ALBERT

Description: In view of the randomness in the selection of kernel parameters in the traditional kernel independent component analysis (KICA) algorithm, this paper proposes a CPSO-KICA algorithm based on Chaotic Particle Swarm Optimization (CPSO) and KICA. In CPSO-KICA, the maximum entropy of the extracted independent component is first adopted as the fitness function of the PSO algorithm to determine the optimal kernel parameters, then the chaotic algorithm (CO) is used to avoid the local optimum existing in the traditional PSO algorithm. Finally, this proposed algorithm is compared with Weighted KICA (WKICA) and PSO-KICA with Tennessee Eastman Process (TEP) as the benchmark. Simulation results show that the proposed algorithm can determine the optimal kernel parameters and perform better in terms of false alarm rates (FAR), detection latency (DL) and fault detection rates (FDR).

Description: We explore the dissipative dynamics of two coupled qubits placed inside a coherent cavity-field under dipole-dipole interplay and 2-photon transitions. The generated non-classical correlations (NCCs) beyond entanglement are investigated via two measures based on the Hilbert-Schmidt norm. It is found that the robustness of the generated NCCs can be greatly enhanced by performing the intrinsic dissipation rate, dipole-dipole interplay rate, initial coherence intensity and the degree of the coherent state superpositions. The results show that the intrinsic decoherence stabilize the stationarity of the non-classical correlations while the dipole interplay rate boost them. The non-classical correlations can be frozen at their stationary correlations by increasing the intrinsic dissipation rate. Also NCCs, can be enhanced by increasing the initial coherent intensity.

Description: Thermodynamic aspects of the theory of nucleation are commonly considered employing Gibbs’ theory of interfacial phenomena and its generalizations. Utilizing Gibbs’ theory, the bulk parameters of the critical clusters governing nucleation can be uniquely determined for any metastable state of the ambient phase. As a rule, they turn out in such treatment to be widely similar to the properties of the newly-evolving macroscopic phases. Consequently, the major tool to resolve problems concerning the accuracy of theoretical predictions of nucleation rates and related characteristics of the nucleation process consists of an approach with the introduction of the size or curvature dependence of the surface tension. In the description of crystallization, this quantity has been expressed frequently via changes of entropy (or enthalpy) in crystallization, i.e., via the latent heat of melting or crystallization. Such a correlation between the capillarity phenomena and entropy changes was originally advanced by Stefan considering condensation and evaporation. It is known in the application to crystal nucleation as the Skapski–Turnbull relation. This relation, by mentioned reasons more correctly denoted as the Stefan–Skapski–Turnbull rule, was expanded by some of us quite recently to the description of the surface tension not only for phase equilibrium at planar interfaces, but to the description of the surface tension of critical clusters and its size or curvature dependence. This dependence is frequently expressed by a relation derived by Tolman. As shown by us, the Tolman equation can be employed for the description of the surface tension not only for condensation and boiling in one-component systems caused by variations of pressure (analyzed by Gibbs and Tolman), but generally also for phase formation caused by variations of temperature. Beyond this particular application, it can be utilized for multi-component systems provided the composition of the ambient phase is kept constant and variations of either pressure or temperature do not result in variations of the composition of the critical clusters. The latter requirement is one of the basic assumptions of classical nucleation theory. For this reason, it is only natural to use it also for the specification of the size dependence of the surface tension. Our method, relying on the Stefan–Skapski–Turnbull rule, allows one to determine the dependence of the surface tension on pressure and temperature or, alternatively, the Tolman parameter in his equation. In the present paper, we expand this approach and compare it with alternative methods of the description of the size-dependence of the surface tension and, as far as it is possible to use the Tolman equation, of the specification of the Tolman parameter. Applying these ideas to condensation and boiling, we derive a relation for the curvature dependence of the surface tension covering the whole range of metastable initial states from the binodal curve to the spinodal curve.

Description: Over the years, the cellular mobile network has evolved from a wireless plain telephone system to a very complex system providing telephone service, Internet connectivity and many interworking capabilities with other networks. Its air interface performance has increased drastically over time, leading to high throughput and low latency. Changes to the core network, however, have been slow and incremental, with increased complexity worsened by the necessity of backwards-compatibility with older-generation systems such as the Global System for Mobile communication (GSM). In this paper, a new virtualized Peer-to-Peer (P2P) core network architecture is presented. The key idea of our approach is that each user is assigned a private virtualized copy of the whole core network. This enables a higher degree of security and novel services that are not possible in today’s architecture. We describe the new architecture, focusing on its main elements, IP addressing, message flows, mobility management, and scalability. Furthermore, we will show some significant advantages this new architecture introduces. Finally, we investigate the performance of our architecture by analyzing voice-call traffic available in a database of a large U.S. cellular network provider.

Description: Direct dependencies and conditional dependencies in restricted Bayesian network classifiers (BNCs) are two basic kinds of dependencies. Traditional approaches, such as filter and wrapper, have proved to be beneficial to identify non-significant dependencies one by one, whereas the high computational overheads make them inefficient especially for those BNCs with high structural complexity. Study of the distributions of information-theoretic measures provides a feasible approach to identifying non-significant dependencies in batch that may help increase the structure reliability and avoid overfitting. In this paper, we investigate two extensions to the k-dependence Bayesian classifier, MI-based feature selection, and CMI-based dependence selection. These two techniques apply a novel adaptive thresholding method to filter out redundancy and can work jointly. Experimental results on 30 datasets from the UCI machine learning repository demonstrate that adaptive thresholds can help distinguish between dependencies and independencies and the proposed algorithm achieves competitive classification performance compared to several state-of-the-art BNCs in terms of 0–1 loss, root mean squared error, bias, and variance.

Description: Identification of denatured biological tissue is crucial to high intensity focused ultrasound (HIFU) treatment. It is not easy for intercepting ultrasonic scattered echo signals from HIFU treatment region. Therefore, this paper employed time-frequency entropy based on generalized S-transform (GST) to intercept ultrasonic echo signals. First, the time-frequency spectra of ultrasonic echo signal is obtained by GST, which is concentrated around the real instantaneous frequency of the signal. Then the time-frequency entropy is calculated based on time-frequency spectra. The experimental results indicate that the time-frequency entropy of ultrasonic echo signal will be abnormally high when ultrasonic signal travels across the boundary between normal region and treatment region in tissues. Ultrasonic scattered echo signals from treatment region can be intercepted by time-frequency entropy. In addition, the refined composite multi-scale weighted permutation entropy (RCMWPE) is proposed to evaluate the complexity of nonlinear time series. Comparing with multi-scale permutation entropy (MPE) and multi-scale weighted permutation entropy (MWPE), RCMWPE not only measures complexity of signal including amplitude information, but also improves the stability and reliability of multi-scale entropy. The RCMWPE and MPE are applied to 300 cases of actual ultrasonic scattered echo signals (including 150 cases in normal status and 150 cases in denatured status). It is found that the RCMWPE and MPE values of denatured tissues are higher than those of the normal tissues. Both RCMWPE and MPE can be used to distinguish normal tissues and denatured tissues. However, there are fewer feature points in the overlap region between RCMWPE of denatured tissues and normal tissues compared with MPE. The intra-class distance and the inter-class distance of RCMWPE are less and greater respectively than MPE. The difference between denatured tissues and normal tissues is more obvious when RCMWPE is used as the characteristic parameter. The results of this study will be helpful to guide doctors to obtain more accurate assessment of treatment effect during HIFU treatment.

Description: Gas-liquid two-phase flow behavior in horizontal channel under heaving motion showed unique dynamic characteristics due to the complex nonlinear interaction. To further establish a description model and investigate the effects of heaving motion on horizontal gas-liquid flow, experiments in a wide range of vibration parameters and working conditions were carried out by combining vibration platform with two-phase flow loop. It was found that the flow regimes under heaving motion showed significant differences compared to the ones expected in steady state flow under the same working conditions. Increasing vibration parameters showed an obvious impact on fluctuation degree of gas-liquid interface by visualizing high-speed photographs. A method based on multi-scale entropy was applied to identify flow regimes and reveal the underlying dynamic characteristics by collecting signals of pressure-difference. The results indicated that the proposed method was effective to analyze gas-liquid two-phase flow transition in horizontal channel under heaving motion by incorporating information of flow condition and change rate of multi-scale entropy, which provided a reliable guide for flow pattern control design and safe operation of equipment. However, for slug-wave and boiling wave flow, an innovative method based on multi-scale marginal spectrum entropy showed more feasible for identification of transition boundary.

Description: The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption.

Description: A mixture of red and blue light-emitting diodes (LEDs; at a ratio of 7:3, respectively) were used to analyze the effects of different photosynthetic photon flux densities (PPFDs) (40, 80, and 120 µmol m−2 s−1 hereafter known as LED 40, 80, and 120, respectively) on the micropropagation of Gerbera jamesonii Bolus shoots. The experiment also examined the effect of 6-benzyladenine (BA) in 1, 2.5, and 5 µM concentrations in the media. Biometrical observations and analyses of leaf morphometry and photosynthetic pigment content were conducted. Shoot multiplication increased with an increasing BA concentration. A PPFD of 80 µmol m−2 s−1 and 5 µM BA is suggested as efficient for shoot propagation and economically viable. LED 120 increased the leaf blade area and its width, and circularity and elongation ratios. The intensity of light did not affect the fresh weight, which increased at higher BA concentrations (2.5 and 5 μM). The dry weight content decreased with increasing cytokinin concentration; the greatest content was observed on media with 1 µM BA under PPFD 120 µmol m−2 s−1. LED 80 increased the photosynthetic pigments content in the leaves in comparison to the standard intensity of LED 40. Increased BA concentration raises the content of chlorophyll a.

Description: We compare the standard Higgs mechanism corresponding to the scalar field, with the dynamical origin of the graviton mass inside the scenario of the dRGT theory of massive gravity. We demonstrate that the effective mass perceived locally by different observers depends on how they define the local time with respect to the preferred notion of time defined by the Stückelberg function T 0 ( r , t ) .

Description: In this paper, we measure the systemic risk with a novel methodology, based on a “spatial-temporal” approach. We propose a new bank systemic risk measure to consider the two components of systemic risk: cross-sectional and time dimension. The aim is to highlight the “time-space dynamics” of contagion, i.e., if the CDS spread of bank i depends on the CDS spread of other banks. To do this, we use an advanced spatial econometrics design with a time-varying spatial dependence that can be interpreted as an index of the degree of cross-sectional spillovers. The findings highlight that the Eurozone banks have strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present and persistent. Moreover, we analyse the role of the European Central Bank in managing contagion risk. We find that monetary policy has been effective in reducing systemic risk. However, the results show that systemic risk does not imply a policy intervention, highlighting how financial stability policy is not yet an objective.

