Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-temperature at booting reduces starch content and yield of wheat by affecting dry matter transportation and starch synthesis

With the continuous change of global climate, the frequency of low-temperature stress (LTS) in spring increased greatly, which led to the increase of wheat yield decline. The effects of LTS at booting on grain starch synthesis and yield were examined in two wheat varieties with differing low-temperature sensitivities (insensitive variety Yannong 19 and sensitive variety Wanmai 52). A combination of potted and field planting was employed. For LTS treatment at booting, the wheat plants were placed in a climate chamber for 24 h at −2°C, 0°C or 2°C from 19:00 to 07:00 then 5°C from 07:00 to 19:00. They were then returned to the experimental field. The effects of flag leaf photosynthetic characteristics, the accumulation and distribution of photosynthetic products, enzyme activity related to starch synthesis and relative expression, the starch content, and grain yield were determined. LTS at booting caused a significant reduction in the net photosynthetic rate (Pn), stomatal conductance (Gs), and transpiration rate (Tr) of the flag leaves at filling. The development of starch grains in the endosperm is also hindere, there are obvious equatorial grooves observed on the surface of the A-type starch granules, and a reduction in the number of B-type starch granules. The abundance of 13C in the flag leaves and grains decreased significantly. LTS also caused a significant reduction in translocation amount of pre-anthesis stored dry matte from vegetative organs to grains and amount of post-anthesis transfer of accumulated dry matte into grains, and the distribution rate of dry matter in the grains at maturity. The grain filling time was shortened, and the grain filling rate decreased. A decrease in the activity and relative expression of enzymes related to starch synthesis was also observed, with a decrease in the total starch content. As a result, a decrease in the grain number per panicle and 1000-grain weight were also observed. These findings highlight the underlying physiological cause of decreased starch content and grain weight after LTS in wheat.
Powered knee and ankle prosthesis use with a K2 level ambulator: a case report

Powered prosthetic knees and ankles have the capability of restoring power to the missing joints and potential to provide increased functional mobility to users. Nearly all development with these advanced prostheses is with individuals who are high functioning community level ambulators even though limited community ambulators may also receive great benefit from these devices. We trained a 70 year old male participant with a unilateral transfemoral amputation to use a powered knee and powered ankle prosthesis. He participated in eight hours of therapist led in-lab training (two hours per week for four weeks). Sessions included static and dynamic balance activities for improved stability and comfort with the powered prosthesis and ambulation training on level ground, inclines, and stairs. Assessments were taken with both the powered prosthesis and his prescribed, passive prosthesis post-training. Outcome measures showed similarities in velocity between devices for level-ground walking and ascending a ramp. During ramp descent, the participant had a slightly faster velocity and more symmetrical stance and step times with the powered prosthesis compared to his prescribed prosthesis. For stairs, he was able to climb with reciprocal stepping for both ascent and descent, a stepping strategy he is unable to do with his prescribed prosthesis. More research with limited community ambulators is necessary to understand if further functional improvements are possible with either additional training, longer accommodation periods, and/or changes in powered prosthesis control strategies
Magnetic Levitating Bearing Control System Simulation Based on Variable Universe Fuzzy PID Algorithm

Magnetic levitating bearing is nonlinear and uncertainty,and the traditional PID control and fuzzy PID control algorithm is not good enough to meet the requirements of the magnetic levitating bearing control system.So,based on the idea of variable universe method,this paper puts forward the theory of variable universe fuzzy PID algorithm——by selecting the right expansion factor to realize domain contraction,it can increase the fuzzy control rules,so as to improve the control precision.The simulation analysis result of the magnetic levitating bearing control system based on SIMULINK show that the variable universe fuzzy PID algorithm improve the control accuracy,the dynamic performance and the static performance.
Inertial Parameter Identification of Unknown Object Captured by a Space Robot

Inertial parameter identification of unknown object captured by a manipulator on a free-floating space robot is studied based on the conservation principle of linear and angular momenta.The momentum-based identification in all the published work is proposed under the assumption that the linear and angular momenta of the whole system are zero,which means the parameters can not be identified when the momenta are actually not zero.In order to resolve this problem,firstly the linear and angular momenta are expressed in the inertial frame rather than in the base body-fixed frame,so that the conservation of components of momenta is ensured.Then,by eliminating the linear and angular momenta from the momentum equations,the momentum increment equations are obtained and the inertial parameters of grasped object become the only unknown.Finally,by using three sets of measurements,the unknown inertial parameters are determined from the combined momentum increment equations.The simulation results demonstrate that the inertial parameters can be accurately identified by using this method in both cases of zero and nonzero momenta,and the problem of singular solution can also be avoided.
Middlesex University Research Repository An open access repository of Middlesex University research

