Order Vector Research Articles

Second order modified objective function method for twice differentiable vector optimization problems over cone constraints

In the paper, a vector optimization problem with twice differentiable functions and cone constraints is considered. The second order modified objective function method is used for solving such a multiobjective programming problem. In this method, for the considered twice differentiable multi-criteria optimization problem, its associated second order vector optimization problem with the modified objective function is constructed at the given arbitrary feasible solution. Then, the equivalence between the sets of (weakly) efficient solutions in the original twice differentiable vector optimization problem with cone constraints and its associated modified vector optimization problem is established. Further, the relationship between an (weakly) efficient solution in the original vector optimization problem and a saddle-point of the second order Lagrange function defined for the modified vector optimization problem is also analyzed.

Observability criterion of angles-only navigation for spacecraft proximity operations

This research studies the angles-only relative navigation problem for spacecraft proximity operations when the camera offset from the vehicle center-of-mass allows for range observability. Emphasis is placed on developing an analytic observability criterion for the camera offset within the context of the Clohessy-Wiltshire (CW) dynamics and evaluating the performance of the navigation algorithm in nonlinear dynamics environment. Further, the newly observability criterion is mathematically derived by changing the analyzing problem of the nonlinear observability into the solving problem of linear equations, and the criterion can be a powerful guideline to be followed to install the camera and execute attitude maneuvers. Moreover, navigation filtering algorithm based on an Unscented Kalman Filter and systematic nonlinear Monte Carlo simulation framework are established to verify the analytic observability criterion and evaluate the performance of the proposed algorithm through typical proximity missions. The sensitivity of the navigation accuracy to spacecraft trajectories and camera offset is presented and discussed. The conclusion is that the camera offset is required to have out-of-plane component and cannot be parallel to the line-of-sight vector in order to supply range observability.

IDENTIFICATION OF THE kdr MUTATIONS IN VGSC GENE OF THE DENGUE MOSQUITOES AEDES ALBOPICTUS COLLECTED FROM HANOI AND HAIPHONG

Vietnam is one of the countries that is affected by dengue fever in Southeast Asia. The dengue epidemic is becoming increasingly more complex so it is necessary to have a well control to vectors in order to limit the spread of the disease. The Aedes albopictus mosquito is determined as one of the two major vectors that transmitted the dengue. Recent research shows that A. albopictus is present in some parts of Hanoi and Haiphong. In order to control the vector as well as the disease, it is necessary to understand the level of resistance and the resistance mechanism of the vector. Two important resistance mechanisms of insect were known as the mutations in the target protein of the insecticides and enhancing the activity of enzymes that participate in the resolution of the insecticides. In this study, the mosquito samples were collected from Hanoi and Haiphong to identify the level of resistance and detect the knock down resistance mutations in voltage gated sodium channel (VGSC) in membrane of nervecell of mosquito. The results of insecticide susceptibility test showed that A. albopictus in Hanoi and Haiphong were still sensitive to organophosphate but resistant to DDT, carbamate and pyrethroid. Ser989Pro, Ile1011Met, Val1016Gly and Phe1534Cys mutations were not deteced in A. albopictus in Hanoi and Haiphong. However, we detected a novel mutation Tyr986His in VGSC protein.

Multistage classifier-based approach for Alzheimer's disease prediction and retrieval

The most prevalent and common type of dementia is Alzheimer's disease (AD). However, it is notable that very few people who are suffering from AD are diagnosed correctly and in a timely manner. The definite cause and cure of the disease are still unavailable. The symptoms might be more manageable and its treatment can be more effective, when the impairment is still at an earlier stage or at MCI (mild cognitive impairment). AD can be clinically diagnosed by physical and neurological examination, so there is an need for developing better and efficient diagnostic tools for AD. In recent years, content-based image retrieval (CBIR) systems have been widely researched and applied in many medical applications. Combining an automated image classification system and the radiologist's professional knowledge, to increase the accuracy of prediction and diagnosis, were the main motives. In this paper, a multistage classifier using machine learning, including Naive Bayes classifier, support vector machine (SVM), and K-nearest neighbor (KNN), was used to classify Alzheimer's disease more acceptably and efficiently. For this, MRI (Magnetic resonance imaging) scans were processed by FreeSurfer, a powerful software tool suitable for processing and normalizing brain MRI images. We also applied a feature selection technique - PSO (particle swarm optimization) to many feature vectors in order to obtain the best features that represent the salient characteristics of AD. The results of the proposed method outperform individual techniques in a benchmark database provided by the Alzheimer's Disease Neuroimaging Institute (ADNI).