Description: Service recommendation is one of the important means of service selection. Aiming at the problems of ignoring the influence of typical data sources such as service information and interaction logs on the similarity calculation of user preferences and insufficient consideration of dynamic trust relationship in traditional trust-based Web service recommendation methods, a novel approach for Web service recommendation based on advanced trust relationships is presented. After considering the influence of indirect trust paths, the improved calculation about indirect trust degree is proposed. By quantifying the popularity of service, the method of calculating user preference similarity is investigated. Furthermore, the dynamic adjustment mechanism of trust is designed by differentiating the effect of each service recommendation. Integrating these efforts, a service recommendation mechanism is introduced, in which a new service recommendation algorithm is described. Experimental results show that, compared with existing methods, the proposed approach not only has higher accuracy of service recommendation, but also can resist attacks from malicious users more effectively.

Description: Soil pH is a key factor affecting the growth of blueberries. Understanding the response mechanism of blueberries to different pH values and selecting suitable evaluation indexes are the basis of breeding new blueberry cultivars with high pH tolerances. The effects of different soil pH treatments for 17 months on the plant growth, fruit yield, photosynthetic characteristics, and leaf microelement concentration of Vaccinium ashei Reade ‘Climax’ and V. corymbosum hybrid ‘Chaoyue No. 1′ were studied. Plant height, main stem diameter, branch number per plant, leaf dry weight, stem dry weight, root dry weight, and total dry weight decreased with increasing soil pH. With an increase in soil pH, the first flowering date, 50% flowering date, first ripening date, and 50% ripening date of the two cultivars were postponed, and the flower bud numbers per plant, the floret numbers per bud, and yield per plant showed a downward trend. Moreover, the fruit quality decreased, which was reflected in the increase in the titratable acid content (TA) and the decrease in the total soluble solids content (TSS) and the TSS:TA ratio in the high pH treatment. With increasing soil pH, the chlorophyll content index (CCI), maximal photochemical efficiency of the PSII (Fv/Fm), quantum photosynthetic yield of the PSII (Y(II)) and net photosynthetic rate (Pn) of the two cultivars showed a downward trend, and some microelement concentrations in the leaves were imbalanced. Under high pH treatment, ‘Chaoyue No. 1′ had a relatively higher plant biomass and fruit yield, so it had a stronger tolerance to high pH than ‘Climax’ did. More strongly acidified rhizosphere soil capacity, as well as higher CCI, Fv/Fm, Y(II), and Pn values were the main reasons for the high pH tolerance of ‘Chaoyue No. 1′. Compared with destructive biomass indicators such as plant weight, nondestructive indicators such as CCI, Fv/Fm, and Y(II) can be more valuable indicators for fast and accurate evaluation of blueberry tolerance to high pH at early stages of treatment.

Description: We explore the class of positive integers n that admit idempotent factorizations n = p ¯ q ¯ such that λ ( n ) ∣ ( p ¯ − 1 ) ( q ¯ − 1 ) , where λ is the Carmichael lambda function. Idempotent factorizations with p ¯ and q ¯ prime have received the most attention due to their cryptographic advantages, but there are an infinite number of n with idempotent factorizations containing composite p ¯ and/or q ¯ . Idempotent factorizations are exactly those p ¯ and q ¯ that generate correctly functioning keys in the Rivest–Shamir–Adleman (RSA) 2-prime protocol with n as the modulus. While the resulting p ¯ and q ¯ have no cryptographic utility and therefore should never be employed in that capacity, idempotent factorizations warrant study in their own right as they live at the intersection of multiple hard problems in computer science and number theory. We present some analytical results here. We also demonstrate the existence of maximally idempotent integers, those n for which all bipartite factorizations are idempotent. We show how to construct them, and present preliminary results on their distribution.

Description: HERO (Highly Eccentric Relativity Orbiter) is a space-based mission concept aimed to perform several tests of post-Newtonian gravity around the Earth with a preferably drag-free spacecraft moving along a highly elliptical path fixed in its plane undergoing a relatively fast secular precession. We considered two possible scenarios—a fast, 4-h orbit with high perigee height of 1047 km and a slow, 21-h path with a low perigee height of 642 km . HERO may detect, for the first time, the post-Newtonian orbital effects induced by the mass quadrupole moment J 2 of the Earth which, among other things, affects the semimajor axis a via a secular trend of ≃4–12 cm yr − 1 , depending on the orbital configuration. Recently, the secular decay of the semimajor axis of the passive satellite LARES was measured with an error as little as 0 . 7 cm yr − 1 . Also the post-Newtonian spin dipole (Lense-Thirring) and mass monopole (Schwarzschild) effects could be tested to a high accuracy depending on the level of compensation of the non-gravitational perturbations, not treated here. Moreover, the large eccentricity of the orbit would allow one to constrain several long-range modified models of gravity and accurately measure the gravitational red-shift as well. Each of the six Keplerian orbital elements could be individually monitored to extract the G J 2 / c 2 signature, or they could be suitably combined in order to disentangle the post-Newtonian effect(s) of interest from the competing mismodeled Newtonian secular precessions induced by the zonal harmonic multipoles J ℓ of the geopotential. In the latter case, the systematic uncertainty due to the current formal errors σ J ℓ of a recent global Earth’s gravity field model are better than 1 % for all the post-Newtonian effects considered, with a peak of ≃ 10 − 7 for the Schwarzschild-like shifts. Instead, the gravitomagnetic spin octupole precessions are too small to be detectable.

Description: Due to its possible utilization in cosmetics, medicine and crop protection, as a valuable alternative to petrochemical-derived products, hemp essential oil is now considered a product with high value added and a promising marketing potential. This experiment was conducted with the aim of evaluating the effect of four different locations of Northern Italy during two years (four environments) and three hemp monoecious varieties on the production and quality of essential oils (EOs) obtained by inflorescences harvested at full flowering of female flowers. The highest inflorescence yield was obtained at Maiano 2017, where a superficial groundwater layer (1.5 m) was present, with values that ranged from 1.69 of Fedora to 2.06 t ha−1 of Futura. EOs production ranged between 3.4 and 4.9 L ha−1, affected mainly by the variety effect. The terpene in EOs, very similar between varieties and environments, was mainly composed of sesquiterpenes (caryophillene and humulene, as the most abundant) rather than monoterpenes (α-pinene, β-myrcene and trans-β-ocimene, in particular). Phytocannabinoids, and in particular cannabidiol (CBD), were not removed from tissues by the steam during hydrodistillation, and if this is confirmed by further experiments, the residual biomass, now considered as waste, could assume significant importance as a source for further utilization.

Description: The logistic chaotic system, as a classical complex phenomenon of nonlinear dynamic systems, has received extensive attention in the field of secure communication. It is generally believed that the characteristics of chaos are suitable for the needs of encryption systems. In this paper, a multi-scale entropy theory analysis and statistical analysis are carried out on the chaotic sequences produced by different parameters and different initial values of logistic systems. According to the simulation results, the complexity of the chaotic system represented by the logistic system is mainly decided by parameter μ. Not all characteristic parameters of the chaotic system depend on the initial values. It is possible to make a reasonable estimation and prediction of the chaotic system from a macroscopic level. A variance estimation method for the parameter μ is proposed and applied to a logistic system and to another chaotic system, which is equally effective.

Description: Google’s Material Design, created in 2014, led to the extended application of floating action buttons (FAB) in user interfaces of web pages and mobile applications. FAB’s roll is to trigger an activity either on the present screen, or it can play out an activity that makes another screen. A few specialists in user experience (UX) and user interface (UI) design are sceptical regarding the usability of FAB in the interfaces of both web pages and mobile applications. They claim that the use of FAB easily distracts users and that it interferes with using other important functions of the applications, and it is unusable in applications designed for iOS systems. The aim of this paper is to investigate by an experiment the quality of experience (QoE) of a static and animated FAB and compare it to the toolbar alternative. The experimental results of different testing methods rejected the hypothesis that the usage and animation of this UI element has a positive influence on the application usability. However, its static and animated utilization enhanced the ratings of hedonic and aesthetic features of the user experience, justifying the usage of this type of button.

Description: We elaborate on the link relating gravitational radiation, vorticity and a flux of super–energy on the plane orthogonal to the vorticity vector. We examine the vorticity appearing in the congruence of observers at the outside of the source, as well as the vorticity of the fluid distribution, the source of the gravitational radiation is made of. The information provided by the study of the physical aspects of the source poses new questions which could, in principle, be solved by the observational evidence. Besides the study of the theoretical issues associated to such relationship, we also stress the new observational possibilities to detect gravitational radiation, appearing as consequence of the above mentioned link. The high degree of development achieved in the gyroscope technology, as well as recent proposals to detect rotations by means of ring lasers, atom interferometers, atom lasers and anomalous spin–precession experiments, lead us to believe that an alternative to the laser interferometers used so far to detect gravitational waves, may be implemented based on the detection of the vorticity associated with gravitational radiation. Additionally, this kind of detectors might be able to elucidate the open question about the physical properties of the tail of the waves appearing as the consequence of the violation of the Huygens’s principle in general relativity.

Description: Recommender systems are nowadays an indispensable part of most personalized systems implementing information access and content delivery, supporting a great variety of user activities [...]

Description: The role of small bioactive molecules (〈500 Da) in mechanisms improving resource use efficiency in plants under stress conditions draws increasing interest. One such molecule is omeprazole (OMP), a benzimidazole derivative and inhibitor of animal proton pumps shown to improve nitrate uptake and exclusion of toxic ions, especially of chloride from the cytosol of salt-stressed leaves. Currently, OMP was applied as substrate drench at two rates (0 or 10 μM) on hydroponic basil (Ocimum basilicum L. cv. Genovese) grown under decreasing NO3−:Cl− ratio (80:20, 60:40, 40:60, or 20:80). Chloride concentration and stomatal resistance increased while transpiration, net CO2 assimilation rate and beneficial ions (NO3−, PO43−, and SO42−) decreased with reduced NO3−:Cl− ratio under the 0 μM OMP treatment. The negative effects of chloride were not only mitigated by the 10 μM OMP application in all treatments, with the exception of 20:80 NO3−:Cl−, but plant growth at 80:20, 60:40, and 40:60 NO3−:Cl− ratios receiving OMP application showed maximum fresh yield (+13%, 24%, and 22%, respectively), shoot (+10%, 25%, and 21%, respectively) and root (+32%, 76%, and 75%, respectively) biomass compared to the corresponding untreated treatments. OMP was not directly involved in ion homeostasis and compartmentalization of vacuolar or apoplastic chloride. However, it was active in limiting chloride loading into the shoot, as manifested by the lower chloride concentration in the 80:20, 60:40, and 40:60 NO3−:Cl− treatments compared to the respective controls (−41%, −37%, and −24%), favoring instead that of nitrate and potassium while also boosting photosynthetic activity. Despite its unequivocally beneficial effect on plants, the large-scale application of OMP is currently limited by the molecule’s high cost. However, further studies are warranted to unravel the molecular mechanisms of OMP-induced reduction of chloride loading to shoot and improved salt tolerance.

