Miracle-dz/UniFAR-FTUs huggingface dataset:
Airway epithelial interferon response to SARS-CoV-2 is inferior to rhinovirus and heterologous rhinovirus infection suppresses SARS-CoV-2 replication

Introduction Common alphacoronaviruses and human rhinoviruses (HRV) induce type I and III interferon (IFN) responses important to limiting viral replication in the airway epithelium. In contrast, highly pathogenic betacoronaviruses including SARS-CoV-2 may evade or antagonize RNA-induced IFN I/III responses. Methods In airway epithelial cells (AECs) from children and older adults we compared IFN I/III responses to SARS-CoV-2 and HRV-16, and assessed whether pre-infection with HRV-16, or pretreatment with recombinant IFN-β or IFN-λ, modified SARS-CoV-2 replication. Bronchial AECs from children (ages 6-18 yrs.) and older adults (ages 60-75 yrs.) were differentiated ex vivo to generate organotypic cultures. In a biosafety level 3 (BSL-3) facility, cultures were infected with SARS-CoV-2 or HRV-16, and RNA and protein was harvested from cell lysates 96 hrs. following infection and supernatant was collected 48 and 96 hrs. following infection. In additional experiments cultures were pre-infected with HRV-16, or pre-treated with recombinant IFN-β1 or IFN-λ2 before SARS-CoV-2 infection. Results Despite significant between-donor heterogeneity SARS-CoV-2 replicated 100 times more efficiently than HRV-16. IFNB1, INFL2, and CXCL10 gene expression and protein production following HRV-16 infection was significantly greater than following SARS-CoV-2. IFN gene expression and protein production were inversely correlated with SARS-CoV-2 replication. Treatment of cultures with recombinant IFNβ1 or IFNλ2, or pre-infection of cultures with HRV-16, markedly reduced SARS-CoV-2 replication. Discussion In addition to marked between-donor heterogeneity in IFN responses and viral replication, SARS-CoV-2 elicits a less robust IFN response in primary AEC cultures than does rhinovirus, and heterologous rhinovirus infection, or treatment with recombinant IFN-β1 or IFN-λ2, markedly reduces SARS-CoV-2 replication.
Spontaneous and piezoelectric polarization effects on the output characteristics of AlGaN/GaN heterojunction modulation doped FETs

We report on the calculation of electrical characteristics of AlGaN/GaN heterojunction field effect transistors (HFETs). The model is based on the self-consistent solution of the Schrodinger and Poisson equations coupled to a quasi-2D model for the current flow. Both single and double heterojunction devices are analyzed for [0001] or [000-1] growth directions. The onset of a parasitic p-channel for particular growth directions and alloy concentrations is also shown.
Efficient Anonymous Authentication Scheme in Body Area Networks Via Signal Propagation Characterization

Owing to its capability to measure the sensitive biological data of patients through embedded sensors and transmit them via open wireless channels to remote medical experts, wireless body area network (WBAN) has been playing an important role in pervasive healthcare systems. However, the open nature of the wireless channels renders the data susceptible to being eavesdropped by an adversary and linked to the identities of the transmitting devices, which can enable the adversary to gain sensitive information and launch targeted physical attacks. Therefore, anonymous authentication and confidentiality of the data in WBAN are vital. In the last few years, numerous anonymous authentication schemes based on cryptographic primitives and physiological features were designed to enhance security in WBAN. However, most of the existing schemes are not computationally efficient or require additional sensing hardware. To address these limitations, we propose an efficient anonymous authentication scheme for WBAN based on signal propagation characteristics. The key idea in the proposed scheme is to utilize the distinct received signal strength (RSS) variation profiles between on-body and off-body communication channels to conceal the identities of communicating devices, thereby ensuring their anonymity during authentication. We perform security and performance analyses of the proposed approach to prove its security strength and computational efficiency, respectively. Moreover, extensive experiments are conducted on human volunteers in indoor and outdoor environments to show the robustness of our approach. The results of the analyses and the experiments show that our scheme can successfully mitigate 88.8% of active attack attempts with less computation overhead.
Treatment Considerations for Obstructive Sleep Apnea in Pediatric Down Syndrome

