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Compared with the classic object detection problem, detecting objects in aerial images has some special challenges including huge orientation variations, complicated and large background, and wide multi-scale distribution. Considering these three challenges together, we propose a novel arbitrary-oriented object detection framework consisting of three main parts. Firstly, the Cascading Attention Network (CA-Net) composed of a patching self-attention module and a supervised spatial attention module is proposed for enhancing the feature representations from objects of interest and suppressing the background noises in Feature Pyramid Network (FPN) from coarse to fine. Then, the Adaptive Feature Concatenate Network (AFC-Net) is proposed to adaptively stack the feature maps pooled from all FPN levels as well as the global semantic features, for dealing with the multi-scale change of objects. Lastly, the OBB Multi-Definition and Selection Strategy (OBB-MDS-Strategy) is proposed to regress rotated bounding boxes more smoothly and detect oriented objects more accurately in the training process. Our experiments are conducted on two common and challenging aerial datasets, i.e., DOTA and HRSC2016. Experiments results show that the proposed method has superior performances in multi-orientated objects detection compared with the representative methods.

In greedy pursuit algorithm, atom selection is commonly a concerned topic for signal compressed recovery. To improve the recovery performance, an optimal atom selection strategy without the prior information is proposed in this paper. The sensing information entropy is defined to prune the possible false atoms in the estimated support set. Fewer iterations are required in the proposed strategy and it can also be applied in the case with high sparsity level or low signal-noise-ratio. Compared with the existing representative algorithms, the superiority of the recovery error and probability is verified by the simulations. Furthermore, the proposed method is applied to recover the real random modulated signal. The results show that the recovered signal has greater consistence with the original input signal.

Component orthogonal arrays, as fractional designs of all possible permutations on experimental factors, are suitable for order-of-addition experiments due to their pairwise balance in any two positions of the orders. The existing component orthogonal arrays are mainly restricted to the case where the number of components is prime power. In this paper, we construct component orthogonal arrays with any number of components. The constructed design may have a number of appealing properties, for example, it may preserve pairwise balance after some components removed from the orders. In addition, a design selection strategy is provided to determine component orthogonal arrays with reasonable run sizes. Illustrative examples are given to demonstrate our methods.

In this paper, we devise and analyse three highly efficient second-order accurate (in time) schemes for solving the Functionalized Cahn–Hilliard (FCH) gradient flow equation where an asymmetric double-well potential function is considered. Based on the Scalar Auxiliary Variable (SAV) approach, we construct these schemes by splitting the FCH free energy in a novel and ingenious way. Utilizing the Crank–Nicolson formula, we firstly construct two semi-discrete second-order numerical schemes, which we denote by CN-SAV and CN-SAV-A, respectively. To be more specific, the CN-SAV scheme is constructed based on the fixed time step, while the CN-SAV-A scheme is a variable time step scheme. The BDF2-SAV scheme is another second-order scheme in which the fixed time step should be used. It is designed by applying the second-order backward difference (BDF2) formula. All the constructed schemes are proved to be unconditionally energy stable and uniquely solvable in theory. To the best of our knowledge, the CN-SAV-A scheme is the first unconditionally energy stable, second-order scheme with variable time steps for the FCH gradient flow equation. In addition, an effective adaptive time selection strategy introduced in Christlieb et al., (2014) is slightly modified and then adopted to select the time step for the CN-SAV-A scheme. Finally, several numerical experiments based on the Fourier pseudo-spectral method are carried out in two and three dimensions, respectively, to confirm the numerical accuracy and efficiency of the constructed schemes.

There are two ways of brand expansion for IT enterprises in the world, i.e. internal accumulation expansion and external expansion, involving internal expansion and external expansion respectively. The international expansion strategy of IT brands mainly includes product positioning strategy, customer group selection strategy, location selection strategy, marketing channel strategy, and global public relations strategy. If IT enterprises want to expand their brands successfully in the world, they need to carry out effective innovation in product positioning, market segmentation, location selection mode, marketing methods, public relations system construction, etc.

On a five-axis CNC machine tool, the pretravel errors of touch-trigger probes are severely affected by gravity and must be compensated to ensure the required measurement accuracy. The situation is more complex than that of the three-axis on-machine inspection system. This paper proposes a simple and accurate modeling and compensation method for the probe pretravel error of a five-axis on-machine inspection system. First, the pretravel error for the 5-axis CNC tool is decoupled into three parts, which are analyzed based on the probe's mechanical structure. Then, a new calibration point selection strategy is proposed to obtain the accurate reference sphere center. Finally, we carry out calibration tests to validate the proposed method. The compensation results show that the proposed compensation method for the probe pretravel error under the influence of gravity (PPEUG) can improve the accuracy considerably.

Cancer treatments become more effective now. One cause of these therapeutic promotions is based on drug selective paradigms in the clinic. A small range of drug selective paradigms (pharmacogenomics, precision-oncology, biomarkers and drug sensitivity testing) have been widely used worldwide. To accelerate the performance of these drug selective strategies, improved understanding of different mechanisms may be useful. Among these four classes of selective systems, drug sensitivity testing (DST) plays a unique role in clinical trials. Biomedical advances of knowledge and technology in current drug selective systems are increasingly importance. In the past two decades, knowledge gap between pathology and therapeutics is gradually filling, especially among different predictive mechanisms. The different drug selective systems are competitive, progress and advantageous in different clinical situations. To face with this diversity of drug selective strategies, guiding principle and technical feasibility need to emphasize. As a powerful platform, DST have some unique characters—high-quality quantification and comparison of responses among anticancer drug of interests. The smooth progress of DST platform depends on increasingly understanding of cancer biology, pathology and pharmacology—including medical knowledge (cancer heterogeneity, plasticity, stem cells and metastasis) and technical versatility (micro-fluidity and apoptotic evaluation). The medical and technical insights into various and uniqueness of DST are especially highlighted in this article.

