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Physiological closed-loop controlled (PCLC) medical devices, such as those designed for blood pressure regulation, can be tested for safety and efficacy in real-world clinical settings. However, relying solely on limited animal and clinical studies may not capture the diverse range of physiological conditions. Credible mathematical models can complement these studies by allowing the testing of the device against simulated patient scenarios. This research involves the development and validation of a low-order lumped-parameter mathematical model of the cardiovascular system’s response to fluid perturbation. The model takes rates of hemorrhage and fluid infusion as inputs and provides hematocrit and blood volume, heart rate, stroke volume, cardiac output and mean arterial blood pressure as outputs. The model was calibrated using data from 27 sheep subjects, and its predictive capability was evaluated through a leave-one-out cross-validation procedure, followed by independent validation using 12 swine subjects. Our findings showed small model calibration error against the training dataset, with the normalized root-mean-square error (NRMSE) less than 10% across all variables. The mathematical model and virtual patient cohort generation tool demonstrated a high level of predictive capability and successfully generated a sufficient number of subjects that closely resembled the test dataset. The average NRMSE for the best virtual subject, across two distinct samples of virtual subjects, was below 12.7% and 11.9% for the leave-one-out cross-validation and independent validation dataset. These findings suggest that the model and virtual cohort generator are suitable for simulating patient populations under fluid perturbation, indicating their potential value in PCLC medical device evaluation.

Postural control is one of the primary body functions for fall prevention. Unexpected perturbation-based balance training is effective for improving postural control. However, the effect of perturbation-based training using assistive devices on muscle activity and co-contraction for standing balance is still unclear. This training is also difficult to perform easily because it requires large instruments or expert guidance. The purpose of this study is to demonstrate the effect of perturbation-based balance training using a wearable balance training device (WBTD) on postural control. In this study, fourteen healthy young adult males were assigned to either a WBTD group or a sham group. In the intervention session, participants in the WBTD group were perturbed either left or right direction at random timing by the WBTD during tandem stance balance training. Participants in the Sham group did not receive external perturbation during tandem stance balance training. Before and after the intervention session, participants of both groups underwent unexpected lateral perturbation postural control testing (pre- and post-test). The normalized integral of electromyography (IEMG), co-contraction index (CCI), and center of pressure (COP) parameters were measured in the pre- and post-test. Experimental results showed that the WBTD group in the post-test significantly decreased left Gluteus Medius IEMG, CCI of both Gluteus Medius, and peak $\text {COP}_{\text {ML}}$ velocity, compared to those of the pre-test ( ${p} \lt 0.001$ , ${p} \,\, =0.024$ , ${p} \,\, =0.031$ , respectively). We conclude that balance training using WBTD could improve flexible postural control adjustment via cooperative muscle activation.

Objective: Metabolic changes have been extensively documented in neurodegenerative brain disorders, including Parkinson’s disease and Alzheimer’s disease (AD). Mutations in the C. elegans swip-10 gene result in dopamine (DA) dependent motor dysfunction accompanied by DA neuron degeneration. Recently, the putative human ortholog of swip-10 (MBLAC1) was implicated as a risk factor in AD, a disorder that, like PD, has been associated with mitochondrial dysfunction. Interestingly, the AD risk associated with MBLAC1 arises in subjects with cardiovascular morbidity, suggesting a broader functional insult arising from reduced MBLAC1 protein expression and one possibly linked to metabolic alterations. Methods: Our current studies, utilizing Mblac1 knockout (KO) mice, seek to determine whether mitochondrial respiration is affected in the peripheral tissues of these mice. We quantified the levels of mitochondrial coenzymes, NADH, FAD, and their redox ratio (NADH/FAD, RR) in livers and kidneys of wild-type (WT) mice and their homozygous KO littermates of males and females, using 3D optical cryo-imaging. Results: Compared to WT, the RR of livers from KO mice was significantly reduced, without an apparent sex effect, driven predominantly by significantly lower NADH levels. In contrast, no genotype and sex differences were observed in kidney samples. Serum analyses of WT and KO mice revealed significantly elevated glucose levels in young and aged KO adults and diminished cholesterol levels in the aged KOs, consistent with liver dysfunction. Discussion/Conclusion: As seen with C. elegans swip-10 mutants, loss of MBLAC1 protein results in metabolic changes that are not restricted to neural cells and are consistent with the presence of peripheral comorbidities accompanying neurodegenerative disease in cases where MBLAC1 expression changes impact risk.