Description: In this paper, we study a dual-channel closed-loop supply chain in which a manufacturer considers the market waste products recovery and remanufacture, and a retailer considers provide services to customers. We build a Stackelberg game model and a centralized game model in a static and dynamic state, respectively, and analyze the two dynamic models by mathematical analysis and explore the stability and entropy of the two models using bifurcation, the basin of attraction, chaotic attractors, and so on. The influences of service level and profit distribution rate on the system’s profit are discussed. The theoretical results show that higher price adjustment speed will lead to the system lose stability with a larger entropy value. In the Stackelberg game model, the stability of the system increases as the service value and the recovery rate increases; in the centralized model, the stability of the system decreases with the increase of the service value and increases with the recovery rate increases. When the Stackelberg game model is in a stable state, the manufacturer’s profit increases first and then decreases, and the retailer’s profit first decreases and then increases as the service value of the retailer increases. The research will serve as good guidance for both the manufacturer and retailer in dual-channel closed-loop supply chains to improve decision making.

Description: When chaotic systems are used in different practical applications, such as chaotic secure communication and chaotic pseudorandom sequence generators, a large number of chaotic systems are strongly required. However, for a lack of a systematic construction theory, the construction of chaotic systems mainly depends on the exhaustive search of systematic parameters or initial values, especially for a class of dynamical systems with hidden chaotic attractors. In this paper, a class of quadratic polynomial chaotic maps is studied, and a general method for constructing quadratic polynomial chaotic maps is proposed. The proposed polynomial chaotic maps satisfy the Li–Yorke definition of chaos. This method can accurately control the amplitude of chaotic time series. Through the existence and stability analysis of fixed points, we proved that such class quadratic polynomial maps cannot have hidden chaotic attractors.

Description: Finite element data form an important basis for engineers to undertake analysis and research. In most cases, it is difficult to generate the internal sections of finite element data and professional operations are required. To display the internal data of entities, a method for generating the arbitrary sections of finite element data based on radial basis function (RBF) interpolation is proposed in this paper. The RBF interpolation function is used to realize arbitrary surface cutting of the entity, and the section can be generated by the triangulation of discrete tangent points. Experimental studies have proved that the method is very convenient for allowing users to obtain visualization results for an arbitrary section through simple and intuitive interactions.

Description: The discovery and study of the Higgs boson at the Large Hadron Collider of CERN has proven the validity of the Brout–Englert–Higgs mechanism of mass creation in the standard model via spontaneous symmetry breaking. The new results obtained by the ATLAS and CMS Collaborations at the LHC show that all measured cross-sections agree within uncertainties with the predictions of the theory. However, the standard model has obvious difficulties (nonzero neutrino masses, hierarchy problem, existence of dark matter, non-existence of antimatter galaxies, etc.), which point towards more possible violated symmetries. We first summarize the present status of the studies of the Higgs boson, including the latest results at 13 TeV p-p collision energy, then enlist some of the problems with possible solutions and the experimental situation regarding them.

Description: A new formalism involving spinors in theories of spacetime and vacuum is presented. It is based on a superalgebraic formulation of the theory of algebraic spinors. New algebraic structures playing role of Dirac matrices are constructed on the basis of Grassmann variables, which we call gamma operators. Various field theory constructions are defined with use of these structures. We derive formulas for the vacuum state vector. Five operator analogs of five Dirac gamma matrices exist in the superalgebraic approach as well as two additional operator analogs of gamma matrices, which are absent in the theory of Dirac spinors. We prove that there is a relationship between gamma operators and the most important physical operators of the second quantization method: number of particles, energy–momentum and electric charge operators. In addition to them, a series of similar operators are constructed from the creation and annihilation operators, which are Lorentz-invariant analogs of Dirac matrices. However, their physical meaning is not yet clear. We prove that the condition for the existence of spinor vacuum imposes restrictions on possible variants of the signature of the four-dimensional spacetime. It can only be (1, − 1 , − 1 , − 1 ), and there are two additional axes corresponding to the inner space of the spinor, with a signature ( − 1 , − 1 ). Developed mathematical formalism allows one to obtain the second quantization operators in a natural way. Gauge transformations arise due to existence of internal degrees of freedom of superalgebraic spinors. These degrees of freedom lead to existence of nontrivial affine connections. Proposed approach opens perspectives for constructing a theory in which the properties of spacetime have the same algebraic nature as the momentum, electromagnetic field and other quantum fields.

Description: There is a growing body of research that recognizes the potentials of biochar application in agricultural production systems. However, little is known about the effects of biochar, especially hydrochar, on production of containerized seedlings under nursery conditions. This study aimed to test the effects of hydrochar application on growth, quality, nutrient and heavy metal contents, and mycorrhizal association of containerized pine seedlings. The hydrochar used in this study was produced through hydrothermal carbonization of paper mill biosludge at 200 °C. Two forms of hydrochar (powder and pellet) were mixed with peat at ratios of 10% and 20% (v/v) under three levels of applied commercial fertilizer (nil, half and full rates). Application of hydrochar had positive or neutral effects on shoot biomass and stem diameter compared with control seedlings (without hydrochar) under tested fertilizer levels. Analysis of the natural logarithmic response ratios (LnRR) of quality index and nutrient and heavy metal uptake revealed that application of 20% (v/v) hydrochar powder or pellet with 50% fertilizer resulted in same quality pine seedlings with similar heavy metal (Cu, Ni, Pb, Zn and Cr) and nutrient (P, K, Ca and Mg) contents as untreated seedlings supplied with 100% fertilizer. Colonization percentage by ectomycorrhizae significantly increased when either forms of hydrochar were applied at a rate of 20% under unfertilized condition. The results of this study implied that application of proper rates of hydrochar from biosludge with adjusted levels of liquid fertilizer may reduce fertilizer requirements in pine nurseries.

Description: The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

Description: The tank cascade system (TCS) has been used for over 2000 years for water management in Sri Lanka. Since surface water is limited in the dry zone of Sri Lanka, agricultural production, especially of upland crops, relies on groundwater for irrigation. We sampled 29 wells in the Ulagalla cascade, a prominent TCS near Anuradhapura city in the dry zone of Sri Lanka, in Yala (dry) and Maha (wet) seasons, the two main cropping seasons in Sri Lanka. We evaluated the suitability of groundwater for irrigation using the analytic hierarchy process and geographical information system. Water quality did not vary notably between seasons. However, it deteriorated with the onset of high intensity heavy rain, especially during the Maha season. A water quality zoning map indicated that groundwater in 4% and 96% of the study area is suitable and moderately suitable for irrigation, respectively. Irrigation water quality in tank cascade landscapes and similar environments can be assessed using this methodology and our results.

Description: The entropic uncertainty relation (EUR) is of significant importance in the security proof of continuous-variable quantum key distribution under coherent attacks. The parameter estimation in the EUR method contains the estimation of the covariance matrix (CM), as well as the max-entropy. The discussions in previous works have not involved the effect of finite-size on estimating the CM, which will further affect the estimation of leakage information. In this work, we address this issue by adapting the parameter estimation technique to the EUR analysis method under composable security frameworks. We also use the double-data modulation method to improve the parameter estimation step, where all the states can be exploited for both parameter estimation and key generation; thus, the statistical fluctuation of estimating the max-entropy disappears. The result shows that the adapted method can effectively estimate parameters in EUR analysis. Moreover, the double-data modulation method can, to a large extent, save the key consumption, which further improves the performance in practical implementations of the EUR.

Description: This paper aims to explore the current status, research trends and hotspots related to the field of infrared detection technology through bibliometric analysis and visualization techniques based on the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) articles published between 1990 and 2018 using the VOSviewer and Citespace software tools. Based on our analysis, we first present the spatiotemporal distribution of the literature related to infrared detection technology, including annual publications, origin country/region, main research organization, and source publications. Then, we report the main subject categories involved in infrared detection technology. Furthermore, we adopt literature cocitation, author cocitation, keyword co-occurrence and timeline visualization analyses to visually explore the research fronts and trends, and present the evolution of infrared detection technology research. The results show that China, the USA and Italy are the three most active countries in infrared detection technology research and that the Centre National de la Recherche Scientifique has the largest number of publications among related organizations. The most prominent research hotspots in the past five years are vibration thermal imaging, pulse thermal imaging, photonic crystals, skin temperature, remote sensing technology, and detection of delamination defects in concrete. The trend of future research on infrared detection technology is from qualitative to quantitative research development, engineering application research and infrared detection technology combined with other detection techniques. The proposed approach based on the scientific knowledge graph analysis can be used to establish reference information and a research basis for application and development of methods in the domain of infrared detection technology studies.

Description: The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

Description: Recently, active learning is considered a promising approach for data acquisition due to the significant cost of the data labeling process in many real world applications, such as natural language processing and image processing. Most active learning methods are merely designed to enhance the learning model accuracy. However, the model accuracy may not be the primary goal and there could be other domain-specific objectives to be optimized. In this work, we develop a novel active learning framework that aims to solve a general class of optimization problems. The proposed framework mainly targets the optimization problems exposed to the exploration-exploitation trade-off. The active learning framework is comprehensive, it includes exploration-based, exploitation-based and balancing strategies that seek to achieve the balance between exploration and exploitation. The paper mainly considers regression tasks, as they are under-researched in the active learning field compared to classification tasks. Furthermore, in this work, we investigate the different active querying approaches—pool-based and the query synthesis—and compare them. We apply the proposed framework to the problem of learning the price-demand function, an application that is important in optimal product pricing and dynamic (or time-varying) pricing. In our experiments, we provide a comparative study including the proposed framework strategies and some other baselines. The accomplished results demonstrate a significant performance for the proposed methods.

Description: A complete quantum cooling cycle may be a useful platform for studying quantum thermodynamics just as the quantum heat engine does. Entropy change is an important feature which can help us to investigate the thermodynamic properties of the single ion cooling process. Here, we analyze the entropy change of the ion and laser field in the single ion cooling cycle by generalizing the idea in Reference (Phys. Rev. Lett. 2015, 114, 043002) to a single ion system. Thermodynamic properties of the single ion cooling process are discussed and it is shown that the Second and Third Laws of Thermodynamics are still strictly held in the quantum cooling process. Our results suggest that quantum cooling cycles are also candidates for the investigation on quantum thermodynamics besides quantum heat engines.