With the increasing operation time and environmental loads, a growing number of bridges are in poor conditions and suffer from continuous structural deformation. In order to understand how those deformations occurred and developed, the knowledge of bridge deformation characteristics and mechanisms is essential for users to manage and maintain bridges sustainably. The Differential Synthetic Aperture Radar Interferometry (DInSAR) technique is an effective tool to observe the bridge deformation and has achieved a few impressive results. Nevertheless, the previous studies often focused on bridges with simple structure and high coherence, avoiding the difficulties in identifying dense and accurate point-like targets (PTs) upon complexly structured bridges with more severe de-correlation effects. Moreover, the traditional two-dimensional (2D) deformation maps of complexly structured bridges are difficult for non-expert users to understand in multi-dimensional views, increasing the difficulty of deformation interpretation. Finally, simply analyzing all the PTs’ deformation velocities equivalently without considering the structural information is insufficient to explore the deformation characteristics and mechanisms of complexly structured bridges. Starting from these limitations, we proposed a bridge-tailored multi-temporal DInSAR approach, and for the first time, explored the deformation characteristics and mechanisms of the complexly structured arch bridge and cable-stayed bridge. The density and accuracy of detectable PTs on bridges are improved by implementing a structure coherence-driven PTs selection strategy. A simple and fast algorithm for 3D visualization of bridge results is accomplished through integrating orthorectified PTs’ measurements from multi-track SAR datasets. Based on the 3D products, the deformation characteristics of different types of bridges are investigated by identifying the damage sensitive points (DSPs) and revealing the global deformations, and the deformation mechanisms of different bridge components are explored by analyzing the different types of PTs classified based on the spatial-temporal semantics. It is derived that the deformation characteristics of bridges are associated with their structural and material characteristics, and the deformation mechanisms of their DSPs are similar. The subsidence of land surface and foundations control the deformation of the pier-DSPs which mainly reflect the trend deformation of bridges, whereas the temperature variation drives the deformation temporal evolution of the span-DSPs which represent the elastic deformation of bridges.

The aim of this study was to use genome-wide SNP data to investigate phenotypic and genetic differences in resistance to Haemonchus contortus between resistant and susceptible South African Dohne Merino sheep. The participating farm (Wauldby) has a history of heavy H. contortus challenge and implemented a selection strategy for resistance to H. contortus in 2011. Faecal egg count (FEC), body condition scores (BCS) and Famacha© scores (FAM) were recorded annually on lambs from weaning in January until the end of June from 2011 to 2014. Animals with FAM scores of ≥ 2.5 or BCS scores

Urban land ecological security plays an important role in alleviating human–land conflicts and in coordinating the development of natural and human resources and the environment. However, the subject of the present urban land ecological security is poorly studied. Given the circumstances, this paper presents a spatial exploring model for urban land ecological security (ULESM) based on the modified artificial bee colony algorithm (MABCA). The paper improves ABCA with a spatial grid, designed a grid-based ABCA calculation framework, ABCA's data structure, information-sharing mechanism and position selection strategy, introduces the model of spatial coupling and coordination degree for urban ecological security, and achieves spatial detection of ecologically hazardous areas. Taking Guangzhou as an example, this paper simulated the hazardous regions of the urban ecological land (assumed area: 50% of total area of Guangzhou), yielding favorable results. The results show that the ULESM based on MABCA exhibits an excellent global search and a rapid global-convergence speed, preventing a bee colony from being trapped in a local optimum. It can also effectively detect regions with urban land ecological hazards and provide intelligence-aided methods for decision making in urban ecological security and spatial planning

Using whole-genome sequence (WGS) data are supposed to be optimal for genome-wide association studies and genomic predictions. However, sequencing thousands of individuals of interest is expensive. Imputation from single nucleotide polymorphisms panels to WGS data is an attractive approach to obtain highly reliable WGS data at low cost. Here, we conducted a genotype imputation study with a combined reference panel in yellow-feather dwarf broiler population. The combined reference panel was assembled by sequencing 24 key individuals of a yellow-feather dwarf broiler population (internal reference panel) and WGS data from 311 chickens in public databases (external reference panel). Three scenarios were investigated to determine how different factors affect the accuracy of imputation from 600 K array data to WGS data, including: genotype imputation with internal, external and combined reference panels; the number of internal reference individuals in the combined reference panel; and different reference sizes and selection strategies of an external reference panel. Results showed that imputation accuracy from 600 K to WGS data were 0.834±0.012, 0.920±0.007 and 0.982±0.003 for the internal, external and combined reference panels, respectively. Increasing the reference size from 50 to 250 improved the accuracy of genotype imputation from 0.848 to 0.974 for the combined reference panel and from 0.647 to 0.917 for the external reference panel. The selection strategies for the external reference panel had no impact on the accuracy of imputation using the combined reference panel. However, if only an external reference panel with reference sizegt;50 was used, the selection strategy of minimizing the average distance to the closest leaf had the greatest imputation accuracy compared with other methods. Generally, using a combined reference panel provided greater imputation accuracy, especially for low-frequency variants. In conclusion, the optimal imputation strategy with a combined reference panel should comprehensively consider genetic diversity of the study population, availability and properties of external reference panels, sequencing and computing costs, and frequency of imputed variants. This work sheds light on how to design and execute genotype imputation with a combined external reference panel in a livestock population.

Histamine (HIS) and tryptamine (TRY) are heterocyclic biogenic amines (BAs) often found in food products. The consumption of products containing high levels of BAs results in a wide range of life-threatening physiological reactions. The work described a Simultaneous coupled with Separate selection strategy for the screening of different aptamers against histamine and tryptamine. Aptamer HIS-3 and TRY-2 exhibited a good binding affinity; specificity and pH stability to histamine and tryptamine were obtained with a dissociation constant of 11.62 ± 3.241 nM and 6.303 ± 1.758 nM, respectively. The preliminary binding mechanism was analyzed using a circular dichromatic (CD) and molecular docking. Finally, the identified aptamers were used for the development of a colorimetric method for the detection of histamine and tryptamine, achieving limits of detection of 1 ng/mL and 10 ng/mL, respectively. This aptamer-based method was further successfully employed to detect the BAs in fish and pork, providing a potential application for sensitive and efficient detecting multiple food risk factors.

