Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Assays for Measuring the Catalytic Activity of Ribosome Inactivating Proteins

Ribosome-inactivating proteins (RIPs) are potent toxins that inactivate ribosomes by catalytically removing a specific adenine from the α-sarcin/ricin loop (SRL) of the large rRNA. Direct assays for measuring depurination activity and indirect assays for measuring the resulting translation inhibition have been employed to determine the enzyme activity of RIPs. Rapid and sensitive methods to measure the depurination activity of RIPs are critical for assessing their reaction mechanism, enzymatic properties, interaction with ribosomal proteins, ribotoxic stress signaling, in the search for inhibitors and in the detection and diagnosis of enteric infections. Here, we review the major assays developed for measuring the catalytic activity of RIPs, discuss their advantages and disadvantages and explain how they are used in understanding the catalytic mechanism, ribosome specificity, and dynamic enzymatic features of RIPs.
Empirical path loss model for 2.4 GHz IEEE 802.15.4 wireless networks in compact cars

Wireless sensor systems are becoming increasingly attractive as a means to flexibly extend sensing capabilities towards more intelligent cars. Wireless sensors can reduce cabling costs and weight, allow for cheaper customization, and allow retrofitting new functionality into older vehicles. The communication system design for these wireless sensor systems requires models for radio propagation in cars. In this paper we present a novel empirical path loss model for the 2.4 GHz based IEEE 802.15. 4 radio channel. Our model is based on extensive measurements in two compact cars. We have extracted model parameters for two types of wireless links, those between sensors in the car and those towards sensors on the outside of the car. We have calibrated the model parameters for both path loss and shadow fading to fit our measurements.
Panacea: A low-latency, energy-efficient neighbor discovery protocol for wireless sensor networks

Neighbor discovery is essential to construct wireless sensor networks. Most existing studies on neighbor discovery are designed on the assumption that either all nodes are within the radio range of each other, or only two nodes compose the network. However, networks are partially-connected in reality: some nodes are within the radio range of each other, while others are not. Low latency and energy efficiency are two common goals, which become even more challenging to achieve at the same time in partially-connected networks. We find the collision caused by simultaneous transmissions is the main obstruction of achieving the two goals. In this paper, by alleviating collisions, we present Panacea, the first low-latency, energy-efficient protocol for a partially-connected network. To begin with, we design Panacea-WCD for nodes that have collision detection mechanisms and Panacea-NCD for nodes that do not have. Then, we prove the discovery latency of Panacea is bounded by a low latency O(n•ln n), where each node has n neighbors on average. Finally, we implement Panacea for evaluations in complicated partially-connected networks. The evaluated results corroborate our analyses: compared with two notable protocols, i.e. PND and Coupon, Panacea achieves up to 5.14 times lower latency with the same energy consumption; and saves 30% energy for similar latency with Coupon.
Low Complexity ZF Receiver Design for Multi-User GFDMA Uplink Systems

This paper presents a low complexity receiver for multi-user uplink transmission using generalized frequency division multiplexing (GFDM). By exploiting the frequency spreading filter in the GFDM, the banded and circulant properties of the frequency transformed modulation matrices of individual users are developed. According to the newly developed properties in the frequency domain, a novel low complexity zero-forcing (ZF) receiver is developed for multi-user GFDM uplink systems in the frequency selective channels. The proposed receiver can be realized by successive discrete Fourier transforms and one-tap equalization, avoiding the huge computation caused by the inverse of the large dimensional equivalent system matrix. Simulation results show that the proposed receiver provides nearly the same symbol error rate as the traditional ZF equalizer with substantially reduced computational complexity.
SMDP-Based Coordinated Virtual Machine Allocations in Cloud-Fog Computing Systems

Heterogeneous computing powered by remote clouds and local fogs is a promising technology to improve the performance of user terminals in the Internet of Things. In this paper, two semi-Markov decision process (SMDP)-based coordinated virtual machine (VM) allocation methods are proposed to balance the tradeoff between the high cost of providing services by the remote cloud and the limited computing capacity of the local fog. We first present a model-based planning method in which it is necessary to train the state transition probabilities and the expected time intervals between adjacent decision epochs. To facilitate training them, the SMDP is degraded into a continuous-time Markov decision process (CTMDP) in which the service requests and ongoing service completions follow a continuous-time Markov chain. The relative value iterative algorithm for the CTMDP is used to find an asymptotically optimal VM allocation policy. In addition, we also propose a model-free reinforcement learning (RL) method, where an optimal coordinated VM allocation policy is approximated by learning from the states and rewards of feedback. The simulation results show that the performance of the model-free RL method can converge to a level similar to that of the model-based planning method and outperform the greedy VM allocation method.
When Regression Verification Meets CEGAR