Machine learning is widely used for autonomous driving because it can recognize surrounding circumstances feasibly from sensor and determine appropriate actions. Most of these sensors are based on micro-electro-mechanical systems (MEMS), which enable autonomous vehicles to judge objects in conjunction with object-detection algorithms. However, recent studies have shown that MEMS are vulnerable to signal-injection attacks, in which the input images are manipulated to force the object detection algorithms to misclassify the results. These attacks can be critical in the wild because they deteriorate state-of-the-art detection techniques, dropping their detection rates until the objects would no longer be detected at all. In this paper, we propose Priest, a novel detection method against prior signal-injection attacks. Priest uses the similarity of pixel values between two consecutive images. Using only two images ensures a low computational cost. According to our performance analysis, Priest detects state-of-the-art signal-injection attacks in real-time with 99% accuracy on average, achieving practical autonomous driving security.

Dynamic searchable symmetric encryption (DSSE) enables users to outsource their data while retaining the capability to search and update on the encrypted database. Although various DSSE schemes have been proposed to achieve higher efficiency and stronger security, many of them incurred information leakages due to the linkability between ciphertexts and queries as side information. The notions of forward and backward privacy are defined to capture such information leakage in DSSE formally. In particular, backward privacy guarantees that queries do not reveal their relationship with the deleted database, which is further classified into four types (Type-I, $\textrm {I}^{-}$ , II, and III) based on the types of information leakage. In this study, we provide a backward privacy attack that exploits the information leakages and apply it to Type- $\textrm {I}^{-}$ backward private schemes to lower their security level to Type-III. We then propose a new DSSE framework, which is robust against the proposed attack. We apply our framework to the previous DSSE scheme (Zuo et al., ESORICS 2019) to build the first forward and backward Type-I private DSSE scheme under the backward privacy attack, and demonstrate its efficacy.

Quantum annealing is a specialized type of quantum computation that aims to use quantum fluctuations in order to obtain global minimum solutions of combinatorial optimization problems. Programmable D-Wave quantum annealers are available as cloud computing resources, which allow users low-level access to quantum annealing control features. In this article, we are interested in improving the quality of the solutions returned by a quantum annealer by encoding an initial state into the annealing process. We explore two D-Wave features that allow one to encode such an initial state: the reverse annealing (RA) and the h-gain (HG) features. RA aims to refine a known solution following an anneal path starting with a classical state representing a good solution, going backward to a point where a transverse field is present, and then finishing the annealing process with a forward anneal. The HG feature allows one to put a time-dependent weighting scheme on linear ($h$) biases of the Hamiltonian, and we demonstrate that this feature likewise can be used to bias the annealing to start from an initial state. We also consider a hybrid method consisting of a backward phase resembling RA and a forward phase using the HG initial state encoding. Importantly, we investigate the idea of iteratively applying RA and HG to a problem, with the goal of monotonically improving on an initial state that is not optimal. The HG encoding technique is evaluated on a variety of input problems including the edge-weighted maximum cut problem and the vertex-weighted maximum clique problem, demonstrating that the HG technique is a viable alternative to RA for some problems. We also investigate how the iterative procedures perform for both RA and HG initial state encodings on random whole-chip spin glasses with the native hardware connectivity of the D-Wave Chimera and Pegasus chips.