This paper investigates the impact of consumer's uncertainty as to quality preference on the retailer's supplier selection decision, and on supply chain pricing decisions. When the retailer adopts a single-supplier structure, both informed (knowing quality preference) and uninformed (not knowing quality preference) consumers are in the market, and the retailer has two different market-targeting strategies. An uninformed consumer can be informed by the presentation of two quality-differentiated products supplied by two suppliers, when the retailer selects dual-supplier structure and sells both products. We show that the dual-supplier structure is not always a dominant strategy when there exists a portion of uninformed consumers in the market. The retailer will choose the dual-supplier structure if the proportion of informed consumers is either low or very high, while it will choose a single high-quality supplier to target both consumer groups if the proportion of informed consumers is moderately high. Moreover, the retailer is more likely to adopt the low-price strategy targeting both informed and uninformed consumers when the proportion of the informed consumer in the market is lower; otherwise, it adopts a high-price strategy targeting informed consumers only. The impacts of the consumer's quality preference uncertainty on the retailer's supplier selection and on suppliers under the retailer's optimal supplier selection strategy are also examined. We also include numerical studies to illustrate main results.

The data presented in this article are related to the research article entitled “The alteration landscape of the cerebral cortex” (Cauda et al., 2018). Here, we applied a metric called alteration negentropy (A-negentropy) on a large human neuroimaging dataset, in order to denote the “low structural alteration variety” of the altered brain areas. Furthermore, we reported the overview of the selection strategy, as well as the description and distribution of the selected studies from the voxel-based morphometry database of BrainMap (Vanasse et al., 2018). For all of the analyzed brain areas, we reported the number of pathologies affecting them (both local maxima and mean value), as well as the peak and average values of A-negentropy. Regions altered by a small number of brain disorders exhibit high values of A-negentropy.

As the key part of a fluid catalytic cracking unit (FCCU), the control of the reactor-regenerator system is very important to the FCCU process. For this purpose, we adopt three multiple inputs/single output Elman neural networks (ENNs) for modeling the reactor-regenerator system. To determine the weight parameters of ENN models optimally, we propose the splicing process inspired cuckoo search algorithm (spCS), in which the new RNA crossover operator is designed to increase the population diversity. And a modified selection strategy based on Euclidean distances and fitness values is presented for the selection of dissimilarity individuals. The searching performance of the spCS is demonstrated by the numeral experiments and comparisons with CS and ACS. Then, spCS and CS are compared to optimize the parameters of ENNs. The simulation results validate that the spCS based ENN model outputs are in better agreement with experimental data in modeling the FCCU reactor-regenerator system.

Recent innovations in network virtualization and elastic optical networks enable the flexible deployment of optical networks. However, the spectrum fragmentation and virtual optical links being embedded to the longer lightpaths decrease the spectrum efficiency of elastic optical networks. To address this issue, a coordinated virtual optical network embedding algorithm based on resources availability-aware (RA-CVONE) is proposed. Firstly, we design the node importance metric to embed the neighboring virtual nodes to the closer physical nodes, and then a coordinated embedding approach is adopted, which can further shorten the length of the lightpaths to which virtual optical links are embedded. Moreover, we jointly evaluate the resources availability of a physical lightpath and its node, then select the candidate lightpaths with less spectrum fragmentations and abundant resources for a virtual optical link. Based on the resources availability, a path selection strategy is proposed to determine a physical lightpath of which the vacant spectrum resources can be effectively used. Simulation results show that the RA-CVONE algorithm can significantly shorten the length of the physical lightpaths which are selected to embed virtual optical links, reduce the bandwidth blocking probability of virtual optical network requests, and improve the spectrum utilization of elastic optical networks.

Multiview video supports observing a scene from different viewpoints. The Joint Video Team (JVT) developed H.264/MVC to enhance the compression efficiency for multiview video, however, MVC encoded multiview video (MVC video) still requires high bitrates for transmission. This paper investigates live MVC video streaming over Peer-to-Peer (P2P) networks. The goal is to minimize the server bandwidth costs whist ensuring high streaming quality to peers. MVC employs intra-view and inter-view prediction structures, which leads to a complicated layer dependency relationship. As the peers’ outbound bandwidth is shared while supplying all the MVC video layers, the bandwidth allocation to one MVC layer affects the available outbound bandwidth of the other layers. To optimize the utilization of the peers’ outbound bandwidth for providing video layers, a maximum flow based model is proposed which considers the MVC video layer dependency and the layer supplying relationship between peers. Based on the model, a layer dependency aware live MVC video streaming method over a BitTorrent-like P2P network is proposed, named MVP2P. The key components of MVP2P include a chunk scheduling strategy and a peer selection strategy for receiving peers, and a bandwidth scheduling algorithm for supplying peers. To evaluate the efficiency of the proposed solution, MVP2P is compared with existing methods considering the constraints of peer bandwidth, peer numbers, view switching rates, and peer churns. The test results show that MVP2P significantly outperforms the existing methods.

Multi-objective harmony optimization algorithm dominated by α in negotiation supply chain of self-adaptive weighted constraint penalty is proposed for high dimension of objective optimization and low algorithm accuracy and computational efficiency in the collaborative supply chain between enterprises in Logistics Industry Park. Firstly, the collaborative scheduling model of supply chain is researched, the collaborative scheduling multi-objective optimization model is constructed, the single collaborative scheduling optimization model is established with the method of self-adaptive weight, and the collaborative scheduling multi-objective compensation model is set up by means of integrated constraint; secondly, the multi-objective evolutionary algorithm is improved, elite gradient selection strategy dominated by α is constructed for the problem of missing guidance in Pareto solution, and multi-objective harmony search optimization algorithm dominated by α is established by combining with harmony search algorithm; finally, scheduling effect of distributors is improved and good results of α dominating set is obtained under the premise that the manufacturers scheduling is ensured.