Children with Down syndrome (DS) are at high risk for developing obstructive sleep apnea (OSA) compared to children without DS. The negative impact of OSA on health, behavior, and cognitive development in children with DS highlights the importance of timely and effective treatment. Due to the higher prevalence of craniofacial and airway abnormalities, obesity, and hypotonia in patients with DS, residual OSA can still occur after exhausting first-line options. While treatment commonly includes adenotonsillectomy (AT) and continuous positive airway pressure (CPAP) therapy, additional therapy such as medical management and/or adjuvant surgical procedures need to be considered in refractory OSA. Given the significant comorbidities secondary to untreated OSA in children with DS, such as cardiovascular and neurobehavioral consequences, more robust randomized trials in this patient population are needed to produce treatment guidelines separate from those for the general pediatric population of otherwise healthy children with OSA. Further studies are also needed to look at desensitization and optimization of CPAP use in patients with DS and OSA.
Analyzing the capabilities of electric field energy harvesting using natural leaves

Electric field energy harvesters (EFEHs) are reliable and sustainable power sources that may be used to power wireless sensor nodes (WSNs) in urban Internet of Things (IoT) networks, replacing traditional batteries. However, the large-scale deployments begin to pose significant environmental concerns regarding fabrication material degradation and recycling. In this context, this work analyses the performance of the natural green leaves as a replacement for standard electrodes in EFEH developments. To this end, different 10×3 cm2 EFEHs were assembled with raw leaves from the following species Magnolia Obovata, Ravenala Madagascariensis, Acanthus Mollis, and Agapanthus Africanus. Each harvester was evaluated at different drying steps, concluding that natural leaves may collect electrostatic charges related to the urban electric field, which might power ultra-low-energy devices. Experimental results reveal that Agapanthus Africanus electrodes perform best, with an open-circuit voltage (VOC) of 111.88 V and a short-circuit current (ISC) of 229.09 nA. On the other hand, the VOC of Magnolia Obavata leaves achieved 76.76 V, and the ISC was 135.30 nA (the worst case). Although the performance for Ravenala Madagascariensis samples is less than Agapanthus Africanus ones, the size leaf is another critical parameter to design functional devices. Therefore, the experimental section also includes the conceptualization, design, and experimental testing of a functional EFEH prototype called Leaf-EFEH, which is assembled with Ravenala Madagascariensis leaves. Finally, numerous experiments have shown that the proposed Leaf-EFEH can power ultra-low-power devices.
Dubins Path Planning of Multiple Unmanned Airborne Vehicles for Communication Relay

This article investigates the path planning strategy of a swarm of unmanned airborne vehicles (UAVs) with the aim of guaranteeing the communication relay between a ground control station and a friendly fleet. A decision-making algorithm mainly relies on waypoint generations and path planning based on Dubins theory and deals with dynamic and strategic constraints such as maximum speed, minimum curvature radius, and no fly zones arising from the mission operative scenario. The example simulations concerning a baseline scenario with a swarm of three UAVs are done to verify the feasibility of the proposed path planning algorithm.
MemXCT: Design, Optimization, Scaling, and Reproducibility of X-ray Tomography Imaging

This work extends our previous research entitled MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization that was originally published at the SC19 conference [1] with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11K 11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original paper at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world at the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this paper.
AN EXPERIMENT ON PROTEIN STRUCTURE PREDICTION USING REINFORCEMENT LEARNING

We are focusing in this paper on investigating a reinforcement learning based model for solving the problem of predicting the bidimen- sional structure of proteins in the hydrophobic-polar model, a well-known NP-hard optimization problem, important within many elds including bioinformatics, biochemistry, molecular biology and medicine. Our model is based on a Q-learning agent-based approach. The experimental evalua- tion conrms a good performance of the proposed model and indicates the potential of our proposal.
An Energy Efficient Routing Protocol Using NCPR and GNDA Algorithms

This Due to high mobility of nodes in mobile ad hoc networks (MANETs), there exist frequent link breakages which lead to frequent path failures and route discoveries and energy level of nodes also frequently reduced. The overhead of a route discovery cannot be neglected. In a route discovery, broadcasting is a fundamental and effective data dissemination mechanism, where a mobile node blindly rebroadcasts the first received route request packets unless it has a route to the destination, and thus it causes the broadcast storm problem due to this more rebroadcast occurs causes more energy consumption. In this paper, we propose a neighbour coverage-based probabilistic rebroadcast protocol with implementation of good node detection algorithm for reducing routing overhead and energy consumption in MANETs. In order to effectively exploit the neighbour coverage knowledge, we propose a novel rebroadcast delay to determine the rebroadcast order, and then we can obtain the more accurate additional coverage ratio by sensing neighbour coverage knowledge. We also define a Good node detection Algorithm to provide the energy efficient network by selecting energy efficient nodes. By combining the Neighbour coverage probabilistic routing and good node detection algorithm, we gradually reduce rebroadcast and choosing energy efficient nodes during routing. Our approach combines the advantages of the neighbour coverage knowledge and Good node detection algorithm which can significantly decrease the number of retransmissions so as to reduce the routing overhead, and can also decrease the energy consumption.
Bilevel multi-objective gray wolf algorithm based on Packet transport network optimization

