Simple Combination Research Articles

An Inconvenient Truth about Forecast Combinations

It is well-known that the weighted averages of two competing forecasts may reduce mean squared prediction errors (MSPE) and may also introduce certain inefficiencies. In this paper, we take an in-depth view of one particular type of inefficiency stemming from simple combination schemes: Mincer and Zarnowitz inefficiency or auto-inefficiency for short. Under mild assumptions, we show that linear convex forecast combinations are almost always auto-inefficient, and, therefore, greater reductions in MSPE are almost always possible. In particular, we show that the process of taking averages of forecasts may induce inefficiencies in the combination, even when individual forecasts are efficient. Furthermore, we show that the so-called “optimal weighted average” traditionally presented in the literature may indeed be inefficient as well. Finally, we illustrate our findings with simulations and an empirical application in the context of the combination of headline inflation forecasts for eight European economies. Overall, our results indicate that in situations in which a number of different forecasts are available, the combination of all of them should not be the last step taken in the search of forecast accuracy. Attempts to take advantage of potential inefficiencies stemming from the combination process should also be considered.

An efficient adaptive multi-mesh phase-field method for simulating rock fractures

Phase-field models have significant advantages in rock fractures, such as simulating multiple crack initiation, propagation and coalescence without requiring fracture criteria. However, the high computational cost is a notable drawback. For this reason, this paper proposes a simple, efficient and robust Adaptive Multi-Mesh phase-field method (AMM) for modelling crack propagation. The main concept of the AMM method is to adopt a coarser adaptive mesh and C0 elements for the displacement field in order to reduce computational cost, while employing a finer adaptive mesh and C1 elements for the damage field to accurately capture in-rock crack paths. The results show that the AMM method is capable of reducing the number of displacement elements by 58.2–72% and the computational time by 43.8–53% compared to the Adaptive Mesh phase-field method (AM), and by 92.4–96.4% and 95.9–98.8%, respectively, compared to the Uniform Refinement mesh method (UR). Additionally, this paper also proposes a simple and practical mesh Refinement Combination Strategy, RCS, and an efficient Local variable transfer method, LEI, which requires only the Exchange of Integration point positions. The proposed RCS makes it possible to perform variable transfer between two independent adaptive meshes without searching the topological relations, thus dramatically decreasing the implementation difficulty and computational time. The proposed LEI method achieves variable transfer simply by exchanging integration point positions, thereby ensuring simplicity and numerical stability.

Energy Efficient Multi User Spatial Modulated NOMA for mmWave Communications

In this paper, we propose a downlink multi-user (MU) generalized spatial modulation (GSM) aided non-orthogonal multiple access (MU-GSM-NOMA) transmission technique for mmWave communications. Given a set of users, a user grouping algorithm is presented to partition the users into different clusters, and each of them is served by a dedicated uniform linear array (ULA) of antennas. The ULAs generate analog beams to steer the amplitude and phase-modulated symbols by employing the principles of NOMA. Further, the analog beams carry additional spatial information bits encoded in GSM for a particular cluster user. We propose simple digital combiners for receiver processing based on matched filtering, for which we have characterized the theoretical guarantee of the perfect detection of spatially modulated analog beams. Also, the work proposes an optimization algorithm to allocate power to the users, ensuring stable successive interference cancellation for a specific case of two users in each cluster. Finally, we present the numerical results, which demonstrate the outperformance of our proposed approach over the existing ones in terms of average sum rate and energy efficiency.

Predicting Turbulent Vertical Velocity in the Ocean Surface Layer under Mixed Convective and Wind/Wave Forcing

Abstract Turbulence in the ocean surface layer is forced by a mixture of buoyancy, wind, and wave processes that evolves over time scales from the diurnal scale of buoyancy forcing, through storm time scales, to the annual cycle. This study seeks a predictor for root-mean-square w (rmsw), a time and surface layer average of turbulent vertical velocity w measured by bottom-mounted vertical-beam acoustic Doppler current profilers, in terms of concurrently measured surface forcing fields. Data used are from two coastal sites, one shallow (LEO, 15-m depth) and one deeper (R2, 26-m depth). The analysis demonstrates that it is possible to predict observed rmsw with a simple linear combination of two scale velocities, one the convective scale velocity familiar from the atmospheric literature, the other a scale velocity wS representing combined wind and wave effects. Three variants are considered for this latter scale velocity, the wind stress velocity alone and two forms using both and US, a Stokes velocity characteristic of the surface wave field. At both sites, the two-parameter fit using alone is least accurate, while fits using the other two variants are essentially indistinguishable. At both sites, the coefficient multiplying is the same, within error bounds, and within the range of previous observations. At the deeper site, the coefficient multiplying the wind/wave scale velocity wS is approximately half that at the shallow site, a difference here attributed to difference in wave character.