Software systems evolve throughout their life cycles. Many revisions are produced over time. Model checking each revision of the software is impractical. Regression verification suggests reusing intermediate results from the previous verification runs. This paper proposes a fully automatic regression verification technique in the context of CEGAR. Procedure summaries, which describe the input/output behaviors of a procedure, are proposed as the intermediate results to be reused. Procedure summaries are reasonably small to store, technically easy to process, and do not require much extra computation effort to be reused. Reusing procedure summaries saves much analysis effort on the corresponding procedures. By combining regression verification and CEGAR, we propose a technique that is able to reuse procedure summaries across different abstract precisions and different program revisions. We performed extensive experiments on a large number of industrial programs (534 revisions of 89 Linux kernel device drivers). The results show that our approach can significantly improve the performance of regression verification.
Noncontact Sleep Stage Estimation Using a CW Doppler Radar

Sleep stage estimation is crucial to the evaluation of sleep quality and is a proven biometric in diagnosing cardiovascular diseases. In this paper, we design a continuous wave (CW) Doppler radar to accurately measure sleep-related signals, including respiration, heartbeat, and body movement. Body movement index, respiration per minute (RPM), variance of RPM, amplitude difference accumulation (ADA) of respiration, rapid eye movement parameter, sample entropy, heartbeat per minute (HPM), variance of HPM, ADA of heartbeat, deep parameter, and time feature have been extracted and fed into different machine learning classifiers. A total of 11 all night polysomnography recordings from 13 healthy examinees were used to validate the proposed CW Doppler radar system and the ability to detect sleep stage information from it. Comparative studies and statistical results have shown that the subspace K-nearest neighbor algorithm outperforms the other classifiers with the highest accuracy of up to 86.6%. With the Relief F algorithm, features have been ranked, and the selected feature subsets have been preliminary tested to identify the optimal feature subset. Meanwhile, comparative analysis of our classification performance under different sleep stage patterns with prior works has been carried out to show the significant improvements over state-of-the-art solutions. These results suggest that the proposed scheme is suitable for long-term sleep monitoring.
On the Benefits of Keeping Path Identifiers Secret in Future Internet: A DDoS Perspective

In recent years, there are increasing interests in using path identifiers (PIDs) as inter-domain routing objects in the future Internet. While PIDs are advertised throughout the Internet in some future Internet architectures, they are kept secret in others. In this paper, we investigate the benefits, from the perspective of preventing distributed denial-of-service (DDoS) attacks, of keeping PIDs secret in future Internet when compared with advertising them throughout the Internet. Our results from extensive simulations show that keeping PIDs secret significantly helps prevent DDoS attacks.
Coarray beamspace transformation based DOA estimation for uniform circular arrays

The beamspace transformation (BT) is utilized to convert a uniform circular array (UCA-BT) to a virtual uniform linear array (VULA) for spatial smoothing and direction-of-arrival (DOA) estimation. The estimation performance of the UCA-BT is significantly affected by the choice of phase mode order. In order to enhance estimation performance, we propose a new coarray beamspace transformation (CBT) algorithm based on the UCA in this paper. The difference coarray of a UCA, which is a uniform concentric circular array (UCCA), is first calculated using the covariance vectorization technique. Subsequently, the BT is applied to the UCCA coarray to obtain a large VULA. The forward spatial smoothing and MUSIC algorithms are then implemented to the VULA for high-resolution DOA estimation. Simulation results demonstrate the effectiveness of UCA-CBT algorithm compared to other estimation methods.
IoT solutions for crop protection against wild animal attacks

Technology plays a central role in our everyday life. There has been a surge in the demand of Internet of Things (IoT) in many sectors, which has drawn significant research attention from both the academia and the industry. In the agriculture sector alone, the deployment of IoT has led to smart farming, precision agriculture, just to mention a few. This paper presents the development of Internet of Things application for crop protection to prevent animal intrusions in the crop field. A repelling and a monitoring system is provided to prevent potential damages in Agriculture, both from wild animal attacks and weather conditions.
Uplink channel estimation for nonorthogonal coded access

A promising multiple access scheme, which is called nonorthogonal coded access (NOCA), has been proposed for the fifth generation wireless communication systems to achieve higher service quality with limited radio resource provisions. However, uplink channel estimation in NOCA has not been well studied and the perfect channel knowledge has been assumed in the literature. In this paper, the uplink channel estimation for NOCA is investigated including the designs of pilot sequences and the channel estimation algorithms. Furthermore, two pilot patterns are proposed and evaluated in different spreading factors. The simulation results show that the proposed pattern designs enable accurate channel estimation using the pilots generated by nonorthogonal Zadoff-Chu sequences. In addition, a valuable observation is that the channel estimation is more accurate using smaller spreading factors in the case of high overloading.
Process variation estimation using a combination of ring oscillator delay and FlipFlop retention characteristics