Description: Soils in tropical croplands are becoming degraded because of soil carbon (C) depletion. Local farmers in South India use a specific management of traditional cultivation, i.e., broadcast seeding. However, for sustainable C management, there is no quantitative data on the CO2 flux under this management. Our objectives were to (1) estimate the annual CO2 flux, and (2) evaluate the effect of traditional cultivation management (seeding rate) on the CO2 flux. Our field experiment was conducted in South India, from 2015 to 2017, including two cultivation periods with four cultivation management treatments (traditional cultivation management plot (T), fixed density plot (FD), no thinning plot (NT), and bare plot (B)). The seeding rate in the FD plot was ca. 50% of the T plot. We applied 1.1 Mg C ha−1 farmyard manure just before the experiment as a C input. We found that broadcasting, thinning, and cultivation increased soil moisture, while the CO2 efflux rate showed no significant difference between treatments throughout the experimental period. This indicates that cultivation management did not affect the CO2 flux. The total CO2 fluxes for two years were estimated at 2.2–2.7 Mg C ha−1. Our results indicate that it is necessary to apply larger or more frequent C inputs to prevent C depletion.

Description: Service Level Agreements are employed to set availability commitments in cloud services. When a violation occurs as in an outage, cloud providers may be called to compensate customers for the losses incurred. Such compensation may be so large as to erode cloud providers’ profit margins. Insurance may be used to protect cloud providers against such a danger. In this paper, closed formulas are provided through the expected utility paradigm to set the insurance premium under different outage models and QoS metrics (no. of outages, no. of long outages, and unavailability). When the cloud service is paid through a fixed fee, we also provide the maximum unit compensation that a cloud provider can offer so as to meet constraints on its profit loss. The unit compensation is shown to vary approximately as the inverse square of the service fee.

Description: The rye (Secale cereale L.) 5R chromosome contains some elite genes that can be used to improve wheat cultivars. In this study, a set of 5RKu dissection lines was obtained, and 111 new PCR-based and 5RKu-specific markers were developed using the specific length amplified fragment sequencing (SLAF-seq) method. The 111 markers were combined with the 52 5RKu-specific markers previously reported, and 65 S. cereale Lo7 scaffolds were physically mapped to six regions of the 5RKu chromosome using the 5RKu dissection lines. Additionally, the 5RLKu arm carried stripe rust resistance gene(s) and it was located to the region L2, the same region where 22 5RKu-specific markers and 11 S. cereale Lo7 scaffolds were mapped. The stripe rust resistance gene(s) located in the 5RLKu arm might be new one(s) because its source and location are different from the previously reported ones, and it enriches the resistance source of stripe rust for wheat breeding programs. The markers and the S. cereale Lo7 scaffolds that were mapped to the six regions of the 5RKu chromosome can facilitate the utilization of elite genes on the 5R chromosome in the improvement of wheat cultivars.

Description: Assessing and prescribing fertilizer use is critical to profitable and sustainable coffee production, and this is becoming a priority concern for the Robusta coffee industry. In this study, annual survey data of 798 farms across selected Robusta coffee-producing provinces in Vietnam and Indonesia between 2008 and 2017 were used to comparatively assess the fertilizer management strategies in these countries. Specifically, we aimed to characterize fertilizer use patterns in the key coffee-growing provinces and discuss the potential for improving nutrient management practices. Four types of chemical (NPK, super phosphate, potassium chloride and urea) and two of natural (compost and lime) fertilizers were routinely used in Vietnam. In Indonesia, NPK and urea were supplemented only with compost. Farmers in Vietnam applied unbalanced quantities of chemical fertilizers (i.e., higher rates than recommended) and at a constant rate between years whereas Indonesian farmers applied well below the recommended rates because of poor accessibility and financial support. The overuse of chemical fertilizers in Vietnam threatens the sustainability of Robusta coffee farming. Nevertheless, there is a potential for improvement in both countries in terms of nutrient management and sustainability of Robusta coffee production by adopting the best local fertilizer management practices.

Description: Term translation quality in machine translation (MT), which is usually measured by domain experts, is a time-consuming and expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems often need to be updated for many reasons (e.g., availability of new training data, leading MT techniques). To the best of our knowledge, as of yet, there is no publicly-available solution to evaluate terminology translation in MT automatically. Hence, there is a genuine need to have a faster and less-expensive solution to this problem, which could help end-users to identify term translation problems in MT instantly. This study presents a faster and less expensive strategy for evaluating terminology translation in MT. High correlations of our evaluation results with human judgements demonstrate the effectiveness of the proposed solution. The paper also introduces a classification framework, TermCat, that can automatically classify term translation-related errors and expose specific problems in relation to terminology translation in MT. We carried out our experiments with a low resource language pair, English–Hindi, and found that our classifier, whose accuracy varies across the translation directions, error classes, the morphological nature of the languages, and MT models, generally performs competently in the terminology translation classification task.

Description: Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p 〈 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p 〈 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale.

Description: High input costs combined with multiple management and material inputs have threatened cotton productivity. We hypothesize that this problem can be addressed by a single fertilization at flowering with late sowing in a moderately populated plant stand. Field experiments were conducted to evaluate the cotton biomass accumulation, phosphorus dynamics, and fiber quality under three planting densities (low, 3 × 104; moderate, 6 × 104; and dense, 9 × 104 ha−1) and two cultivars (Zhongmian-16 and J-4B). High planting density had 6.2 and 12.6% larger stems and fruiting nodes m−2, while low density produced a 37.5 and 59.4% maximum height node ratio. Moderate density produced 26.4–15.5%, 24.7–12.6%, and 10.5–13.6% higher biomass accumulation rate at the peak bloom, boll set, and plant removal stages over low and high density in both years, respectively. J-4B produced a higher reproductive organs biomass yield when compared with Zhongmian-16 in both years. This higher biomass formation was due to both the higher average (0.8 VT kg·ha−1·d−1) and maximum (1.0 VM kg·ha−1·d−1) reproductive organ phosphorus uptake, respectively. Plants with low density had 5.3–18.5%, 9.5–15%, and 7.8–12.8% greater length, strength, and micronaire values over moderate and dense plants, respectively. Conclusively, moderate density with J-4B is a promising option for improved biomass, phosphorus acquisition, and fiber quality under a short season.

Description: Three rust diseases namely; stem rust caused by Puccinia graminis f. sp. tritici (Pgt), leaf rust caused by Puccinia triticina (Pt), and stripe rust caused by Puccinia striiformis f. sp. tritici (Pst), are the most common fungal diseases of wheat (Triticum aestivum L.) and cause significant yield losses worldwide including Australia. Recently characterized stripe rust resistance genes Yr51 and Yr57 are effective against pre- and post-2002 Pst pathotypes in Australia. Similarly, stem rust resistance genes Sr22, Sr26, and Sr50 are effective against the Pgt pathotype TTKSK (Ug99) and its derivatives in addition to commercially important Australian pathotypes. Effectiveness of these genes make them good candidates for combining with known pleiotropic adult plant resistance (PAPR) genes to achieve durable resistance against three rust pathogens. This study was planned to transfer rust resistance genes Yr51, Yr57, Sr22, Sr26, and Sr50 into two Australian (Gladius and Livingston) and two Indian (PBW550 and DBW17) wheat cultivars through marker assisted selection (MAS). These cultivars also carry other rust resistance genes: Gladius carries Lr37/Yr17/Sr38 and Sr24/Lr24; Livingston carries Lr34/Yr18/Sr57, Lr37/Yr17/Sr38, and Sr2; PBW550 and DBW17 carry Lr34/Yr18/Sr57 and Lr26/Yr9/Sr31. Donor sources of Yr51 (AUS91456), Yr57 (AUS91463), Sr22 (Sr22/3*K441), Sr26 (Sr26 WA1), and Sr50 (Dra-1/Chinese Spring ph1b/2/3* Gabo) were crossed with each of the recurrent parents to produce backcross progenies. Markers linked to Yr51 (sun104), Yr57 (gwm389 and BS00062676), Sr22 (cssu22), Sr26 (Sr26#43), and Sr50 (Sr50-5p-F3, R2) were used for their MAS and markers csLV34 (Lr34/Yr18/Sr57), VENTRIUP-LN2 (Lr37/Yr17/Sr38), Sr24#12 (Sr24/Lr24), and csSr2 (Sr2) were used to select genes present in recurrent parents. Progenies of selected individuals were grown and selected under field conditions for plant type and adult plant rust responses. Final selections were genotyped with the relevant markers. Backcross derivatives of these genes were distributed to breeding companies for use as resistance donors.

Description: Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.

Description: Reducing soil tillage can lead to many benefits, but this practice often increases weed abundance and thus the need for herbicides, especially during the transition phase from inversion tillage to non-inversion tillage. We evaluated if subsidiary crops (SCs, e.g., cover crops) can mitigate the effects of non-inversion tillage on weed abundance. Two-year experiments studying SC use, tillage intensity, and nitrogen (N) fertilization level were carried out twice at six sites throughout northern and central Europe. SCs significantly reduced weed cover throughout the intercrop period (−55% to −1% depending on site), but only slightly during the main crops. Overall weed abundance and weed biomass were higher when using non-inversion tillage with SCs compared to inversion tillage without SCs. The effects differed due to site-specific weed pressure and management. With increasing weed pressure, the effect of SCs decreased, and the advantage of inversion over non-inversion tillage increased. N fertilization level did not affect weed abundance. The results suggest that SCs can contribute by controlling weeds but cannot fully compensate for reduced weed control of non-inversion tillage in the transition phase. Using non-inversion tillage together with SCs is primarily recommended in low weed pressure environments.

Description: In this paper, a new three-dimensional chaotic system is proposed for image encryption. The core of the encryption algorithm is the combination of chaotic system and compressed sensing, which can complete image encryption and compression at the same time. The Lyapunov exponent, bifurcation diagram and complexity of the new three-dimensional chaotic system are analyzed. The performance analysis shows that the chaotic system has two positive Lyapunov exponents and high complexity. In the encryption scheme, a new chaotic system is used as the measurement matrix for compressed sensing, and Arnold is used to scrambling the image further. The proposed method has better reconfiguration ability in the compressible range of the algorithm compared with other methods. The experimental results show that the proposed encryption scheme has good encryption effect and image compression capability.

