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Dynamical controls on terahertz (THz) wavefronts are crucial for many applications, but available mechanism requests tunable elements with sub-micrometer sizes that are difficult to find in the THz regime. Here, different from the local-tuning mechanism, we propose an alternative approach to construct wavefront-control meta-devices combining specifically designed metasurfaces and globally tuned graphene layers. Coupled-mode-theory (CMT) analyses reveal that graphene serves as a tunable loss to drive the whole meta-device to transit from one functional phase to another passing through an intermediate regime, exhibiting distinct far-field (FF) reflection wavefronts. As a proof of concept, we design/fabricate a graphene meta-device and experimentally demonstrate that it can reflect normally incident THz wave to pre-designed directions with different polarizations under appropriate gating voltages. We finally design a graphene meta-device and numerically demonstrate that it can generate vectorial THz beams with continuously varying polarization distributions upon gating. These findings pave the road to realizing a wide range of THz applications, such as sensing, imaging, and wireless communications.

Abstract Background Infant and young child feeding is critical for child health and survival. Proportion of infants 0–5 months who are fed exclusively with breast milk is a common indicator used for monitoring and evaluating infant and young child feeding in a given country and region. Despite progress made since 1990, a previous review in 2006 of global and regional trends found improvement to be modest. The current study provides an update in global and regional trends in exclusive breastfeeding from 1995 to 2010, taking advantage of the wealth of data from recent household surveys. Methods Using the global database of infant and young child feeding maintained by the United Nations Children’s Fund, the authors examined estimates from 440 household surveys in 140 countries over the period between 1995 and 2010 and calculated global and regional averages of the rate of exclusive breastfeeding among infants 0–5 months for the two time points to assess the trends. Results Trend data suggest the prevalence of exclusive breastfeeding among infants younger than six months in developing countries increased from 33% in 1995 to 39% in 2010. The prevalence increased in almost all regions in the developing world, with the biggest improvement seen in West and Central Africa. Conclusions In spite of the well-recognized importance of exclusive breastfeeding, the practice is not widespread in the developing world and increase on the global level is still very modest with much room for improvement. Child nutrition programmes worldwide continue to require investments and commitments to improve infant feeding practices in order to have maximum impact on children’s lives.

Abstract Background The Bayesian shrinkage technique has been applied to multiple quantitative trait loci (QTLs) mapping to estimate the genetic effects of QTLs on quantitative traits from a very large set of possible effects including the main and epistatic effects of QTLs. Although the recently developed empirical Bayes (EB) method significantly reduced computation comparing with the fully Bayesian approach, its speed and accuracy are limited by the fact that numerical optimization is required to estimate the variance components in the QTL model. Results We developed a fast empirical Bayesian LASSO (EBLASSO) method for multiple QTL mapping. The fact that the EBLASSO can estimate the variance components in a closed form along with other algorithmic techniques render the EBLASSO method more efficient and accurate. Comparing with the EB method, our simulation study demonstrated that the EBLASSO method could substantially improve the computational speed and detect more QTL effects without increasing the false positive rate. Particularly, the EBLASSO algorithm running on a personal computer could easily handle a linear QTL model with more than 100,000 variables in our simulation study. Real data analysis also demonstrated that the EBLASSO method detected more reasonable effects than the EB method. Comparing with the LASSO, our simulation showed that the current version of the EBLASSO implemented in Matlab had similar speed as the LASSO implemented in Fortran, and that the EBLASSO detected the same number of true effects as the LASSO but a much smaller number of false positive effects. Conclusions The EBLASSO method can handle a large number of effects possibly including both the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTL mapping.

Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding.

We first develop a reduced-rank minimum mean squared error (MMSE) detector for direct-sequence (DS) code division multiple access (CDMA) by forcing the linear MMSE detector to lie in a signal subspace of a reduced dimension. While a reduced-rank MMSE detector has lower complexity, it cannot outperform the full-rank MMSE detector. We then concentrate on the blind reduced-rank MMSE detector which is obtained from an estimated covariance matrix. Our analysis and simulation results show that when the desired user′s signal is in a low-dimensional subspace, there exists an optimal subspace so that the blind reduced-rank MMSE detector lying in this subspace has the best performance. By properly choosing a subsspace, we guarantee that the optimal blind reduced-rank MMSE detector is obtained. An adaptive blind reduced-rank MMSE detector, based on a subspace tracking algorithm, is developed. The adaptive blind reduced-rank MMSE detector exhibits superior steady-state performance and fast convergence speed.

Perovskite-based materials have become a new direction for peroxymonosulfate (PMS) activation in pollutants degradation. However, the mechanism towards PMS activation remains insufficient. In this paper, a series of LaMxMn1−xO3 materials with B-site doping were developed by sol-gel self-propagating combustion method. The prepared LaFe0.3Mn0.7O3/PMS system exhibited the desired catalytic activity, almost 100% removal efficiency of TC can be achieved within 50 min and 98% initial catalytic activity could be maintained after 5 cycles. Quenching experiments and EPR tests together certified the vital roles of •O2− and 1O2 during the TC degradation procedure. Simultaneously, the catalytic performance of the LaFe0.3Mn0.7O3/PMS system was examined in relation to varying pH values, coexisting chemicals, and water conditions. DFT calculations demonstrated an increased electron concentration in the reactive sites after Fe doping. In addition, LaFexMn1−xO3 at the electron-rich state increased the bond strength with O atoms and encouraged the adsorption/desorption of intermediates in the PMS activation process. The aforementioned outcomes showed the possibility for application in intricate, realistic aquatic environments. [ABSTRACT FROM AUTHOR]

Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-dimensional space. We then utilized these learned features to train a classifier to categorize tumors into a high- or low-risk group of recurrence. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that CL can significantly improve classification accuracy. Specifically, our CL-based classifiers achieved an area under the receiver operating characteristic curve (AUC) greater than 0.8 for 14 types of cancer, and an AUC greater than 0.9 for 3 types of cancer. We also developed CL-based Cox (CLCox) models for predicting cancer prognosis. Our CLCox models trained with the TCGA data outperformed existing methods significantly in predicting the prognosis of 19 types of cancer under consideration. The performance of CLCox models and CL-based classifiers trained with TCGA lung and prostate cancer data were validated using the data from two independent cohorts. We also show that the CLCox model trained with the whole transcriptome significantly outperforms the Cox model trained with the 16 genes of Oncotype DX that is in clinical use for breast cancer patients. The trained models and the Python codes are publicly accessible and provide a valuable resource that will potentially find clinical applications for many types of cancer. [ABSTRACT FROM AUTHOR]

This study investigates the mechanisms of flame acceleration and deflagration-to-detonation transition (DDT) in supersonic flows using transverse hydrogen injection and downstream ignition. Utilizing the graphics processing unit accelerated adaptive mesh refinement approach, we examine the influence of downstream ignition jet pressure on DDT through high-resolution computational simulations. Our results indicate that the transverse injection of hydrogen into the supersonic mainstream generates strong turbulence and numerous vortices due to Kelvin–Helmholtz instability, enhancing fuel mixing efficiency along the flow but deviating from the ideal premixed state. Following the injection of the downstream ignition jet into the supersonic main flow, initial flame acceleration is less effective than in the premixed state due to the non-uniformity of the incoming flow. However, within the boundary layer, the flame remains stable, and the intense turbulence fosters shock–flame interactions. The convergence of multiple compression waves into a shock wave facilitates energy deposition, coupling with the flame to trigger local detonation via the reactive gradient mechanism. The detonation wave exhibits complex wavefront structures, including vertical and oblique fronts induced by boundary layer interactions. Ignition jet pressure significantly impacts the DDT process and detonation wave characteristics, reducing ignition time and affecting the detonation temperature, pressure, and propagation speed. This study provides valuable insights into the dynamics of flame acceleration and DDT in supersonic flows with non-uniform fuel distribution and downstream jet ignition. The findings highlight the critical role of ignition jet pressure in optimizing ignition and detonation processes, offering new perspectives for achieving low-energy, rapid detonation initiation within the tube. [ABSTRACT FROM AUTHOR]

In the information age, semantic parsing technology drives efficiency improvement and accelerates the process of intelligence. However, it faces complex understanding, data inflation, inappropriate evaluation, and difficult application of advanced large models. This study analyses the current challenges and looks forward to the development trend of the technology. Specific approaches include: this study adopts a systematic review method and strictly follows the PRISMA framework, deeply analyzes the key ideas, methods, problems, and solutions of traditional and neural network methods, and explores the model performance, API application, dataset, and evaluation mechanism. Through literature analysis, the technology is classified according to its application scenarios. Then, the practical application contributions are summarized, current limitations such as data size, model performance, and resource requirements are analyzed, and future directions such as dataset expansion, real-time performance enhancement, and industrial applications are envisioned. The results of the study show significant advances in semantic parsing technology with far-reaching impacts. Traditional and neural network methods complement each other to promote theoretical and practical innovation. In the future, with the continuous progress and in-depth application of machine learning technology, semantic parsing technology needs to further deepen the research on logical reasoning and evaluation, to better cope with technical challenges and lead the new development of natural language processing and AI. [ABSTRACT FROM AUTHOR]

Pachylophus belongs to the subfamily Chloropinae, the second most diverse subfamily of Chloropidae. However, there have been few complete mitochondrial genomes of Chloropinae reported in the public database. Consequently, we sequenced and annotated the complete mitochondrial genome of Pachylophus rufescens (de Meijere, 1904). The whole mitochondrial genome is 17, 926 bp in length, consisting of 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs) and two ribosomal RNAs (rRNAs). The mitochondrial genome exhibits high A + T bias, accounting for 79.7% of its entirety. All PCGs start with ATN codon and end with TAN or incomplete stop codon TA or single T. The Maximum likelihood phylogenetic tree revealed a close relationship between Pachylophus and Cetema. This study contributes to the expansion of the mitochondrial genome library of Chloropinae, providing a valuable resource for gaining insights into the evolutionary history of Chloropidae. [ABSTRACT FROM AUTHOR]

When constructing long-span spatial steel structures, the unformed structure is often incomplete and unstable. The construction sequence significantly influences the mechanical state of the structure during the construction stages (CSs), affecting both the path and time effects. This study examined the mechanical properties of the construction process using an actual project as a case study, comparing two methods: one-step forming and stage-by-stage forming. Critical turning points of stress and displacement during the CSs were identified as the initial installation and unloading stages. Stress concentrations frequently occurred at temporary support points, and peak displacements often appeared at the outer overhanging bars of the structure. A well-planned construction sequence can effectively manage the structure's formation, boundaries, and loading to ensure construction safety and stability. The conclusions and analysis methods from this study provide valuable references for the design and construction of similar long-span spatial steel structures. [ABSTRACT FROM AUTHOR]