Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth-dependent signals drive an increase in early G1 cyclin concentration to link cell cycle entry with cell growth

Entry into the cell cycle occurs only when sufficient growth has occurred. In budding yeast, the cyclin Cln3 initiates cell cycle entry by inactivating a transcriptional repressor called Whi5. Growth-dependent changes in the concentrations of Cln3 or Whi5 have been proposed to link cell cycle entry to cell growth. However, there are conflicting reports regarding the behavior and roles of Cln3 and Whi5. Here, we found no evidence that changes in the concentration of Whi5 play a major role in controlling cell cycle entry. Rather, the data suggest that cell growth triggers cell cycle entry by driving an increase in the concentration of Cln3. We further found that accumulation of Cln3 is dependent upon homologs of mammalian SGK kinases that play roles in control of cell growth and size. Together, the data are consistent with models in which Cln3 serves as the crucial link between the cell cycle and signals that control cell growth and size.
MPFSLP: Masqueraded Probabilistic Flooding for Source-Location Privacy in VANETs

A major impediment to the adoption of vehicular ad-hoc networks (VANETs) today is their privacy and security concerns. In this paper, we address the privacy aspects of VANETs. Specifically we build a scheme that provides non-repudiation, message authentication, integrity, and non-traceability (or source location privacy) while maintaining low overhead in communication to support the security requirements for the beaconing mechanism. Our scheme leverages the fact that an endpoint only requires the knowledge of the location and velocity of another endpoint and not its identity, thereby our mechanism introduces confusion in the mapping form identity of nodes to packets. Each node can send any other node's packet masquerading as its own packet. We further introduce causal dependence amongst masqueraded packets to ensure non-repudiation. Our scheme reduces traceability by 6× when compared to baseline pseudonym changing schemes.
Calculation of Windings Differential Inductances of Electrical Devices in Mathematical Models with a Total Magnetic Flux

The calculation of differential electromagnetic parameters (differential inductances and mutual inductances of windings) of electric machines and electromagnetic devices based on the mathematical models of their magnetic circuits with a total magnetic flux is proposed. These parameters are determined using the geometric dimensions and design features of the devices, the main magnetization curve of the magnetic core material and the winding currents.
On Bandwidth Constrained Distributed Detection of a Known Signal in Correlated Gaussian Noise

We consider a Neyman-Pearson (NP) distributed binary detection problem in a bandwidth constrained wireless sensor network, where the fusion center (FC) makes a final decision about the presence or absence of a known signal in correlated Gaussian noises. Our goals are (i) to investigate whether or not randomized transmission can improve detection performance, under communication rate constraint, and (ii) to explore how the correlation among observation noises impacts performance. We propose two novel schemes that combine the concepts of censoring and randomized transmission (which we name CRT schemes) and compare them with pure censoring scheme. In CRT (pure censoring) schemes we map randomly (deterministically) a sensor's observation to a ternary transmit symbol <inline-formula><tex-math notation="LaTeX">$u_k \in \lbrace -1,0,1\rbrace$</tex-math></inline-formula> where “0” corresponds to no transmission (sensor censors). We model the randomization in CRT schemes using two independent Bernoulli random variables with parameters <inline-formula><tex-math notation="LaTeX">$g,f$</tex-math></inline-formula>. Assuming sensors transmit over orthogonal fading channels, we formulate and address two system-level constrained optimization problems: in the first problem we minimize the probability of miss detection at the FC, subject to constraints on the probabilities of transmission and false alarm at the FC; in the second (dual) problem we minimize the probability of transmission, subject to constraints on the probabilities of miss detection and false alarm at the FC. The optimization variables include <inline-formula><tex-math notation="LaTeX">$g,f$</tex-math></inline-formula>. Both problems are non-convex and their solutions can be found via exhaustive search. Seeking to shed some light on the qualitative behavior of randomization, we propose methods to find sub-optimal solutions of the original problems and study the relation between these solutions. Through analysis and numerical evaluations, we explore and provide the conditions under which CRT schemes outperform pure censoring scheme.
Design of A Novel Virtual Sensor for the Vascular Interventional Robotic System

In order to solve existed problems in the force measurement part of vascular interventional surgical robots. In this paper, A new concept was proposed to obtain more accurate force information and solve the problems of traditional sensors in vascular interventional surgery. This sensorless measurement is based on kinematic equations in robotics. First of all, the virtual sensor mentioned in this paper firstly collects the pictures of the catheter in the blood vessel, extracts the deformation variables, then establishes the catheter kinematics model, and imports the extracted deformation variables into the catheter kinematics model. The final output force value. Finally, experimental results showed that force measurement in this way can avoid the problem of sterilization caused by the sensor entering the blood vessel and the problem of insufficient accuracy caused by indirect measurement.
Improved Hybrid Switched Inductor/Switched Capacitor DC–DC Converters