One of the major challenges of model-free visual tracking problem has been the difficulty originating from the unpredictable and drastic changes in the appearance of objects we target to track. Existing methods tackle this problem by updating the appearance model on-line in order to adapt to the changes in the appearance. Despite the success of these methods however, inaccurate and erroneous updates of the appearance model result in a tracker drift. In this paper, we introduce a novel real-time visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods. The tracking algorithm utilizes this strategy to choose the appropriate template for tracking a given frame. The template selection strategy is self-learned by utilizing a simple policy gradient method on numerous training episodes randomly generated from a tracking benchmark dataset. Our proposed reinforcement learning framework is generally applicable to other confidence map based tracking algorithms. The experiment shows that our tracking algorithm runs in real-time speed of 43 fps and the proposed policy network effectively decides the appropriate template for successful visual tracking.

The inverse medium problem with discontinuous refractive index is a kind of challenging inverse problem. We employ the primal dual theory and fast solution of integral equations, and propose a new iterative imaging method. The selection criteria of regularization parameter is given by the method of generalized cross-validation. Based on multi-frequency measurements of the scattered field, a recursive linearization algorithm has been presented with respect to the frequency from low to high. We also discuss the initial guess selection strategy by semi-analytical approaches. Numerical experiments are presented to show the effectiveness of the proposed method.

In this paper, two enhanced Binary Differential Evolution (BDE) algorithms are proposed to select variables for nonlinear process soft sensor development. Firstly, the Parallel BDE (PBDE) algorithm is presented to extract the optimal individuals of several parallel short evolution paths of basic BDE, where the spurious variables are effectively eliminated. And the most relevant variables are selected through a double-layer selection strategy with the validating Root Mean Square Error (RMSE) for evaluating criterion. Secondly, the Boosting BDE (BBDE) algorithm is proposed through applying the boosting technique to the parallel evolution paths. The performance of the previous path needs to be taken into account when conducting the current evolution path. The selected probabilities of variables are given through the weighted summation of the selection results of all paths. Also, a double-layer selection is conducted on BBDE algorithm. The feasibility and effectiveness of the proposed methods are demonstrated through a nonlinear numerical example and a real industrial process.

The Internet of Things (IoT) is overpopulated by a large number of objects and millions of services and interactions. Therefore, the ability to search for the right object to provide a specific service is important. The merger of the IoT and social networking, the Social Internet of Things (SIoT), has made this possible. The main idea in the SIoT is that every object in the IoT can use its friends’ or friends-of-friends’ relationships to search for a specific service. However, this is usually a slow process because each node (object) is required to manage a large number of friends. This paper addresses the issue of link selection of friends and analyzes five strategies in the literature. Then it proposes a link selection strategy using the Genetic Algorithm (GA) to find the near optimal solution. The results show an improvement over the examined strategies in terms of several parameters.

Genetic and phenotypic trends of a breed indicate its progress over decades, and it is important to develop an effective futuristic selection strategy. The aim of this study was to estimate the genetic and phenotypic progress of an important synthetic sheep breed of India, Kashmir Merino. Breeding values, phenotypic averages, heritability and inbreeding coefficients were estimated for the Kashmir Merino breed for body weights at various ages reared in Government Sheep Breeding Farms (GSBF); Kralpathri (1997–2016) and GSBF, Goabal (2013–2016) for body weights of animals. Breeding value and heritability were estimated using Animal Models (Best Linear Unbiased Predictions). Trends were generated for each using R programming language. A change from 3.258 kg to 3.810 kg for birth, 21.489 to 19.660 for 6-month weights, 24.710 to 24.460 for 12-month weights was observed from 1997 to 2016. Trends obtained for phenotypic values for 6-month body weight and 12-month body weights were negative and trends for birth weight were positive. Trends obtained for breeding values for birth weight, 6-month body weight and 12-month weights were positive except for birth weight which had a negative slope. The slopes for breeding values for birth weight, 6-month weight and 12-month weights were -0.00021 ± 0.00012, 0.00068 ± 0.00045, 0.00148 ± 0.00093, respectively. Trends obtained for heritability for birth weight, 6-month weight were negative and 12-month weight were positive. The heritability slopes for birth, 6-month and 12-month body weights were 0.001912 ± 0.00180, 0.00335 ± 0.00222 and 0.00894 ± 0.00469 respectively. Trends obtained for inbreeding coefficients through linear regression found for Kashmir Merino were positive and near zero. It is concluded that an optimal selection strategy that considers both genetic merits as well as the pedigree should be adopted to sustain the sheep breeding programs in Jammu and Kashmir.

Sine cosine algorithm (SCA) is a newly developed optimization tool that has received increasing attention in the field of computational intelligence. Nevertheless, SCA has a combination of strong exploration and weak exploitation abilities, which undermines the performance of the algorithm. A dynamic selection strategy (DSS) has been proposed in this paper to overcome this limitation. The central idea is that the selection pressure of the parent is dynamically regulated during the course of evolution, and the parent with higher fitness have higher selection probability. Accordingly, the weak exploitation ability of SCA can be enhanced. This approach can be readily applied to other SCA variants as well. Results from systematic experiments conducted on CEC2014 competition benchmark suite show that the proposed DSS method can significantly improve the performance of the SCAs. In addition, the proposed DSS-SCA algorithm has been successfully applied to six engineering design problems, which shows that the algorithm has the potential to solve other practical problems.

Weakly supervised temporal action localization is a challenging computer vision problem that uses only video-level labels and lacks the supervision of temporal annotations. In this task, the majority of existing methods usually identify the most discriminative snippets and ignore other relevant snippets. To address this problem, we propose a deep feature enhancing and selecting network. It generates multiple masks for both capturing more complete temporal interval of actions and keeping its high classification accuracy. After that, we further propose a novel selection strategy to balance the influence of multiple masks and improve the model performance. In the experiments, we evaluate the proposed method on the THUMOS’14 and ActivityNet datasets, and the results show the effectiveness of our approach for weakly supervised temporal action localization.

