Hierarchicality of Trade Flow Networks Reveals Complexity of Products

With globalization, countries are more connected than before by trading flows, which currently amount to at least 36 trillion dollars. Interestingly, approximately 30-60 percent of global exports consist of intermediate products. Therefore, the trade flow network of a particular product with high added values can be regarded as a value chain. The problem is weather we can discriminate between these products based on their unique flow network structure. This paper applies the flow analysis method developed in ecology to 638 trading flow networks of different products. We claim that the allometric scaling exponent $\eta$ can be used to characterize the degree of hierarchicality of a flow network, i.e., whether the trading products flow on long hierarchical chains. Then, the flow networks of products with higher added values and complexity, such as machinery&transport equipment with larger exponents, are highlighted. These higher values indicate that their trade flow networks are more hierarchical. As a result, without extra data such as global input-output table, we can identify the product categories with higher complexity and the relative importance of a country in the global value chain solely by the trading network.Comment: 14 pages,7 figure

The global sliding mode tracking control for a class of variable order fractional differential systems

In this paper, a novel variable order fractional control approach is proposed for tracking control of both of variable order fractional and constant order fractional order system with uncertain and external disturbance terms. In term of the global sliding mode control theory and terminal sliding mode control method, the system states are guaranteed to stay on the switching surface from the initial time, and then converge to the origin by the designed controllers which are continuous input signals. Such controllers avoid the undesirable chattering and remove the effects of uncertain and external disturbance completely. Finally, the comparison between the variable order fraction controller and the constant order fractional controller is given by numerical simulation, furthermore, numerical results on the effects of the tracking control are provided.This paper has been supported by National Natural Science Foundation of China (No.12002194; No.12072178; No.11732005), Natural Science Foundation of Shandong Province (No.ZR2020QA037; No.ZR2020MA054), Ministerio de Ciencia, Innovación y Universidades (No. PGC2018-097198-B-I00) and Fundación Séneca de la Región de Murcia (No.20783/PI/18)

Efficient Resources Provisioning Based on Load Forecasting in Cloud

Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements

Performance of the fixed-point autoencoder

Model autodavača (autoencodera) je jedan od najtipičnijih modela temeljitog učenja koji se najčešće koriste u učenju neupravljačkog obilježja za mnoge aplikacije kao što su prepoznavanje, identifikacija i pretraživanje. Algoritmi autodavača predstavljaju opsežne računarske zadatke. Stvaranje opsežnog modela autodavača može zadovoljiti potrebe u analizi ogromnog broja podataka. Međutim, vrijeme učenja katkada postaje nepodnošljivo, što dovodi do potrebe istraživanja nekih platformi hardvera za ubrzavanje, kao što je FPGA. Verzije softvera autodavača često koriste izraze jednostruke ili dvostruke preciznosti. Ali implementiranje jedinica s promjenjivom točkom je vrlo skupo za postavljanje u FPGA. Kod implementacije autodavača na hardver stoga se često primjenjuje aritmetika nepromjenjive točke. No često se zanemaruje gubitak točnosti i nije proučavan u ranijim radovima. Ima tek nekoliko radova koji se bave akceleratorima koji koriste fiksne širine bita na drugim modelima neuronskih mreža. U našem se radu daje opsežna procjena prikaza preciznosti implikacija nepromjenjive točke na autodavač, postizanje najbolje značajke i područja učinkovitosti. Metoda konverzije formata podataka, metode blokiranja matrice i aproksimacija kompleksnim funkcijama predstavljaju ključne razmatrane čimbenike u skladu s mjestom implementacije hardvera. U radu se procjenjuju metoda simulacije konverzije podataka, blokiranje matrice različitim paralelizmom i jednostavna metoda evaluacije. Rezultati su pokazali da je širina bita s nepromjenjivom točkom uistinu utjecala na učinkovitost autodavača. Višestruki čimbenici mogu postići suprotan učinak. Svaki čimbenik može imati dvostruki učinak odbacivanja "brojnih" informacija i "korisnih" informacija u isto vrijeme. Područje predstavljanja treba pažljivo odabrati u skladu s računarskim paralelizmom. Rezultat je također pokazao da se primjenom aritmetike nepromjenjive točke može garantirati preciznost algoritma autodavača i postići prihvatljiva brzina konvergencije.The model of autoencoder is one of the most typical deep learning models that have been mainly used in unsupervised feature learning for many applications like recognition, identification and mining. Autoencoder algorithms are compute-intensive tasks. Building large scale autoencoder model can satisfy the analysis requirement of huge volume data. But the training time sometimes becomes unbearable, which naturally leads to investigate some hardware acceleration platforms like FPGA. The software versions of autoencoder often use single-precision or double-precision expressions. But the floating point units are very expensive to implement on FPGA. Fixed-point arithmetic is often used when implementing autoencoder on hardware. But the accuracy loss is often ignored and its implications for accuracy have not been studied in previous works. There are only some works focused on accelerators using some fixed bit-widths on other neural networks models. Our work gives a comprehensive evaluation to demonstrate the fix-point precision implications on the autoencoder, achieving best performance and area efficiency. The method of data format conversion, the matrix blocking methods and the complex functions approximation are the main factors considered according to the situation of hardware implementation. The simulation method of the data conversion, the matrix blocking with different parallelism and a simple PLA approximation method were evaluated in this paper. The results showed that the fixed-point bit-width did have effect on the performance of autoencoder. Multiple factors may have crossed effect. Each factor would have two-sided impacts for discarding the "abundant" information and the "useful" information at the same time. The representation domain must be carefully selected according to the computation parallelism. The result also showed that using fixed-point arithmetic can guarantee the precision of the autoencoder algorithm and get acceptable convergence speed

