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Molecular imprinting is a technique for creating artificial recognition sites on polymer matrices that complement the template in terms of size, shape, and spatial arrangement of functional groups. The main advantage of Molecularly Imprinted Polymers (MIP) as the polymer for use with a molecular imprinting technique is that they have high selectivity and affinity for the target molecules used in the molding process. The components of a Molecularly Imprinted Polymer are template, functional monomer, cross-linker, solvent, and initiator. Many things determine the success of a Molecularly Imprinted Polymer, but the Molecularly Imprinted Polymer component and the interaction between template-monomers are the most critical factors. This review will discuss how to find the interaction between template and monomer in Molecularly Imprinted Polymer before polymerization and after polymerization and choose the suitable component for MIP development. Computer simulation, UV-Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Proton-Nuclear Magnetic Resonance (1H-NMR) are generally used to determine the type and strength of intermolecular interaction on pre-polymerization stage. In turn, Suspended State Saturation Transfer Difference High Resolution/Magic Angle Spinning (STD HR/MAS) NMR, Raman Spectroscopy, and Surface-Enhanced Raman Scattering (SERS) and Fluorescence Spectroscopy are used to detect chemical interaction after polymerization. Hydrogen bonding is the type of interaction that is becoming a focus to find on all methods as this interaction strongly contributes to the affinity of molecularly imprinted polymers (MIPs).

In brain mapping, structural templates derived from population-specific MRI scans are essential for normalizing individual brains into a common space. This normalization facilitates accurate group comparisons and statistical analyses. Although templates have been developed for various populations, none currently exist for the Saudi population. To our knowledge, this work introduces the first structural brain template constructed and evaluated from a homogeneous subset of T1-weighted MRI scans of 11 healthy Saudi female subjects aged 25 to 30. Our approach combines the symmetric model construction (SMC) method with a covariance-based weighting scheme to mitigate bias caused by over-represented anatomical features. To enhance the quality of the template, we employ a patch-based mean-shift intensity estimation method that improves image sharpness, contrast, and robustness to outliers. Additionally, we implement computational optimizations, including parallelization and vectorized operations, to increase processing efficiency. The resulting template exhibits high image quality, characterized by enhanced sharpness, improved tissue contrast, reduced sensitivity to outliers, and minimized anatomical bias. This Saudi-specific brain template addresses a critical gap in neuroimaging resources and lays a reliable foundation for future studies on brain structure and function in this population.

•NeuroMark combines templates with data-driven methods for biomarker extraction.•This study developed spatiotemporal functional templates throughout the lifespan.•The unique and shared networks across age populations were investigated.•Templates for other modalities were constructed using T1w and diffusion scans.•The reproducibility of network templates was evaluated across large-scale data. A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Template matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally, during the bin-picking process, the template needs to be frequently updated to maintain detection performance, and finding an adaptive template from a vast dataset poses another challenge. To address these challenges, we propose a novel template searching method in a latent space trained by a Variational Auto-Encoder (VAE), which generates an adaptive template dynamically based on the current environment. The proposed method was evaluated experimentally under various conditions, and in all scenarios, it successfully completed the tasks, demonstrating its effectiveness and robustness for bin-picking applications. Furthermore, we integrated our proposed method with YOLO, and the experimental results indicate that our method effectively improves YOLO’s detection performance.

The development of an efficient catalyst for formic acid electrocatalytic oxidation reaction (FAEOR) is of great significance to accelerate the commercial application of direct formic acid fuel cells (DFAFC). Herein, palladium phosphide (PdxPy) porous nanotubes (PNTs) with different phosphide content (i.e., Pd3P and Pd5P2) are prepared by combining the self‐template reduction method of dimethylglyoxime‐Pd(II) complex nanorods and succedent phosphating treatment. During the reduction process, the self‐removal of the template and the continual inside–outside Ostwald ripening phenomenon are responsible for the generation of the one‐dimensional hollow and porous architecture. On the basis of the unique synthetic procedure and structural advantages, Pd3P PNTs with optimized phosphide content show outstanding electroactivity and stability for FAEOR. Importantly, the strong electronic effect between Pd and P promotes the direct pathway of FAEOR and inhibits the occurrence of the formic acid decomposition reaction, which effectively enhances the FAEOR electroactivity of Pd3P PNTs. In view of the facial synthesis, excellent electroactivity, high stability, and unordinary selectivity, Pd3P PNTs have the potential to be an efficient anode electrocatalyst for DFAFC. Efficient formic acid oxidation reaction (FAEOR) electrocatalyst Pd3P porous nanotubes (PNTs) are synthesized by simple self‐template pyrolysis and phosphating treatment. Benefiting from the abundant defect atoms and excellent self‐stability, Pd3P PNTs reveal outstanding electroactivity and durability for FAEOR. The introduction of phosphorus could effectively improve the FAEOR electroactivity of Pd nanomaterials and simultaneously inhibit FADR, highlighting an efficient strategy for designing a DFAFC anode electrocatalyst.