The aptamers screened by the classic SELEX strategy or other approaches do not undergo the real sample analysis during the selection process, and the environmental inconsistency between the selection and final detection systems might influence the aptamer characteristics and detection performance. Herein, a novel real sample assisted SELEX selection strategy has been proposed to rationally select the suitable aptamers with high detection performance, using the selection of a sarafloxacin (SAR) specific aptamer as an example. Based on the developed strategy, the aptamer Apt-F2 with higher specificity and SAR binding affinity was obtained as compared to the aptamer Apt-B4 selected by the standard SELEX strategy. Apt-F2 also exhibited a stronger conformational change after binding as compared to Apt-B4. Apt-B4 and Apt-F2 were used as a platform for developing the fluorescent aptasensor. Under optimized conditions, the limit of detection of Apt-B4- and Apt-F2-based aptasensors for SAR was 0.19 and 0.12 ng/mL respectively. Moreover, Apt-F2-based aptasensor also revealed a slightly superior SAR recovery rate from the spiked milk samples. The findings obtained in the study confirmed the advantage of the real SELEX strategy as compared to the traditional version. Further, the characteristics of the aptasensor proposed here also revealed its application potential for detecting SAR in the real milk or other animal-sourced food samples.

A growing number of studies use crude oil price measures possibly in combination with other financial and macroeconomic variables, such as the dividend yield and earnings-to-price ratio as predictors in econometric models to evaluate whether they improve the accuracy of equity premium point predictions out-of-sample. In this study, we suggest relying on widely used crude oil price measures not as regressors in our predictive model but rather using them to devise a prediction selection strategy. Particularly, we use the crude oil price measure of interest to select between predictions produced under the benchmark and the predictive model augmented with a financial or macroeconomic variable. Using this prediction selection strategy, we succeed in obtaining point prediction accuracy improvements close to 10% relative to the benchmark and commonly used alternatives.

Sugarcane is one of the crops of greatest interest in the search for efficient biomass for the production of bioenergy. In addition to the production of sugar and ethanol from sugarcane juice, the industrialization process obtains the biomass, a lignocellulosic material with high energy content, which can be converted into biofuels. Recognized this importance, the objective of this study was to indicate crosses between potential sugarcane genotypes, aiming at the production of sugar and ethanol, and at the use of biomass for the production of bioelectricity or cellulosic ethanol. Ninety sugarcane genotypes of the Active Germplasm Bank of the Federal University of Vicosa were evaluated for 15 chemical and technological variables that characterize the juice and the bagasse. Crosses were defined by the genotypes which presented the best means using the simultaneous selection index, excluding the crosses between the most similar genotypes based on genetic dissimilarity verified by the analyses of diversity. Thus, it was possible to indicate 40 crosses between potential parents with high quality for production of sugar and ethanol and with efficient biomass for production of bioenergy, 19 crosses being for production of bioelectricity, 15 for production of cellulosic ethanol, and 6 for both purposes.

Background and purpose: Radiotherapy (RT) of rectal cancer is challenged by potentially large inter-fractional anatomy changes. The risk of radiation-induced morbidity is a particular concern in patients receiving re-irradiation for recurrent disease. We propose an adaptive RT plan selection strategy for these patients and report on its clinical feasibility and normal tissue sparing potential. Material and methods: Eight patients with pelvic recurrence were re-irradiated according to a hyper-fractionation protocol (ReRAD-I; 40.8 Gy) using margins around the clinical target volume (CTV) of 15 mm trimmed to anatomical barriers (Plan L). Two new library plans (S and M) were created for each patient, with the target volumes covering the CTV with isotropic margins of 5 and 10 mm. Pre-treatment cone beam CTs were assessed to determine which plan would cover the CTV following soft-tissue match. The selected plans were compared to the clinically delivered plan in terms of normal tissue volume receiving 95% of the dose (V95%) and the volume of bone receiving 30 Gy (V30 Gy). Results: Plan selections could be performed on all CBCTs for all patients. Plan S was chosen in 213 fractions (79%), plan M in 53 (20%) and plan L in 2 fractions. Normal tissue V95% was reduced by 67% (median; range 30–79%) while bone V30 Gy was reduced by 66% (median; range 40–100%). Conclusion: The CTV and/or surrogate structures were visible on all CBCTs. Margins smaller than those used clinically would have accounted for 99% of the observed target deformations, translating into a considerable normal tissue sparing potential. Keywords: Adaptive radiotherapy, Rectal recurrence, Plan selection

Production of polyhydroxyalkanoates (PHAs) by open mixed microbial cultures (MMCs) has been attracting increasing interest as an alternative technology to PHA production by pure cultures, due to the potential for lower costs associated with the use of open systems (eliminating the requirement for sterile conditions) and the utilisation of cheap feedstock (industrial and agricultural wastes). Such technology relies on the efficient selection of an MMC enriched in PHA-accumulating organisms. Fermented cheese whey, a protein-rich complex feedstock, has been used previously to produce PHA using the feast and famine regime for selection of PHA accumulating cultures. While this selection strategy was found efficient when operated at relatively low organic loading rate (OLR, 2g-CODL-1d-1), great instability and low selection efficiency of PHA accumulating organisms were observed when higher OLR (ca. 6g-CODL-1d-1) was applied. High organic loading is desirable as a means to enhance PHA productivity. In the present study, a new selection strategy was tested with the aim of improving selection for high OLR. It was based on uncoupling carbon and nitrogen supply and was implemented and compared with the conventional feast and famine strategy. For this, two selection reactors were fed with fermented cheese whey applying an OLR of ca. 8.5g-CODL-1 (with 3.8g-CODL-1 resulting from organic acids and ethanol), and operated in parallel under similar conditions, except for the timing of nitrogen supplementation. Whereas in the conventional strategy nitrogen and carbon substrates were added simultaneously at the beginning of the cycle, in the uncoupled substrates strategy, nitrogen addition was delayed to the end of the feast phase (i.e. after exogenous carbon was exhausted). The two different strategies selected different PHA-storing microbial communities, dominated by Corynebacterium and a Xantomonadaceae, respectively with the conventional and the new approaches. The new strategy originated a more efficient PHA-production process than the conventional one (global PHA productivity of 6.09g-PHAL-1d-1 and storage yield of 0.96 versus 2.55g-PHAL-1d-1 and 0.86, respectively). Dissociation between the feast to famine length ratio (F/F) and storage efficiency was shown to be possible with the new strategy, allowing selection of an efficient PHA-storing culture with complex feedstock under high organic loading rates.

