networks. Unfortunately, most previous studies for community mining in temporal networks ignore the periodic patterns of communities. In this paper, we study the problem of seeking periodic communities in a temporal network, where each edge is associated with a set of timestamps. We propose novel models, including $\sigma$ σ -periodic $k$ k -core and $\sigma$ σ -periodic $k$ k -clique, that represent periodic communities in temporal networks. Specifically, a $\sigma$ σ -periodic $k$ k -core (or $\sigma$ σ -periodic $k$ k -clique) is a $k$ k -core (or clique with size larger than $k$ k ) that appears at least $\sigma$ σ times periodically in the temporal graph. The problem of searching periodic core is efficient but the resulting communities may be not enough cohesive; the problem of enumerating all periodic cliques is not efficient (NP-hard) but the resulting communities are very cohesive. To compute all of them efficiently, we first develop two effective graph reduction techniques to significantly prune the temporal graph. Then, we transform the temporal graph into a static graph and prove that mining the periodic communities in the temporal graph equals mining communities in the transformed graph. Subsequently, we propose a decomposition algorithm to search maximal $\sigma$ σ -periodic $k$ k -core, a Bron-Kerbosch style algorithm to enumerate all maximal $\sigma$ σ -periodic $k$ k -cliques, and a branch-and-bound style algorithm to find the maximum $\sigma$ σ -periodic clique. The results of extensive experiments on five real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is the leading cause of mortality worldwide and its incidence is rising due to an aging population. The development and progression of CVD is directly linked to adverse vascular hemodynamics and biomechanics, whose in-vivo measurement remains challenging but can be simulated numerically and experimentally. The ability to evaluate these parameters in patient-specific CVD cases is crucial to better predict future disease progression, risk of adverse events, and treatment efficacy. While significant progress has been made toward patient-specific hemodynamic simulations, blood vessels are often assumed to be rigid, which does not consider the compliant mechanical properties of vessels whose malfunction is implicated in disease. In an effort to simulate the biomechanics of flexible vessels, fluid-structure interaction (FSI) simulations have emerged as promising tools for the characterization of hemodynamics within patient-specific cardiovascular anatomies. Since FSI simulations combine the blood's fluid domain with the arterial structural domain, they pose novel challenges for their experimental validation. This paper reviews the scientific work related to FSI simulations for patient-specific arterial geometries and the current standard of FSI model validation including the use of compliant arterial phantoms, which offer novel potential for the experimental validation of FSI results. [ABSTRACT FROM AUTHOR]

In the wide field of tumor treatment, thermal ablation procedures are very promising. Alternating magnetic fields are used to transfer the energy from outside the patient to the tumor area located anywhere in the body. This energy is converted to heat by implanted devices located in the tumor area. In this paper, the spatial distribution of the magnetic field of different circular coil configurations is analyzed and optimized with focus on patients’ safety and on coil configuration performance. The analysis is based on several performance criteria and is conducted with respect to the worst case scenario of a contactless thermal tumor ablation of deep-seated tumors, in which the energy has to be transferred over a considerably large distance. The magnetic field and the specific absorption rate (SAR) are calculated numerically and the performance criteria are evaluated based on a model of a human body including a tumor area. The most suitable coil configurations for different application scenarios are presented and a thermal analysis is done. Based on this, the minimum required heating power, coil current and number of coil windings, and the corresponding maximum SAR to achieve an adequate rise of tissue temperature are evaluated. For a heating power of 1.45 W, a minimum SAR of 130 mW/kg, a maximum power transfer efficiency of 1.05%, and a maximum coupling coefficient of 0.00695 is achieved. This paper shows the potential to enhance the safety of the patients significantly by choosing the appropriate coil configuration for a specific application scenario. [ABSTRACT FROM AUTHOR]

Prosthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous controller treats a gait cycle in a unified way. Thus it is expected to better facilitate normative biomechanics by providing a gait predictive model to contribute a non-switching controller that supports a continuum of tasks. Furthermore, a better method is to train a personalized trajectory prediction model suitable for personal characteristics according to personal walking data. This paper proposes a Gaussian process enhanced Fourier series (GPEFS) method to construct a gait prediction model that represents the human locomotion as a continuous function of phase, speed and slope. Firstly the joint trajectories are transformed into the Fourier coefficient space by least square method. Then the relationship between each Fourier coefficient and task input can be learned by multiple Gaussian process regression (GPRs) model respectively. Compared with directly using GPR to fit the joint trajectory under multi task, our method greatly reduces the computational burden, so as to meet the real-time application scenario. In addition, in Fourier coefficient space, the difference in all tasks between the Fourier coefficient of personal data and the one of statistical data follows the same trend. Therefore, a personalized prediction model is built to predict an individual’s kinematics over a continuous range of slopes and speeds given only one personalized task at level ground and normal speed. The experimental results show that the gait prediction model and the personalized prediction model are feasible and effective. [ABSTRACT FROM AUTHOR]