Crystallographic texture engineering through templated grain growth (TGG) has gained prominence as a highly effective strategy for optimizing the electromechanical performance of lead-free piezoelectric ceramics, offering a pathway toward sustainable alternatives to lead-based systems like lead zirconate titanate (PZT). By achieving high degrees of texture, with Lotgering factors (LFs) often exceeding 90%, these systems have demonstrated piezoelectric properties that rival or even surpass their lead-based counterparts. Despite these advancements, the field lacks a comprehensive understanding of how specific template parameters influence the texture quality and functional properties across different material systems. This review provides an in-depth analysis of the influence of the template morphology, composition, and crystallographic orientation on the texturing of key lead-free systems, including BaTiO3 (BT), (K0.5Na0.5)NbO3 (KNN), and Bi0.5Na0.5TiO3 (BNT). Furthermore, it explores how the template selection affects the induced crystallographic direction, and how this impacts the material’s phase structure and domain configurations, ultimately influencing the piezoelectric and dielectric properties. By consolidating the existing knowledge and identifying current challenges, this work highlights key strategies for optimizing the texture and electromechanical performance in lead-free ceramics, providing essential insights for future research aimed at advancing high-performance, environmentally friendly piezoelectric materials for applications such as sensors, actuators, and energy-harvesting devices.

This paper presents a weld seam tracking system using cross mark structured light. The hardware of the proposed system consists of a two degrees of freedom (DOF) welding robot, a camera with cross mark structured light, and two computers. The system has two parts namely visual sensing and motion control. In the visual sensing part, the cross mark of the structured light is utilized to set a region of interest (ROI). In the ROI, an adapted line fitting algorithm is employed to estimate the lines. Then, intersections of the lines are computed and used as the pin points for templates creating. During the matching process, a modified template matching is used to detect the edges of V-groove weld seam. By using this technique, a huge computational cost in image processing can be reduced, and therefore the tracking can be made in real time. The position based visual servoing with proportional-derivative(PD) and velocity feedback controller is designed for seam tracking. The experimental results show that the proposed method performs the real-time tracking efficiently with sufficient accuracy.

Stimuli that resemble the content of visual working memory (VWM) capture attention. However, theories disagree on how many VWM items can bias attention simultaneously. According to some theories, there is a distinction between active and passive states in VWM, such that only items held in an active state can bias attention. The single-item-template hypothesis holds that only one item can be in an active state and thus can bias attention. In contrast, the multiple-item-template hypothesis posits that multiple VWM items can be in an activate state simultaneously, and thus can bias attention. Recently, Van Moorselaar, Theeuwes, and Olivers ( Journal of Experimental Psychology: Human Perception and Performance , 40 (4):1450, 2014 ) and Hollingworth and Beck ( Journal of Experimental Psychology: Human Perception and Performance , 42 (7):911–917, 2016 ) tested these accounts, but obtained seemingly contradictory results. Van Moorselaar et al. ( 2014 ) found that a distractor in a visual-search task captured attention more when it matched the content of VWM (memory-driven capture). Crucially, memory-driven capture disappeared when more than one item was held in VWM, in line with the single-item-template hypothesis. In contrast, Hollingworth and Beck ( 2016 ) found memory-driven capture even when multiple items were kept in VWM, in line with the multiple-item-template hypothesis. Considering these mixed results, we replicated both studies with a larger sample, and found that all key results are reliable. It is unclear to what extent these divergent results are due to paradigm differences between the studies. We conclude that is crucial to our understanding of VWM to determine the boundary conditions under which memory-driven capture occurs.

Graphitic carbon nitride (g-C3N4), as a polymeric semiconductor, is promising for ecological and economical photocatalytic applications because of its suitable electronic structures, together with the low cost, facile preparation, and metal-free feature. By modifying porous g-C3N4, its photoelectric behaviors could be facilitated with transport channels for photogenerated carriers, reactive substances, and abundant active sites for redox reactions, thus further improving photocatalytic performance. There are three types of methods to modify the pore structure of g-C3N4: hard-template method, soft-template method, and template-free method. Among them, the hard-template method may produce uniform and tunable pores, but requires toxic and environmentally hazardous chemicals to remove the template. In comparison, the soft templates could be removed at high temperatures during the preparation process without any additional steps. However, the soft-template method cannot strictly control the size and morphology of the pores, so prepared samples are not as orderly as the hard-template method. The template-free method does not involve any template, and the pore structure can be formed by designing precursors and exfoliation from bulk g-C3N4 (BCN). Without template support, there was no significant improvement in specific surface area (SSA). In this review, we first demonstrate the impact of pore structure on photoelectric performance. We then discuss pore modification methods, emphasizing comparison of their advantages and disadvantages. Each method’s changing trend and development direction is also summarized in combination with the commonly used functional modification methods. Furthermore, we introduce the application prospects of porous g-C3N4 in the subsequent studies. Overall, porous g-C3N4 as an excellent photocatalyst has a huge development space in photocatalysis in the future.

Since the notion of biometric template is not well defined, various concepts are used in biometrics practice. In this paper we present a systematic view on a family of template concepts based on the L1 or L2 dissimilarities. In particular, for sample vectors of independent components we find out how likely it is for the median code to be a sample vector.