Objective To evaluate the repeatability of (Motor Unit Number Index) MUNIX under repeatability conditions, specify the origin of variations and provide strategies for quality control. Methods MUNIX calculations were performed on the bicep brachii muscles of eight healthy subjects. Negative effect of suboptimal electrode positions on MUNIX accuracy was eliminated by employing the high-density surface electromyography technique. MUNIX procedures that utilized a variety of surface interferential pattern (SIP) epoch recruitment strategies (including the original MUNIX procedure, two proposed improvement strategies and their combinations) were described. For each MUNIX procedure, ten thousands of different SIP pools were constructed by randomly recruiting necessary SIP epochs from a large SIP epoch pool (3 datasets, 9 independent electromyography recordings at different contraction levels per dataset and 10 SIP epochs per recording) and implemented for MUNIX calculation. The repeatability of each MUNIX procedure was assessed by summarizing the resulting MUNIX distribution and compared to investigate the effect of SIP epoch selection strategy on repeatability performance. Results SIP epochs selected at lower contraction levels have a stronger influence on the repeatability of MUNIX than those selected at higher contraction levels. MUNIX under repeatability conditions follows a normal distribution and the standard deviation can be significantly reduced by introducing more epochs near the MUNIX definition line. Conclusions The MUNIX technique shows an inherent variation attributable to SIP epochs at low contraction levels. It is recommended that more epochs should be sampled at these low contraction levels to improve the repeatability. Significance The present study thoroughly documented the inherent variation of MUNIX and the causes, and offered practical solutions to improve the repeatability of MUNIX.

With the increasing of Network-on-Chip size, deterministic routing algorithms suffer from a poor performance under heavy traffic. Therefore, routing schemes with varied candidate paths and fair selection strategy are required to relieve congestion. We propose an alterable priority arbitration for the fair granting of packet transmission from all possible directions permitted by the odd-even turn model. To achieve fairness, the proposed arbitration strategy alters the priority orders based on an optimized pointer, its granting decisions are made according to fixed mechanism under varied priority orders. Under a cycle-accurate simulator based on System C, the proposed alterable priority arbiter is verified to achieve better fairness than the RR arbiter. Simulation results show that the average packet latency of the odd-even turn model is reduced by 26.46%, and the saturation throughput is improved by 29.79% with the introduction of proposed arbitration scheme. Furthermore, the proposed arbiter achieves ignorable increasing of the hardware implementation overhead.

In this paper, transfer trajectories from the Moon to near-Earth asteroids with different escaping trajectory options, Earth flyby and direct escaping, are investigated. The former executes an Earth flyby maneuver to increase the orbital energy during the transfer. The latter realizes the transfer by a single impulse at perilune. In this work, these two kinds of escaping orbits in the Earth-Moon planar circular-restricted three-body (PCRTB) system are analyzed firstly. The abilities to achieve the same Earth hyperbolic excess velocity for both orbits with minimum velocity impulse expenditure are calculated and analyzed. Then, the asteroid flyby trajectory in ephemeris model via two types of escaping orbits is designed. Asteroids 4179 Toutatis from Apollo and 3908 Nyx from Amor, represent the small and large transfer energy, respectively, are chosen as targets. The selection strategy of escaping trajectories for small and large-energy transfers is discussed.

Turning points are the Achilles' heel of time-series momentum portfolios. Slow signals fail to react quickly to changes in trend while fast signals are often false alarms. We examine theoretically and empirically how momentum portfolios of various intermediate speeds, formed by blending slow and fast strategies, cope with turning points. Our model predicts a mean-variance optimal dynamic speed selection strategy. We apply this strategy across domestic and international equity markets and document efficient out-of-sample performance. We also propose a novel decomposition of momentum strategy alpha, highlighting the role of volatility timing.

There are three main classic decomposition approaches, i.e., weighted sum (WS), Tchebycheff (TCH) and penalty-boundary based intersection (PBI), which have different improvement regions (IRs) during the evolutionary search process. Hence, these three decomposition approaches have their own corresponding advantages and weaknesses on the performance of convergence or diversity due to their different features of IRs. In this paper, in order to take advantages of different decomposition methods, a novel multi-objective evolutionary algorithm using a self-organizing decomposition selection strategy (called MOEA/D-SDSS) is presented for solving multi-objective optimization problems (MOPs) and the crashworthiness design problem. In this approach, two competitive populations are respectively run based on the TCH and PBI decomposition methods. Then, based on their respective performance on both diversity and convergence, a self-organizing selection strategy is performed during the whole evolutionary process, which aims to select one suitable decomposition approach for the next generation. In other words, the two populations based on different decomposition approaches are complementary with each other, which is crucial and beneficial for improving the robustness of evolutionary algorithms on solving various kinds of MOPs. At last, one population with a better performance will be outputted as the final result. In order to evaluate the performance of our proposed MOEA/D-SDSS, thirty-six well-known benchmark problems and the crashworthiness design problem are studied in our experiments. The experimental results validate the promising performance of our proposed algorithm over six state-of-the-art decomposition-based evolutionary algorithms and six other competitive evolutionary algorithms.

Many recent studies have shown that the characteristics of human activities such as mobility and network structure have significant impacts on information spreading. This paper proposes a destination selection strategy in a metapopulation model, there are two different destination selection preferences about the strategy. The Markov chain method is utilized to discuss the information spreading dynamics. In the experimental results of Erdos-Renyi (ER) network structure, when the movement preference is determined, the range of information propagation will increase slightly with the increase of mobility probability. Mobility has little impact on the information outbreak threshold. When exploring the impact of destination selection strategy on the scope of information dissemination, compared with moving to the neighbor nodes with lower information prevalence, individuals who prefer to move to the neighbors with higher information prevalence will bring more extensive information coverage. Similar phenomena are revealed on the Barabasi-Albert (BA) model.