Accurate prediction of the traffic state can help to address the issue of traffic congestion, providing guiding advices for people’s travel and traffic regulation. In this paper, we propose a novel short-term traffic flow prediction approach based on empirical mode decomposition and combination model fusion. First, we explore the amplitude-frequency characteristics of short-term traffic flow series, and use empirical mode decomposition to decompose traffic flow to several components with different frequency. Second, based on the results of self-similarity analysis of each component, improved extreme learning machine, seasonal auto regressive integrated moving average and auto regressive moving average are selected to predict different components. Meanwhile, an improved fruit fly optimization algorithm is proposed to optimize the weight coefficient of the combination model. Third, the prediction results of each prediction model are multiplied by their respective weight coefficient to get the final prediction results. We evaluate our prediction approach by doing thorough experiment on a real traffic data set. Moreover, experimental results show that the proposed approach has superior performance than state-of-the-art prediction methods or models in short-term traffic flow prediction. [ABSTRACT FROM AUTHOR]

This paper demonstrates that false data injection (FDI) attacks are extremely limited in their ability to cause physical consequences on $N-1$ reliable power systems operating with real-time contingency analysis (RTCA) and security constrained economic dispatch (SCED). Prior work has shown that FDI attacks can be designed via an attacker-defender bi-level linear program (ADBLP) to cause physical overflows after re-dispatch using DCOPF. In this paper, it is shown that attacks designed using DCOPF fail to cause overflows on $N-1$ reliable systems because the system response modeled is inaccurate. An ADBLP that accurately models the system response is proposed to find the worst-case physical consequences, thereby modeling a strong attacker with system level knowledge. Simulation results on the synthetic Texas system with 2000 buses show that even with the new enhanced attacks, for systems operated conservatively due to $N-1$ constraints, the designed attacks only lead to post-contingency overflows. Moreover, the attacker must control a large portion of measurements and physically create a contingency in the system to cause consequences. Therefore, it is conceivable but requires an extremely sophisticated attacker to cause physical consequences on $N-1$ reliable power systems operated with RTCA and SCED. [ABSTRACT FROM AUTHOR]

Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts. A nonuniform pricing mechanism, referred to as the temporal locational marginal pricing (TLMP), is proposed. As an extension of the standard locational marginal pricing (LMP), TLMP takes into account both generation and ramping-induced opportunity costs. It eliminates the need for the out-of-the-market uplifts and guarantees full dispatch-following incentives regardless of the accuracy of the demand forecasts used in the dispatch. It is also shown that, under TLMP, a price-taking market participant has incentives to bid truthfully with its marginal cost of generation. Part II of the paper extends the theoretical results developed in Part I to more general network settings. It investigates a broader set of performance measures, including the incentives of the truthful revelation of ramping limits, revenue adequacy of the operator, consumer payments, generator profits, and price volatility under the rolling-window dispatch model with demand forecast errors. [ABSTRACT FROM AUTHOR]

Palmprint recognition is one of the most popular biometric technologies. Recent researches mainly focus on the recognition performance, while pay less attention to the data protection issues. In this paper, we propose an active biometric data protection model for securing palmprint images in transmission or storage scenarios, called Dynamic Random Invisible Watermark Embedding (DRIWE) model. The DRIWE model implicitly embeds a watermark in each original palmprint ROI image, and then separates the embedded watermark from the watermarked image before identification. If the separated watermark is consistent with the original watermark, it indicates that the image is trustworthy and can be used in the subsequent recognition process. Otherwise, it proves that the image has been illegally tampered with. Furthermore, a two-dimensional image information entropy loss is proposed to enhance the generalization of the model to different watermarks. It ensures that the model can always assign enough information to the host image (i.e., original palmprint image) when different watermarks are applied. Thus, it enables the separator to extract the complete watermark from the watermarked image. This greatly enhances the dynamics and randomness of the watermark embedding process and further improves the ability to secure the data. Adequate experiments are conducted on two benchmark palmprint databases. The results show that the proposed DRIWE model has satisfactory attack resistance and strong generalization ability: even if only one watermark is used in training stage, it can be generalized to a dozen of other new watermark images in the testing stage. In addition, the optimal accuracy of the watermarked data is only reduced by 0.07% compared with the original data. [ABSTRACT FROM AUTHOR]

Wind turbines (WTs) can beseriously damaged by lightning strikes and they can be struck by a significant number of flashes. This should be taken into account when the WT lightning protection system is designed. Moreover, WTs represent a path for the lightning current that can modify the well-known effects of the lightning discharge in terms of radiated electromagnetic fields, which are a source of damage and interference for nearby structures and systems. In this paper, a WT struck by a lightning discharge is analyzed with a full-wave modelling approach, taking into account the details of the WT and its interactions with the lightning channel. The effects of first and subsequent return strokes are analyzed as well as that of the rotation angle of the struck blade. Results show that the lightning current along the WT is mainly affected by the ground reflection and by the reflection between the struck blade and the channel. The computed electromagnetic fields show that, for subsequent return strokes, the presence of a WT almost doubles their magnitude with respect to a lightning striking the ground. Such enhancement is emphasized when the inclined struck blade is considered. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and sparsity recovery consistency of IHT has long been known in compressed sensing. From the perspective of statistical learning, another fundamental question is how well the IHT estimation would predict on unseen data. This paper makes progress towards answering this open question by introducing a novel sparse generalization theory for IHT under the notion of algorithmic stability. Our theory reveals that: 1) under natural conditions on the empirical risk function over $n$ samples of dimension $p$ , IHT with sparsity level $k$ enjoys an $\tilde {\mathcal {O}}(n^{-1/2}\sqrt {k\log (n)\log (p)})$ rate of convergence in sparse excess risk; 2) a tighter $\tilde {\mathcal {O}}(n^{-1/2}\sqrt {\log (n)})$ bound can be established by imposing an additional iteration stability condition on a hypothetical IHT procedure invoked to the population risk; and 3) a fast rate of order $\tilde {\mathcal {O}}\left ({n^{-1}k(\log ^{3}(n)+\log (p))}\right)$ can be derived for strongly convex risk function under proper strong-signal conditions. The results have been substantialized to sparse linear regression and sparse logistic regression models to demonstrate the applicability of our theory. Preliminary numerical evidence is provided to support our theoretical predictions. [ABSTRACT FROM AUTHOR]