High voltage gain dc–dc converters are widely used in various applications like low-voltage sustainable sources. In traditional boost converters, the voltage gain is limited by high voltage stress, high current ripple, and low efficiency due to employing a high duty cycle ratio. In this article, we propose converters that are a combination of four substructures, namely active switched inductor, passive switched inductor, switched capacitor cell, as well as an auxiliary switch with nonisolated configuration. The proposed structures have high voltage gain compared to the conventional either switched inductor-based or switched capacitor-based ones in addition to a low duty cycle ratio. By adding an auxiliary switch, the efficiency is improved, particularly for high voltage gains. The principle of operation and steady-state analysis are discussed in detail. Also, simulation results from PSCAD/EMTDC software are validated by a prototype built for experimental examination. The results demonstrated that the voltage gain and efficiency could be improved by utilizing the auxiliary switch.
Classification of High - Voltage Ignition Waveforms Using Deep Learning

A detailed analysis of secondary voltage waveforms displayed by an oscilloscope is commonly acknowledged as a key to diagnosing automobile ignition systems as well as some mechanical components of spark - ignited combustion engines. Many ignition systems troubles may be identified by experienced service technicians due to an assessment of some characteristic parts of a high-voltage waveform: the dwell time, firing line, burn time and oscillations. However, the analysis of many data collected over a long period, if done manually, can be awkward and time-consuming. The focus of the article is an application of a deep learning method for the detection and classification of selected parts of highvoltage waveforms generated by an ignition coil. The study concluded that an application of the ADAM optimization algorithm for deep learning can help develop a model for highvoltage signal segmentation which yields the average classification accuracy greater than 99%.
Agent-Based Modeling in Electricity Market Using Deep Deterministic Policy Gradient Algorithm

Game theoretic methods and simulations based on reinforcement learning (RL) are often used to analyze electricity market equilibrium. However, the former is limited to a simple market environment with complete information, and difficult to visually reflect the tacit collusion; while the conventional RL algorithm is limited to low-dimensional discrete state and action spaces, and the convergence is unstable. To address the aforementioned problems, this paper adopts deep deterministic policy gradient (DDPG) algorithm to model the bidding strategies of generation companies (GenCos). Simulation experiments, including different settings of GenCo, load and network, demonstrate that the proposed method is more accurate than conventional RL algorithm, and can converge to the Nash equilibrium of complete information even in the incomplete information environment. Moreover, the proposed method can intuitively reflect the different tacit collusion level by quantitatively adjusting GenCos’ patience parameter, which can be an effective means to analyze market strategies.
Hankel Singular Values and LQG Characteristic Values of Discrete-Time Linear Systems in Cascade With Inner Systems

Recent results have shown that, for continuous-time systems obtained by cascading an asymptotically stable system with an inner system, the first $n$ Hankel singular values are greater than or equal to those of the original system. Similarly, cascading a system in minimal form with an inner system, the same property holds for the linear quadratic Gaussian (LQG) characteristic values. In this article, we consider the discrete-time case and demonstrate that the property also holds for these systems. A very important consequence stemming from these results is that the Hankel singular values and the LQG characteristic values of input-delayed discrete systems are greater than or equal to those of their zero-delay counterpart.
Pricing Based Distributed Traffic Allocation for 5G Heterogeneous Networks

Next generation (5G and beyond) mobile networks will highly rely on distributed heterogeneous infrastructure, which is foreseen to create a versatile resource ecosystem. In such network deployments, efficient traffic allocation and management is of prominent priority. The increasing congestion conditions of cellular infrastructures have steered extensive research focus towards Heterogeneous Network (HetNet) access solutions. The deployment of dense multiple radio access technology (RAT) networks combined with the enhanced capabilities of mobile devices to maintain concurrent connections through different RATs, add up to the complexity of the traffic allocation problem. Traffic allocation becomes more challenging when using diverse technologies concurrently to serve multiple traffic classes of different Quality of Service (QoS) demands. Towards a viable solution, we have proposed a pricing-based network selection process for heterogeneous networks, running in a distributed manner, which has proved to lead to equilibrium under the developed policies. In this paper, we extend our pricing algorithm by providing a new approach that introduces personalized pricing based on the load pressure that an allocation decision will impose to a RAT. The central system decisions are confined to dynamically select the UE polling rank policy that is presenting the closest potential performance to that of the optimal centralized solution, used as the benchmark ranking. The optimal policy is based on proportional fairness and is used as a reference centralized policy to drive our distributed solutions. We assess the enhanced pricing scheme proposed in this work, and we employ extensive simulations and testbed experimentation to evaluate its performance in terms of efficient bandwidth allocation, price variation, fairness and induced cost to clients served by the HetNet.
Visual Orientation Sequence Task enhances Working Memory in Younger and Older Adults