Matrix effects are major concerns in pesticide residue analysis, which can adversely affect the identification and quantification of pesticide residues especially for complex matrices. In this study, a flexible and effective electrospray ionization tandem mass spectrometry method based on matrix-matched monitoring ion selection strategy was developed to eliminate the interferences of complex matrice co-extractives in pesticide residue analysis. The detection ions of 27 typical pesticides were optimized to improve qualitative accuracy of pesticide residues in complex matrices Chrysanthemum. The optimized ionization and chromatographic conditions were used for the detection of 196 pesticides in Chrysanthemum. Using this strategy, the limit of quantification was determined to be ≤5 μg/kg and the recoveries were within 70–120% for the majority of the pesticides. This work provides a practical strategy and theoretical guidance for the development of pesticide detection in complex matrices.

With the rapid increase of renewables, the unit commitment (UC) problem with high time resolution should be implemented to guarantee the economy and security of the power system. However, as a mixed-integer programming problem, its computational burden exponentially increases with the time resolution. Because of the unacceptable computational burden, many markets still adopt the UC with an hourly scheduling horizon. In this paper, a new approach is proposed to perform a fast calculation of the UC problem with high time resolution. The basic idea is to identify the representative scheduling points (RSPs) and reduce the size of optimization while causing a limited impact on the commitment solution. A selection strategy is proposed to identify RSPs. Based on RSPs, the construction of commitment solution is presented to obtain the result with original high time resolution. To guarantee feasibility, a correction strategy is proposed. Numerical results based on the IEEE 30-bus and 118-bus systems and a 661-bus utility system show that 1) the proposed method can substantially accelerate the computational speed, 2) the proposed approach can provide a feasible commitment solution within the desired accuracy loss, and 3) the market-clearing payments solved by the proposed method reflect a more accurate price signal.

Recombination of knowledge elements has been recognized as important innovation activities. This study aims to develop a new measurement of recombinative innovation and firstly explores its antecedents at the country-dyad level. We analyze 41,007 US alternative energy patents granted between 1976 and 2012. Based on multi-source data and longitudinal design, Quadratic Assignment Procedure (QAP) model results indicate that two countries’ technological proximity (TP) takes an inverted U-shaped relationship with their recombinative innovation (RI), which means that TP could raise the potential of joint recombination, but should not become too high because of great knowledge homogenization. Furthermore, we test two types of distances (i.e., cultural and geographical) as moderators of the relationship between TP and RI. Cultural distance negatively moderates the relationship between TP and RI, but moderating role of geographical distance is not supported in this research. The findings of this study, besides having implications for management and policy, have implications on the research of recombinative innovation, inter-national collaboration and partner selection strategy.

Because of its physical and physiological characteristics, the horse has a natural predisposition for use in sport. However, the use of horses as “competition tools” frequently results in several problems related to stress that directly or indirectly bias their sporting performance. Hence, the aim of this manuscript was to examine literature on the influence of stress on sport horse performance and highlight potential avenues for improvements in their breeding and welfare. The stress response is initiated when a stimulus (the stressor) is perceived as a potential threat, resulting in a combination of biological responses designed to alleviate the effects of the perceived stressor. Stress can have positive as well as negative effects on the body helping the animal to cope with routine short-term stressors that in some circumstances can enhance performance, but in others, it can impair performance. A good sport performer has a combination of superior conformation, an appropriate temperament, a healthy physical condition and primed physiological mechanisms, and function for optimal fitness that keeps the horse performing well. In addition, it is more valuable if it has the appropriate genetic background and good reproductive ability. How stress can potentially affect these characteristics is discussed in this review. The development of a broader selection strategy for breeding of sport horses that takes into account aspects as conformation, temperament, genetics, health, and the physiology of the horse, together with an adequate control of the environment, is likely to improve the welfare of horses during competitions.

Due to the circuit aging effect, the minimum leakage vector (MLV) found by the traditional input vector control method may not obtain the optimal leakage power reduction result when the circuit begins to degrade. To solve this problem, we present an adaptive MLV selection strategy based on a linear programming approach. The method divides the total lifetime of the circuit into a succession of time intervals, and the MLV used in each interval is periodically updated according to the transistor's threshold voltage degradation so that the best overall power reduction result can be achieved. Experimental results on various benchmark circuits show the effectiveness of our method. An NBTI-aware adaptive minimum leakage vector selection method has been proposed.The impact of the NBTI effect on leakage power and MLV selection has been analyzed.SVR is used to build the model between threshold voltage change and leakage power.MLV is updated according to aging condition by ILP approach to reduce leakage power.The impact of MLV update on NBTI-induced path delay increase has been analyzed.

Using a stability result for variable time-step Runge-Kutta methods applied to nonautonomous real linear scalar test problem that decays exponentially fast, a step-size selection algorithm is devised. The step-size selection algorithm is based partly on stability information obtained by estimating the discrete Lyapunov exponent of a Runge-Kutta method applied to a nonautonomous linear scalar problem. The utility of the approach is illustrated in numerical experiments that demonstrate how the new algorithm performs against a standard accuracy based step-size selection strategy.

The Knowledge sharing among virtual alliance members is intended to avoid repetitive investment in knowledge innovation and waste of knowledge resources, so as to get a higher starting point in knowledge innovation, thus improving the efficiency of organizational knowledge innovation. On the basis of analyzing knowledge sharing conditions in virtual alliance activities, this paper expounds symbiosis of alliance member of virtual enterprises and knowledge sharing levels. Knowledge sharing is further divided into four knowledge sharing models according to knowledge sharing types, flow directions and relations between alliance members. Moreover, a questionnaire survey is made to investigate the four knowledge sharing models and knowledge sharing activities of alliance members, to provide decision-making support to alliance enterprises to develop applicable solutions for knowledge sharing, and offer theoretical guidance to alliance organizers to develop management and control strategies.