Bio-inspired neuron models are the key building blocks of brain-like neural networks for brain-science exploration and neuromorphic engineering applications. The efficient hardware design of bio-inspired neuron models is one of the challenges to implement brain-like neural networks, as the balancing of model accuracy, energy consumption and hardware cost is very challenging. This paper proposes a high-accuracy and energy-efficient Fast-Convergence COordinate Rotation DIgital Computer (FC-CORDIC) based Izhikevich neuron design. For ensuring the model accuracy, an error propagation model of the Izhikevich neuron is presented for systematic error analysis and effective error reduction. Parameter-Tuning Error Compensation (PTEC) method and Bitwidth-Extension Error Suppression (BEES) method are proposed to reduce the error of Izhikevich neuron design effectively. In addition, by utilizing the FC-CORDIC instead of conventional CORDIC for square calculation in the Izhikevich model, the redundant CORDIC iterations are removed and therefore, both the accumulated errors and required computation are effectively reduced, which significantly improve the accuracy and energy efficiency. An optimized fixed-point design of FC-CORDIC is also proposed to save hardware overhead while ensuring the accuracy. FPGA implementation results exhibit that the proposed Izhikevich neuron design can achieve high accuracy and energy efficiency with an acceptable hardware overhead, among the state-of-the-art designs. [ABSTRACT FROM AUTHOR]

In this paper, a theoretical model has been proposed for the first time to illustrate the living Tulsi (Holy Basil) leaf evolution in describing the branching systems in the intermediate range of frequencies. The proposed fitting function following the applied model portrays the whole stages of the temporal evolution of Tulsi (Holy Basil, “Ocimumtenuiflorum”) leaf during the six days of impedance measurements with a high degree of accuracy (with fitting error less than 0.1 percent). The developed fitting parameter enables to identify the three stages of temporal evaluation of the Tulsi leaf as “vital leaf activity” (first stage), “quasi-chaotic behaviour” (second stage), and the “dying” (third stage). The theoretical model proposed in this work incorporated a fractal element having complex conjugated power law of exponents and a fractal element with a time lagged branching process. This novel approach introduces an additional degree of freedom over the previously proposed impedance models in terms of its imaginary part of complex conjugated power-law exponent. Further, it increases flexibility and versatility in accurately modeling the behavioral variations of complex branching systems, whose seemingly nondeterministic temporal nature had been considered earlier as counterintuitive and random. The incorporation of branching processes in comprehensively explaining the complex biological systems allows us to gain a deeper insight into the transfer of charge processes in the intermediate range of available frequency scales. Furthermore, the proposed model validates the presence of new fractal elements with a complex conjugated power law of exponents in naturally occurring biological processes. The experimental confirmation can play a key role in explaining a wide class of branching processes in complex systems and enrich the modern theory of fractional calculus. [ABSTRACT FROM AUTHOR]

Over the past decades, Chemical-induced Disease (CID) relations have attracted extensive attention in biomedical community, reflecting wide applications in biomedical research and healthcare field. However, prior efforts fail to make full use of the interaction between local and global contexts in biomedical document, and the derived performance needs to be improved accordingly. In this paper, we propose a novel framework for document-level CID relation extraction. More specifically, a stacked Hypergraph Aggregation Neural Network (HANN) layers are introduced to model the complicated interaction between local and global contexts, based on which better contextualized representations are obtained for CID relation extraction. In addition, the CID Relation Heterogeneous Graph is constructed to capture the information with different granularities and improve further the performance of CID relation classification. Experiments on a real-world dataset demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

This study is motivated by the fact that localization in millimeter wave(mmWave) Vehicle-to-Vehicle communication becomes a more critical problem because both the terminals of the communication link are in motion. The positional awareness merely based on GPS or local sensors has an error margin of around 10 meters, which can worsen in uncertain real-time conditions such as road topology and highway traffic. The paper analyses the relation between vehicle dynamics, beamforming, and beam alignment for highly directive antennas. When the subsystem models presented in this paper are taken into consideration, the joint vehicle dynamics-beamforming approach will optimize the link connectivity and SNR. The vehicle dynamics model is designed to be more realistic considering the non-linear acceleration based on the throttle-brake jerks due to induced noises. The Unscented Kalman Filter based prediction subsystem predicts the alignment values even during non-linear acceleration and jerks. The feasibility of antenna design for the beamforming strategies is supported by the requirements of phase shifters and the number of elements. [ABSTRACT FROM AUTHOR]