Working memory is an important component of higher-order cognitive processes which are negatively impacted by aging effects. Recent researches have shown that plentiful behavioral training had positive impact on working memory. However, it remains difficult to achieve significant training results and thus improve the ability of working memory and general cognitive function for older adults who was suffered with cognitive decline. Based on this premise, our study aims to compare the working memory training effects in younger and older participants, and hopes to found reliable ways to improve training effects for older and cognitive ability-damaged subjects. To achieve this purpose, we recruited 42 younger (12 men, 4 women; mean age: 25.4 years) and older adults (12 men, 12 women; mean age: 69.9 years) who were divided into two subgroups randomly to participate the current experiment. We executed a visual orientation discrimination task and a modified visual orientation sequence tasks to train the visual working memory for all participants, and evaluated the training effects by comparing results of the same pre and posttests. Our results showed that both younger and older participants could benefit from the designed training tasks. These findings indicated the robust plasticity of working memory even in aging brain, and our results further suggest that more effective training gains could be achieved by adjusting the difficulty of training task for participants with different cognitive ability.
Classification, fixation techniques, complications and outcomes of femur fractures in dogs and cats: 61 cases (2015-2016)

ABSTRACT: The aim of this study was to retrospectively evaluate the frequency of femur fractures in dogs and cats in the routine of a school hospital, determining their classification, fixation methods, complications, and outcomes. A total of 61 animals, 50 (82.0%) dogs and 11 (18.0%) cats, had femoral fractures that were submitted to osteosynthesis. Sixty-two femoral fractures were evaluated. Single fractures in the distal epiphysis (n=25) were the most frequent (P=0.0001). Intramedullary pins were used in association with cerclage and tension band for osteosynthesis in proximal fractures. In diaphyseal fractures, bone plates and screws, two intramedullary pins (insulated or with cerclage) and Tie-In configuration were used. In distal fractures, modified Rush intramedullary pins, cross pins and Tie-In configuration were used. Comparing complication frequencies at fracture sites that required reintervention after osteosynthesis, a significant difference was observed (P=0.0253) between the diaphyseal (31.25%) and distal (7.14%) fractures independent of the technique used. We concluded that distal epiphyseal fractures were the most frequent in the routine of a school hospital. Distal epiphyseal fractures presented a lower frequency of complications for consolidation when treated with modified Rush intramedullary pins or crossed pins.
Secrecy Performance of NOMA Systems With Energy Harvesting and Full-Duplex Relaying

This paper studies the physical layer security for a cooperative non-orthogonal multiple access system with energy harvesting (EH) and full-duplex (FD) relaying. In particular, the base station transmits a superposed message to a dedicated EH-FD relay according to NOMA principle. Then, the relay forwards the decoded message to users. At the same time, the message transmitted by the relay can be wiretapped by an passive eavesdropper. The lower bound on the ergodic secrecy rates and the approximated secrecy outage probabilities of users are analyzed with closed-form solutions considering imperfect SIC. Finally, simulation results demonstrate the correctness of theoretical analysis, reflect the influence of the key parameters on the system performance and verify the better secrecy performance of the proposed scheme than its counterpart.
Voltage Source Multilevel Inverters With Reduced Device Count: Topological Review and Novel Comparative Factors

Multilevel inverters (MLIs) have gained increasing interest for advanced energy-conversion systems due to their features of high-quality produced waveforms, modularity, transformerless operation, voltage, and current scalability, and fault-tolerant operation. However, these merits usually come with the cost of a high number of components. Over the past few years, proposing new MLIs with a lower component count has been one of the most active topics in power electronics. The first aim of this article is to update and summarize the recently developed multilevel topologies with a reduced component count, based on their advantages, disadvantages, construction, and specific applications. Within the framework, both single-phase and three-phase topologies with symmetrical and asymmetrical operations are taken into consideration via a detailed comparison in terms of the used component count and type. The second objective is to propose a comparative method with novel factors to take component ratings into account. The effectiveness of the proposed method is verified by a comparative study.
Genetic Algorithm Parametrization for Informed Exploration of Short Peptides Chemical Space

Chemical space is vast and its exploration by hand-picking chemical compounds with desirable features can appear slow and demanding. With the advancement in the field of search based algorithms such exploration can be rationally controlled. Our goal was to construct a genetic algorithm that searches through chemical space of short peptides in intelligent and fast manner. We achieved our goal by adapting the multi-objective NSGA-II algorithm through the implementation of the early stopping criterion and mitigation of possible memory issues by using simulated annealing. The contribution of this paper is the design strategy for multiple peptide libraries that cover greater area of search space in exploration of new active peptides.
Software-Defined Storage Optimization of Distributed ALICE Resources