Packet transport network (PTN), as an efficient transmission network technology in mobile communications in the big data era, is used by more and more communication operators. The existing PTN resource utilization rate is low, the network security is poor, so the existing PTN needs to be optimized in all aspects. For the optimization of the PTN, it is necessary to consider the decision of both the operator user and the service product supplier. Therefore, this paper proposes a bilevel multi-objective gray wolf algorithm based on PTN optimization problem. The operator user is the upper-level decision maker, and the objective function is to pay the product supplier the lowest cost. The product supplier is the lower-level decision maker, it mainly includes two major objective functions. The first objective function is to maximize the Label switching path overlap rate(LSPOR) evaluation score to solve the abnormal Label Switching Path (LSP) problem in the network, and the second is to maximize the committed bandwidth with utilizing rate(CBWUR) evaluation score to solve the problem of excessive Committed Information Rate(CIR) bandwidth usage in the network. According to the three scale network situation in Hubei, China, the improved multi-objective gray wolf algorithm is used to solve the PTN bilevel programming problem. The experimental results show that the model increases the utilization rate of network resources, and reduces the cost to be paid by the upper-level decision makers.
Effects of Open Road Tolling on Safety Performance of Freeway Mainline Toll Plazas

Advances in intelligent transportation systems technologies promoted the implementation of open road tolling (ORT) on tolled freeways worldwide. This new tolling solution converts existing barrier toll booths to express lanes that are capable of collecting tolls at high speed. ORT has demonstrated numerous benefits in reducing traffic congestion and air pollution. However, the effects of ORT on safety are still not clear because most ORT systems have been operated for a relatively short period. Therefore, this study aimed to evaluate the safety impacts of ORT by examining recent practices in New Jersey. Multiple-year crash data at toll plazas before and after the implementation of the ORT systems were analyzed. The full Bayes methodology was employed to develop crash frequency models as a function of traffic and toll plaza configurations. These models were used to estimate the crash frequency on the assumption that the ORT systems were not installed. The estimations were then compared with the observed crash counts that occurred after the deployment of the ORT systems. Comparisons showed that crash reductions were obtained at most of the toll plazas. The overall comparison showed that crash frequency decreased by about 24% after the deployment of the ORT systems and suggested that the use of ORT is a beneficial solution to improve the safety of the toll plazas. From an implementation point of view, the analysis results also indicated that special attention should be paid to operational elements such as signage, diversion, and merge designs of the ORT systems.
Creative Process Experiences with Digital Storytelling: A Tale of Two Engineering Students

Creativity is an essential trait for engineering students to be innovative and successful in current and future global economy. Thus, it is important for their creative potential to be nurtured. Since digital storytelling has been portrayed by previous studies as being influential in enhancing creativity, the purpose of this study is to explore the creative processes of two engineering students in their development of digital stories in the English reading class. Both of them were chosen based on their reading performance in an English reading placement test. One of them was considered an above-average reader, while the other a below-average reader. The duration for the digital storytelling project was three weeks. The study employed a case study research method. Data sources included observational field notes, interview transcripts and digital stories. The findings of this study showed that both students exhibited the preparation, incubation, illumination and verification steps of the creative process as proposed by Wallas (1926). However, it was found that the above-average reader prepared extensively, wanted to be different from the other students, and was more self-critical of and not easily satisfied with his work. The below-average reader, on the other hand, completed his work through trial and error, did not worry much of the project and was generally satisfied with his work.
Stable Convergence Control of the Buck Converter Based on Iterative Learning Method

To enhance the stability and robustness of the DC converter, a two-dimensional (2D) iterative learning control method suitable for the Buck converter is proposed. This control method is derived through a continuous-discrete model. Combining the error dynamics equation and the iterative learning method (ILM) described with Roesser theory, effective learning rules and sufficient conditions for convergence are obtained. By contrast with the iterative learning algorithm of the conventional Buck converter, this algorithm clarifies the learning gain, deeply learns the structure and parameters of the converter, and obtains an accurate learning dynamics model. Moreover, numerical simulations of the Buck converter with specific circuit parameters are also conducted. The simulation results demonstrate that the control learning rules are less restrictive with a swift transient response, which are robust without complex feedback compensation circuits. Thereby, it elucidates a new approach to the application of the Buck converter.
Efficient Privacy-Preserving Federated Learning With Unreliable Users