Steam reforming of ethanol over Ni/MgAl2O4 catalysts

Hydrogen is considered one of the most promising energy vectors in order to match the current energy and environmental issues. Bioethanol steam reforming is a sound opportunity and close to the industrialization considering an integrated biorefinery concept. MgAl2O4 was selected as a stable support, with improved activity, selectivity and stability due to negligible acidity. Increasing the Ni loading from 1.5 to 10 wt% over MgAl2O4 improved the conversion of ethanol as well as the yield of hydrogen, while the carbon deposition and yield of byproducts decreased.Small acidity characterised the samples, attributed exclusively to the Ni active phase. This prevented extensive catalyst coking due to ethylene formation and subsequent polymerisation. Consequently, small coke amount was found on the spent catalysts, mainly amorphous, allowing rather easy regeneration.DRIFT analysis of adsorbed ethanol at variable temperature evidenced the intermediates of reaction and their evolution with temperature, allowing to suggest the main reaction paths. Acetaldehyde was found as intermediate, rapidly evolving to reformate. Among the possible evolution paths of acetaldehyde, the oxidation to acetate and carbonate species (likely stabilised by the support) was preferred with respect to decomposition to methane and CO. This is reflected in the products distribution evidenced through activity testing.

205 Towards the correction of meconium ileus with cystic fibrosis transmembrane conductance regulator (CFTR) intestinal expression in CFTR knockout sheep

Cystic fibrosis (CF) is a human autosomal genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, which is responsible for Cl − anion transport in epithelial cells. We previously generated CFTR +/− and CFTR −/− lambs using CRISPR/Cas9 and somatic cell nuclear transfer (SCNT) techniques. The CFTR −/− lambs display many features similar to humans with CF, including meconium ileus (MI), pancreatic fibrosis, portal fibrosis and biliary hyperplasia, small gallbladder, and absence of the vas deferens. Although MI affects only 15 to 20% of human babies with CF, it was observed in 100% of newborn CFTR −/− lambs and was the primary cause of death. We here hypothesized that the transgenic expression of ovine CFTR cDNA under regulation of the rat intestinal fatty acid binding protein (iFABP) promoter would promote the correction of MI in CFTR −/− sheep. In this study, we constructed the pcDNA3.1>iFABP>CFTR expression vector in order to generate iFABP>CFTR transgenic CFTR −/− sheep. PCR and sequence analysis of all junction regions confirmed the presence of the inserts in the vector. We transfected male and female CFTR −/− sheep fetal fibroblasts with the construct. Three days after transfection, limiting dilution and G418 selection were used for the isolation of resistant single cell derived colonies. A total of 15 male colonies containing the pcDNA3.1>iFABP>CFTR vector was confirmed by PCR and sequence analysis. Three male cell colonies were used as nuclear donors for SCNT. In total, 129 one-cell-stage cloned embryos were generated and transferred into 11 estrus-synchronized recipients. Four recipients (4/11; 36.3%) were confirmed pregnant at Day 40 to 45 of gestation. One pregnancy went to term and resulted in the birth of a single lamb. The necropsy indicated the same abnormalities, including MI, seen in CFTR −/− lambs. A PCR assay confirmed the presence of the iFABP>CFTR transgene in the genome of the cloned offspring. The lack of MI correction is likely due to the low expression of CFTR in this particular colony. Western blot analysis is in progress. Additional embryo transfers will be performed using different single-cell-derived colonies.

Sieve bootstrap for functional time series

A bootstrap procedure for functional time series is proposed which exploits a general vector autoregressive representation of the time series of Fourier coefficients appearing in the Karhunen–Loeve expansion of the functional process. A double sieve-type bootstrap method is developed, which avoids the estimation of process operators and generates functional pseudo-time series that appropriately mimics the dependence structure of the functional time series at hand. The method uses a finite set of functional principal components to capture the essential driving parts of the infinite dimensional process and a finite order vector autoregressive process to imitate the temporal dependence structure of the corresponding vector time series of Fourier coefficients. By allowing the number of functional principal components as well as the autoregressive order used to increase to infinity (at some appropriate rate) as the sample size increases, consistency of the functional sieve bootstrap can be established. We demonstrate this by proving a basic bootstrap central limit theorem for functional finite Fourier transforms and by establishing bootstrap validity in the context of a fully functional testing problem. A novel procedure to select the number of functional principal components is introduced while simulations illustrate the good finite sample performance of the new bootstrap method proposed.