Context-awareness is an essential component of systems developed in areas like Intelligent Environments, Pervasive & Ubiquitous Computing and Ambient Intelligence. In these emerging ﬁelds, there is a need for computerized systems to have a higher understanding of the situations in which to provide services or functionalities, to adapt accordingly. The literature shows that researchers modify existing engineering methods in order to better ﬁt the needs of context-aware computing. These e ﬀ orts are typically disconnected from each other and generally focus on solving speciﬁc development issues. We encourage the creation of a more holistic and uniﬁed engineering process that is tailored for the demands of these systems. For this purpose, we study the state-of-the-art in the development of context-aware systems, focusing on: A) Methodologies for developing context-aware systems, analysing the reasons behind their lack of adoption and features that the community wish they can use; B) Context-aware system engineering challenges and techniques applied during the most common development stages; C) Context-aware systems conceptualization.
Triangle Mesh Simplification Algorithm Based on Edge Collapse

This paper presents an improved quadric error metric edge-collapse based algorithm which can keep the speed and efficiency of quadric error metric and reserve more important shape features even after performing drastic level of simplification. It defines an important degree of a triangle and embeds it into the original Garland's quadric error metric, so that the metric can not only measure distance error but also reflect geometric variations of local surface. The experimental results show that the new algorithm can reserve quite a number of important shape features and reduce visual distortion effectively at low levels of detail.
An efficient Kernel Principal Component Analysis Algorithm for large-scale data set *

Kernel principal component analysis (KPCA) is a popular nonlinear feature extraction method in the field of machine learning. It uses eigen-decomposition technique to extract the principal components. But the method is infeasible for large-scale data set because of the store and computational problem. To overcome these disadvantages, a new covariance-free method of computing kernel principal components is proposed. First, a matrix, called Gram-power matrix, is constructed using the original Gram matrix. It is proven by the theorem of linear algebra that the eigenvectors of newly constructed matrix are the same as the ones of the Gram matrix. Therefore, we can treat each column of the Gram matrix as the input sample for the covariance-free algorithm. Thus, the kernel principle components can be iteratively computed without the eigen-decomposition. The space complexity of proposed method is only , the time complexity is reduced to . The effectiveness of proposed method is validated from experimental results. More important, it still can be used even if traditional eigen-decomposition technique cannot be applied when faced with the extremely large-scale data set.
OPTIMUM RECURRENCE FILTERING METHOD FOR SINGULAR DISCRETE STOCHASTIC LINEAR SYSTEMS (II)

In this paper, properties of the error covariance matrix of the optimal estimate given in [1] are analyzed for singular discrete stochastic linear systems. Under some conditions, upper and lower bounds of the matrix have been obtained. In view of the above results, the stability of the optimal filter in [1] has been discussed.
Aero-Acoustic Performance of Fractal Spoilers

One of the major environmental problems facing the aviation industry is that of aircraft noise. The work presented in this paper, done as part of the EU’s OPENAIR Project, looks at reducing spoiler noise whilst maintaining aerodynamic performance, through means of large-scale fractal porosity. It is hypothesised that the highly turbulent flow generated by fractal grids from the way the multiple-length-scales are organised in space, would reduce the impact of the re-circulation region and with it, the low frequency noise it generates. In its place, a higher frequency noise is introduced which is more susceptible to atmospheric attenuation and is less offensive to the human ear. A total of nine laboratory scaled spoilers were looked at, seven of which had a fractal design, one with a regular grid design and one solid for reference. The spoilers were inclined at an angle of 30◦. Force, acoustic and flow visualisation experiments on a flat plate were carried out and it was found that the present fractal spoilers reduce the low frequency noise by 2.5dB. Results show that it is possible to improve the acoustic performance by modifying a number of parameters defining the fractal spoiler, some of them very sensitively. From these experiments, two fractal spoilers were chosen for a detailed aero-acoustic study on a three-element wing system, where it was found that the fractal spoilers had a reduction of up to 4dB in the sound pressure level while maintaining similar aerodynamic performances as conventional solid spoilers on the measured wing system.
Time Is Money, Friend! Timing Side-channel Attack against Garbled Circuit Frameworks

