Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms Affecting the Biosynthesis and Incorporation Rate of Selenocysteine

Selenocysteine (Sec) is the 21st non-standard proteinogenic amino acid. Due to the particularity of the codon encoding Sec, the selenoprotein synthesis needs to be completed by unique mechanisms in specific biological systems. In this paper, the underlying mechanisms for the biosynthesis and incorporation of Sec into selenoprotein were comprehensively reviewed on five aspects: (i) the specific biosynthesis mechanism of Sec and the role of its internal influencing factors (SelA, SelB, SelC, SelD, SPS2 and PSTK); (ii) the elements (SECIS, PSL, SPUR and RF) on mRNA and their functional mechanisms; (iii) the specificity (either translation termination or translation into Sec) of UGA; (iv) the structure–activity relationship and action mechanism of SelA, SelB, SelC and SelD; and (v) the operating mechanism of two key enzyme systems for inorganic selenium source flow before Sec synthesis. Lastly, the size of the translation initiation interval, other action modes of SECIS and effects of REPS (Repetitive Extragenic Palindromic Sequences) that affect the incorporation efficiency of Sec was also discussed to provide scientific basis for the large-scale industrial fermentation for the production of selenoprotein.
Seismic Fragility Evaluation of RC Frame Structures Retrofitted with Controlled Concrete Rocking Column and Damping Technique

Acceleration response of simple yielding structure is proportional to its own weight, but it is limited by yield strength. Thus, using rocking columns that reduces global yield strength, a limited acceleration is achieved. However, the displacement becomes large due to lower strength and higher inelasticity, but it can be controlled by adding damping. Performing fragility analyses, the seismic response of R/C frame structures with rocking columns and viscous dampers is investigated. Near field MCEER ground motions are considered. The analyses show that the story accelerations are reduced by using rocking columns, while the story displacements are controlled by using viscous dampers.
Nonlinear Optical Properties of ZnO Thin Film at Low Laser Intensity Using Z-Scan Technique

In this work, a highly sensitive well-known z-scan technique was employed to study the nonlinear optical properties of Zinc Oxide thin films as a function of low laser fluencies. The transmissions of the continues-wave red laser diode with wavelength of (650 nm) were measured from the ZnO thin film sample with thickness of ( 425 nm). The thin film used in this study was deposited on the glass substrates based on atmospheric pressure chemical vapor deposition (APCVD) technique. The measurements were obtained at low laser powers ranging from (1.9-2.5) mW. The results indicated that the nonlinear absorption coefficient, refractive index and the third-order nonlinear optical susceptibility increase with increasing the laser intensity. The obtained curves of the closed aperture showed a positive sign of the nonlinear refractive index which in turn attributed to the self-defocusing of the material. The optical parameters obtained in this work are relatively comparable with that obtained elsewhere. The results also confirm the reliability of the z-scan approach even at low laser intensities.
Unified framework based on multiscale transform and feature learning for infrared and visible image fusion

Abstract. Infrared image and visible image can provide the thermal radiation and optical information of objects respectively. Hence, fusing their complementary information for a scene is essential for both human vision and machine perception. The adaptability of image fusion by multiscale transform (MST) is weak because of the fixed fusion rule. Meanwhile, the image fusion algorithm by supervised learning is hard to realize for lack of large labeled data. Therefore, we propose a unified framework for infrared and visible-image fusion via MST and feature learning. The reasonable fusion rule is designed by learning the attention response via training a novel attention architecture built-in the attention-guided generative adversarial network, which does not need the fused result as the ground truth. The experiment results demonstrate the validity of feature learning and the superiority of the proposed algorithm against other state-of-the-art methods in image fusion task. And the proposed method is one of the effective ways to promote the development of deep learning in the field of insufficient labeled data.
Optimization of Window Design for Daylight and Thermal Comfort in Cold Climate Conditions

Window design affects the overall performance of a building. It is important to include window design during the initial stages of a project since it influences the performance of daylight and thermal comfort as well as the energy demand for heating and cooling. The Norwegian building code facilitates two alternative methods for achieving a sufficient daylight, and only guidelines for adequate indoor thermal comfort. In this study, a typical Norwegian residential building was modeled to investigate whether the criteria and methods facilitate consistent and good performance through different scenario changes and furthermore, how the national regulations compare to European standards. A better insulated and more air-tight building has usually a lower annual heating demand, with only a marginal decrease in the daylight performance when the window design is unchanged. A more air-tight construction increases the risk of overheating, even in cold climates. This study confirms that a revision of the window design improves the overall performance of a building, which highlights the importance of proper window design. The pursuit of lower energy demand should not be at the expense of indoor thermal comfort considering the anticipated future weather conditions. This study indicates that criteria for thermal comfort and daylight, if clearly defined, can affect the energy demand for heating and cooling, as well as the indoor climate positively, and should be taken into account at the national level. A comparison between the national regulations and the European standards was made, and this study found that the results are not consistent.
Vertebraic Soft Robotic Joint Design With Twisting and Antagonism