Distinguishing Wavelengths of Optical Beams by OAM Beams Based on an OAM Optical Fiber

In order to separate the multiplexed beams with different wavelengths in an all-fiber network, a novel concept of distinguishing different wavelengths by the orbital angular momentum (OAM) beams [i.e., L(topological charge) =1 and $L=-1$ ] based on the OAM fiber is proposed and investigated in the theory and experiments. Meanwhile, a new OAM fiber supporting the first order vector modes (i.e., TE0,1, HE2,1, and TM0,1) is also designed and fabricated. For the sake of verifying the feasibility of the proposed scheme, an experimental platform is anticipated and established. Bit error rates (BERs) and constellation diagrams are employed to evaluate the performance of the proposed schemes with respect to the transmission rate of data (V) and the difference of wavelength between the two input lasers. The simulated and measured results demonstrate that two different wavelengths (i.e., 1548 and 1545 nm) with the cycle of 14 and 10 nm can be distinguished though two OAM beams (i.e., $L=1$ and $L=-1$ ), respectively. The measured BERs and constellation diagrams with respect to data rates (V) depict that the performance of the proposed schemes degenerates with the increment of data rates (V) from 5 to 40 Gb/s. Similarly, the measured BERs and constellation diagrams with respect to the difference of wavelengths also demonstrate that the performance of the proposed scheme can be improved by increasing the difference of wavelengths from 1 to 4 nm.

SAR image time-series analysis framework using morphological operators and global and local information-based linear discriminant analysis

Fusion of spectral, spatial, and temporal information is an effective method used in many satellite remote sensing applications. On the other hand, one drawback of this fusion is an increase in complexity. In this paper, we focus on developing a fast and well-performed classification method for agricultural crops using time-series SAR data. In order to achieve this, a novel two-stage approach is proposed. In the first stage, a high-dimensional feature space is obtained using time-series dual-pol SAR data and morphological operators. Spectral, spatial, and temporal information is combined into a single high-dimensional feature space. In the second stage, a dimension reduction technique is applied to the feature vector in order to decrease time complexity and increase classification accuracy by considering the global and local pattern information in the high-dimensional feature space. The contribution of the morphological profiles to the classification performance is significant; however the time complexity is increased drastically. The proposed method overcomes the time complexity stemming from high-dimensional feature space; it also improves the classification performance. The superiority of the proposed method to the comparative methods in agricultural crop classification is experimentally shown with the improvements in both classification and time performance using time-series TerraSAR-X images.

Acoustic signal processing with robust machine learning algorithm for improved monitoring of particulate solid materials in a gas flowline

The flow of particulate solid materials in a gas flowline can significantly erode mechanical equipment. Hence, real-time quantitative monitoring is a timely need for the oil and gas industry to achieve real-time control and production optimisation. Although a considerable amount of research has been conducted employing acoustic signals for qualitative monitoring, there is still an unmet demand for a simple and robust real-time quantitative monitoring system. Acoustic signal processing with machine learning is a simple and robust method that has the potential to meet this demand but has not been previously exploited for real-time quantitative monitoring of particulate solid materials in a gas flowline. This paper proposes a novel instrumentation system for on-line measurement of solid flow rate, solid concentration, line pressure drop and gas velocity in a gas-solid multiphase flow using acoustic sensing technology coupled with signal processing techniques and machine learning algorithm. The acoustic sensor is used to capture the acoustic wave emitted from the impingements of the solid particles on the bend component of the flowline. Signal processing techniques are used to extract relevant features about the impingements. An integrated, conventional Artificial Neural Network (ANN) is used to capture the distribution of the acoustic feature vectors in order to establish the relationship between the measurands and the acoustic signal. However, conventional ANNs are mainly concerned with capturing systematic patterns in a distribution of measurements fixed in time and in this case the dynamics of the generated acoustic signal varies with time. A modification, called Time-Delay Neural Network (TDNN) is used to capture such dynamics. The proposed system compares the performance of the classical ANN and the TDNN models. Results obtained demonstrate that with the classical ANN, the normalised root mean square error (NRMSE) is 0.66, 0.29, 0.26 and 0.46 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. With the TDNN model, the NRMSE is 0.18, 0.17, 0.20 and 0.16 for the solid flow rate, solid concentration, line pressure drop and gas velocity respectively. In comparison with the ANN model, the TDNN model has better performance as the NRMSE values are lower for all the models for the measurands. Overall, this study lays the basis for employing signal processing techniques and machine learning algorithm in the development of a simple, reliable and low cost real-time quantitative particulate solid flow monitoring system.