We propose an extraction method of process variation utilizing D-Flip-Flop (DFF) data retention characteristics and Ring Oscillator(RO) oscillation delay. Extracted process variation is modeled as PMOS and NMOS threshold voltage variations. Retention characteristics of the DFF circuit has different sensitivity to threshold voltage variation from the RO circuit. A DFF circuit is newly introduced as a complementary test structure of the conventional RO circuit for process variation extraction. By combining the RO circuit and the DFF circuits, we can accurately estimate the shift of global process variation. The test structure is implemented into silicon chip and the amount of global variation shift is extracted from measured data.
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition

Speech recognizers trained on close-talking speech do not generalize to distant speech and the word error rate degradation can be as large as 40% absolute. Most studies focus on tackling distant speech recognition as a separate problem, leaving little effort to adapting close-talking speech recognizers to distant speech. In this work, we review several approaches from a domain adaptation perspective. These approaches, including speech enhancement, multi-condition training, data augmentation, and autoencoders, all involve a transformation of the data between domains. We conduct experiments on the AMI data set, where these approaches can be realized under the same controlled setting. These approaches lead to different amounts of improvement under their respective assumptions. The purpose of this paper is to quantify and characterize the performance gap between the two domains, setting up the basis for studying adaptation of speech recognizers from close-talking speech to distant speech. Our results also have implications for improving distant speech recognition.
Analyze users' online shopping behavior using interconnected online interest-product network

In recent years, with the rapid development of Internet, more and more users shop and socialize online, which produces a large amount of traffic data. Analyzing users' online shopping behavior is important for merchants to improve profit. Social networks are widely used in studying the relationship among users. In this paper, we apply the concept of social networks to online shopping behavior, and present a novel network perspective on the interconnected nature of online interest and product, allowing us to capture the attribute of online products reflecting users' sociability. Sociability in this paper is not a traditional social connection but common interests(defined by produce visiting behavior) among users. First, we build an interconnected online interest-product network which includes online shopping based social network (OSSN), online product network and intermediate layer, and we define popular online products and non-popular online products according to the number of distinct visitors of one product. Then, we analyze some indicators about OSSN, and find that OSSN has similar characteristics with traditional social networks, such as small world feature and homophily. Finally, we analyze how online products reflect users' sociability. With the number of online products users have visited increasing, the number of neighbors with similar interests first presents a positive correlation, and then disappears, which implies that the number of users' neighbors depends on the attribute of online products rather than the number of online products users have browsed. We define a formula to measure the attribute of online products reflecting users' sociability, and find that there is a distinct difference among different categories of online products, and non-popular online products often have high value of attribute to reflect users' sociability and the value is low in popular online products.
Suppression of focal adhesion formation may account for the suppression of cell migration, invasion and growth of non-small cell lung cancer cells following treatment with polyisoprenylated cysteinyl amide inhibitors

Migratory cells form extracellular matrix attachments called focal-adhesions. Focal adhesion assembly and disassembly are regulated by the Rho family of small GTPases. We previously reported that polyisoprenylated cysteinyl amide inhibitors (PCAIs) suppress Rho protein levels, disrupting F-actin cytoskeleton remodeling in the formation of lamellipodia and filopodia. In this study, we investigated whether these observations effect focal adhesion formation, which involves cell surface receptors known as integrins and several signaling/adaptor proteins such as vinculin, α-actinin, Rock kinases and phospho-Myosin Light Chain-2 (p-MLC-2), that foster the linkage of the actin cytoskeleton to the extracellular matrix. We observed that treatment of H1299 cells with 5 μM PCAIs for 24 h markedly diminished the level of full-length integrin α4 by at least 24% relative to controls. PCAIs at 5 μM, diminished the levels of vinculin by at least 50%. Immunofluorescent analysis showed at least a 76% decrease in the number of vinculin-focal adhesion punctates. In addition, PCAIs diminished Rock1 levels by 25% and its substrate, p-MLC-2 by 75%. PCAIs did not significantly alter the levels of integrin β5, α-actinin, and Rock2, suggesting that the effects of the PCAIs are target specific. Our data indicate that the PCAIs alter the levels of the Rho proteins and their effectors to abrogate their functions in cytoskeleton remodeling thereby suppressing focal adhesion formation. This in turn results in a PCAIs-induced decrease in cell invasion, thus making the PCAIs propitious agents for the inhibition of cancer growth and metastasis.
On the Problem and Promise of Metaphor Use in Science and Science Communication