Unsupervised Cross-Modal Hashing With Modality-Interaction

Recently, numerous unsupervised cross-modal hashing methods have been proposed to deal the image-text retrieval tasks for the unlabeled cross-modal data. However, when these methods learn to generate hash codes, almost all of them lack modality-interaction in the following two aspects: (1) The instance similarity matrix used to guide the hashing networks training is constructed without image-text interaction, which fails to capture the fine-grained cross-modal cues to elaborately characterize the intrinsic semantic similarity among the datapoints. (2) The binary codes used for quantization loss are inferior because they are generated by directly quantizing a simple combination of continuous hash codes from different modalities without the interaction among these continuous hash codes. Such problems will cause the generated hash codes to be of poor quality and degrade the retrieval performance. Hence, in this paper, we propose a novel Unsupervised Cross-modal Hashing with Modality-interaction, termed UCHM. Specifically, by optimizing a novel hash-similarity-friendly loss, a modality-interaction-enabled (MIE) similarity generator is first trained to generate a superior MIE similarity matrix for the training set. Then, the generated MIE similarity matrix is utilized as guiding information to train the deep hashing networks. Furthermore, during the process of training the hashing networks, a novel bit-selection module is proposed to generate high-quality unified binary codes for the quantization loss with the interaction among continuous codes from different modalities, thereby further enhancing the retrieval performance. Extensive experiments on two widely used datasets show that the proposed UCHM outperforms state-of-the-art techniques on cross-modal retrieval tasks.

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics.

Physics-supervised deep learning–based optimization (PSDLO) with accuracy and efficiency

Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.

Sugar-Assisted Kinetic Resolutions in Metal/Chiral Amine Co-Catalyzed α-Allylations and [4+2] Cycloadditions: Highly Enantioselective Synthesis of Sugar and Chromane Derivatives.

Functionalized triose-, furanose and chromane-derivatives were synthesized by the titled reactions. The sugar-assisted kinetic resolution/C-C bond-forming cascade processes generate a functionalized sugar derivative with a quaternary stereocenter in a highly enantioselective fashion (up to >99 % ee) by using a simple combination of metal and chiral amine co-catalysts. Notably, the interplay between the chiral sugar substrate and the chiral amino acid derivative allowed for the construction of a functionalized sugar product with high enantioselectivity (up to 99 %) also when using a combination of racemic amine catalyst (0 % ee) and metal catalyst.

Enhancing plane-wave emission with a combination device based on acoustic metamaterial

Generating high sound-intensity plane waves typically requires transducer arrays with complex synchronizing systems and high-output amplifiers. This work proposes a simple combination device to convert fan-shaped waves into elevated sound-pressure plane waves. The proposed device primarily relies on phase reconstruction of near-zero index metamaterials and the superposition of reflected waves in the reverberation field. The compact device, which is easy to manufacture, consists of a short waveguide equipped with two types of converters. One of the converters makes use of inverted cone's sound-guiding properties to convert fan-shaped waves into cylindrical waves over a broad frequency band. The other converter is comprised of rigid cylinders with triangular lattice arrays, effectively acting as the near-zero index metamaterial to transform cylindrical waves into plane waves. Both the simulated and experimental results show that the emitted plane waves can be linearly amplified by increasing the number of point sources that are separated by integer multiples of the wavelength in the vertical cavity. This study presents a straightforward and efficient approach to generate plane waves with a low-cost array assembly design, thereby enabling the widespread use of acoustic testing and underwater detection.

Synthesis of PDMS-μ-PCL Miktoarm Star Copolymers by Combinations (Є) of Styrenics-Assisted Atom Transfer Radical Coupling and Ring-Opening Polymerization and Study of the Self-Assembled Nanostructures.