Reducibility of first order linear operators on tori via Moser's theorem

In this paper we prove reducibility of a class of first order, quasi-linear, quasi-periodic time dependent PDEs on the torus∂tu+ζ⋅∂xu+a(ωt,x)⋅∂xu=0,x∈Td,ζ∈Rd,ω∈Rν. As a consequence we deduce a stability result on the associated Cauchy problem in Sobolev spaces. By the identification between first order operators and vector fields this problem can be formulated as the problem of finding a change of coordinates which conjugates a weakly perturbed constant vector field on Tν+d to a constant diophantine flow. For this purpose we generalize Moser's straightening theorem: considering smooth perturbations we prove that the corresponding straightening torus diffeomorphism is smooth, under the assumption that the perturbation is small only in some given Sobolev norm and that the initial frequency belongs to some Cantor-like set. In view of applications in KAM theory for PDEs we provide also tame estimates on the change of variables.

Rank-Based Comparative Research Flow Benchmarking the Effectiveness of AHP–GTMA on Aiding Decisions of Shredder Selection by Reference to AHP–TOPSIS

The AHP–GTMA (analytic hierarchy process and graph theory and matrix approach) has been applied to select the best paper shredder before a company was making a bulk purchase order. However, there is a question as to whether one such relatively recent approach is effective to aid the selection decision problems in industrial/commercial practice. In this paper, a novel multi-measure, rank-based comparative research flow is proposed. The real decision problem case mentioned above is solved using the AHP–GTMA and the AHP–TOPSIS methods, respectively, with relevant datasets sourced. Several measures in the proposed flow, i.e., the arithmetical, geometrical, or even statistical ones, are multiplexed and used to validate the similarity between the rank order vectors (ROVs) (and thus between the final preferential orders determined over the alternatives) that are obtained using these two different methods. While AHP–TOPSIS is a confident multi-attribute decision-making (MADM) approach which has been successfully applied to many other fields, the similarity validated between these individual results using the proposed method is used to confirm the efficacy of the AHP–GTMA approach and to determine its applicability in practice. In addition, along with this study, some contributable points are also rendered for implementing the decision models, e.g., the optimized recursive implementation in R to compute the permanent value of a square ASAM (alternative selection attribute matrix, which is the computational basis required by AHP–GTMA) of any dimension. The proposed methodological flow to confirm the similarity based on the ordinal rank information is not only convenient in operational practice with ubiquitous tool supports (e.g., the vector-based R statistical platform), but also generalizable (to verify between another pair of results obtained using any other MADM methods). This gives options for future research.

Updating of structural multi-scale monitoring model based on multi-objective optimisation

Structural safety assessments are implemented based on measured data, but the limited number of sensors restricts the comprehensive acquisition of response information in large complex structures. A concurrent multi-scale model utilises global and local simulation characteristics to expand the insufficient measured data. Thus, good global and local simulation capability is necessary for structural health monitoring-oriented multi-scale model, and the updating of this monitoring model needs to consider the multi-type responses that are obtained from different structural scales. However, the existing methods usually integrate multi-type responses into a single-objective function, which cannot ensure the acquisition of the optimal parameters. Moreover, in common parameter screening method, the perturbation and threshold are set artificially, which causes a strong subjectivity, and the common polynomial response surface fits poorly for highly non-linear problem. Therefore, an updating method of the structural multi-scale monitoring model based on multi-objective optimisation is proposed. For the proposed method, a variance analysis based on the orthogonal experimental design is used to screen the unique significant influence parameters. The Kriging spatial interpolation technique is used to establish the approximate surrogate model between the response and its corresponding influence parameters. Simultaneously, the responses obtained from the global and local structural scales are used to define the sub-objectives of the multi-objective function vector in order to avoid the introduction of weight coefficients. Then, the multi-objective optimisation algorithm NSGA-II is used to obtain the optimal parameter values and realise the comprehensive updating of the initial multi-scale monitoring model. Finally, based on the health monitoring system of the large shell structure of the Zhuhai Opera House, the initial multi-scale monitoring model is updated using the proposed method. The structural dynamic characteristics and local stress obtained from the initial model, updated model and the real structure are compared to validate the effectiveness of the proposed method.