With the advent of secure function evaluation (SFE), distrustful parties can jointly compute on their private inputs without disclosing anything besides the results. Yao’s garbled circuit protocol has become an integral part of secure computation thanks to considerable efforts made to make it feasible, practical, and more efficient. These efforts have resulted in multiple optimizations on this primitive to enhance its performance by orders of magnitude over the last years. The advancement in protocols has also led to the development of general-purpose compilers and tools made available to academia and industry. For decades, the security of protocols offered in those tools has been assured with regard to sound proofs and the promise that during the computation, no information on parties’ input would be leaking. In a parallel effort, however, side-channel analysis (SCA) has gained momentum in connection with the real-world implementation of cryptographic primitives. Timing side-channel attacks have proven themselves effective in retrieving secrets from implementations, even through remote access to them. Nevertheless, the vulnerability of garbled circuit frameworks to timing attacks has, surprisingly, never been discussed in the literature. This paper introduces Goblin, the first timing attack against commonly employed garbled circuit frameworks. Goblin is a machine learning-assisted, non-profiling, single-trace timing SCA, which successfully recovers the garbler’s input during the computation under different scenarios, including various GC frameworks, benchmark functions, and the number of garbler’s input bits. Furthermore, we discuss Gob-lin’s success factors and countermeasures against that. In doing so, Goblin hopefully paves the way for further research in this matter.
Value-creating organizational leadership

Despite their many contributions, each of the most prevalent approaches to leadership – the micro interpersonal leadership models such as transformational theory, trait theory and charismatic leadership, and the macro strategic management – has notable ‘ blind spots ’ and relies on biased or partial assumptions. Furthermore, the macro – micro polarization of major leadership theories overlooks important meso perspective processes, such as structuring, which leaders can use to attain a more compounded and sustained effect on organizational outcomes. The goal of this paper is to propose an integrative theoretical framework – value-creating leadership – which provides what is missing from the theory of organizational leadership. Value-creating leadership combines micro and macro perspectives regarding management and leadership along with a meso perspective to create a uni ﬁ ed model of corporate leadership.
Bioactive Potential and Chemical Composition of Coffee By-Products: From Pulp to Silverskin

Processing the coffee cherry into roasted beans generates a large amount of by-products, which can negatively impact the environment. The aim of this study was to analyze the bioactive potential and chemical composition of different coffee by-products (pulp, husk, parchment, silverskin, defective beans, and green coffee sieving residue) having in mind their bioactive potential for health and well-being. The coffee by-products showed a distinct nutritional composition. The content of ash, protein, fat, and total dietary fiber was significantly higher (p < 0.05) in coffee pulp (10.72% dw), silverskin (16.31% dw), defective beans (8.47% dw), and parchment (94.19% dw), respectively. Defective beans and the sieve residue exhibited a higher content of total phenolics (6.54 and 5.11 g chlorogenic acid eq./100 g dw, respectively) as well as higher DPPH• scavenging activity (3.11 and 2.85 g Trolox eq./100 g, respectively) and ferric-reducing antioxidant power (17.68 and 17.56 g ferrous sulfate eq./100 g dw, respectively). All the coffee by-products considered in this study are sources of caffeine and chlorogenic acids, in particular 5-caffeoylquinic acid (5.36–3787.58 mg/100 g dw, for parchment and defective beans, respectively). Thus, they can be recycled as functional ingredients for food, cosmetic and/or pharmaceutical industries, contributing to the social, economic, and environmental sustainability of the coffee industry.
Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning

Since unmanned aerial vehicles (UAVs), such as drones, are used in various fields due to their high utilization and agile mobility, technologies to deal with multiple UAVs are becoming more important. There are many advantages to using multiple drones in a swarm, but, at the same time, each drone requires a strong connection to some or all of the other drones. This paper presents a superior approach for the UAV network’s routing system without wasting memory and computing power. We design a routing system called the geolocation ad hoc network (GLAN) using geolocation information, and we build an adaptive GLAN (AGLAN) system that applies reinforcement learning to adapt to the changing environment. Furthermore, we increase the learning speed by applying a pseudo-attention function to the existing reinforcement learning. We evaluate the proposed system against traditional routing algorithms.
Is the Renewable Portfolio Standard in China Effective? Research on RPS Allocation Efﬁciency in Chinese Provinces Based on the Zero-Sum DEA Model

: As one of the countries with the most rapid development of new energy, China has been committed to exploring countermeasures to the challenges of new energy consumption. After more than ten years of consideration and consultation, the “renewable portfolio standard“(RPS) for “renewable energy power consumption responsibility weighting” has landed in China. However, in the ofﬁcial afﬁrmation issued by the National Energy Administration, theoretical support for the basis of the initial quota allocation is still lacking. In this study, we examine the efﬁciency of the weight allocation scheme for renewable energy power consumption responsibilities, which was announced by the National Energy Administration in 2018 and which is based on the BCC-DEA efﬁciency model. The results indicate that most provinces have low allocation efﬁciency under this allocation scheme. Therefore, we propose an optimal allocation scheme for a renewable energy consumption quota, based on the ZSG-DEA model. With the achievement of its target, this study’s allocation scheme would ensure 100% efﬁciency in all provinces, improve provincial economic efﬁciency, and simultaneously bring economic growth. After analyzing the fairness before and after adjustment of the RPS, our ﬁndings suggest that the adjusted RPS allocation scheme can promote equity in per capita renewable electricity consumption.
Flow: Specialized Proof of Conﬁdential Knowledge ( SPoCK ) – Technical Note –