Due to their diverse and unique physical properties, miktoarm star copolymers (μ-SCPs) have garnered significant attention. In our study, we employed α-monobomoisobutyryl-terminated polydimethylsiloxane (PDMS-Br) to carry out styrenics-assisted atom transfer radical coupling (SA ATRC) in the presence of 4-vinylbenzyl alcohol (VBA) at 0 °C. By achieving high coupling efficiency (χc = 0.95), we obtained mid-chain functionalized PDMS-VBAm-PDMS polymers with benzylic alcohols. Interestingly, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) analysis revealed the insertion of only two VBA coupling agents (m = 2). Subsequently, the PDMS-VBA2-PDMS products underwent mid-chain extensions using ε-caprolactone (ε-CL) through ring-opening polymerization (ROP) with an efficient organo-catalyst at 40 °C, resulting in the synthesis of novel (PDMS)2-μ-(PCL)2 μ-SCPs. Eventually, novel (PDMS)2-μ-(PCL)2 μ-SCPs were obtained. The obtained PDMS-μ-PCL μ-SCPs were further subjected to examination of their solid-state self-assembly through small-angle X-ray scattering (SAXS) experiments. Notably, various nanostructures, including lamellae and hexagonally packed cylinders, were observed with a periodic size of approximately 15 nm. As a result, we successfully developed a simple and effective reaction combination (Є) strategy (i.e., SA ATRC-Є-ROP) for the synthesis of well-defined PDMS-μ-PCL μ-SCPs. This approach may open up new possibilities for fabricating nanostructures from siloxane-based materials.

Design, Synthesis and Application of a Robust d3‐Methylthiolating Reagent†

Comprehensive SummaryA deuterated methylthiolating reagent with a broad spectrum, designated as DMTP, has been ingeniously formulated. This new reagent can be produced with high efficiency through the simple combination of commercially available methyl 3‐thiopropanoate and CD3I. This novel SCD3 reagent exhibits excellent reactivity with electrophiles such as alkyl iodides, benzyl chlorides, aryl halides (I, Br) and aryl triflates, resulting in high levels of deuterium content (99% D). Representative deuterated methylthiolated biomolecules, such as Sulfoxaflor, have been synthesized for use in pharmaceutical discovery and development.

Entropy stable non-oscillatory fluxes: An optimized wedding of entropy conservative flux with non-oscillatory flux

Abstract This work frames the problem of constructing non-oscillatory entropy stable fluxes as a least square optimization problem. A flux sign stability condition is defined for a pair of entropy conservative flux (F∗ ) and a non-oscillatory flux (Fs ). This novel approach paves a way to construct non-oscillatory entropy stable flux (F̂) as a simple combination of (F∗ and Fs ) which inherently optimize the numerical diffusion in the entropy stable flux (F̂) such that it reduces to the underlying non-oscillatory flux (Fs ) in the flux sign stable region. This robust approach is (i) agnostic to the choice of flux pair (F∗, Fs ), (ii) does not require the computation of costly dissipation operator and high order reconstruction of scaled entropy variable to construct the diffusion term. Various non-oscillatory entropy stable fluxes are constructed and exhaustive computational results for standard test problems are given which show that fully discrete schemes using these entropy stable fluxes do not exhibit nonphysical spurious oscillations in approximating the discontinuities compared to the non-oscillatory schemes using underlying fluxes (Fs ) only. Moreover, these entropy stable schemes maintain the formal order of accuracy of the lower order flux in the pair.

Exploring Architectural Shapes Based on Parametric Shape Grammars: A Case Study of the “Three Lanes and Seven Alleys” Historic District in Fuzhou City, China

With the development of information technology, the introduction of information technology into architectural modelling and façade design and the systematic definition of historic districts is a problem that the architectural industry continues to explore and pursue. As a shape-based and self-defining generation rule, shape grammar provides significant help in the process of the automatic generation of architectural shapes to obtain design results that meet the requirements of the original historic district. Based on the simple combination of the application of shape grammar in architectural design, combined with the field investigation method, the representative buildings in the “Three Lanes and Seven Alleys” historic district are investigated and understood in detail, and the corresponding shape grammar rules are established. The courtyard types of the historic district are divided into: “” shape, “T” shape, “=” shape, “” shape, “” shape, “” shape, “” shape, “U” shape, “” shape, and garden. In detail, the façade components include the entrance, patio, main seat, wings, cloister, pavilion, etc. The elements of its façade include saddle walls, grey sculptures, carved stones, grey tiles, wooden grilles, wooden railings, wood grain flowers, etc. On this basis, parametric design is introduced to convert the design syntax into parametric programs. Grasshopper in Rhino is mainly used to visualize and simulate the regulation, and finally, achieve the purpose of automatically generating the architectural shape and façade of the “Three Alleys and Seven Alleys” historic district by adjusting its parameters.

Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network

Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method.

Evaluation of trap type and attractant composition for potential mass trapping of Hylobius abietis

Abstract Different trap types and attractant compositions were tested with the goal of attracting as many individuals of Hylobius abietis as possible while assuring a minimal bycatch of nontarget species with regard to the family Carabidae on three study sites in the east of Germany. The results revealed a modified pitfall trap based on a model by Nordlander (1987, Scandinavian Journal of Forest Research, 2, 199–213.) as the most effective trap type with a high degree of selectivity and minimal ecological side effects. The modified Nordlander trap is suitable for potential H. abietis mass trapping as an instrument of integrated pest management. Two field trials testing different attractant compositions showed that a simple attractant combination of (−)‐α‐pinene and ethanol is as effective as other more complex combinations and thus preferable for mass trapping efforts. The addition of the monoterpenes camphene, Δ‐3‐carene, ocimene and terpinolene to the combination of (−)‐α‐pinene and ethanol did not increase trapping numbers significantly. Trapping numbers of H. abietis indicate that a reduction of the local H. abietis population by trapping is possible, though further research about the influence of mass trapping on regeneration damage is recommended.

Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes

Abstract The objective of this study is to quantify the spatial dependency and trend of annual maxima precipitation (annual highest daily precipitation, from 1970 to 2020) across selected weather stations in the Nelson Churchill River Basin (NCRB) of North America. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation. The generalized extreme value (GEV) parameters are expressed as simple linear combinations of geographical coordinates (i.e., longitude and latitude) and topography. The results show that topography, geographical coordinates, and time (as a temporal covariate) were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted SPEV was used to predict the 25- and 50-year return period levels. The fitted Extremal-t max-stable process model captured the spatial dependency structure of the extreme precipitation in the NCRB. The study is relevant in quantifying the spatial dependency structure of extreme precipitation in the Northern Great Plains. The result will contribute as a decision-support system in climate adaptation strategies in the United States and Canada.

Novel Strategy for Optimizing the Antibacterial Activity of Psidium guajava Against Clinical Isolates of Escherichia coli, Staphylococcus aureus, Salmonella spp., and Streptococcus spp.

The development of a new antibiotic is a challenging task, with an estimated failure rate of 95%. Minor changes in the chemical structure of a drug, such as stereochemistry, geometry, or functional group modifications, can significantly impact its medicinal activity. In this study, we aim to devise novel strategies for optimizing the antimicrobial properties of guava leaf extract through simple reactions, either by self-reaction or combination reactions with a reagent, drug, or different plant extract. Fourier transform infrared spectroscopy analysis revealed conjugation and formation of new functional groups in the prepared sample of Guava Guava (GG) and Guava Aspirin Guava (GAG), which were further confirmed by weight analysis. The results demonstrated that the antimicrobial activity of medicinal plants can be improved or optimized through simple reactions, such as combining the plant extract with a non-antimicrobial drug like aspirins or adding a small volume of concentrated sulfuric acid to the plant extract by heating at a temperature range of 100 – 110°C. Among the two combinatory methods used, GG exhibited better performance in inhibiting the growth of all tested bacterial strains at a concentration of 0.1 mg/mL compared to GAG at the same concentration, which inhibited the growth of only two bacterial strains: Escherichia coli and Streptococcus spp. These methods can be further explored and applied in various studies, including antifungal, anti-inflammatory, antiviral, and anticancer research, leveraging the availability and diverse range of natural products found in medicinal plants.