Description: Wolf–Rayet stars (WRs) represent the end of a massive star’s life as it is about to turn into a supernova. Obtaining complete samples of such stars across a large range of metallicities poses observational challenges, but presents us with an exacting way to test current stellar evolutionary theories. A technique we have developed and refined involves interference filter imaging combined with image subtraction and crowded-field photometry. This helps us address one of the most controversial topics in current massive star research: the relative importance of binarity in the evolution of massive stars and formation of WRs. Here, we discuss the current state of the field, including how the observed WR populations match with the predictions of both single and binary star evolutionary models. We end with what we believe are the most important next steps in WR research.

Description: Chamomile (Matricaria chamomilla L.) is a well-known medicinal plant species in which the products requested from the market are those that are derived from the organic system. The study was conducted to assess the allelopathic effects, as natural herbicides, of two essential oils extracted from oregano (Origanum vulgare L.) and rosemary (Rosmarimum officinalis L.), with the objective of exploring the possibility of their utilization for future weed management. A field experiment was conducted over two seasons, when the infestation of 15 different weed species was detected. Each essential oil was applied at two different concentrations (50% diluted and undiluted), three times during the chamomile crop under an organic farm system. The results demonstrated that the germination of different weed species was affected differently by the type of essential oils and especially by their concentrations. The undiluted oils inhibited most of the germination of several weed species, highlighting a significantly higher percentage of Weed Control Efficiency (WCE) and suggesting the potential to be used as bio-herbicides. Bioherbicidal weed control methods could offer an advantage with respect to hand weeding, particularly from an economic point of view.

Description: In this paper, the Gaussian wiretap feedback channel is revisited, and some new results on its secrecy capacity are obtained. To be specific, first, we show that the Schalkwijk–Kailath (SK) feedback scheme, which achieves the secrecy capacity of the degraded Gaussian wiretap feedback channel, also achieves the secrecy capacity of the non-degraded Gaussian wiretap feedback channel. Second, applying the existing secret key-based feedback schemes to Gaussian wiretap feedback channels, we derive some new lower bounds on the secrecy capacities of these models. Finally, we compare the performances of the above feedback schemes in the degraded and non-degraded Gaussian wiretap feedback channels and show which feedback scheme performs better for these channel models.

Description: A multiobjective optimization of an organic Rankine cycle (ORC) evaporator, operating with toluene as the working fluid, is presented in this paper for waste heat recovery (WHR) from the exhaust gases of a 2 MW Jenbacher JMS 612 GS-N.L. gas internal combustion engine. Indirect evaporation between the exhaust gas and the organic fluid in the parallel plate heat exchanger (ITC2) implied irreversible heat transfer and high investment costs, which were considered as objective functions to be minimized. Energy and exergy balances were applied to the system components, in addition to the phenomenological equations in the ITC2, to calculate global energy indicators, such as the thermal efficiency of the configuration, the heat recovery efficiency, the overall energy conversion efficiency, the absolute increase of engine thermal efficiency, and the reduction of the break-specific fuel consumption of the system, of the system integrated with the gas engine. The results allowed calculation of the plate spacing, plate height, plate width, and chevron angle that minimized the investment cost and entropy generation of the equipment, reaching 22.04 m2 in the heat transfer area, 693.87 kW in the energy transfer by heat recovery from the exhaust gas, and 41.6% in the overall thermal efficiency of the ORC as a bottoming cycle for the engine. This type of result contributes to the inclusion of this technology in the industrial sector as a consequence of the improvement in thermal efficiency and economic viability.

Description: Rough set theory is an important approach for data mining, and it refers to Shannon’s information measures for uncertainty measurements. The existing local conditional-entropies have both the second-order feature and application limitation. By improvements of hierarchical granulation, this paper establishes double-granule conditional-entropies based on three-level granular structures (i.e., micro-bottom, meso-middle, macro-top ), and then investigates the relevant properties. In terms of the decision table and its decision classification, double-granule conditional-entropies are proposed at micro-bottom by the dual condition-granule system. By virtue of successive granular summation integrations, they hierarchically evolve to meso-middle and macro-top, to respectively have part and complete condition-granulations. Then, the new measures acquire their number distribution, calculation algorithm, three bounds, and granulation non-monotonicity at three corresponding levels. Finally, the hierarchical constructions and achieved properties are effectively verified by decision table examples and data set experiments. Double-granule conditional-entropies carry the second-order characteristic and hierarchical granulation to deepen both the classical entropy system and local conditional-entropies, and thus they become novel uncertainty measures for information processing and knowledge reasoning.

Description: Nowadays, the images are transferred through open channels that are subject to potential attacks, so the exchange of image data requires additional security in many fields, such as medical, military, banking, etc. The security factors are essential in preventing the system from brute force and differential attacks. We propose an Enhanced Logistic Map (ELM) while using chaotic maps and simple encryption techniques, such as block scrambling, modified zigzag transformation for encryption phases, including permutation, diffusion, and key stream generation to withstand the attacks. The results of encryption are evaluated while using the histogram, correlation analysis, Number of Pixel Change Rate (NPCR), Unified Average Change Intensity (UACI), Peak-Signal-to-Noise Ratio (PSNR), and entropy. Our results demonstrate the security, reliability, efficiency, and flexibility of the proposed method.

Description: Intrinsically disordered proteins (IDPs) represent a distinct class of proteins and are distinguished from globular proteins by conformational plasticity, high evolvability and a broad functional repertoire. Some of their properties are reminiscent of early proteins, but their abundance in eukaryotes, functional properties and compositional bias suggest that IDPs appeared at later evolutionary stages. The spectrum of IDP properties and their determinants are still not well defined. This study compares rudimentary physicochemical properties of IDPs and globular proteins using bioinformatic analysis on the level of their native sequences and random sequence permutations, addressing the contributions of composition versus sequence as determinants of the properties. IDPs have, on average, lower predicted secondary structure contents and aggregation propensities and biased amino acid compositions. However, our study shows that IDPs exhibit a broad range of these properties. Induced fold IDPs exhibit very similar compositions and secondary structure/aggregation propensities to globular proteins, and can be distinguished from unfoldable IDPs based on analysis of these sequence properties. While amino acid composition seems to be a major determinant of aggregation and secondary structure propensities, sequence randomization does not result in dramatic changes to these properties, but for both IDPs and globular proteins seems to fine-tune the tradeoff between folding and aggregation.

Description: In semi-autonomous robot conferencing, not only the operator controls the robot, but the robot itself also moves autonomously. Thus, it can modify the operator’s movement (e.g., adding social behaviors). However, the sense of agency, that is, the degree of feeling that the movement of the robot is the operator’s own movement, would decrease if the operator is conscious of the discrepancy between the teleoperation and autonomous behavior. In this study, we developed an interface to control the robot head by using an eye tracker. When the robot autonomously moves its eye-gaze position, the interface guides the operator’s eye movement towards this autonomous movement. The experiment showed that our interface can maintain the sense of agency, because it provided the illusion that the autonomous behavior of a robot is directed by the operator’s eye movement. This study reports the conditions of how to provide this illusion in semi-autonomous robot conferencing.

Description: Appropriate business processes management (BPM) within an organization can help attain organizational goals. It is particularly important to effectively manage the lifecycle of these processes for organizational effectiveness in improving ever-growing performance and competitivity-building across the company. This paper presents a process discovery and how we can use it in a broader framework supporting self-organization in BPM. Process discovery is intrinsically associated with the process lifecycle. We have made a pre-evaluation of the usefulness of our facts using a generated log file. We also compared visualizations of the outcomes of our approach with different cases and showed performance characteristics of the cash loan sales process.

Description: Correlations between observed data are at the heart of all empirical research that strives for establishing lawful regularities. However, there are numerous ways to assess these correlations, and there are numerous ways to make sense of them. This essay presents a bird’s eye perspective on different interpretive schemes to understand correlations. It is designed as a comparative survey of the basic concepts. Many important details to back it up can be found in the relevant technical literature. Correlations can (1) extend over time (diachronic correlations) or they can (2) relate data in an atemporal way (synchronic correlations). Within class (1), the standard interpretive accounts are based on causal models or on predictive models that are not necessarily causal. Examples within class (2) are (mainly unsupervised) data mining approaches, relations between domains (multiscale systems), nonlocal quantum correlations, and eventually correlations between the mental and the physical.

Description: The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing.

Description: The capacity of private information retrieval (PIR) from databases coded using maximum distance separable (MDS) codes was previously characterized by Banawan and Ulukus, where it was assumed that the messages are encoded and stored separably in the databases. This assumption was also usually made in other related works in the literature, and this capacity is usually referred to as the MDS-PIR capacity colloquially. In this work, we considered the question of if and when this capacity barrier can be broken through joint encoding and storing of the messages. Our main results are two classes of novel code constructions, which allow joint encoding, as well as the corresponding PIR protocols, which indeed outperformed the separate MDS-coded systems. Moreover, we show that a simple, but novel expansion technique allows us to generalize these two classes of codes, resulting in a wider range of the cases where this capacity barrier can be broken.

Description: The Boltzmann–Gibbs (BG) entropy has been used in a wide variety of problems for more than a century. It is well known that BG entropy is additive and extensive, but for certain systems such as those dictated by long-range interactions, it is speculated that the entropy must be non-additive and non-extensive. Tsallis entropy possesses these characteristics, and is parameterized by a variable q ( q = 1 being the classic BG limit), but unless q is determined from microscopic dynamics, the model remains a phenomenological tool. To this day, very few examples have emerged in which q can be computed from first principles. This paper shows that the space plasma environment, which is governed by long-range collective electromagnetic interaction, represents a perfect example for which the q parameter can be computed from microphysics. By taking the electron velocity distribution function measured in the heliospheric environment into account, and considering them to be in a quasi-equilibrium state with electrostatic turbulence known as quasi-thermal noise, it is shown that the value corresponding to q = 9 / 13 = 0 . 6923 , or alternatively q = 5 / 9 = 0 . 5556 , may be deduced. This prediction is verified against observations made by spacecraft, and it is shown to be in excellent agreement. This paper constitutes an overview of recent developments regarding the non-equilibrium statistical mechanical approach to understanding the non-extensive nature of space plasma, although some recent new developments are also discussed.