Flow is a high-throughput blockchain with a dedicated step for executing the transactions in a block and a subsequent veriﬁcation step performed by Veriﬁcation Nodes [1, 2, 3]. To enforce integrity of the blockchain, the protocol requires a component that prevents Veriﬁcation Nodes from approving execution results without checking. In our preceding work, we have sketched out an approach called Specialized Proof of Conﬁdential Knowledge ( SPoCK ). Using SPoCK , nodes can provide evidence to a third party that they both executed the same transaction sequence without revealing the resulting execution trace. The previous white paper [3] presented a basic implementation of such scheme. In this note, we introduce a new SPoCK implementation that is more concise and more eﬃcient than the previous proposal. We ﬁrst provide a formal generic description of a SPoCK scheme as well as its security deﬁnition. Then we propose a new construction of SPoCK based on the BLS signature scheme [4]. We support the new scheme with its proof of security under the appropriate computation assumptions.
Populations in the Kepler field

Using a population synthesis model I have created a synthetic catalogue of stars in the Kepler field of view. This model has then been subjected to the same biases and selection effects inherent in the selection of stars for the Kepler transit survey mission. This produced a synthetic Kepler Input Catalogue (KIC) which was subjected to the Kepler Stellar Classification Program (SCP) method for determining stellar parameters. I achieve a satisfactory match between the synthetic KIC and the real KIC in the logg vs log Teff diagram. I find a median difference of ΔTeff = +500 K and ~ Δlogg = -0.2 dex for main sequence stars, although there is a large variation across parameter space. I find no significant difference between ΔTeff and Δlogg for single stars and the primary star in a binary system. I also re-created the Kepler target selection method and found that the binary fraction is unchanged by the target selection. The fraction of main sequence stars in the sample increases from 75% to 80%, and the giant star fraction decreases from 25% to 20%. I have then used the synthetic KIC to build a of synthetic sample of eclipsing binaries (EBs) in the Kepler field. Comparing the synthetic catalogue to the Kepler EB catalogue I find that the Kepler EB pipeline introduces significant biases into the derived temperature ratio and fractional radii. I then tested the effect of different initial mass ratio distributions (IMRDs) and initial binary fraction distributions (IBFDs). At this time, all distributions fail to match the data, such that their parameters can not be constrained. Modelling the population of asteroseismic binaries, where both stars have a detectable asteroseismic signal, have shown a way to constrain the IMRD for equal mass systems. This method is independent of the binary period and orbital orientation. The number of detectable asteroseismic binaries increases from 87 for the IMR parameter s = -0.5 to 256 for s = 1.0. The number of detectable asteroseismic EBs increases from 34.0 ± 6.0 (s = -0.5) to 59.0 ± 6.0 (s = 1.0). This number shows disagreement with the number of actual systems detected (2 for Porb < 40 days), which can not be explained by incompleteness alone.
Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks

Clinicians and researchers divide sleep periods into different sleep stages to analyze the quality of sleep. Despite advances in machine learning, sleep-stage classification is still performed manually. The classification process is tedious and time-consuming, but its automation has not yet been achieved. Another problem is low accuracy due to inconsistencies between somnologists. In this paper, we propose a method to classify sleep stages using a convolutional neural network. The network is trained with EEG and EOG images of time and frequency domains. The images of the biosignal are appropriate as inputs to the network, as these are natural inputs provided to somnologists in polysomnography. To validate the network, the sleep-stage classifier was trained and tested using the public Sleep-EDFx dataset. The results show that the proposed method achieves state-of-the-art performance on the Sleep-EDFx (accuracy 94%, F1 94%). The results demonstrate that the classifier is able to learn features described in the sleep scoring manual from the sleep data.
Quasineutral Limit of a Nonlinear Drift Diffusion Model for Semiconductors: The Fast Diffusion Case

The limit of vanishing Debye length (charge neutral limit) in a nonlinear bipolar drift-diffusion model for semiconductors without pn-junction (i.e. with a unipolar background charge) is studied. The quasineutral limit (zero-Debye-length limit) for the fast diffusion case is performed rigorously by using the compactness argument and the so-called entropy functional which yields appropriate uniform estimates.