The interaction on HSA and the antibacterial activity from four polyoxometalate hybrids based on berberine

Four organic-polyoxometalate hybrids BR4[SiW12O40] (BR–SiW), BR3[PMo12O40] (BR–PMo), BR4K[EuSiW11O40]·2H2O (BR–EuSiW) and BR6Na3[EuW10O36] (BR–EuW) were fabricated by the polyoxometalates (POMs) anions and berberine cations (BR) noted for the alkaloids in traditional Chinese herbal medicine. These hybrids have been characterized and confirmed. The interaction between hybrids and human serum albumin (HSA) was investigated in a buffer solution (pH 7.4) using ultraviolet–visible light absorption and fluorescence techniques. The classical Stern–Volmer equation was used to analyze the fluorescence quenching at three temperatures (296, 303 and 310 K), and the static quenching mechanism for interaction was proposed. The Thermodynamic parameters, enthalpy, entropy change, and Gibbs free energy of hybrids interacting on HSA were calculated by Scatchard equation. The results indicated that therewas one binding site on the protein and BR–POMs all showed stronger binding force than that of raw materials. Synchronous fluorescence results showed that the binding sites of BR–POMs and HSA were not effectively affected the surrounding microenvironment. The following antibacterial experiments implied that inhibitory effect of hybrids were synergistic effect from organic active ingredient and POMs but the simple combination. All these data were prepared for further research on biology.

Four-dimensional electron energy-loss spectroscopy

Recent advances in scanning transmission electron microscopy have enabled atomic-scale focused, coherent, and monochromatic electron probes, achieving nanoscale spatial resolution, meV energy resolution, sufficient momentum resolution, and a wide energy detection range in electron energy-loss spectroscopy (EELS). A four-dimensional EELS (4D-EELS) dataset can be recorded with a slot aperture selecting the specific momentum direction in the diffraction plane and the beam scanning in two spatial dimensions. In this paper, the basic principle of the 4D-EELS technique and a few examples of its application are presented. In addition to parallelly acquired dispersion with energy down to a lattice vibration scale, it can map the real space variation of any EELS spectrum features with a specific momentum transfer and energy loss to study various locally inhomogeneous scattering processes. Furthermore, simple mathematical combinations associating the spectra at different momenta are feasible from the 4D dataset, e.g., the efficient acquisition of a reliable electron magnetic circular dichroism (EMCD) signal is demonstrated. This 4D-EELS technique provides new opportunities to probe the local dispersion and related physical properties at the nanoscale.

Modelling the cross-sectional profile of the kerf generated in overlapped pass erosion in abrasive waterjet milling of Al6061-T6 alloy

Machining complex surfaces on difficult-to-cut materials by conventional methods is challenging due to the issues that arise from both the ‘freeform surface generation’ and the ‘machining difficult-to-cut materials’. On the other hand, abrasive waterjets (AWJs) have proven their unique capabilities in machining a wide range of materials and 3D shapes by maneuvering the jet strategically. In realizing 3D shapes through AWJ milling, the cross-sectional profile (CP) of the kerf, i.e. (i) trench — formed in a single pass erosion and (ii) cavity — formed in an overlapped pass erosion, need to be predicted apriori. However, the stochastic and aggressive behaviour of the AWJ, and the lack of complete understanding of the kerf generation, make the accurate prediction of its CP a difficult task. Furthermore, the cavity generated (by overlapping two trenches) is not a simple linear combination of trenches generated in two single AWJ passes. Also, the modelling efforts that consider both the dynamic jet characteristics and the non-linearity in the kerf formation are very limited. In this work, analytical models are proposed to predict CP the kerf (trench and the cavity) milled by the AWJs by considering the experimental understanding gained on the kerf generation in single and overlapped pass erosion. The modelling strategy evaluates the local erosion volume and sweeps this over the CP along the top width of the kerf. This strategy enables the incorporation of region-wise physical phenomenon in the material removal. Furthermore, it considers the (i) aggressive and stochastic nature of the AWJ, and (ii) particle erosion theories applicable to ductile materials, which pose multiple challenges during conventional machining. The model developed for the trench considers the effective jet divergence, spatial abrasive mass- and velocity-distributions in the jet plume, as inputs. Towards capturing the non-linearity in the cavity generation, the non-flat surface from the previous pass and its effect on the variation of local particle impact angles, uneven jet deflection during subsequent passes around the jet axis, and the degree of overlap were considered. From the experimental validation, it was observed that in the single pass and overlapped passes at different degrees of overlaps (5 %–80 %), the proposed model predicts the kerf geometry with a maximum mean absolute error (MAE) of 36 μm and 49 μm, and the maximum depth of CP with a maximum error of 7 % and 11 %, respectively.