Wheels are vital components on railway vehicles and may encounter various problems during long-term operation, e.g., wear and defects. The existing preventative reprofiling strategy with fixed reprofiling interval and cutting depth causes huge maintenance costs. In order to reduce life cycle cost (LCC), this paper proposes an optimal life cycle reprofiling (LCR) strategy for risk-based preventative maintenance of train wheels. First, the probabilistic models of wheel wear, reprofiling, and defects are built. Then, Markov decision process is constructed to model a complete wheel degradation process in its life cycle with sequential decision-making of reprofiling. Meanwhile, the reward matrices are designed considering LCC and safety-related costs. Further, a two-loop optimization scheme is proposed to pursue the optimal LCR strategy minimizing LCC. In the inner loop, policy iteration is adopted to pursue the optimal reprofiling policy. In the outer loop, gradient descent with momentum is employed to optimize the overhaul limits. This scheme achieves fast convergence and global optimization of maintenance decision-making for both routine and overhaul reprofiling. Finally, the mechanism of optimal reprofiling policy is exemplified, including the principles of prolonging, protecting, and abandoning. The main trade-offs in cutting depth and overhaul limits provide the feasibility for optimization. A case study and field tests demonstrate that through controlling wheel degradation and life span, LCR strategy can not only reduce the LCC of wheel maintenance, but also improve the reliability and safety of wheel operation. [ABSTRACT FROM AUTHOR]

A key challenge in the task of human pose and shape estimation is occlusion, including self-occlusions, object-human occlusions, and inter-person occlusions. The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild. In this paper, we focus on the estimation of human pose and shape in the case of inter-person occlusions, while also handling object-human occlusions and self-occlusion. We propose a novel framework that synthesizes occlusion-aware silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh renderer is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. In addition, keypoints-and-silhouette-driven training data in panoramic viewpoints are synthesized to compensate for the lack of viewpoint diversity in any existing dataset. Experimental results show that we are among the state-of-the-art on the 3DPW and 3DPW-Crowd datasets in terms of pose estimation accuracy. The proposed method evidently outperforms Mesh Transformer, 3DCrowdNet and ROMP in terms of shape estimation. Top performance is also achieved on SSP-3D in terms of shape prediction accuracy. Demo and code will be available at https://igame-lab.github.io/LASOR/. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary data and enables interpretability of the factors. We factorize the Bernoulli parameter and consider an additional Beta prior on one of the factors to further improve the model’s expressive power. While similar models have been proposed in the literature, they only exploit the Beta prior as a proxy to ensure a valid Bernoulli parameter in a Bayesian setting; in practice it reduces to a uniform or uninformative prior. Besides, estimation in these models has focused on costly Bayesian inference. In this paper, we propose a simple yet very efficient majorization-minimization algorithm for maximum a posteriori estimation. Our approach leverages the Beta prior whose parameters can be tuned to improve performance in matrix completion tasks. Experiments conducted on three public binary datasets show that our approach offers an excellent trade-off between prediction performance, computational complexity, and interpretability. [ABSTRACT FROM AUTHOR]

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput. [ABSTRACT FROM AUTHOR]

Medical applications have driven many areas of engineering to optimize diagnostic capabilities and convenience. In the near future, wireless body area networks (WBANs) are expected to have widespread impact in medicine. To achieve this impact, however, significant advances in research are needed to cope with the changes of the human body’s state, which make coherent communications difficult or even impossible. In this paper, we consider a realistic noncoherent WBAN system model where transmissions and receptions are conducted without any channel state information due to the fast-varying channels of the human body. Using distributed reception, we propose several symbol detection approaches where on-off keying (OOK) modulation is exploited, among which a supervised-learning-based approach is developed to overcome the noncoherent system issue. Through simulation results, we compare and verify the performance of the proposed techniques for noncoherent WBANs with OOK transmissions. We show that the well-defined detection techniques with a supervised-learning-based approach enable robust communications for noncoherent WBAN systems. [ABSTRACT FROM AUTHOR]

