Core Building Research Articles

A heptazine-based polymer photocatalyst with donor-acceptor configuration to promote exciton dissociation and charge separation

Polymeric carbon nitride (PCN) is an emerging class of polymer semiconductor photocatalysts, but bulk PCN typically suffers from low visible-light-harvesting ability, high activation energy, and rapid charge recombination. In this context, using the same core building block, a donor-acceptor (D-A) type heptazine-based polymer, namely BPCN, was proposed via Friedel-Crafts arylation reaction to tackle these issues. Comparatively, the affording BPCN features extended light absorption, reduced activation energy, and suppressed charge recombination, triggered by the electron push-pull interactions as a consequence of the D-A configuration. BPCN is elucidated to be an effective heterogeneous photocatalyst for aerobic organic transformations and a wide range of substrate scopes and reactions were realized. Besides, BPCN also showed an advantage in mediating the photocatalytic water oxidation reaction, achieving a nearly 10-fold oxygen evolution reaction (OER) rate over PCN. These findings demonstrate the great potential of the rational design of heptazine-based polymers with D-A configurations for artificial photosynthesis.

POSS-Al-porphyrin-imidazolium cross-linked network as catalytic bifunctional platform for the conversion of CO2 with epoxides

Two heterogeneous catalysts were prepared with the aim of following the promising path of CO2 fixation into epoxides. The synthetic procedure involves a radical copolymerization of an octavinylsilsesquioxane as inorganic core building block and tetrastyrylporphyrin aluminum chloride monomer (TSP-AlCl) in presence (POSS-TSP-AlCl-imiBr) or in absence (POSS-TSP-AlCl) of a bis-vinylimidazolium bromide salt (bis-imiBr), in order to investigate if the bifunctional heterogeneous material can display better catalytic performance than the separate species. All the solids were fully characterized and tested in the synthesis of cyclic carbonates starting from CO2 and several epoxides. The synergic cooperation of the two co-catalytic species (Lewis acid/imiBr) was observed. POSS-TSP-AlCl-imiBr showed to be a recyclable material with an excellent activity and TON and TOF values up to 16,000 and 5000, respectively. These excellent results, even under mild reaction and solvent-free conditions, were attributed to a twofold effect: the proximity between the two active sites due to the direct covalent bond between the porphyrin and the imidazolium component and the increasing local concentration of active sites effect that is obtained by functionalizing the silica cage at all its vertices.

Nucleobase-modified nucleosides and nucleotides: Applications in biochemistry, synthetic biology, and drug discovery.

. DNA is often referred to as the "molecule of life" since it contains the genetic blueprint for all forms of life on this planet. The core building blocks composing DNA are deoxynucleotides. While the deoxyribose sugar and phosphate group are ubiquitous, it is the composition and spatial arrangement of the four natural nucleobases, adenine (A), cytosine (C), guanine (G), and thymine (T), that provide diversity in the coding information present in DNA. The ability of DNA to function as the genetic blueprint has historically been attributed to the formation of proper hydrogen bonding interactions made between complementary nucleobases. However, recent chemical and biochemical studies using nucleobase-modified nucleotides that contain "non-hydrogen bonding" functional groups have challenged many of the dogmatic views for the necessity of hydrogen-bonding interactions for DNA stability and function. Based on years of exciting research, this area has expanded tremendously and is thus too expansive to provide a comprehensive review on the topic. As such, this review article provides an opinion highlighting how nucleobase-modified nucleotides are being applied in diverse biomedical fields, focusing on three exciting areas of research. The first section addresses how these analogs are used as mechanistic probes for DNA polymerase activity and fidelity during replication. This section outlines the synthetic logic and medicinal chemistry approaches used to replace hydrogen-bonding functional groups to examine the contributions of shape/size, nucleobase hydrophobicity, and pi-electron interactions. The second section extends these mechanistic studies to provide insight into how nucleobase-modified nucleosides are used in synthetic biology. One example is through expansion of the genetic code in which changing the composition of DNA makes it possible to site-specifically incorporate unnatural amino acids bearing unique functional groups into enzymes and receptors. The final section describes results of pre-clinical studies using nucleobase-modified nucleosides as potential therapeutic agents against diseases such as cancer.