The soft robotic manipulators attract extensive interest of researchers due to its conformity to the unstructured environment, safe-interaction with human and fragile objects. The movement of the soft manipulator often include elongation, contraction, 2-DOF rotations due to the parallelly arranged fluidic-actuated linear soft actuators. Few design achieves bending and twisting around the longitudinal axis concurrently, thus hindering the implementation of such manipulator in scenarios requiring local position adjustment. Here, we proposed a soft robotic joint with a vertebra in the center of the tilt-arranged soft origami actuators, which enables twisting motion in addition to the rotation X and Y. Moreover, the tilted actuator configuration and the vertebra formed an antagonism pair, which endowed the soft joint with improved payload and stiffness variability. Due to the vertebra and the antagonism it provided, the joint prototype generated 22.6 N, 27.4 N force and 1.7 Nm torque in respective rotations with a light weight of 90.3 g; The resist force of the joint increased about 70% when inflated to 90 kPa; the RMSE between the actual and the desired output in a range of frequencies reduced considerably compared to the soft robotic joints without vertebra. A dedicated model was proposed and verified, obtaining the relationship between the input pressure and the output position/posture of the joint, providing a mapping model for the control loop.
Reinforcement Learning Control for 6 DOF Flight of Fixed-Wing Aircraft

Flight control is a key technique for the autonomous unmanned aircraft. The traditional model-based controller design approaches often aim at concrete plant and are short in universality. Reinforcement learning provides a general controller design paradigm that is adaptive, optimized, model-free and widely applicable, and it is a promising way for the intelligent control. In contrast to the 3 Degree-of-freedom (DOF) flight, the 6 DOF motion better describes the aircraft real flight, while the implementation of the intelligent control is much harder. Based on the multiple continuous states input and multiple continuous action output deep reinforcement learning, the integrated intelligent control for the cruise flight, directly from the vehicle flight states to the aero-surfaces and thrust control, is developed for the full-sized fixed-wing aircraft. It avoids the artificial trajectory and attitude loop separation in the traditional controller design, and is advantageous to the exploitation of the aerodynamics and the nonlinear inertia coupling.
Smart concretes and structures: A review

Smart concretes and structures are intelligent systems that have properties different from normal concrete, such as self-sensing and self-healing properties, or have the ability to react upon an external stimulus, such as stress and temperature. The “smartness” of concrete and structures is achieved through material composition design, special processing, introduction of other functional components, or modification of the microstructure. They are designed to meet specific requirements through tailored properties for improving serviceability, safety, reliability, longevity, and durability of the infrastructures and reducing the life-cycle costs of the infrastructures. This article reviews the recent researches of various smart concretes and structures, with attentions to their principles, fabrication, and properties. Future challenges in the development and applications of smart concretes and structures are also discussed.
A low-cost implementation method on deep neural network using stochastic computing

The deep neural network (DNN) as the computing core of the multiplication operation consumes a lot of hardware resources, which is not conducive to the development of DNN on a hardware platform with limited resources. In order to solve the above problems, this paper proposes an acceleration method of basing on a deep neural network (DNN) to accelerate multiplication operations on resource-constrained hardware platforms. This method can support sparse calculations to optimize calculation delays. At the same time, inspired by neurosynaptic plasticity and stochastic computing (SC), an acceleration method using simple logic gates for reasoning tasks is proposed. Experimental results show that the hardware resource consumption of the proposed acceleration method is 1.4 times lower than that of the traditional method solution in DNN.
2.2.2 What is Technical Integrity and How to Measure It?