Identifying position errors for Graves’ ophthalmopathy (GO) patients using electronic portal imaging device (EPID) transmission fluence maps is helpful in monitoring treatment. However, most of the existing models only extract features from dose difference maps computed from EPID images, which do not fully characterize all information of the positional errors. In addition, the position error has a three-dimensional spatial nature, which has never been explored in previous work. To address the above problems, a deep neural network (DNN) model with structural similarity difference and orientation-based loss is proposed in this paper, which consists of a feature extraction network and a feature enhancement network. To capture more information, three types of Structural SIMilarity (SSIM) sub-index maps are computed to enhance the luminance, contrast, and structural features of EPID images, respectively. These maps and the dose difference maps are fed into different networks to extract radiomic features. To acquire spatial features of the position errors, an orientation-based loss function is proposed for optimal training. It makes the data distribution more consistent with the realistic 3D space by integrating the error deviations of the predicted values in the left-right, superior-inferior, anterior-posterior directions. Experimental results on a constructed dataset demonstrate the effectiveness of the proposed model, compared with other related models and existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Secret-key agreement using physical identifiers is a promising security protocol for the authentication of users and devices with small chips, owing to its lightweight security. In the previous studies, the fundamental limits of such a protocol were analyzed, and the results showed that two auxiliary random variables were involved in the capacity region expressions. However, with two auxiliary random variables, it is difficult to directly apply the expressions to derive the computable forms of the capacity regions for certain information sources such as binary and Gaussian sources, which hold importance in practical applications. In this paper, we explore the structure of authentication channels and reveal that for the classes of degraded and less noisy authentication channels, a single auxiliary random variable is sufficient to express the capacity regions. As specific examples, we use the expressions with one auxiliary random variable to derive the computable forms for binary and Gaussian sources. Numerical calculations for the Gaussian case show the trade-off between secret-key and privacy-leakage rates under a given storage rate, which illustrates how the noise in the enrollment phase affects the capacity region. [ABSTRACT FROM AUTHOR]

Emotion analysis based on peripheral physiological signals has attracted increasing attention recently in affective computing. Previous works usually predict emotional states using a single emotion label for each discrete time. However, in real-world scenarios, it is not sufficient due to the fact that the real-world emotional state is usually a mixture of basic emotions. In this paper, we formulate the emotion analysis as an emotion distribution learning (EDL) problem and make two contributions. First, we establish a standardised dataset containing four negative emotions (anger, disgust, sadness, fear) and three positive emotions (tenderness, joy, amusement), which could be a useful benchmark for the EDL task. Second, we propose an emotion distribution prediction system which has the following distinct characteristics: (1) after processing raw peripheral physiological signals, we compute totally 89 representative features from four channels, i.e., GSR, SKT, ECG and HR, (2) an adaptive feature selection strategy based on recursive feature elimination (RFE) is used to select the most significant features in our EDL task, and (3) we design a dedicated EDL model based on convolution neural networks that takes information from both the feature correlation and the time domain into consideration. Experiments were conducted to validate our proposed system, and the results indicated that (1) the proposed feature selection strategy effectively selects significant features and improves algorithmic performance, and (2) the proposed EDL model can obtain good results in terms of six evaluation measures and outperform existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs. [ABSTRACT FROM AUTHOR]

We present here the Arkansas AI-Campus solution method for the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Our Arkansas AI-Campus team participated the KiTS19 Challenge for four months, from March to July of 2019. This paper provides a summary of our methods, training, testing and validation results for this grand challenge in biomedical imaging analysis. Our deep learning model is an ensemble of U-Net models developed after testing many model variations. Our model has consistent performance on the local test dataset and the final competition independent test dataset. The model achieved local test Dice scores of 0.949 for kidney and tumor segmentation, and 0.601 for tumor segmentation, and the final competition test earned Dice scores 0.9470 and 0.6099 respectively. The Arkansas AI-Campus team solution with a composite DICE score of 0.7784 has achieved a final ranking of top fifty worldwide, and top five among the United States teams in the KiTS19 Competition. [ABSTRACT FROM AUTHOR]

Long non-coding RNA(lncRNA) can interact with microRNA(miRNA) and play an important role in inhibiting or activating the expression of target genes and the occurrence and development of tumors. Accumulating studies focus on the prediction of miRNA-lncRNA interaction, and mostly are concerned with biological experiments and machine learning methods. These methods are found with long cycles, high costs, and requiring over much human intervention. In this paper, a data-driven hierarchical deep learning framework was proposed, which was composed of a capsule network, an independent recurrent neural network with attention mechanism and bi-directional long short-term memory network. This framework combines the advantages of different networks, uses multiple sequence-derived features of the original sequence and features of secondary structure to mine the dependency between features, and devotes to obtain better results. In the experiment, five-fold cross-validation was used to evaluate the performance of the model, and the zea mays data set was compared with the different model to obtain better classification effect. In addition, sorghum, brachypodium distachyon and bryophyte data sets were used to test the model, and the accuracy reached 0.9850, 0.9859 and 0.9777, respectively, which verified the model’s good generalization ability. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributionally robust dispatch for integrated transmission-distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually require synchronization of all subproblems, which could be hard to scale, resulting in the under-utilization of computation resources due to the subsystem heterogeneity in ITD systems. Moreover, the most commonly used distributionally robust individual chance-constrained dispatch models cannot systematically and robustly ensure simultaneous security constraint satisfaction. To address these limitations, this paper presents a novel distributionally robust joint chance-constrained (DRJCC) dispatch model for ITD systems via asynchronous decentralized optimization. Using the Wasserstein-metric based ambiguity set, we propose data-driven DRJCC models for transmission and distribution systems, respectively. Furthermore, a combined Bonferroni and conditional value-at-risk approximation for the joint chance constraints is adopted to transform the DRJCC model into a tractable conic formulation. Meanwhile, considering the different grid scales and complexity of subsystems, a tailored asynchronous alternating direction method of multipliers (ADMM) algorithm that better adapts to the star topological ITD systems is proposed. This asynchronous scheme only requires local communications and allows each subsystem operator to perform local updates with information from a subset of, but not all, neighbors. Numerical results illustrate the effectiveness and scalability of the proposed model. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable generations has given rise to a novel energy management mechanism, peer-to-peer (P2P) energy trading. The concept of Energy Internet (EI) is proposed as a new energy system framework to facilitate P2P energy trading where all distributed electrical devices are interconnected via energy routers (ERs). A proper market clearing approach is required to coordinate decentralized decision making in P2P market for demand-supply balance and feasible end-to-end energy delivery. In this paper, a decentralized market clearing mechanism considering energy path conflict management is proposed for P2P energy trading in energy local area network (e-LAN). An adjusted conflict-based search (ACBS) algorithm is proposed to deal with the non-convexity of the social welfare maximization problem where the nonconvex problem is transformed into multiple independent convex problems. Considering the privacy protection of participants, a dual decomposition based approach is proposed to solve these convex problems in a decentralized manner where self rationality of each participant can be satisfied by maximizing its personal welfare. In addition, energy path conflicts caused by decentralized optimization is resolved by imposing penalty fee and observing the rule of energy sharing maximization. Numerical simulations are presented to verify the effectiveness of the proposed market clearing approach. [ABSTRACT FROM AUTHOR]