Semantic segmentation neural networks require pixel-level annotations in large quantities to achieve a good performance. In the medical domain, such annotations are expensive because they are time-consuming and require expert knowledge. Active learning optimizes the annotation effort by devising strategies to select cases for labeling that are the most informative to the model. In this work, we propose an uncertainty slice sampling (USS) strategy for the semantic segmentation of 3D medical volumes that selects 2D image slices for annotation and we compare it with various other strategies. We demonstrate the efficiency of USS on a CT liver segmentation task using multisite data. After five iterations, the training data resulting from USS consisted of 2410 slices (4% of all slices in the data pool) compared to 8121(13%), 8641(14%), and 3730(6%) slices for uncertainty volume (UVS), random volume (RVS), and random slice (RSS) sampling, respectively. Despite being trained on the smallest amount of data, the model based on the USS strategy evaluated on 234 test volumes significantly outperformed models trained according to the UVS, RVS, and RSS strategies and achieved a mean Dice index of 0.964, a relative volume error of 4.2%, a mean surface distance of 1.35mm, and a Hausdorff distance of 23.4mm. This was only slightly inferior to 0.967, 3.8%, 1.18mm, and 22.9mm achieved by a model trained on all available data. Our robustness analysis using the 5th percentile of Dice and the 95th percentile of the remaining metrics demonstrated that USS not only resulted in the most robust model compared to other strategies, but also outperformed the model trained on all data according to the 5th percentile of Dice (0.946 vs. 0.945) and the 95th percentile of mean surface distance (1.92mm vs. 2.03mm).

A major obstacle when developing convolutional neural networks (CNNs) for medical imaging is the acquisition of training labels: Most current approaches rely on manual class labels from physicians, which may be challenging to obtain. Clinical biomarkers, often measured alongside medical images and used in diagnostic workup, may provide a rich set of data that can be collected retrospectively and utilized to train diagnostic models. In this work, we focused on assessing the potential of blood serum biomarkers, B-type natriuretic peptide (BNP) and NT-pro B-type natriuretic peptide (BNPP), indicative of acute heart failure (HF) and cardiogenic pulmonary edema to be used as continuously valued labels for training a radiographic deep learning algorithm. For this purpose, a CNN was trained using 27748 radiographs to automatically infer BNP and BNPP, and achieved strong performance (AUC =0.903, sensitivity =0.926, specificity =0.857, $r=0.787$ ). Also, the trained models achieved strong performance (AUC =0.801) for pulmonary edema detection when evaluated with radiologist labels. Since relevant radiographic features visible to the CNN may vary greatly based on image resolution, we also assessed the impact of image resolution on model learning and performance, comparing CNNs trained at five image sizes ( $64\times 64$ to $1024\times 1024$ ). Increasing image resolutions had diminishing but positive gains in AUC. Perhaps more importantly, experiments using three activation mapping techniques (saliency, Grad-CAM, XRAI) revealed considerably increased attention in the lungs with larger image sizes. This result emphasizes the need to utilize radiographs near native resolution for optimal CNN performance, which may not be fully captured by summary metrics like AUC.

Range query on encrypted data has been widely studied to achieve fast query processing while keeping data and queries secret. Prior work hides even the ordering information of the data, but sacrifices accuracy (e.g., false positives) and efficiency (e.g., exponential complexity in the domain size) as a trade-off. Moreover, hiding the order alone is not sufficient to prevent the recent database reconstruction attacks on range query abusing access pattern and volume leakage. Several countermeasures prevent such information leakage but incur practically unacceptable storage or computational overhead due to fake records embedded in the database and database re-encryption procedures. In this paper, we first propose a range query scheme $\mathsf{RevealRE}$ , which is highly efficient, but reveals ordering information. We then extend $\mathsf{RevealRE}$ to construct $\mathsf{HideRE}$ , a range query scheme that does not leak data ordering information while accurate (i.e., no false positives/negatives) and efficient (i.e., logarithmic search time). $\mathsf{HideRE}$ can mitigate the database reconstruction attacks by introducing deceptive queries, which are indistinguishable from regular queries while obfuscating the queried ranges. We experimentally confirm efficiency of our construction under various data distribution models, demonstrating the practicality. Resilience to database reconstruction attacks of our order-hiding range query scheme is formally proved.

A Medical Body Area Network (MBAN) is an ensemble of collaborating, potentially heterogeneous, medical devices located inside, on the surface of or around the human body with the objective of tackling one or multiple medical conditions of the MBAN host. These devices-which are a special category of Wireless Body Area Networks (WBANs)–collect, process and transfer medical data outside of the network, while in some cases they also administer medical treatment autonomously. Since communication is so pivotal to their operation, the newfangled IEEE 802.15.6 standard is aimed at the communication aspects of WBANs. It places a set of physical and communication constraints while it also includes association/disassociation protocols and security services that WBAN applications need to comply with. However, the security specifications put forward by the standard can be easily shown to be insufficient when considering realistic MBAN use cases and need further enhancements. The present work addresses these shortcomings by, first, providing a structured analysis of the IEEE 802.15.6 security features and, afterwards, proposing comprehensive and tangible recommendations on improving the standard’s security.