Description: Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

Description: We present a quantum scheme for signing contracts between two clients (Alice and Bob) using entangled states and the services of a third trusted party (Trent). The trusted party is only contacted for the initialization of the protocol, and possibly at the end, to verify clients’ honesty and deliver signed certificates. The protocol is fair, i.e., the probability that a client, say Bob, can obtain a signed copy of the contract, while Alice cannot, can be made arbitrarily small, and scales as N − 1 / 2 , where 4 N is the total number of rounds (communications between the two clients) of the protocol. Thus, the protocol is optimistic, as cheating is not successful, and the clients rarely have to contact Trent to confirm their honesty by delivering the actual signed certificates of the contract. Unlike the previous protocol (Paunković et al., Phys. Rev. A 84, 062331 (2011)), in the present proposal, a single client can obtain the signed contract alone, without the need for the other client’s presence. When first contacting Trent, the clients do not have to agree upon a definitive contract. Moreover, even upon terminating the protocol, the clients do not reveal the actual contract to Trent. Finally, the protocol is based on the laws of physics, rather than on mathematical conjectures and the exchange of a large number of signed authenticated messages during the actual contract signing process. Therefore, it is abuse-free, as Alice and Bob cannot prove they are involved in the contract signing process.

Description: The advent of utility computing has revolutionized almost every sector of traditional software development. Especially commercial cloud computing services, pioneered by the likes of Amazon, Google and Microsoft, have provided an unprecedented opportunity for the fast and sustainable development of complex distributed systems. Nevertheless, existing models and tools aim primarily for systems where resource usage—by humans and bots alike—is logically and physically quite disperse resulting in a low likelihood of conflicting resource access. However, a number of resource-intensive applications, such as Massively Multiplayer Online Games (MMOGs) and large-scale simulations introduce a requirement for a very large common state with many actors accessing it simultaneously and thus a high likelihood of conflicting resource access. This paper presents a systematic mapping study of the state-of-the-art in software technology aiming explicitly to support the development of MMOGs, a class of large-scale, resource-intensive software systems. By examining the main focus of a diverse set of related publications, we identify a list of criteria that are important for MMOG development. Then, we categorize the selected studies based on the inferred criteria in order to compare their approach, unveil the challenges faced in each of them and reveal research trends that might be present. Finally we attempt to identify research directions which appear promising for enabling the use of standardized technology for this class of systems.

Description: It is becoming increasingly clear that causal analysis of dynamical systems requires different approaches than, for example, causal analysis of interconnected autoregressive processes. In this study, a correlation dimension estimated in reconstructed state spaces is used to detect causality. If deterministic dynamics plays a dominant role in data then the method based on the correlation dimension can serve as a fast and reliable way to reveal causal relationships between and within the systems. This study demonstrates that the method, unlike most other causal approaches, detects causality well, even for very weak links. It can also identify cases of uncoupled systems that are causally affected by a hidden common driver.

Description: The large amount of cosmological data already available (and in the near future) makes the development of efficient numerical codes necessary. Many software products have been implemented to perform cosmological analyses considering one or few probes. The need of multi-task software is rapidly increasing, in order to combine numerous cosmological probes along with their specificity (e.g., astrophysical descriptions and systematic errors). In this work, we mention some of these libraries, bringing out some challenges they will face in the few-percent error era (on the cosmological parameters). We review some concepts of the standard cosmological model, and examine some specific topics on their implementation, bringing, for example, the discussion on how some quantities are numerically defined in different codes. We also consider implementation differences between public codes, mentioning their advantages/disadvantages.

Description: Crop models are useful tools to evaluate the effects of agricultural management on ecosystem services. However, before they can be applied with confidence, it is important to calibrate and validate crop models in the region of interest. In this study, the Environmental Policy Integrated Climate (EPIC) model was evaluated for its potential to simulate maize yield using limited data from field trials on two maize cultivars. Two independent fields at the Cradock Research Farm were used, one for calibration and one for validation. Before calibration, mean simulated yield was 8 t ha−1 while mean observed yield was 11.26 t ha−1. Model calibration improved mean simulated yield to 11.23 t ha−1 with a coefficient of determination, (r2) = 0.76 and a model efficiency (NSE) = 0.56. Validation with grain yield was satisfactory with r2 = 0.85 and NSE = 0.61. Calibration of potential heat units (PHUs) and soil-carbon related parameters improved model simulations. Although the study only used grain yield to calibrate and evaluate the model, results show that the calibrated model can provide reasonably accurate simulations. It can be concluded that limited data sets from field trials on maize can be used to calibrate the EPIC model when comprehensive experimental data are not available.

Description: Lemon processing generates thousands of tons of residues that can be preserved as flours by thermal treatment to obtain phenolic compounds with beneficial bioactivities. In this study, the effect of different drying temperatures (40, 50, 60, 70, 80, 90, 100 and 110 °C) on the Total Phenolic Content (TPC), antioxidant and antimicrobial activities of phenolic compounds present in Citrus. lemon (L.) Burn f waste was determined. Identification and quantification of phenolic compounds were also performed by UPLC-PDA and UPLC-ESI-MS analysis. Eriocitrin (19.79–27.29 mg g−1 DW) and hesperidin (7.63–9.10 mg g−1 DW) were detected as the major phenolic compounds in the flours by UPLC-PDA and confirmed by UPLC-ESI-MS. Antimicrobial activity determined by Minimum Inhibitory Concentration (MIC) against Salmonella typhimurium, Escherichia coli and Staphylococcus aureus was observed. Accordingly, a stable functional flour as a source of bioactive phenolic compounds obtained from lemon residues at 50 °C may be produced as a value-added product useful in various industrial sectors.

Description: According to the asymptotic safety conjecture, gravity is a renormalizable quantum field theory whose continuum limit is defined by an interacting fixed point of the renormalization group flow. In these proceedings, we review some implications of the existence of this nontrivial fixed point in cosmological contexts. Specifically, we discuss a toy model exemplifying how the departure from the fixed-point regime can explain the approximate scale-invariance of the power spectrum of temperature fluctuations in the cosmic microwave background.

Description: Fusarium Head Blight (FHB, scab) is a destructive fungal disease that causes extensive yield and quality losses in wheat and other small cereals. Biological control of FHB is considered to be an alternative disease management strategy that is environmentally benign, durable, and compatible with other control measures. In this study, to screen antagonistic bacteria with the potential to manage FHB, 113 endophytes were isolated from the stems, leaves, panicles, and roots of wheat. Among them, six strains appeared to effectively inhibit Fusarium graminearum growth and one isolate, XS-2, showed a highly antagonistic effect against FHB. An in vitro antagonistic test of XS-2 on wheat heads confirmed that XS-2 could suppress the disease severity of FHB. The 16S rDNA sequence analysis revealed that XS-2 is a strain of Bacillus amyloliquefaciens. Antagonistic spectrum analyses showed that XS-2 had antagonistic effects against two and four types of cotton and fruit tree pathogens, respectively. The fermentation condition assays showed that glucose and peptone are the most suitable nutrient sources for XS-2, and that the optimal pH value and temperature for fermentation were 7.4 and 28 °C, respectively. Our study indicates that XS-2 has a good antagonistic effect on FHB and lays a theoretical foundation for the application of the strain as a biological agent in the field to control FHB.

Description: We review the current status of large ring laser gyroscopes having the potential to contribute to terrestrial measurements of general relativistic precessions. At this point in time, although these devices possess the raw sensitivity for such a measurement, they remain limited by long-term geometric instability, detection noise and imperfections in the physical models required to isolate geophysical effects. Furthermore, minute non-reciprocal biases provide a null-shift error and therefore no currently constructed laser system meets the requirement of absolute rotation rate sensing. Nevertheless, we are of the view that these are surmountable problems and the ability of ring laser gyroscopes to measure low frequency to DC signals has vastly increased in the last decade.

Description: In this survey paper, we review various concepts of graph density, as well as associated theorems and algorithms. Our goal is motivated by the fact that, in many applications, it is a key algorithmic task to extract a densest subgraph from an input graph, according to some appropriate definition of graph density. While this problem has been the subject of active research for over half of a century, with many proposed variants and solutions, new results still continuously emerge in the literature. This shows both the importance and the richness of the subject. We also identify some interesting open problems in the field.

Description: One of the main challenges in the design and implementation of fluidized desiccant cooling (FDC) systems is increasing their low COP (coefficient of performance). Exergy analysis is one of the tools especially suitable for improvement and optimization of FDC systems. The improvement of performance is impossible as long as the main sources of exergy destruction are not identified and evaluated. In this paper, the exergy analysis was applied in order to identify these components and processes of the FDC system that are mainly responsible for exergy destruction. Moreover, the exergy efficiency of a simple fluidized desiccant cooler was determined. The results showed that fluidized beds and regenerative heat exchanger were the main exergy destruction sources with a 32% and 18% share of total exergy destruction, respectively. On the other hand, the direct evaporative cooler and air cooler placed after the desorbing fluidized bed were characterized by the lowest exergy efficiencies. This work contributes to better understanding of FDC operation principles and improvement of the performance of FDC technology.

Description: China has attained rice sufficiency with the increased use of nitrogen (N) fertilizer, but this has led to serious N pollution. China has the world’s highest use of N with the lowest N use efficiency (NUE). Including livestock production, China’s agriculture sector has surpassed industry as the greatest polluter of water. Using plastic film on raised-beds, combined with improved agronomic practices, can boost rice yield by 50% with 36% less N fertilizer use, 30% higher NUE, and stabilized the yield of 9.75 t ha−1. It also counters the effects of drought and low ambient temperature. A six-year study was conducted combining no-tillage, crop-residue mulch, and plastic cover, alternating organic rice and rapeseed production. All the treatments, fertilized with biogas slurry and rapeseed meal, gave rice yields of 7.0 to 10.7 t ha−1, well above China’s average of 6.5 t ha−1. In this time, soil organic matter increased from 1.6% to 4.2%. In the first four years, the combination of crop-residue mulch with plastic cover had a slightly higher yield than mulch alone. In the fifth and sixth years, the latter treatment surpassed the use of plastic cover with crop-residue mulch. Trials with a biodegradable film show that plastic pollution can be dealt with.

Description: The aim of the study was to determine the Actinobacteria structure in cultivated (C) versus non-cultivated (NC) soils divided into three groups (autogenic, hydrogenic, lithogenic) with consideration its formation process in order to assess the Actinobacteria sensitivity to agricultural soil use and soil genesis and to identify factors affecting their abundance. Sixteen C soil samples and sixteen NC samples serving as controls were taken for the study. Next generation sequencing (NGS) of the 16S rRNA metagenomic amplicons (Ion Torrent™ technology) and Denaturing Gradient Gel Electrophoresis (DGGE) were applied for precise determination of biodiversity. Generally, greater abundance of Actinobacteria in the NC soils relative to the C soils was found. Moreover, it was indicated that the actinobacterial diversity depended on both the soil genesis and the land use; however, this effect directly depended on the particular family and genera. Two factors: redox potential (Eh) and total carbon (TC) seemed to had a significant effect on the diversity of Actinobacteria. More precisely, Actinobacteria from the NC soils displayed a greater affinity for each other and were clearly influenced by Eh, whilst those from the C soils were mostly influenced by TC.