Medical practitioners generally rely on multimodal brain images, for example based on the information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis. Hence, to further utilize the 3D information embedded in such datasets, this paper proposes a multi-view dynamic fusion framework (hereafter, referred to as MVFusFra) to improve the performance of brain tumor segmentation. The proposed framework consists of three key building blocks. First, a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view. Second, the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views into an integrated method. Then, two different fusion methods (i.e., voting and weighted averaging) are used to evaluate the fusing process. Third, the multi-view fusion loss (comprising segmentation loss, transition loss, and decision loss) is proposed to facilitate the training process of multi-view learning networks, so as to ensure consistency in appearance and space, for both fusing segmentation results and the training of the learning network. We evaluate the performance of MVFusFra on the BRATS 2015 and BRATS 2018 datasets. Findings from the evaluations suggest that fusion results from multi-views achieve better performance than segmentation results from the single view, and also implying effectiveness of the proposed multi-view fusion loss. A comparative summary also shows that MVFusFra achieves better segmentation performance, in terms of efficiency, in comparison to other competing approaches. [ABSTRACT FROM AUTHOR]

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed MNL-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to be able to accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, MNL-ARD is able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) based biometric system has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper, we have designed a novel attention based hierarchical long short-term memory (HLSTM) model to learn the biometric representation corresponding to a person. The HLSTM model proposed in this paper can learn the temporal variation of the ECG signal in different abstractions. This addresses the long term dependency issue of the LSTM network in our application. The attention mechanism of the model learns to capture the ECG complexes that have more biometric information corresponding to each person. These ECG complexes are given more weight to learn better biometric representation. The proposed system is less complex and more efficient as it does not require the detection of any fiducial points. We have evaluated the proposed model for both the person verification and identification problems using three on-the-person ECG databases and two off-the-person ECG databases. The proposed framework is found to perform better than the existing fiducial and non-fiducial point based methods. [ABSTRACT FROM AUTHOR]

Tree reconciliation is a general framework for investigating the mutual influence between gene and species trees according to the parsimony principle, that is, to each evolutionary event a cost is assigned and the goal is to find a reconciliation of minimum total cost. The resulting optimization problem is known as the reconciliation problem. Usually, the considered events are: co-divergence, gene Duplication, horizontal gene Transfer, and gene Loss (DTL model), while in a more conservative setting, gene transfers are not allowed (DL model). The reconciliation problem requires, in the DL model, time linear in the dimension of the two trees and at least quadratic time in the DTL model. Hence, it is reasonable to argue that the introduction of horizontal gene transfers increases the complexity of the problem. Instead, we introduce horizontal gene transfers with some constraints and prove that the problem is still linear in the dimension of the trees. Namely, we allow gene transfers of length bounded by $k=2$ k = 2 , on the basis of the observation that transfers are more likely to occur between closely related species than between distantly related ones. Then we extend the same reasonings to the case in which $k>2$ k > 2 under additional constrains. In this paper we study also another problem related to the reconciliation one, that is optimally rooting one of the two trees when it is not, and also for it we prove similar results. The relevance of this contribution lies in showing that, in the transit from the DL to the DTL model, the computational time does not increase suddenly to quadratic but remains linear in the case when gene transfers are very short (i.e., happening between very close genes). [ABSTRACT FROM AUTHOR]

Motivation: To minimize the accelerating amount of time invested on the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. Results: In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in a multi-task learning (MTL)framework, and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves over all the baselines (deep learning techniques)and single-task models for predicting the relationships, without compromising on the performance of all the tasks. [ABSTRACT FROM AUTHOR]