Interference-Aware Adaptive Beam Alignment for Hyper-Dense IEEE 802.11ax Internet-of-Things Networks.

The increasing use of Internet of Things (IoT) devices in specific areas results in an interference among them and the quality of communications can be severely degraded. To deal with this interference issue, the IEEE 802.11ax standard has been established in hyper-dense wireless networking systems. The 802.11ax adopts a new candidate technology that is called multiple network allocation vector in order to mitigate the interference problem. In this paper, we point out a potential problem in multiple network allocation vector which can cause delays to communication among IoT devices in hyper-dense wireless networks. Furthermore, this paper introduces an adaptive beam alignment algorithm for interference resolution, and analyzes the potential delays of communications among IoT devices under interference conditions. Finally, we simulate our proposed algorithm in densely deployed environments and show that the interference can be mitigated and the IEEE 802.11ax-based IoT devices can utilize air interface more fairly compared to conventional IEEE 802.11 distributed coordination function.

Driving factors for the changing epidemiology of vector-borne diseases – a review

Vector-borne diseases regularly occurred in moderate climate areas of Europe in past decades. The changing epidemiology of vector-borne diseases in Europe is caused by changes of environmental factors – amongst these climate change -and human activity (transport mobility, urbanisation, renaturation etc.). The occurrence of invasive vector species (for example the Asian tiger mosquito [ Aedes albopictus] and the Asian bush mosquito [ Aedes japonicus japonicus]) in new habitats pose a risk of pathogen transmission in naive host populations. On the other hand, the presence of native competent vectors and adequate habitats are sufficient for the effective spread of a pathogen as seen in the Bluetongue Virus epidemic in Europe in 2006 onwards or the spread of West Nile Virus (WNV) lineage 1 in USA in 1999. The vector-borne diseases posing threat of spread are very often of viral origin (WNV fever, Chikungunya fever, Dengue fever, Tick-borne encephalitis, Yellow fever, Japanese encephalitis), but there is also the potential for parasitic (Malaria, Dirofilariosis, Piroplasmosis and Leishmaniosis) or bacterial diseases (Tularaemia, Rickettsiosis). Recently, the fast spreading epidemic of mosquito-borne Zika virus infection implicated the need of preparedness and constant monitoring of vector-borne diseases and their vectors in order to provide fast answers to emerging disease control and prevention. In this paper, we review major driving factors for the changing epidemiology of selected vector-borne diseases, provide a risk assessment for the introduction of new emerging diseases to Europe and present a new concept to face future challenges.

Low-Complexity Statistically Robust Precoder/Detector Computation for Massive MIMO Systems

Massive MIMO is a variant of multi-user MIMO in which the number of antennas at the base station (BS) $M$ is very large and typically much larger than the number of served users (data streams) $K$ . Recent research has widely investigated the system-level advantages of the massive MIMO, and in particular, the beneficial effect of increasing the number of antennas $M$ . These benefits, however, come at the cost of a dramatic increase in hardware and computational complexity. This is partly due to the fact that the BS needs to compute precoding/receiving vectors in order to coherently transmit/detect data to/from each user, where the resulting complexity grows proportionally to the number of antennas $M$ and the number of served users $K$ . Recently, different algorithms based on tools from asymptotic random matrix theory and/or approximated message passing have been proposed to reduce such complexity. The underlying assumption in all these techniques, however, is that the exact statistics (covariance matrix) of the channel vectors of the users is a priori known. This is far from being realistic, especially taking into account that, in the high-dim regime of $M \gg 1$ , estimating the channel covariance matrices of the users is also challenging in terms of both computation and storage requirements. In this paper, we propose a novel technique for computing the precoder/detector in a massive MIMO system. Our method is based on the randomized Kaczmarz algorithm and does not require a priori knowledge of the statistics of users’ channel vectors. We analyze the performance of our proposed algorithm theoretically and compare its performance with that of other techniques based on random matrix theory and approximate message passing via numerical simulations. Our results indicate that our proposed technique is computationally very competitive and yields quite a comparable performance while it does not require the knowledge of the statistics of users’ channel vectors.