The language of science is largely metaphorical. Scientists rely on metaphor and analogy to make sense of scientific phenomena and communicate their findings to each other and to the public. Yet, despite their utility, metaphors can also constrain scientific reasoning, contribute to public misunderstandings, and, at times, inadvertently reinforce stereotypes and messages that undermine the goals of inclusive science. This paper 1) examines the generative potential of metaphors to the advancement of scientific knowledge and science communication, 2) highlights the ways in which outdated metaphors may limit scientific inquiry and contribute to public misunderstandings, and 3) critically analyzes the implications of cryptic social and political messages embedded in common metaphors in the life sciences.
Bridging the digital divide in Tonga through a sustainable multi-tenancy broadband infrastructure: Are we ready?

The digital divide refers to the gap between those who have access to modern information and communications technology (ICT) and those who do not or have constrained access. This technology could include telephones, television, computers and the Internet. The arrival of high-speed Internet connectivity to the Kingdom of Tonga continues to bring opportunities such as business growth and jobs creation; however, there is also a growing digital divide affecting the country and other regions of the world. These motivate us to debate and seek the best way to reduce the divide in Tonga. Much of the research on this area focuses on the accessibility to the technologies, while some works have addressed the inequality in the ability and skills to use the technology. Motivated by the United Nations' sustainable development goals (SDGs), especially Goal 9 on Industry, Innovation and Infrastructure and Goal 10 on Reduced Inequalities, we examine both perspectives of digital divide in Tonga. Moreover, we introduce the most recent broadband technologies and discuss their potential to enable Tonga's digital transformation from the perspectives of reliability, security and affordability. We then envision a sustainable multi-tenancy broadband infrastructure to integrate various access technologies in order to offer multi-services, which can best accommodate Tongan's various ICT needs according to their different geographical and socio-economic characteristics. This paper intends to contribute to the current debate, and stimulate further discussion and research on reducing the digital divide in Tonga.
Different fast charging methods and topologies for EV charging

In order to promote clean and green environment and to reduce carbon emission, the Government of India aspires for a nation with 100 percent electric vehicle mobility by the year 2030. For such a large number of electric vehicles to be running on Indian roads, there is a critical need for fast charging stations and infrastructure. This paper presents different fast charging schemes along with different power converter topologies for electric vehicles. Simulation results of different topologies of power converter are presented and compared. To charge battery of electric vehicle, the safety and charging time of battery must be considered.
Mitigation of automotive radar interference

This paper presents a new approach to mitigating radar interference and focuses on the application of automotive radar. Traditional interference mitigation techniques in automotive radar depend on detection and identification of the interference. With this paper, we propose a novel method based on advanced signal separation techniques which do not need any prior detection of the interference. The success of the proposed method is demonstrated into simulated and real automotive radar data sets, in the presence of Continuous Wave (CW) and Frequency Modulated Continuous Wave (FMCW) interference. Significant improvement in Signal-to-Interference-plus-Noise Ratio (SINR) is observed after range-Doppler processing.
Effects of Dietary Daidzein Supplementation on Reproductive Performance, Serum Hormones, and Reproductive-Related Genes in Rats

The aim of this study was to investigate the effect of dietary daidzein supplementation on reproductive performance in rats. A total of twenty-four female Sprague–Dawley (SD) rats were randomly allocated to two groups and fed either with a basal diet (CON) or basal diet containing 50 mg/kg daidzein (DAI) from gestation until delivery stage. The results show that daidzein supplementation significantly increased the total litter weight and the total viable newborn weight (p < 0.05). Interestingly, daidzein supplementation acutely elevated the concentrations of serum estrogen, progesterone and insulin-like growth factor-1 (p < 0.01) after the maternal rats’ delivery. The concentrations of serum immunoglobulin A (IgA) and immunoglobulin G (IgG) were also significantly higher in the DAI maternal rats than in the CON maternal rats (p < 0.05). Moreover, daidzein significantly increased the total antioxidant capacity (T-AOC) in maternal rats’ sera and in newborns (p < 0.05) and elevated the concentration of superoxide dismutase (SOD) in both the maternal rats’ sera and their ovaries (p < 0.05). Importantly, daidzein supplementation significantly elevated the expression levels of estrogen receptor β (ERβ) and NR5A2 genes in maternal rats’ ovaries (p < 0.05) and downregulated the expression level of prolactin receptor (PRLR) in newborns (p < 0.05). These results suggest that dietary daidzein supplementation improves reproductive performance and fetal development in rats, which is associated with changes in serum hormones, tissue antioxidant capacity, and expression levels of reproductive-related genes, both in maternal rats and their offspring.