Background Applications to pediatric residency programs have increased in the last decade, creating pressure for applicants & programs. It is unclear how programs’ interviewing & ranking practices relate to match outcomes. Objective: Describe pediatric residency program interview & ranking strategies. Determine if these strategies would change if USMLE becomes pass/fail. Design We created a survey regarding current invitation & ranking practices, and if these practices would change if USMLE becomes pass/fail. We distributed the APPD approved survey to residency coordinators & associate program directors (APDs) in Spring 2019. We analyzed the data using nonparametric correlation & multivariate regression analysis. Results Of 153 programs surveyed, 60 coordinators and 32 APDs completed the survey, with 21% of programs completing the study. Interviewing (A=-0.38) & ranking (A=-0.36) more applicants correlated significantly with the rank list percentile of the lowest-matched applicant, but did not correlate with the top-matched applicant's rank. There were no significant correlations between number of interviews offered & average USMLE scores or type of matched applicant (US MD vs. non-US IMG). However, more invitations (A=0.46, 0.58), interviews (A=0.42, 0.54), & ranks (A=0.53, 0.60) were associated with higher average COMLEX (1, 2) scores. Having more available PGY1 positions was associated with higher incoming USMLE step (1, 2) scores (A=0.27, 0.61), & a greater proportion of US MD residents (A=0.29). On average, programs ranked USMLE/COMLEX (Fig. 1) scores as the primary criteria for offering interviews, therefore if the USMLE becomes pass/fail, many programs would need to change their selection strategy. Conclusion When controlling for the number of PGY1 positions, offering more interviews did not correlate with most characteristics of matched applicants, suggesting that pediatric residency programs could interview fewer applicants without meaningfully affecting match outcomes. Next steps include studying the correlation between application strategies & outcomes for applicants.

DC-link voltage and output current control loops are two cascaded loops in the control structure of grid-connected inverters. A high DC-link voltage loop bandwidth (DCL-BW) provides more disturbance rejection capability for the control loop and is preferred from control system perspective. However, for stability issues, this BW is limited and must be sufficiently less than that of the current control loop. Among the different control schemes, instantaneous active reactive control (IARC) method provides the highest possible DCL-BW (i.e., 0.02 × switching frequency). Having this degree of freedom in the controller design, a proper methodology should be defined for selection of DCL-BW. In this paper, different factors including the output current harmonic distortion, low-order harmonics on DC link voltage, and the inverter nominal rating are introduced as affecting parameters for DCL-BW selection. Accordingly, a DCL-BW selection strategy based on output current harmonic distortion is proposed. The proposed method enhances the power quality indices of the grid-connected inverters, which especially is useful for few kVA power inverters. Finally, to validate the conducted analysis, experimental and simulation results are provided.

Microalgae from several ecosystems in the state of Parana, Brazil, near the Atlantic Forest, were isolated using selected culture media. From 30 isolates, 20 with moderate growth were evaluated for its suitability as industrial strains. A multicriteria analysis was used considering process and composition dimensions: 1) kinetic parameters versus biomass production, 2) one of the kinetic parameters versus biomass composition (carbohydrates, lipids, proteins, and carotenoids), 3) one of the kinetic parameters versus composition after cellular stress and 4) the self-sedimentation and cellular disruption potential. The strategy was adequate to select 9 microalgae from which two are especially good producers. Selection is essential for assessing the biodiversity in search of new industrial strains, and the multicriteria selection strategy proved to be a sound approach for microalgae selection from culture collections. Parameters such as the efficiency of sedimentation and cell disruption allow potentializing the search for the best strains further.

This paper proposes two innovative approaches to address the two challenges of project portfolio selection: (i) Determining how project interactions influence the final values of project portfolios, and (ii) selecting the best solution from non-dominated project portfolios. First, based on the dependence relationship between projects and technologies, we construct a project co-utilization network indicating project interactions using the co-citation network method. We also consider project interaction as a regularization item of the value function to indicate the influence of project interactions on the value of project portfolios. Second, we propose a refined selection strategy. All the projects are ranked in the order of their appearance in the ranked association rules. Project portfolios having no highly ranked projects are deleted successively until only one of them remains. Finally, a case of multi-objective project portfolio selection is studied and an optimal project portfolio recommended. A comparison shows the availability and advantages of the proposed approaches.

This paper formulates a nonlinear mixed-integer multiobjective optimization model for service provider selection and order allocation in different procedures under the mass-customization logistics service mode. The optimal provider selection strategy, order allocation strategy and optimal CODP location are determined simultaneously by a quantitative method. We transform the multiobjective programming model into a single-objective model by means of different methods and design an improved genetic algorithm based on multilayer coding technology to solve the model. Numerical experiments show that the algorithm designed in this paper can effectively solve the model.

Signal transducers and activators of transcription (STATs) are a family of proteins activated by different stimulating factors, including interferons, interleukins, growth factors and oncoproteins. Their role in many diseases has been proven, so there is a strong demand to find specific strategies for STAT inhibition. Our searches for specific STAT-targeting compounds focused on exploring the phosphotyrosine (pTyr) SH2 interaction area. Current selection strategies are insufficient, thus, we developed a new pipeline strategy. In this work we used our recently built 3D models for all human (h)STATs (1–4, 5A, 5B and 6). To select specific inhibitors for the STAT protein of interest, we designed and implemented a five step comparative virtual screening tool, which we named – CAVS (comparative approach for virtual screening). CAVS introduces the ‘comparative binding affinity value’ (CBAV) and ‘ligand binding pose variation’ (LBPV) as selection criteria to identify specific inhibitors of STATs. In a five-step approach, including pre-screen, primary filtering of inhibitors, re-screen, secondary filtering of inhibitors and graphical inspection and final selection, CAVS leads to selection of specific STAT inhibitory compounds. CAVS was tested on a small ligand library of 130 000 natural products and 5.7 million of clean leads for the selection of STAT1 or STAT3-specific inhibitors. With the use of a designed set of Python scripts for data managing and filtering, CAVS allows to convert the comparative virtual screening procedure into an automatic pipeline and to effectively analyze virtual screening results from standard Surflex-Dock 2.6 output files. We also adapted CAVS as a general-purpose pipeline, which will allow adapting written code to different experiments and protein families in the future.