Privacy-preserving similarity search is a method of data retrieval from potentially untrusted hosts based on the similarity between encrypted data items. In this setting, a major concern is how to support searches when multiple users (multireader) request for searching similar items over data encrypted by multiple data owners (multiwriter). Unfortunately, previous similarity search schemes address this by enforcing users to communicate with data owners. This limitation incurs a significant communication overhead. Moreover, these schemes use deterministic algorithms to encrypt data, which not only violates the privacy of data but also complicates the proof of semantic security. In this paper, we propose an efficient and secure multiwriter/multireader similarity search scheme over encrypted data in cloud storage. In the proposed scheme, the cloud server is able to perform searches without incurring any interaction between users and data owners. Thus, we achieve asymptotically optimal communication cost. We provide rigorous proofs of data privacy in the standard model. Then, we show the proposed scheme achieves semantic security based on the data privacy. An in-depth experiment on an INRIA image dataset demonstrates the practicality of the proposed scheme.

In recent years, numerous new approaches that rely on data-intensive methods have been developed for maritime assistance systems, leading to a compelling need for more elaborate verification and validation procedures. Modern testbeds that can meet these demands are often developed separately from the system itself and provided as generically usable services. However, the joint usage of such testbeds by multiple stakeholders from research and industry confronts them with various challenges in terms of data management: Data control and protection is required to preserve possible competitive advantages or comply with legal framework conditions. The resulting decentralization in data management complicates collaboration, especially in the joint processing and analysis of testbed data. In this paper, we present a decentralized software system, which can deal with these challenges by modelling interrelationships between the stakeholders in a data space, considering their various interests. With the help of a modular data management architecture, the organization of a testbed data basis, as well as the support of verification and validation processes and the evaluation of data streams is made possible. This is achieved with a workflow model for mapping complex and distributed data processing steps. We demonstrate the applicability of the system in an application scenario for the development of a maritime assistance system.

In multiband transmitters where two or more radios share the same power amplifier (PA), interferences can usually be kept below an acceptable level with the help of multi-band digital predistortion (DPD) techniques. This article reports on novel frequency scenarios in dual-band transmitters where additional cross-modulation products not reported before, are found to overlap and interfer with both the lower and upper-band signals. These cross-modulation products arise in-band when the frequency separation between the bands is close to a sub-harmonic of the band frequencies. The leading nonlinear cross-modulation products are identified for various frequency plans and found to be of increasing order as the frequency interval between the two carriers shrinks. A PA model featuring the four leading cross-modulation products is proposed and found to exhibit an accuracy close to the measurement noise floor when applied to three different PAs. The additional in-band distortions generated by the PA under such operation cannot be filtered or compensated using conventional dual-band DPD. When using the proposed model architecture with the indirect-learning predistortion methodology for linearizing dual-band PAs, DPD is found experimentally to improve the ACPR and EVM by 25 dBc and 11 percentage points, respectively. These and the other experimental results reported indicate that the proposed linearization algorithm provides a suitable method with high efficacy for these special dual-band scenarios.

The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21–64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s−1 – 3.0 m s−1), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode.