A Space Vector Modulation Scheme of the Quasi-Z-Source Three-Level T-Type Inverter for Common-Mode Voltage Reduction

The conventional three-level inverter suffers the limitation of voltage buck operation. In order to give both voltage buck and boost operation capability, the quasi-Z-source three-level T-type inverter (3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> I) has been proposed. This paper further proposes a space vector modulation (SVM) scheme for the quasi-Z-source 3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> I to reduce the magnitude and slew rate of common-mode voltage (CMV). By properly selecting the shoot-through phase, the shoot-through states are inserted within zero vector in order not to affect the active states and output voltage. Doing so, the CMV generated by the quasi-Z-source 3LT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> I is restricted within one-sixth of dc-link voltage, and voltage boosting and CMV reduction can be simultaneously realized. In addition, high dc-link voltage utilization can be maintained. The proposed scheme has been verified in both simulations and experiments. Comparisons are conducted with the conventional SVM method and the phase-shifted sinusoidal PWM method.

Time Optimal Trajectories for a Mobile Robot Under Explicit Acceleration Constraints

Mobile robots are becoming increasingly important for different tasks in various industries. The time-optimal path planning problem in two-dimensional for a point mass robot, navigating in an obstacle free environment, is the focus of this research work. The main challenge is to compute an explicit solution path which takes into account the constraints on the robot's along-track and cross-track accelerations, while minimizing the overall travel time. We analyze this problem by employing Pontryagin's minimum principle and Kelley's condition, to derive time-optimal path primitives. Using these path primitives, the paper proposes a methodology that synthesizes time-optimal paths, and analytically solves (up to simple quadratures) for the corresponding adjoint vector in order to prove compliance with necessary conditions for optimality. Representative examples are studied in order to demonstrate the method, and are compared with numerical results using direct collocation.

Development of a strictly regulated xylose-induced expression system in Streptomyces

BackgroundGenetic tools including constitutive and inducible promoters have been developed over the last few decades for strain engineering in Streptomyces. Inducible promoters are useful for controlling gene expression, however only a limited number are applicable to Streptomyces. The aim of this study is to develop a controllable protein expression system based on an inducible promoter using sugar inducer, which has not yet been widely applied in Streptomyces.ResultsTo determine a candidate promoter, inducible protein expression was first examined in Streptomyces avermitilis MA-4680 using various carbon sources. Xylose isomerase (xylA) promoter derived from xylose (xyl) operon was selected due to strong expression of xylose isomerase (XylA) in the presence of d-xylose. Next, a xylose-inducible protein expression system was constructed by investigating heterologous protein expression (chitobiase as a model protein) driven by the xylA promoter in Streptomyces lividans. Chitobiase activity was detected at high levels in S. lividans strain harboring an expression vector with xylA promoter (pXC), under both xylose-induced and non-induced conditions. Thus, S. avermitilis xylR gene, which encodes a putative repressor of xyl operon, was introduced into constructed vectors in order to control protein expression by d-xylose. Among strains constructed in the study, XCPR strain harboring pXCPR vector exhibited strict regulation of protein expression. Chitobiase activity in the XCPR strain was observed to be 24 times higher under xylose-induced conditions than that under non-induced conditions.ConclusionIn this study, a strictly regulated protein expression system was developed based on a xylose-induced system. As far as we could ascertain, this is the first report of engineered inducible protein expression in Streptomyces by means of a xylose-induced system. This system might be applicable for controllable expression of toxic products or in the field of synthetic biology using Streptomyces strains.

Coexisting spinons and magnons in the frustrated zigzag spin- 12 chain compound β−TeVO4

We investigate magnetic excitations in the frustrated zigzag spin-1/2 chain compound $\beta$-TeVO$_4$ by inelastic neutron scattering. In the magnetically ordered ground state, the excitation spectrum exhibits coexisting magnon dispersion, characteristic of long-range magnetic order, and a spinon-like continuum that prevails above 2 meV, indicating the dominance of intrachain interactions. Combining linear-spin-wave-theory and pre-calculated spinon-continuum results, we reproduce the experimental spectrum. Our analysis offers a minimal exchange-network model which determines dominant intrachain interactions, their anisotropies and weak interchain interactions. The obtained parameters explain the magnetic ordering vector and spin excitations in the magnetic ground state.