In today’s big data era, data and information are the main capital. High Energy Physics (HEP) as a data-intensive field of research drives significant demands for managing large datasets. Physics data need to be stored, accessed, and properly understood. This paper deals with the increasing resource requirements of the Large Hadron Collider (LHC) experiments at European Laboratory for Particle Physics (CERN). We analyze the unique requirements of the A Large Ion Collider Experiment (ALICE). This paper proposes a software-defined storage (SDS) model based on cloud and edge computing to optimize and organize the data storage of the current ALICE grid computing model. Also, it analyses how software-defined storage and distributed computing technologies could potentially be used to address the challenges and unique requirements faced by the ALICE experiment. The proposed model represents the basis for future research in which the parameters will be further examined, with an emphasis on the scalability of storage and computing resources.
Pressure optimal control model of high-pressure tubing based on fourth-order Runge-Kutta method

The pressure control strategy of the high-pressure fuel pipe not only directly affects the stability and speed of the injection pressure, but also affects the performance of the diesel engine starting, idle speed, transition and normal operating conditions. Under different high-pressure conditions, the density and elastic modulus of the oil will change greatly. In addition, due to the viscosity, the oil flow and injection rate are also affected. Therefore, the simulation of the control system of the high-pressure oil pipe pressure is very complicated. At present, there is no strategy for the high-pressure oil pipe pressure control. This paper proposes a pressure control strategy for high-pressure tubing during transportation and injection, including stable pressure control and continuous boost pressure control strategies. Finally, a fourth-order Runge-Kutta method is applied to solve the integral numerical solution in a specific model. The experimental simulation results prove that the control strategy proposed in this paper has a good performance in the regulation and boost process control.
Nonlinear Attitude Control of Satellite Using Optimal Adaptive and Fuzzy Control Methods

Several different control theories have been designed for the problem of the satellite attitude control. According to presence of uncertainties in the space missions, adaptive control techniques are useful. In this paper, the satellite attitude control problem is studied using a novel optimal adaptive controller. The new control method is developed based on rotation matrices. This optimized adaptive controller uses Markov parameters. The controller is a direct adaptive controller which is applicable for following the control command. Also, for comparison, fuzzy control of satellite attitude is included. Finally, the results of simulations indicate that the desired conditions are achieved by the satellite, and the response of the system utilizing the fuzzy control method is faster than when the mentioned adaptive controller is used.
Bonded Wireless Networks: the Gateway to Real-Time Mobile Applications

Real-time mobile applications require reliable wireless communication and usually the major problem is the last mile bottleneck: from the end user to the Internet gateway. To deal with this problem, we implemented a sub-layer between the IP and TCP/UDP layers and named it Layer3.5, which bonds all communication links available to the user and improve the performance: throughput, latency, and reliability. This paper introduces the Layer3.5 architecture and also preliminary evaluation results. For example, we verified that the Layer 3.5 doubles the network throughput when using two LTE routers in parallel. Further, we included also TCP Proxies in our sublayer to deal with TCP performance problems on packet losses in wireless networks. Based on our tests, the the Layer3.5 with only a single LTE router achieves a throughput two times higher than the standard TCP in scenarios with 1% packet error rate. The Layer3.5 guarantees the continuity of services on network problems by switching the network traffic to other links and the TCP/UDP connection is not broken, even when the IP address changes. Further, end user devices and applications benefit from the Layer3.5 immediately, no adaptation are needed, provided the Internet gateway implements this new sub-layer.
An Efficient Polar Coding Scheme for Uplink Data Transmission in Narrowband Internet of Things Systems

Narrowband Internet of Things (NB-IoT) is an emerging IoT cellular-based wireless technology that builds based on the existing LTE standard to extend its network coverage suited for Low Power Wide Area Network (LPWAN) connectivity. In the NB-IoT system, LTE turbo codes and Cyclic Redundancy Check (CRC) codes are adopted as the main channel coding technique for Narrowband Physical Uplink Shared Channel (NPUSCH). However, turbo codes require iterative decoding algorithm at the expense of high computation complexity of decoding hence is crucial to achieving the LPWAN requirement of NB-IoT systems. Polar codes are best suited in this regard due to its capacity approaching and complexity reduction in short block length code. In this paper, we proposed an efficient polar coding technique using the Belief Propagation (BP) decoding algorithm for uplink data transmission on the NPUSCH channel. Furthermore, the BP algorithm is incorporated with the CRC stoppage criterion to decrease the number of decoding iteration and reduce computational complexity. In this scheme, single-tone numerology of NPUSCH using 3.75 kHz and 15 kHz subcarrier spacing is adopted. Then, the encoded data is generated with different NPUSCH resources. The theoretical formulation and simulation demonstrate that the proposed scheme provides better error rate performance over the adopted LTE turbo codes and other polar decoding algorithms while reducing the computational complexity.