This paper presents a novel deep learning-based arterial pulse wave analysis (PWA) approach to diagnosis of peripheral artery occlusive disease (PAD). Naïve application of deep learning to PAD diagnosis can be hampered by the fact that securing a large amount of longitudinal dataset encompassing diverse PAD severity as well as anatomical and physiological variability presents formidable challenge. Training of a deep neural network (DNN) to a small training dataset raises the risk of overfitting the PAD diagnosis algorithm only to the individuals in the training dataset while deteriorating its ability to generalize also to other individuals who may exhibit a large variability in anatomical and physiological characteristics beyond the training dataset. To overcome these obstacles, we propose a continuous property-adversarial regularization (CPAR) approach to robust generalization of a DNN against scarce datasets. Our approach fosters the exploitation of latent features that can facilitate the intended task independently of confounding property-induced disturbances. by regularizing the extraction of disturbance-dependent latent features in the network’s feature extraction layer. By training and testing a deep convolutional neural network (CNN) for PAD diagnosis using scarce virtual datasets, we illustrated that the CNN trained by our approach was superior to a conventionally trained CNN in detecting and assessing the severity of PAD against disturbances originating from diversity in the patients’ height and arterial stiffness: when trained with one-time pulse wave signal measurement at ankle and brachial arteries in a small number of patients, our approach achieved detection accuracy of >90% and severity assessment of 0.83 in r2 value, which were >15% and >40% improvement over conventional approach without CPAR. In addition, we ascertained the advantage of our approach in efficient training and robust generalization of DNN by contrasting it to multi-task learning which promotes the exploitation (as opposed to regularization in CPAR) of disturbance-dependent latent features in fulfilling the intended tasks.

This paper presents a 2-D analysis of synchronous reluctance motors (SynRMs) considering the end-plate effect. In an electric machine with a laminated steel core, an end-plate is often attached to the rotor core to support the structural robustness of the machine. End-plates occasionally comprise carbon-steel, which changes the magnetic behavior of the electric machine. Thus, an electric machine that utilizes the 3-D finite element analysis (FEA) should be designed to consider the comprehensive magnetic flux path. However, the application of 3-D FEA for the whole design process is computationally expensive. Therefore, we proposed a 2-D FEA-based design strategy that can consider the additional magnetic path of the end-plate for SynRMs. The proposed analysis method was developed based on the magnetic equivalent circuit analysis and $d$ - and $q$ -axis flux linkage calculation. The effectiveness of the proposed method was verified by presenting the actual design case of a SynRM and via an experimental study.

In this work, the dynamic behavior of gallium nitride on silicon high electron mobility transistors (GaN/Si HEMT) with carbon doped buffer is modeled using a finite state machine embedded into the core Advanced SPICE Model for High Electron Mobility Transistor (ASM-HEMT). The model is based on the physics of trapping and detrapping of electrons in carbon at nitrogen-site acceptor trap (denoted here as $\text{C}_{N}$ ) and does not require an equivalent Resistance-Capacitance circuit. The model is validated against three off-state stress drain voltages of 50 V, 100 V, and 150 V using only $\text{C}_{N}$ as trap species.

Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods under-perform in-the-wild. Deep learning approaches have had limited success due to the small size and diversity of existing VQA datasets, either artificial or authentically distorted. We introduce a new in-the-wild VQA dataset that is substantially larger and diverse: KonVid-150k. It consists of a coarsely annotated set of 153,841 videos having five quality ratings each, and 1,596 videos with a minimum of 89 ratings each. Additionally, we propose new efficient VQA approaches (MLSP-VQA) relying on multi-level spatially pooled deep-features (MLSP). They are exceptionally well suited for training at scale, compared to deep transfer learning approaches. Our best method, MLSP-VQA-FF, improves the Spearman rank-order correlation coefficient (SRCC) performance metric on the commonly used KoNViD-1k in-the-wild benchmark dataset to 0.82. It surpasses the best existing deep-learning model (0.80 SRCC) and hand-crafted feature-based method (0.78 SRCC). We further investigate how alternative approaches perform under different levels of label noise, and dataset size, showing that MLSP-VQA-FF is the overall best method for videos in-the-wild. Finally, we show that the MLSP-VQA models trained on KonVid-150k sets the new state-of-the-art for cross-test performance on KoNViD-1k and LIVE-Qualcomm with a 0.83 and 0.64 SRCC, respectively. For KoNViD-1k this inter-dataset testing outperforms intra-dataset experiments, showing excellent generalization.