The identification of biotic stress of rice crops using handheld sensing devices is a challenge, as computationally intensive machine learning models are difficult to be executed in these devices. This challenge is exacerbated in farmers’ fields located in remote regions with limited internet connectivity. Thus, an individualiased plant-specific solution to detect biotic stress due to crop infections is required in farms adopting digital agricultural practices. The existing biotic stress detection solutions are deficient in their ability to make decisions in real-time. It is required to have a system that is capable of making decisions at the edge in handheld devices having limited computational capability. This paper proposes RiceBioS, an AI-based deep learning-enabled handheld device for identifying biotic stress in rice crops using the computational capabilities of handheld devices. RiceBioS adopts Edge-as-a-Service (EaaS) as an approach for classifying rice crop images into two categories – healthy and stressed. The biotic stress condition is further diagnosed into two types of infections, fungal (rice blast) and bacterial (bacterial leaf blight of rice) by pruning the shrunk deep learning classification model and incorporating an automated RoI detection and feature extraction workflow, which makes use of adaptive thresholding and hierarchial masking techniques to perform dimensionality reduction. While RiceBioS demonstrates a test accuracy of 93.25%, it exhibits a negligible tradeoff on a smartphone after deployment. This cutting edge solution helps the farmers make informed decisions based on real-time insights provided by the user-friendly mobile application interface of RiceBioS. [ABSTRACT FROM AUTHOR]

Deep neural networks and other machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especially for applications involving high-stakes decision, including the identification of bacterial infections. This paper considers fast Raman spectroscopy data and demonstrates that a logistic regression model with carefully selected features achieves accuracy comparable to that of neural networks, while being much simpler and more transparent. Our analysis leverages wavelet features with intuitive chemical interpretations, and performs controlled variable selection with knockoffs to ensure the predictors are relevant and non-redundant. Although we focus on a particular data set, the proposed approach is broadly applicable to other types of signal data for which interpretability may be important. [ABSTRACT FROM AUTHOR]

The inevitable voltage sag events cause losses to distributed generation (DG) and sensitive users. Reasonable planning for both of them helps to mitigate the impact of voltage sag. In this paper, a joint optimal planning method is proposed for DG and large sensitive user (LSU) considering the influence of voltage sag. Firstly, the new models of accelerated trip and chain trip for DG under voltage sag are established, to characterize the influence of DG tripping on other DG and sag characteristics during the sag event accurately. Secondly, a new voltage sag frequency assessment method is proposed, which considers models of accelerated trip and chain trip for DG, the tripping characteristics of distance protection (DP), to quantify the voltage sag severity accurately of DGs and LSUs in the gird which is with DP. Lastly, a joint optimal planning model is established considering the losses of DG and LSU under voltage sag, to support the planning of DG and LSU in different scenarios. The method is applied to IEEE RTS-30 for verification, proving that the method can give a direction of the most beneficial solution for the planner, to reduce the losses of voltage sag to both the power system and users. [ABSTRACT FROM AUTHOR]

In recent years, biometric authentication technology for smartphones has become widespread, with the mainstream methods being fingerprint authentication and face recognition. However, fingerprint authentication cannot be used when hands are wet, and face recognition cannot be used when a person is wearing a mask. Like the face and fingers, the ear as a biometric contains features that enable human identification and has been the subject of research on personal authentication. Authentication systems based on the acoustic transfer function of the pinna (PRTF: Pinna Related Transfer Function) have been investigated. However, the authentication accuracy decreases due to the positional fluctuation across each measurement. In this paper, we propose multimodal personal authentication on smartphones using PRTF. The pinna image and positional sensor information are used with the PRTF, and the effectiveness of the authentication method is examined. Half total error rate (HTER) of 9.3% for single-modal authentication using only PRTF was improved to 1.6% for multimodal authentication using images and sensor data. We demonstrate that the proposed authentication system can compensate for the positional changes in each measurement and improve the robustness. [ABSTRACT FROM AUTHOR]

Solar energy and bioenergy are two leading renewable forms of energy in the move toward a near-zero-emission electric power industry. Concentrated solar power units coupled with thermal storage and biomass power plant offer dispatchable electricity, raising their ever-growing role in future renewable-dominated networks. This paper proposes a day-ahead and intraday dispatch model for maximizing the profit of an Integrated Biomass-Concentrated Solar (IBCS) system considering the synergies arising from their coupled operation. To sensibly capture uncertainty and decision sequence of real-life electricity markets, a two-stage stochastic structure is proposed, while the solar-related uncertainty is involved using Information Gap Decision Theory (IGDT). The model is complemented with a novel multi-objective architecture based on the compound of IGDT and Conditional Value-at-Risk (CVaR), which allows handling risk exposure to both stochastic and IGDT inputs. The Pareto strategies in the multi-objective model are extracted through an expanded form of the $\epsilon$ -constraint method, whereas a posteriori approach based upon the out-of-sample assessment is applied to derive the optimal dispatch pattern among the generated Pareto strategies. The simulation results demonstrate that: 1) the proposed integrated dispatch model achieves substantial profitability, and 2) the performance of the suggested CVaR-IGDT model is superior to conventional approaches. [ABSTRACT FROM AUTHOR]