Description: The role of local electron–vibration and electron–electron interactions on the thermoelectric properties of molecular junctions is theoretically analyzed focusing on devices based on fullerene molecules. A self-consistent adiabatic approach is used in order to obtain a non-perturbative treatment of the electron coupling to low frequency vibrational modes, such as those of the molecule center of mass between metallic leads. The approach also incorporates the effects of strong electron–electron interactions between molecular degrees of freedom within the Coulomb blockade regime. The analysis is based on a one-level model which takes into account the relevant transport level of fullerene and its alignment to the chemical potential of the leads. We demonstrate that only the combined effect of local electron–vibration and electron–electron interactions is able to predict the correct behavior of both the charge conductance and the Seebeck coefficient in very good agreement with available experimental data.

Description: Coherence is associated with transient quantum states; in contrast, equilibrium thermal quantum systems have no coherence. We investigate the quantum control task of generating maximum coherence from an initial thermal state employing an external field. A completely controllable Hamiltonian is assumed allowing the generation of all possible unitary transformations. Optimizing the unitary control to achieve maximum coherence leads to a micro-canonical energy distribution on the diagonal energy representation. We demonstrate such a control scenario starting from a given Hamiltonian applying an external field, reaching the control target. Such an optimization task is found to be trap-less. By constraining the amount of energy invested by the control, maximum coherence leads to a canonical energy population distribution. When the optimization procedure constrains the final energy too tightly, local suboptimal traps are found. The global optimum is obtained when a small Lagrange multiplier is employed to constrain the final energy. Finally, we explore the task of generating coherences restricted to be close to the diagonal of the density matrix in the energy representation.

Description: Solar magnetism is believed to originate through dynamo action in the tachocline. Statistical mechanics, in turn, tells us that dynamo action is an inherent property of magnetohydrodynamic (MHD) turbulence, depending essentially on magnetic helicity. Here, we model the tachocline as a rotating, thin spherical shell containing MHD turbulence. Using this model, we find an expression for the entropy and from this develop the thermodynamics of MHD turbulence. This allows us to introduce the macroscopic parameters that affect magnetic self-organization and dynamo action, parameters that include magnetic helicity, as well as tachocline thickness and turbulent energy.

Description: This paper is aimed to dissociate nonlocality from quantum theory. We demonstrate that the tests on violation of the Bell type inequalities are simply statistical tests of local incompatibility of observables. In fact, these are tests on violation of the Bohr complementarity principle. Thus, the attempts to couple experimental violations of the Bell type inequalities with “quantum nonlocality” is really misleading. These violations are explained in the quantum theory as exhibitions of incompatibility of observables for a single quantum system, e.g., the spin projections for a single electron or the polarization projections for a single photon. Of course, one can go beyond quantum theory with the hidden variables models (as was suggested by Bell) and then discuss their possible nonlocal features. However, conventional quantum theory is local.

Description: Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.

Description: Mathematical modeling of the heat and mass transfer processes in the evaporating droplet–high-temperature gas medium system is difficult due to the need to describe the dynamics of the formation of the quasi-steady temperature field of evaporating droplets, as well as of a gas-vapor buffer layer around them and in their trace during evaporation in high-temperature gas flows. We used planar laser-induced fluorescence (PLIF) and laser-induced phosphorescence (LIP). The experiments were conducted with water droplets (initial radius 1–2 mm) heated in a hot air flow (temperature 20–500 °C, velocity 0.5–6 m/s). Unsteady temperature fields of water droplets and the gas-vapor mixture around them were recorded. High inhomogeneity of temperature fields under study has been validated. To determine the temperature in the so called dead zones, we solved the problem of heat transfer, in which the temperature in boundary conditions was set on the basis of experimental values.

Description: In this research work, a 3D rotating flow of carbon nanotubes (CNTs) over a porous stretchable sheet for heat and mass transfer is investigated. Kerosene oil is considered as a base liquid and two types of CNTs, (Single & Multi) WCNTs are added as the additives to the base liquid. The present analysis further comprises the combined effect of the Hall, ion-slip, and thermal radiation, along with heat generation/absorption. The appropriate ordinary differential system of equations after applying appropriate transformation is calculated. The resulting nonlinear system of equations (conservation of mass, momentum, temperature) is explained by HAM (Homotopy Analysis Method). Solution of velocities and thermal fields are obtained and discussed graphically. Expression of C f and N u are intended for both type of nanoliquids. The influences of prominent physical factors are plotted for velocities and thermal profiles using Methematica. These graphical results are qualitatively in excellent agreement with the previous published results. Also, single wall nanoparticles are found to have higher temperatures than multi wall CNTs nanoparticles.

Description: Chromatin immunoprecipitation combined with next-generation sequencing (ChIP-Seq) technology has enabled the identification of transcription factor binding sites (TFBSs) on a genome-wide scale. To effectively and efficiently discover TFBSs in the thousand or more DNA sequences generated by a ChIP-Seq data set, we propose a new algorithm named AP-ChIP. First, we set two thresholds based on probabilistic analysis to construct and further filter the cluster subsets. Then, we use Affinity Propagation (AP) clustering on the candidate cluster subsets to find the potential motifs. Experimental results on simulated data show that the AP-ChIP algorithm is able to make an almost accurate prediction of TFBSs in a reasonable time. Also, the validity of the AP-ChIP algorithm is tested on a real ChIP-Seq data set.

Description: This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast-moving-consumer-goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail-level forecasts—this does not reveal the exact business strategy, due to the effect of omni-channel sales—yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than “betting” on any component model.

Description: We present the results of our Power Spectral Density (PSD) analysis for the BL Lac object PKS 2155−304, utilizing the nightly-binned long-term light curve from the decade-long monitoring, as well as the minute-binned intra-night light curve from the High Energy Stereoscopic Survey (H.E.S.S.; 〉200 GeV). The source is unique for exhibiting the shortest flux-doubling timescale at Very High Energy (VHE) among its class and thus provides a rare opportunity to study the particle acceleration on the smallest spatial scales in blazar jets. The light curves are modeled in terms of the Continuous-Time Auto-Regressive Moving Average (CARMA) process. The combined long-term and intra-night PSD extends up to ∼6 decades in the temporal frequency range; unprecedented at the TeV energies for a blazar source. Our systematic approach reveals that PKS 2155−304 shows, on average, a complex shape of variability power spectrum, with more variability power on longer timescales. The long-term variability is best modeled by the CARMA(2,1) process, while the intra-night variability is modeled by a CARMA(1,0) process. We note that the CARMA(1,0) process refers to an Ornstein–Uhlenbeck process where the power-law PSD slope (PSD varies as a function of variability frequency to the power of the negative slope) changes from two to zero, above a certain “characteristic/relaxation” timescale. Even though the derived power spectrum of the intra-night light curve did not reveal a flattening, we speculate such relaxation must occur on timescales longer than a few hours for the source.

Description: Adoption of no-till systems in Eastern Washington has been slow due to the difficulty of managing wheat (Triticum aestivum L.) straw residue and the unknown decomposition potential of cultivars. We hypothesize that by analyzing wheat straw with near-infrared spectroscopy (NIRS), calibration models can be developed to accurately predict fiber and chemical constituents of wheat, determining straw decomposition potential. Straw from a panel of 480 soft winter wheat cultivars adapted to the Pacific Northwest are analyzed for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, carbon (C), and nitrogen (N). Using modified partial least squares regression and cross validation techniques, specific environment and broad-based NIRS models are calibrated and predictive ability is validated. R2cal values from broad models are better than the specific models, and are 0.85 (NDF), 0.86 (ADF), 0.65 (ADL), 0.88 (cellulose), 0.42 (hemicellulose), 0.67 (C), and 0.73 (N). The corresponding SEP values are 1.68% (NDF), 1.54% (ADF), 0.62% (ADL), 1.14% (cellulose), 1.11% (hemicellulose), 1.23% (C), and 0.06% (N). A Finch × Eltan breeding population is used to further validate models and prediction accuracies for variety selection within a breeding program scenario. The broad NIRS models prove useful for estimating high and low ranges of NDF, ADF, and cellulose in wheat cultivars which translate into characteristics of slow and fast decomposition potential.

Description: An important problem in machine learning is that, when using more than two labels, it is very difficult to construct and optimize a group of learning functions that are still useful when the prior distribution of instances is changed. To resolve this problem, semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms are combined to form a systematic solution. A semantic channel in G theory consists of a group of truth functions or membership functions. In comparison with the likelihood functions, Bayesian posteriors, and Logistic functions that are typically used in popular methods, membership functions are more convenient to use, providing learning functions that do not suffer the above problem. In Logical Bayesian Inference (LBI), every label is independently learned. For multilabel learning, we can directly obtain a group of optimized membership functions from a large enough sample with labels, without preparing different samples for different labels. Furthermore, a group of Channel Matching (CM) algorithms are developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions in a two-dimensional feature space, only 2–3 iterations are required for the mutual information between three classes and three labels to surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maximization (EM) algorithm is improved to form the CM-EM algorithm, which can outperform the EM algorithm when the mixture ratios are imbalanced, or when local convergence exists. The CM iteration algorithm needs to combine with neural networks for MMI classification in high-dimensional feature spaces. LBI needs further investigation for the unification of statistics and logic.

Description: The inclusion of species with allelopathic activity in crop rotation systems may have benefits for crop management such as weed control, but less is understood about their wider impacts on succeeding crops. The main objective of this study was to investigate the effects of two allelopathic species (spearmint and peppermint) on growth, physiological characteristics, and yield of a following maize crop. Thus, field experiments were carried out at two sites, according to a randomized complete block design, while the examined treatments were: (a) fallow–fallow–maize crop rotation system (FFM), (b) spearmint–spearmint–maize crop rotation system (SSM), and (c) peppermint–peppermint–maize crop rotation system (PPM). Our results indicated that the inclusion of spearmint or peppermint in crop rotation systems negatively affect the growth of maize plants. The highest plant height was recorded for FFM rotation system where no allelopathic species were used. At both sites and for four sampling dates, the aboveground dry biomass was also affected by the implemented crop rotation system. In particular, dry biomass was significantly lower in the PPM and SSM crop rotation systems comparing to the FFM system, whereas there were no significant differences between these two crop rotation systems. Similar to the maize biomass, the highest values of photosynthetic rate, stomatal conductance, and relative chlorophyll content were observed for the FFM rotation system. Additionally, differences in grain yield were observed among the tested crop rotation systems. Grain yield ranged from 10,200 to 13,346 kg ha−1 and from 11,773 to 14,106 kg ha−1 at site A and B, respectively, while it was reduced by 16.54–23.58% and 12.16–17.83% in the SSP and PPM rotation systems comparing to the FFM system. In conclusion, our results indicate that the inclusion of peppermint or spearmint in crop rotation may inhibit plant growth and reduce grain yield of maize as successive crop, an effect that could be attributed to the allelopathic activity of spearmint and peppermint.