The impact of formal technical regulation and certification frameworks on the technical integrity of complex systems is poorly understood. In researching this problem the author is seeking a means by which to measure technical integrity. Technical integrity is defined and discussed, in both the context of complex systems for the Australian Defence Force and in wider civilian and military contexts. Technical integrity is defined as being composed of two separable attributes that reflect the intended, desired emergent properties of a system and the minimisation of unintended, undesired emergent properties. An approach for formulating a composite measure of technical integrity is developed from this definition and is presented for critical review.
Big Brother As Driver: New Demands and Problems for the Man at the Wheel1

This is a speculative exposition of human factors problems which may accompany semi-automation of highway vehicles. It emphasizes the problems of manual control takeover from an automatic system under both normal and emergency conditions. The paper hypothesizes a new way to consider driver alertness in terms of stability margins of control theory, proposes a three-time-scale model of driver decision-making, and suggests two simulation techniques especially applicable to studying driver alertness and manual control takeover.
Model Output Statistics (MOS) applied to CAMS O3 forecasts: trade-offs between continuous and categorical skill scores

Abstract. Air quality (AQ) forecasting systems are usually built upon physics-based numerical models that are affected by a number of uncertainty sources. In order to reduce forecast errors, first and foremost the bias, they are often coupled with Model Output Statistics (MOS) modules. MOS methods are statistical techniques used to correct raw forecasts at surface monitoring station locations, where AQ observations are available. In this study, we investigate to what extent AQ forecasts can be improved using a variety of MOS methods, including persistence (PERS), moving average (MA), quantile mapping (QM), Kalman Filter (KF), analogs (AN), and gradient boosting machine (GBM). We apply our analysis to the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble median O3 forecasts over the Iberian Peninsula during 2018–2019. A key aspect of our study is the evaluation, which is performed using a very comprehensive set of continuous and categorical metrics at various time scales (hourly to daily), along different lead times (1 to 4 days), and using different meteorological input data (forecast vs reanalyzed). Our results show that O3 forecasts can be substantially improved using such MOS corrections and that this improvement goes much beyond the correction of the systematic bias. Although it typically affects all lead times, some MOS methods appear more adversely impacted by the lead time. When considering MOS methods relying on meteorological information and comparing the results obtained with IFS forecasts and ERA5 reanalysis, the relative deterioration brought by the use of IFS is minor, which paves the way for their use in operational MOS applications. Importantly, our results also clearly show the trade-offs between continuous and categorical skills and their dependencies on the MOS method. The most sophisticated MOS methods better reproduce O3 mixing ratios overall, with lowest errors and highest correlations. However, they are not necessarily the best in predicting the highest O3 episodes, for which simpler MOS methods can give better results. Although the complex impact of MOS methods on the distribution and variability of raw forecasts can only be comprehended through an extended set of complementary statistical metrics, our study shows that optimally implementing MOS in AQ forecast systems crucially requires selecting the appropriate skill score to be optimized for the forecast application of interest.
Class-E/sub M/ switching-mode tuned power amplifier-high efficiency with slow-switching transistor

In class-E switching-mode power amplifiers, the switch-current waveform includes a step change ("jump"), approximated by a ramp of <15% of the period. At a transistor's highest useful frequency, the large input drive required for fast-enough switching yields marginal power gain. Objective: a high-efficiency power amplifier with jumpless current and voltage waveforms. Previously, that was proven impossible for amplifiers using only linear passive components and an ideal switch. We present the theory of a new topology that does achieve the objective: a class-E amplifier with nonlinear passive or active components in the load network. A "biharmonic" version was simulated, built, and tested. It comprises a main stage switching at the output frequency f/sub 1/, drawing DC power of approximately 3/4(P/sub OUT RF//drain efficiency), and an auxiliary amplifier switching at 2f/sub 1/, injecting 2f/sub 1/-current into the circuit node at the main-stage transistor's output port to shape jumpless voltage and current waveforms. That switching (nonlinear) output port converts 2f/sub 1/ power from the auxiliary amplifier to approximately 1/4 of the f/sub 1/ power at the load. Computer simulation, and measurement on a scaled-frequency 3.5-MHz prototype, show that switching losses practically disappear when the main-stage switch is operated in the jumpless regime.
A domain decomposition method based on the AD algorithm

We first establish the relationship between the grid cell connectivity matrix bandwidth and the efficiency of parallel computing, which shows that parallel efficiency can be improved by reducing the bandwidth so that the external communication is reduced. An algorithm based on the AD algorithm is developed which can reduce the connectivity matrix bandwidth, so that parallel efficiency can be improved. The purposed algorithm uses cell ponderation to relabel cell labels. A domain decomposition method is then developed based on the new algorithm. Parts of unstructured grid of global ocean are researched based on the developed domain decomposition method. The investigation shows that our method has feasibility and effectiveness for domain decomposition of large-scale grids since the speedup and efficiency are obtained from our method. Keywords-domain decomposition; parallel computing; AD algorithm; unstructured grid; parallel computing efficiency
Shanxi Highway Earthquake Disaster Risk Division Based on GIS