This paper proposes an improved emotional voice conversion (EVC) method with emotional strength and duration controllability. EVC methods without duration mapping generate emotional speech with identical duration to that of the neutral input speech. In reality, even the same sentences would have different speeds and rhythms depending on the emotions. To solve this, the proposed method adopts a sequence-to-sequence network with an attention module that enables the network to learn attention in the neutral input sequence should be focused on which part of the emotional output sequence. Besides, to capture the multi-attribute aspects of emotional variations, an emotion encoder is designed for transforming acoustic features into emotion embedding vectors. By aggregating the emotion embedding vectors for each emotion, a representative vector for the target emotion is obtained and weighted to reflect emotion strength. By introducing a speaker encoder, the proposed method can preserve speaker identity even after the emotion conversion. Objective and subjective evaluation results confirm that the proposed method is superior to other previous works. Especially, in emotion strength control, we achieve in getting successful results.

Automatic scoring and feedback tools have become critical components of online learning proliferation. These tools range from multiple-choice questions to grading essays using machine learning (ML). Learning environments such as massive open online courses (MOOCs) would not be possible without them. The usage of this mechanism has brought many exciting areas of study, from the design of questions to the ML grading tools’ precision and accuracy. This paper analyzes the findings of 125 studies published in journals and proceedings between 2016 and 2020 on the usages of automatic scoring and feedback as a learning tool. This analysis gives an overview of the trends, challenges, and open questions in this research area. The results indicate that automatic scoring and feedback have many advantages. The most important benefits include enabling scaling the number of students without adding a proportional number of instructors, improving the student experience by reducing the time between submission grading and feedback, and removing bias in scoring. On the other hand, these technologies have some drawbacks. The main problem is creating a disincentive to develop innovative answers that do not match the expected one or have not been considered when preparing the problem. Another drawback is potentially training the student to answer the question instead of learning the concepts. With this, given the existence of a correct answer, such an answer could be leaked to the internet, making it easier for students to avoid solving the problem. Overall, each of these drawbacks presents an opportunity to look at ways to improve technologies to use these tools to provide a better learning experience to students.

Nanogrids are customer deployments that can generate and inject electricity into the power grid. These deployments are based on behind-the-meter renewable energy resources and are labeled as “prosumer setups”, allowing customers to not only consume electricity, but also produce it. A residential nanogrid is comprised of a physical layer that is a household-scale electric power system, and a cyber layer that is used by manufacturers and/or grid operators to remotely monitor and control the nanogrid. With the increased penetration of renewable energy resources, nanogrids are at the forefront of a paradigm shift in the operational landscape and their correct operation is vital to the electric power grid. In this paper, we perform a cybersecurity assessment of a state-of-the art residential nanogrid deployment. For this purpose, we deployed a real-world experimental nanogrid setup that is based on photovoltaic (PV) generation. We analyzed the security and the resiliency of this system at both the cyber and physical layers. While we noticed improvements in the cybersecurity measures employed in the current nanogrid compared to previous generations, there are still major concerns. Our experiments show that these concerns range from exploiting well-known protocols, such as Secure Shell (SSH) and Domain Name Service (DNS), to the leakage of confidential information, and major shortcomings in the software updating mechanism. While the compromise of multiple nanogrids can have a negative effect on the entire power grid, we focus our analysis on individual households and have determined through Simulink-based simulations the economic loss of a compromised deployment.

The growing number of consumer-grade network-enabled Distributed Energy Resources (DER) installations introduces new attack vectors that could impact grid operations through coordinated attacks. This work presents a cyber-physical model and risk assessment methodology for analyzing the emerging nexus between Internet of Things-based energy devices and the bulk transmission grid. The cyber model replicates the device-level interconnectivity and software components interaction found within these architectures to understand the feasibly of coordinated attacks, while the physical model is used to assess the attack's impacts on the grid. The manuscript questions the validity of previous papers' claims regarding IoT-based grid attacks by addressing key limitations in both the power grid and cyber infrastructure models of those works. The resulting methodology is then evaluated using the Western Electricity Coordinating Council (WECC) electrical model coupled with DER's operational statistics from California. The results suggest that current DER penetration rates are not yet significant enough to present serious risk, but continued DER growth may be problematic. Furthermore, the work identifies policies that mitigate these risks through increased device diversity and cybersecurity requirements.