Description: The Midwestern U.S. landscape is one of the most highly altered and intensively managed ecosystems in the country. The predominant crops grown are maize (Zea mays L.) and soybean [Glycine max (L.) Merr]. They are typically grown as monocrops in a simple yearly rotation or with multiple years of maize (2 to 3) followed by a single year of soybean. This system is highly productive because the crops and management systems have been well adapted to the regional growing conditions through substantial public and private investment. Furthermore, markets and supporting infrastructure are highly developed for both crops. As maize and soybean production have intensified, a number of concerns have arisen due to the unintended environmental impacts on the ecosystem. Many areas across the Midwest are experiencing negative impacts on water quality, soil degradation, and increased flood risk due to changes in regional hydrology. The water quality impacts extend even further downstream. We propose the development of an innovative system for growing maize and soybean with perennial groundcover to recover ecosystem services historically provided naturally by predominantly perennial native plant communities. Reincorporating perennial plants into annual cropping systems has the potential of restoring ecosystem services without negatively impacting grain crop production and offers the prospect of increasing grain crop productivity through improving the biological functioning of the system.

Description: Rice blast is a serious fungal disease of rice (Oryza sativa L.) that is threatening global food security. It has been extensively studied due to the importance of rice production and consumption, and because of its vast distribution and destructiveness across the world. Rice blast, caused by Pyricularia oryzae Cavara 1892 (A), can infect aboveground tissues of rice plants at any growth stage and cause total crop failure. The pathogen produces lesions on leaves (leaf blast), leaf collars (collar blast), culms, culm nodes, panicle neck nodes (neck rot), and panicles (panicle blast), which vary in color and shape depending on varietal resistance, environmental conditions, and age. Understanding how rice blast is affected by environmental conditions at the cellular and genetic level will provide critical insight into incidence of the disease in future climates for effective decision-making and management. Integrative strategies are required for successful control of rice blast, including chemical use, biocontrol, selection of advanced breeding lines and cultivars with resistance genes, investigating genetic diversity and virulence of the pathogen, forecasting and mapping distribution of the disease and pathogen races, and examining the role of wild rice and weeds in rice blast epidemics. These tactics should be integrated with agronomic practices including the removal of crop residues to decrease pathogen survival, crop and land rotations, avoiding broadcast planting and double cropping, water management, and removal of yield-limiting factors for rice production. Such an approach, where chemical use is based on crop injury and estimated yield and economic losses, is fundamental for the sustainable control of rice blast to improve rice production for global food security.

Description: A study was conducted in an irrigated arid agroecosystem in southwestern USA, to compare two conservation tillage systems (strip tillage (ST) and no-tillage (NT)) to conventional, plow-based tillage (PT) system. Corn silage (Zea mays L.) was planted in this trial. Growth parameters (plant population and height) of corn silage were measured during the season and yield was evaluated at harvest. Soil physical measurements assessed included mean weight diameter of dry aggregates, wet aggregate stability, and penetrometer resistance. While soil biological measurements included total microbial biomass, diversity index (DI), total bacteria biomass, total fungi biomass (TFB), arbuscular mycorrhizae fungi (AMF), and total saprophytes. Results showed that plant population and silage yield at 65% moisture content were not significant with tillage during both trial years. Soil physical parameters were mostly not significant with tillage, while three out of the six biological measurements (DI, TF, and AM) were significant with tillage at p ≤ 0.05. No-tillage had higher DI and TFB than the ST, but not different from PT, while AMF was significantly higher in PT than ST, but not different from NT. The study demonstrates that farmers in the study region can adopt conservation tillage without yield losses during the early years of transition.

Description: We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete probability distribution defined by a deep neural network as a statistical model. Our strategy is first to estimate the cluster labels of unlabeled data points selected from a high-density region, and then to conduct semi-supervised learning to train the model by using the estimated cluster labels and the remaining unlabeled data points. Lastly, by using the trained model, we obtain the estimated cluster labels of all given unlabeled data points. The advantage of our method is that it does not require key conditions. Existing clustering methods with deep neural networks assume that the cluster balance of a given dataset is uniform. Moreover, it also can be applied to various data domains as long as the data is expressed by a feature vector. In addition, it is observed that our method is robust against outliers. Therefore, the proposed method is expected to perform, on average, better than previous methods. We conducted numerical experiments on five commonly used datasets to confirm the effectiveness of the proposed method.

Description: Query complexity is a common tool for comparing quantum and classical computation, and it has produced many examples of how quantum algorithms differ from classical ones. Here we investigate in detail the role that oracles play for the advantage of quantum algorithms. We do so by using a simulation framework, Quantum Simulation Logic (QSL), to construct oracles and algorithms that solve some problems with the same success probability and number of queries as the quantum algorithms. The framework can be simulated using only classical resources at a constant overhead as compared to the quantum resources used in quantum computation. Our results clarify the assumptions made and the conditions needed when using quantum oracles. Using the same assumptions on oracles within the simulation framework we show that for some specific algorithms, such as the Deutsch-Jozsa and Simon’s algorithms, there simply is no advantage in terms of query complexity. This does not detract from the fact that quantum query complexity provides examples of how a quantum computer can be expected to behave, which in turn has proved useful for finding new quantum algorithms outside of the oracle paradigm, where the most prominent example is Shor’s algorithm for integer factorization.

Description: In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

Description: In conventional textbook thermodynamics, entropy is a quantity that may be calculated by different methods, for example experimentally from heat capacities (following Clausius) or statistically from numbers of microscopic quantum states (following Boltzmann and Planck). It had turned out that these methods do not necessarily provide mutually consistent results, and for equilibrium systems their difference was explained by introducing a residual zero-point entropy (following Pauling), apparently violating the Nernst theorem. At finite temperatures, associated statistical entropies which count microstates that do not contribute to a body’s heat capacity, differ systematically from Clausius entropy, and are of particular relevance as measures for metastable, frozen-in non-equilibrium structures and for symbolic information processing (following Shannon). In this paper, it is suggested to consider Clausius, Boltzmann, Pauling and Shannon entropies as distinct, though related, physical quantities with different key properties, in order to avoid confusion by loosely speaking about just “entropy” while actually referring to different kinds of it. For instance, zero-point entropy exclusively belongs to Boltzmann rather than Clausius entropy, while the Nernst theorem holds rigorously for Clausius rather than Boltzmann entropy. The discussion of those terms is underpinned by a brief historical review of the emergence of corresponding fundamental thermodynamic concepts.

Description: Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

Description: In ecology and evolution, entropic methods are now used widely and increasingly frequently. Their use can be traced back to Ramon Margalef’s first attempt 70 years ago to use log-series to quantify ecological diversity, including searching for ecologically meaningful groupings within a large assemblage, which we now call the gamma level. The same year, Shannon and Weaver published a generally accessible form of Shannon’s work on information theory, including the measure that we now call Shannon–Wiener entropy. Margalef seized on that measure and soon proposed that ecologists should use the Shannon–Weiner index to evaluate diversity, including assessing local (alpha) diversity and differentiation between localities (beta). He also discussed relating this measure to environmental variables and ecosystem processes such as succession. Over the subsequent decades, he enthusiastically expanded upon his initial suggestions. Finally, 2019 also would have been Margalef’s 100th birthday.

Description: The frequency of drought periods influences the yield potential of crops under field conditions. The change in morphology and anatomy of plants has been tested during drought stress under controlled conditions but the change in physiological processes has not been adequately studied in separate studies but needs to be reviewed collectively. This review presents the responses of green peas, snap beans, tomatoes and sweet corn to water stress based on their stomatal behaviour, canopy temperature, chlorophyll fluorescence and the chlorophyll content of leaves. These stress markers can be used for screening the drought tolerance of genotypes, the irrigation schedules or prediction of yield.

Description: A combination of organic and conservation approaches have not been widely tested, neither considering agronomic implications nor the impacts on the environment. Focussing on the effect of agricultural practices on greenhouse gas (GHG) emissions from soil, the hypothesis of this research is that the organic conservation system (ORG+) may reduce emissions of N2O, CH4 and CO2 from soil, compared to an integrated farming system (INT) and an organic (ORG) system in a two-year irrigated vegetable crop rotation set up in 2014, in a Mediterranean environment. The crop rotation included: Savoy cabbage (Brassica oleracea var. sabauda L. cv. Famosa), spring lettuce (Lactuca sativa L. cv. Justine), fennel (Foeniculum vulgare Mill. cv. Montebianco) and summer lettuce (L. sativa cv. Ballerina). Fluxes from soil of N2O, CH4 and CO2 were measured from October 2014 to July 2016 with the flow-through non-steady state chamber technique using a mobile instrument equipped with high precision analysers. Both cumulative and daily N2O emissions were mainly lower in ORG+ than in INT and ORG. All the cropping systems acted as a sink of CH4, with no significant differences among treatments. The ORG and ORG+ systems accounted for higher cumulative and daily CO2 emissions than INT, maybe due to the stimulating effect on soil respiration of organic material (fertilizers/plant biomass) supplied in ORG and ORG+. Overall, the integration of conservation and organic agriculture showed a tendency for higher CO2 emissions and lower N2O emissions than the other treatments, without any clear results on its potential for mitigating GHG emissions from soil.

Description: Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.

Description: The skyline query and its variant queries are useful functions in the early stages of a knowledge-discovery processes. The skyline query and its variant queries select a set of important objects, which are better than other common objects in the dataset. In order to handle big data, such knowledge-discovery queries must be computed in parallel distributed environments. In this paper, we consider an efficient parallel algorithm for the “K-skyband query” and the “top-k dominating query”, which are popular variants of skyline query. We propose a method for computing both queries simultaneously in a parallel distributed framework called MapReduce, which is a popular framework for processing “big data” problems. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithm on both real and synthetic datasets.

Description: A generalization of Ding’s construction is proposed that employs as a defining set the collection of the sth powers ( s ≥ 2 ) of all nonzero elements in G F ( p m ) , where p ≥ 2 is prime. Some of the resulting codes are optimal or near-optimal and include projective codes over G F ( 4 ) that give rise to optimal or near optimal quantum codes. In addition, the codes yield interesting combinatorial structures, such as strongly regular graphs and block designs.

Description: The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.