The paper take Shanxi Province as the object of study, through the analysis of the type after shaking the Shanxi Province road secondary geology disaster, the distribution rule and send the disaster mechanism's investigation and the analysis, the Shanxi Province earthquake secondary disaster mainly have the pitch geology disaster, the mud-rock flow disaster as well as the roadbed cave in three broad headings. The paper selection earthquake intensity, the terrain slope, the ground type, the surface cutting density, the surface cutting depth, the vegetation, the drainage density, the rainfall amount and so on take the pitch geology disaster's risk evaluating indicator, established three has ordered by type lives the geological disaster risk evaluating indicator system and the rank division system, and determined each influencing factor the weight. Has completed each influencing factor topic chart level using the GIS technology, through the GIS spatial analysis function, the superimposition has completed each kind of disaster's risk factor index chart. Finally can obtained the figure of shaanxi roadbed disaster risk classification.
One-to-One Stable Vehicle Cargo Matching in the Fourth Party Logistics

This paper proposes an effective solution to the problem of one-to-one vehicle cargo matching in the fourth party logistics (4PL) platform. Firstly, base on the attributes of multiple vehicles and cargoes, both a preference matrix of cargoes to each vehicle considering vehicle type and a preference matrix of vehicles to each cargo considering profit can be obtained. Based on the preference matrices, a stable matching model is established with the objective of maximizing the platform revenue, among which, stability constraints are designed to avoid blocking pairs of the matching. Then, CPLEX is used to solve the proposed problem. After that, numerical experiments are carried out to demonstrate the performance of the proposed stable matching model by comparing it with the traditional matching model without considering stable constraints in the one-to-one vehicle cargo matching cases with either the quantities of vehicles and cargoes balance or imbalance. The results show that compared with the traditional matching model without stability constraints, the stable matching model can achieve higher revenue and higher stability rate for the platform.
Stabilization of Residue Containing Heavy Metals By Means of Matrices Generating Calcium Trisulphoaluminate and Silicate Hydrates

Two binding matrices based on blast furnace slag or fly ash/lime mixtures were studied in relation to the stabilization/solidification of a residue from a municipal solid waste incinerator. Their use is of interest because of the formation of calcium trisulphoaluminate and silicate hydrates. Mixtures of the above matrices with up to 80% of the incinerator residue were paste-hydrated and cured for 28 days at 25°C and 100% relative humidity. The effect of added waste on the hydration phenomena was studied by means of differential thermal analysis and X-ray diffraction analysis. The effectiveness of these systems as stabilization/solidification matrices was checked by means of mechanical and leaching tests. Cured samples with up to 20-40% of added waste gave compressive strengths sufficiently high for reuse as building materials. The U.S. Environmental Protection Agency leaching test has shown that disposal in a landfill in which no facilities for collecting and treating percolating waters are provided, is possible with 40-60% of waste content. Finally, the American Nuclear Society dynamic leaching test has shown that the entrapment is mainly dependent on the physical binding potential of the matrices. © 1996 ISWA
Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Common electric powered wheelchairs cannot safely negotiate architectural barriers (i.e., curbs) which could injure the user and damage the wheelchair. Robotic wheelchairs have been developed to address this issue; however, proper alignment performed by the user is needed prior to negotiating curbs. Users with physical and/or sensory impairments may find it challenging to negotiate such barriers. Hence, a Curb Recognition and Negotiation (CRN) system was developed to increase user’s speed and safety when negotiating a curb. This article describes the CRN system which combines an existing curb negotiation application of a mobility enhancement robot (MEBot) and a plane extraction algorithm called Polylidar3D to recognize curb characteristics and automatically approach and negotiate curbs. The accuracy and reliability of the CRN system were evaluated to detect an engineered curb with known height and 15 starting positions in controlled conditions. The CRN system successfully recognized curbs at 14 out of 15 starting positions and correctly determined the height and distance for the MEBot to travel towards the curb. While the MEBot curb alignment was 1.5 ± 4.4°, the curb ascending was executed safely. The findings provide support for the implementation of a robotic wheelchair to increase speed and reduce human error when negotiating curbs and improve accessibility.