Federated learning (FL) has emerged as a powerful technology widely applied in Internet of Things (IoT). Recently, researchers have shown an increased interest in privacy-preserving FL with unreliable users. The goal of such works is to achieve private training under ciphertext mode while ensuring that the FL model is mainly derived from the contributions of users with high-quality data. However, the existing work is still in its infancy, and the main challenge faced by many researchers is how to achieve their schemes for meeting the demands of high accuracy and efficiency. To combat that, we propose an efficient privacy-preserving FL (EPPFL) scheme with unreliable users. Specifically, we design a novel scheme to mitigate the negative impact of unreliable users, where the targeted model is guaranteed to be updated with high-quality data. Through iteratively executing our “Excluding Irrelevant Components” and “Weighted Aggregation,” the FL model converges rapidly while taking limited communication and computation overhead. As a result, not only the model accuracy can be optimized, but also the training efficiency can be improved. Meanwhile, we conduct a secure framework based on the threshold Paillier cryptosystem, which can rigorously protect all user-related private information during the training process. Furthermore, the extensive experiments demonstrate our EPPFL with high-level performance in terms of accuracy and efficiency.
A Kinematics Based Evolutionary Approach for Molecular Conformational Search

AbstractThis paper presents a new approach called Kinematics Differential Evolution (kDE) to model flexible biological molecules of different type and size through the rapid identification of low-energy molecular conformations. One of the main benefits of our proposed methodology is that a population of low-energy solutions is provided for each tested molecular structure compared with a single low-energy solution usually obtained by traditional molecular modeling approaches. This population includes a number of different molecular conformations that attain similar low energy values and hence correspond to an energetic state with high probability of occurrence. Results show that the kDE model provides a set of molecular structures comparable to those obtained by traditional molecular dynamics.
Macro-Active Bond Mutual Funds

We document significant outperformance by government bond funds on important macro announcement days such as FOMC and GDP. Attributing the macro-day alpha to fund managers’ macro-timing skills, we provide evidence that fund managers do actively trade on their predictions of macro news, their ability to time the level factor is positive on both FOMC and GDP announcements and they can time the slope factor on GDP announcements, and their correct macro-day prediction does lead to higher macro-day alpha. Exploring its variation, we find the macro-day alpha to be larger during times of high macro disagreement and macro surprise, and stronger for more active funds with larger idiosyncratic volatility. Revealing their macro activeness, such active funds suffered the largest losses on the FOMC day of September 16, 2008, when, counter to their prediction, the Fed kept the rate unchanged despite the Lehman default. Finally, we find the macro-day outperformance to be persistent – past macroday outperformers continue to outperform on future macro days. ∗Hong (yrhong@saif.sjtu.edu.cn) and Tian (swtian.18@saif.sjtu.edu.cn) are from the Shanghai Advanced Institute of Finance at Shanghai Jiao Tong University. Pan (junpan@saif.sjtu.edu.cn) is from the Shanghai Advanced Institute of Finance at Shanghai Jiao Tong University and CAFR. We benefited from discussions with Alex Zhou, Bing Han, Jingzhi Huang, Dun Jia, and Julia Juric. We thank seminar participants at SAIF at Shanghai Jiao Tong University, Shanghai University of Finance and Economics, and conference participants at CICF 2021, World Finance Conference 2021, FMA Annual Meeting 2021.
Advanced Stirling Convertor Dual Convertor Controller Testing at NASA Glenn Research Center in the Radioisotope Power Systems System Integration Laboratory

NASA Glenn Research Center developed a nonnuclear representation of a Radioisotope Power System (RPS) consisting of a pair of Advanced Stirling Convertors (ASCs), Dual Convertor Controller (DCC) EMs (engineering models) 2 and 3, and associated support equipment, which were tested in the Radioisotope Power Systems System Integration Laboratory (RSIL). The DCC was designed by the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to actively control a pair of ASCs. The first phase of testing included a Dual Advanced Stirling Convertor Simulator (DASCS), which was developed by JHU/APL and simulates the operation and electrical behavior of a pair of ASCs in real time via a combination of hardware and software. RSIL provides insight into the electrical interactions between a representative radioisotope power generator, its associated control schemes, and realistic electric system loads. The first phase of integration testing included the following spacecraft bus configurations: capacitive, battery, and super-capacitor. A load profile, created based on data from several missions, tested the RPS's and RSIL's ability to maintain operation during load demands above and below the power provided by the RPS. The integration testing also confirmed the DCC's ability to disconnect from the spacecraft when the bus voltage dipped below 22 volts or exceeded 36 volts. Once operation was verified with the DASCS, the tests were repeated with actual operating ASCs. The goal of this integration testing was to verify operation of the DCC when connected to a spacecraft and to verify the functionality of the newly designed RSIL. The results of these tests are presented in this paper.