The objective of this study was to investigate the efficacy of pulse transit time (PTT)-pulse wave analysis (PWA) fusion in cuff-less blood pressure (BP) trend tracking based on the wearable wrist ballistocardiogram (BCG). We constructed PTT and 36 candidate BCG PWA features based on the BCG and photoplethysmogram (PPG) signals acquired at the wrist. We performed a model-based analysis to select 6 candidate BCG PWA features most sensitive to BP. We aggregated the 6 BCG PWA features into novel predictors of diastolic, systolic, and pulse BP (DP, SP, and PP) orthogonal to PTT by covariance-maximizing dimensionality reduction. Then, we evaluated and compared the efficacy of BCG PTT and BCG PTT-PWA fusion in tracking the trend of DP, SP, and PP. Unique innovations of this study, generalizable to a range of physiological signals beyond the BCG, are (i) the systematic selection of PWA features based on model-based analysis and (ii) the development of PWA-based BP predictors orthogonal to PTT. Using the experimental wrist BCG and PPG signals collected from 23 human subjects, we demonstrated that (i) BCG PTT-PWA fusion may significantly outperform BCG PTT; (ii) PTT may play the primary role in tracking BP while BCG PWA features may still have an independent and complementary value (especially in tracking PP); and (iii) model-based selection of BCG PWA features can enhance the robustness of BP trend tracking based on BCG PTT-PWA fusion.

In the current blockchain network, many participants rationally migrate the pool to receive a better compensation according to their contribution in situations where the pools they engage encounter undesirable attacks. The Nash equilibria of attacked pool has been widely analyzed, but the analysis of practical methodology for obtaining it is still inadequate. In this paper, we propose an evolutionary game theoretic analysis of Proof-of-Work (PoW) based blockchain network in order to investigate the mining pool dynamics affected by malicious infiltrators and the feasibility of autonomous migration among individual miners. We formulate a revenue model for mining pools which are implicitly allowed to launch a block withholding attack. Under our mining game, we analyze the evolutionary stability of Nash equilibrium with replicator dynamics, which can explain the population change with time between participated pools. Further, we explore the statistical approximation of successful mining events to show the necessity of artificial manipulation for migrating. Finally, we construct a better response learning based on the required block size which can lead to our evolutionarily stable strategy (ESS) with numerical results that support our theoretical discoveries.

Monitoring systems for malicious behavior increasingly requires aggregating and analyzing data from various sources, such as network flows, host logs, and end-point monitoring platforms. However, there's currently a lack of metrics and methodologies to compute the observability and efficiency of a security monitoring strategy. This manuscript introduces TOMATO (Threat Observability & Monitoring Assessment Tool), which is a platform to evaluate the effectiveness of a security monitoring strategy by exploring both the number of known adversarial techniques that can be detected within a network, along with evaluating the number of false-positives produced by the monitoring strategy. The output produces both an observability score and efficiency score of a set of deployed monitoring techniques, which are evaluated based on the data from the environment, and simulated attacks generated from MITRE ATT&CK. The proposed approach is then integrated into an ELK stack and evaluated on real SCADA devices within the WSU Smart City Testbed.

Recently, instantaneous wave-free ratio (iFR) or diastolic pressure-ratio (dPR) have been used in practice. For these indices, the reliability of electrocardiography (ECG)-independent algorithm for pressure-only data is essential. The current study sought to compare the current to a new ECG-independent algorithm for calculating resting physiologic indices. The main purpose of developing a new ECG-independent algorithm was to raise the detection rates over the entire heart cycle despite irregular heartbeats. Both iFR and dPR were calculated from resting pressure tracings using current and new algorithms by a core laboratory in 975 vessels (393 patients). The diagnostic performance of resting physiologic indices with a new algorithm to predict fractional flow reserve (FFR) was compared with the current algorithm. Both algorithms provided nearly identical values of iFR or dPR without systemic bias. iFR and dPR, which were calculated using current and new ECG-independent algorithms, provided comparable discrimination ability and diagnostic performance to predict functionally significant stenosis defined by FFR≤0.80. However, detection rates of the new algorithm were significantly higher than current algorithm in the patients with irregular heartbeats (for per patient [59.5% vs. 83.8%] and per unit-heartbeats analysis [84.3% vs. 90.3%]), such as arterial fibrillation or multiple premature ventricular contractions.