Wind power curve modeling has important applications in many fields, such as wind turbine condition monitoring and wind power forecasting. There are various studies on wind power curve construction though, the relation between wind speed and errors is not taken into account when constructing wind power curves. To fill this gap, a hybrid copula-based wind power curve model (HCCM) is proposed in this paper. The independent distribution of wind speed is modeled by a Weibull distribution, and the independent distribution of errors is modeled by a mixture of asymmetric gaussian models. Then a hybrid copula model, including four copula functions, is designed to construct the joint distribution. Finally, a grey wolf optimization algorithm is applied to optimize the regression parameters of wind power curves by taking the log-likelihood function of the joint distribution as the fitness function. The proposed model is compared with ten benchmark models on four wind farms and two wind turbines. Experiments show that in terms of RMSE the HCCM achieves 99.3131%, 99.1197%, 99.1715% and 98.9339% on four wind farms, respectively, and around 80% improvement in RMSE on two wind turbines. [ABSTRACT FROM AUTHOR]

With the energy transition, the cyber-physical system (CPS) is expanded to the cyber-physical-social system (CPSS), and connected by the communication network, which is introduced in the form of a highly reliable cellular star network in this paper. Due to the uncertainties of wind turbine/photovoltaic power outputs, an improved rolling ultra-short-term forecast method with high reliability is proposed, which is based on long short-term memory networks, and the superiority is proved by comparing with multi-layer perception neural networks, linear regression, support vector regression and random forest. Then, two stochastic optimization models are constructed, one is based on CPS, the other is based on CPSS. As for social factors, electric vehicle aggregator is stimulated by cost to actively participate in the dispatch operation. Moreover, in order to ensure the stable operation of the smart energy system, the frequency deviation of automatic generation control system is studied as one security constraint. The optimal results display that social factors can not only make the system more economic and flexible, but also further increase the consumption of RE. Moreover, accurate forecast results can improve the performance of optimization dispatch operations. [ABSTRACT FROM AUTHOR]

Objective: A tendon-transfer is a reconstructive orthopedic surgery where tendons are re-routed from a non-functioning muscle and attached a functioning muscle. Prior work has shown that using a passive implanted device in the ECRL-to-FDP tendon-transfer surgery significantly improves hand grasping function. However, it is still unclear how hand-function improvement, measured by finger joint range of motion and torque, is dependent on the implant’s geometry and location within the tendon network. This paper presents a genetic algorithm that determines the device’s optimal geometry and location. Methods: Hand biomechanical simulation platform was developed to model hand function and also model the tendon-transfer surgery with and without the implant. Finger kinematics and joint torque were used to develop three unique objective functions to optimize the implant’s parameters. Results: The optimized device resulted in an 11X increase in finger kinematics with only a 0.9% decrease in joint torque when compared with the biomechanical function enabled by the current suture-based surgery. Conclusion: Designing implantable devices that modify musculoskeletal function is challenging. Factors like tendon routing and joint kinematics create a complex nonlinear system when considering biomechanical function. A genetic algorithm is an effective tool to tackle these nonlinear landscapes to produce optimized designs. Significance: The state-of-the-art surgical procedure to repair high median-ulnar nerve palsy leads to poor hand function and severely limits the patient’s ability to perform activities of daily life. This work provides a method for defining relevant objective functions for hand biomechanical function and then uses those objective functions with genetic algorithms to optimize the geometry of an orthopedic implant across multiple variables. The achieved biomechanical function is significantly better than hand function enabled by current surgical procedure. [ABSTRACT FROM AUTHOR]

The paper explores the comparative biosensing analysis of single Gate (SG) and double Gate (DG) Extended Source Tunnel FET (ESTFET). The dielectric modulation technique has been incorporated for both the proposed biosensors with increased capture area by introducing nano-cavities near the source and drain region. The biosensing performance parameters like Threshold Voltage Sensitivity ($\Delta\text{V}_{th}$), ON-OFF Current Ratio Sensitivity ($\Delta\text{I}_{\text{ON}}/\text{I}_{\text{OFF}}$), and Drain Current Sensitivity ($\Delta \text{S}_{Id}$) are calculated and compared for both the SG-ES and DG-ESTFET based biosensors. Sentaurus TCAD simulator is used for the investigation reported in this paper. The analysis includes neutral, positively and negatively charged biomolecules for various dielectric constants at immobilization layer and biomolecule analogous insulator interface. Further, a comprehensive analysis of partly filled nano-cavity originated from steric hindrance has been conferred to engulf the real time scenario. Different cases of partly filled nano-cavity have been considered to investigate the sensitivity parameters such as convex, concave, increasing and decreasing step profile along with asymmetric probe placement. It is observed that SG-ESTFET based label free biosensor outperform the DG-ESTFET due to the impact of back gate on the drain side nano-cavity. Some important bio-sensing parameters are estimated to have the subsequent values for SG-ESTFET (DG-ESTFET) as 80 mV (74 mV) for threshold voltage sensitivity, $23\,\,\times10$ 3 ($15\,\,\times10$ 3) for ON-OFF current ratio sensitivity and $60\,\,\times10$ 2 ($45\,\,\times10$ 2) for drain current sensitivity, respectively. Lastly, different sensitivity parameters of SG-ES and DG-ESTFET are gauged against sensitivities of state of the art biosensors to set out the high sensitivity of our proposed biosensors. [ABSTRACT FROM AUTHOR]