Structure-Based Peptide Inhibitor Design of Amyloid-β Aggregation

Many human neurodegenerative diseases are associated with amyloid fibril formation. Inhibition of amyloid formation is of importance for therapeutics of the related diseases. However, the development of selective potent amyloid inhibitors remains challenging. Here based on the structures of amyloid β (Aβ) fibrils and their amyloid-forming segments, we designed a series of peptide inhibitors using RosettaDesign. We further utilized a chemical scaffold to constrain the designed peptides into β-strand conformation, which significantly improves the potency of the inhibitors against Aβ aggregation and toxicity. Furthermore, we show that by targeting different Aβ segments, the designed peptide inhibitors can selectively recognize different species of Aβ. Our study developed an approach that combines the structure-based rational design with chemical modification for the development of amyloid inhibitors, which could be applied to the development of therapeutics for different amyloid-related diseases

Photochemical origin of SiC2_2 in the circumstellar envelope of carbon-rich AGB stars revealed by ALMA

Whether SiC$_2$ is a parent species, that is formed in the photosphere or as a by-product of high-temperature dust formation, or a daughter species, formed in a chemistry driven by the photodestruction of parent species in the outer envelope, has been debated for a long time. Here, we analyze the ALMA observations of four SiC$_2$ transitions in the CSEs of three C-rich AGB stars (AI Vol, II Lup, and RAFGL 4211), and found that SiC$_2$ exhibits an annular, shell-like distribution in these targets, suggesting that SiC$_2$ can be a daughter species in the CSEs of carbon-rich AGB stars. The results can provide important references for future chemical models.Comment: Accepted in Frontiers in Astronomy and Space Science

Nicotinamide mononucleotide adenylyltransferase uses its NAD+ substrate-binding site to chaperone phosphorylated Tau.

Funder: Science and Technology Commission of Shanghai Municipality; FundRef: http://dx.doi.org/10.13039/501100003399Funder: Dr. John T. MacDonald Foundation; FundRef: http://dx.doi.org/10.13039/100010239Tau hyper-phosphorylation and deposition into neurofibrillary tangles have been found in brains of patients with Alzheimer's disease (AD) and other tauopathies. Molecular chaperones are involved in regulating the pathological aggregation of phosphorylated Tau (pTau) and modulating disease progression. Here, we report that nicotinamide mononucleotide adenylyltransferase (NMNAT), a well-known NAD+ synthase, serves as a chaperone of pTau to prevent its amyloid aggregation in vitro as well as mitigate its pathology in a fly tauopathy model. By combining NMR spectroscopy, crystallography, single-molecule and computational approaches, we revealed that NMNAT adopts its enzymatic pocket to specifically bind the phosphorylated sites of pTau, which can be competitively disrupted by the enzymatic substrates of NMNAT. Moreover, we found that NMNAT serves as a co-chaperone of Hsp90 for the specific recognition of pTau over Tau. Our work uncovers a dedicated chaperone of pTau and suggests NMNAT as a key node between NAD+ metabolism and Tau homeostasis in aging and neurodegeneration

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Long Time Behavior and Global Dynamics of Simplified Von Karman Plate Without Rotational Inertia Driven by White Noise

Without the assumption that the coefficient of weak damping is large enough, the existence of the global random attractors for simplified Von Karman plate without rotational inertia driven by either additive white noise or multiplicative white noise are proved. Instead of the classical splitting method, the techniques to verify the asymptotic compactness rely on stabilization estimation of the system. Furthermore, a clear relationship between in-plane components of the external force that act on the edge of the plate and the expectation of radius of the global random attractors can be obtained from the theoretical results. Based on the relationship between global random attractor and random probability invariant measure, the global dynamics of the plates are analyzed numerically. With increasing the in-plane components of the external force that act on the edge of the plate, global D-bifurcation, secondary global D-bifurcation and complex local dynamical behavior occur in motion of the system. Moreover, increasing the intensity of white noise leads to the dynamical behavior becoming simple. The results on global dynamics reveal that random snap-through which seems to be a complex dynamics intuitively is essentially a simple dynamical behavior

Research and Design of LC Series Resonant Wireless Power Transfer System with Modulation Control Method for Supercapacitor Charging in Linear Motion Systems

With the hot topic of “Carbon Neutrality”, energy efficiency and saving practices such as reducing fuel consumption, vigorously advocating new energy power and modern rail are now becoming the main research topics of energy conversion technologies. Supercapacitors, with their ability of higher power density, fast charging, and instantaneous high current output, have become an indispensable energy storage element in modern traction systems for modern rail. This proposal introduced wireless power transfer technologies by using LC series resonant technology for charging the supercapacitors. To match the voltage and current level of the supercapacitor, a four-switch buck-boost converter was applied on the secondary side of the load-matching converter. To regulate the wireless transfer power and charging power of the supercapacitor, the active modulation control method was introduced on both the primary and secondary sides of the transfer system. On the primary side, the power is controlled by controlling the current in resonant inductance through the phase shift control method, while on the secondary side, the charging power is controlled by regulating the input voltage of the four-switch buck-boost converter followed by inductance current control. The theoretical analysis under phase shift mode for the primary side and pulse width modulation for a four-switch buck-boost converter with a supercapacitor load (voltage source) were proposed in detail, and the state-space model of the load matching converter was established for controller design to obtain precise voltage and current control. Both open loop and closed loop simulation models were built in the MATLAB/SIMULINK environment, and simulations were carried out to evaluate the system characteristics and control efficiency. The experimental platform was established based on a dsPIC33FJ64GS606 digital controller. Experiments were carried out, and the results successfully verified the effectiveness of the system