TRANSFORMATION OF RURAL HOMESTEADS IN THE URBAN FRINGE AREAS OF BANGLADESH: A CASE OF KAKON HUT, RAJSHAHI

The early agrarian community was established in the northern part of Bangladesh because of its unique Barind tract formation. Rural homesteads were the core building block of these settlements. Though 70% of Bangladeshi still live in rural areas, the rural homesteads are being transformed rapidly. Many socio-economic indicators such as population density, limited expansion opportunity, access to modern technology and diverse types of materials and many more are working as a core driving force behind these changes. This article investigates the transformation pattern of the rural homesteads in Kakon Hut Paurashava of Godagari Upazilla in Rajshahi District, in terms of both spatial pattern and construction materials, to develop an in-depth understanding of the impact of adaptation of modern techniques, technologies, and materials in the urban fringe areas. This research is based on an empirical research strategy and consequently progressed through qualitative data collection, observation, questionary survey, and key person interviews. The study produces a thorough analysis of three core types of homesteads in the site area. Finally, a comparative analysis of traditional homesteads with the existing types that have gone through the transformation process is produced to identify the spatial and material transformation in these settlements.

Core loss reduction through a laser scribe on Fe-based amorphous ribbon surface for transformer cores

Core loss reduction of an Fe-based amorphous ribbon for distribution transformer cores by laser scribe is researched and the efficacy of core loss reduction by laser scribe is compared with that by fish scale-formation during ribbon casting. Core loss reduction by laser scribe is found to be more effective than fish scale and the reduction is prominent in a 53 kg single phase core tested. Core loss reduction is realized through the reduction of anomalous eddy current loss by domain refinement. Although a fish scale ribbon shows refined magnetic domains in static magnetic domain observation, effective magnetic domains are not refined and the reduction of anomalous eddy current loss by fish scale is small. Core building factor, the ratio of core loss of a transformer core to that of a strip ribbon, is found to be increased due to higher anomalous eddy current loss in a 53 kg single phase core tested. Building factor is reduced from about 2 to 1.5 by reducing anomalous eddy current loss by laser scribe.

Robust one-sided self-testing of two-qubit states via quantum steering

Entangled two-qubit states are the core building blocks for constructing quantum communication networks. Their accurate verification is crucial to the functioning of the networks, especially for untrusted networks. In this work we study the self-testing of two-qubit entangled states via steering inequalities, with robustness analysis against noise. More precisely, steering inequalities are constructed from the tilted Clauser-Horne-Shimony-Holt inequality and its general form, to verify the general two-qubit entangled states. The study provides a good robustness bound, using both local extraction map and numerical semidefinite-programming methods. In particular, optimal local extraction maps are constructed in the analytical method, which yields the theoretical optimal robustness bound. To further improve the robustness of one-sided self-testing, we propose a family of three measurement settings steering inequalities. The result shows that three-setting steering inequality demonstrates an advantage over two-setting steering inequality on robust self-testing with noise. Moreover, to construct a practical verification protocol, we clarify the sample efficiency of our protocols in the one-sided device-independent scenario.

Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection

Defects on the surface of yarn-dyed fabrics are one of the important factors affecting the quality of fabrics. Defect detection is the core link of quality control. Due to the diversity of yarn-dyed fabric patterns and the scarcity of defect samples, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. However, most existing deep learning algorithms cannot fully extract shallow, high-frequency and high-level information, which limits their ability to reconstruct yarn-dyed fabric images. In this article, we propose an Attention-based Feature Fusion Generative Adversarial Network framework for unsupervised defect detection of yarn-dyed fabrics. The framework utilizes a modified Feature Pyramid Network to fuse multi-level information and utilizes an attention mechanism to enhance the model's feature representation capabilities. The Attention-based Feature Fusion Generative Adversarial Network consists of an attention fusion generator and a patch-level discriminator. In the attention fusion generator, the Feature Pyramid Network with EfficientNetV2 as the backbone is used as the core building block, and different feature fusion methods are used to avoid the loss of information in the process of network deepening. The attention mechanism is used to enhance the channel and spatial-wise correlation of features, which helps the model to focus on more meaningful information by recalibrating the feature maps. In the discriminator, the patch-level discriminator is used to calculate the similarity between the reconstructed image and the original image from a local perspective, thereby improving the model's attention to texture details. Experimental results on public datasets demonstrate the effectiveness of the proposed method compared to other methods.

Ultra-high-linearity integrated lithium niobate electro-optic modulators

Integrated lithium niobate (LN) photonics is a promising platform for future chip-scale microwave photonics systems owing to its unique electro-optic properties, low optical loss, and excellent scalability. A key enabler for such systems is a highly linear electro-optic modulator that could faithfully convert analog electrical signals into optical signals. In this work, we demonstrate a monolithic integrated LN modulator with an ultra-high spurious-free dynamic range (SFDR) of 120.04 dB · Hz 4 / 5 at 1 GHz, using a ring-assisted Mach–Zehnder interferometer configuration. The excellent synergy between the intrinsically linear electro-optic response of LN and an optimized linearization strategy allows us to fully suppress the cubic terms of third-order intermodulation distortions (IMD3) without active feedback controls, leading to ∼ 20 dB improvement over previous results in the thin-film LN platform. Our ultra-high-linearity LN modulators could become a core building block for future large-scale functional microwave photonic integrated circuits by further integration with other high-performance components like low-loss delay lines, tunable filters, and phase shifters available on the LN platform.

Molecular Engineering of Peripheral Substitutions to Construct Efficient Acridine Core-Based Hole Transport Materials for Perovskite Solar Cells.

The development of highly efficient hole transport materials (HTMs) for perovskite solar cells (PSCs) has been a hot research topic. Acridine and its derivatives are gradually utilized as new blocks for optoelectronic applications, which stems from its rigid conjugated structure, shedding a new light on this old molecule. Meanwhile, its application in PSCs as a HTM has not been well explored, and the efficiency of 9,10-dihydroacridine (ACR)-based HTMs is relatively low. In this work, we conduct a systematic modulation of the peripheral substituents for ACR core building block-based HTMs and investigate the effects of the electron-donating ability and π-conjugation of peripheral groups on the photovoltaic performance of the corresponding HTMs. It is found that the peripheral groups with a weaker electron-donating ability and stronger π-conjugation are more suitable for the acridine core, which itself has a stronger electron-donating ability. Through molecular engineering, the newly developed HTM ACR-PhDM achieves an impressive power conversion efficiency of 23.5%. Our work lays the foundation for the design and development of efficient HTMs in the future.

Hyperplane patch mixing-and-folding decoder and weighted chamfer distance loss for 3D point set reconstruction

3D point set reconstruction is an important and challenging 3D shape analysis task. Current state-of-the-art algorithms for 3D point set reconstruction employ a deep neural network (DNN) having an encoder–decoder architecture. Recently, the decoder DNNs that transform multiple 2D planar patches to reconstruct a 3D shape have seen some success. These “patch-folding” decoders are adept at approximating smooth surfaces in 3D objects. However, 3D point sets generated by these decoders often lack local geometrical details, as 2D planar patches tend to overly constrain the patch folding process. In this paper, we propose a novel decoder DNN for 3D point sets called Hyperplane Mixing and Folding Net (HMF-Net). HMF-Net uses less constrained hyperplane, not 2D plane, patches as its input to the folding process. HMF-Net has, as its core building block, a stack of token-mixing layers to effectively learn global consistency among the hyperplane patches. In addition to HMF-Net, we also propose a novel loss for 3D point set reconstruction called Weighted Chamfer Distance (WCD). WCD tries to weight, or amplify, loss from parts of shape that are highly variable across training samples by emphasizing higher point-pair distance values between a generated point set and a groundtruth point set. This helps the decoder DNN learn shape details better. We comprehensively evaluate our algorithm under three 3D point set reconstruction scenarios, that are, shape completion, shape upsampling, and shape reconstruction from 2D images. Experimental results demonstrate that our algorithm yields accuracies higher than the existing algorithms for 3D point set reconstruction.

HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning

Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human cognition, e.g., as it relates to navigation, as well as in work on robotics and artificial intelligence. Although previous work has mainly focused on various spatial and temporal calculi, more recently representation learning techniques such as embedding have been applied to reasoning and inference tasks such as query answering and knowledge base completion. These subsymbolic and learnable representations are well suited for handling noise and efficiency problems that plagued prior work. However, applying embedding techniques to spatial and temporal reasoning has received little attention to date. In this paper, we explore two research questions: (1) How do embedding-based methods perform empirically compared to traditional reasoning methods on QSR/QTR problems? (2) If the embedding-based methods are better, what causes this superiority? In order to answer these questions, we first propose a hyperbolic embedding model, called HyperQuaternionE, to capture varying properties of relations (such as symmetry and anti-symmetry), to learn inversion relations and relation compositions (i.e., composition tables), and to model hierarchical structures over entities induced by transitive relations. We conduct various experiments on two synthetic datasets to demonstrate the advantages of our proposed embedding-based method against existing embedding models as well as traditional reasoners with respect to entity inference and relation inference. Additionally, our qualitative analysis reveals that our method is able to learn conceptual neighborhoods implicitly. We conclude that the success of our method is attributed to its ability to model composition tables and learn conceptual neighbors, which are among the core building blocks of QSR/QTR.

A robust methodology for output-only modal identification of civil engineering structures

Output-only modal identification has been attracting increasing research efforts in civil engineering community. The aim of this study was to develop a robust output-only modal identification methodology to improve the performance of the existing methods. The proposed method combines the second-order blind identification (SOBI) with the covariance driven stochastic subspace identification (SSI-COV) by introducing a new system model. Instead of employing single degree-of-freedom fitting methods to obtain local modal information of each mode like it usually does in SOBI, modal responses identified by SOBI are regarded as the output of a system to form the block Toeplitz matrix of the SSI-COV. More accurate global modal parameters are expected to be obtained since a parametric system model is included and the contributions of different modes are considered. It is also easy to select physical modes based on the time and frequency characteristics of source signals and thus can avoid the ambiguous work related to the stabilization diagrams. The proposed method is validated by a simulated 8-DOF system with emphasize on identification accuracy and parameter sensitivity. The results confirm the insensitivity of input parameters and demonstrate the high accuracy. It is then applied to estimate the modal parameters of a reinforced concrete shear core building and a concrete arch-gravity dam. It is demonstrated that a total of twelve building modes and six dam modes are successfully identified respectively, and the results in both applications are accord well with the existing modal identification methods, especially SSI-COV. The promising results show that the physical modes can be well separated, and the associated modal parameters can be reliably identified with high accuracy, even if closely-spaced modes and spurious harmonics occur. As expected, the modes discrimination step that associated with the proposed method tends to be more distinct and intuitive than the stabilization diagrams.

Recent Progress of Using Knowledge Graph for Cybersecurity

In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.

Creating Inclusive Classrooms for Highly Dysregulated Students: What Can We Learn from Existing Literature?

The ability to self-regulate is a key focus for educators, especially for neurodivergent students, such as those with ADHD, fetal alcohol syndrome, mental health difficulties, autism, and/or anxiety. Students not being able to self-regulate frequently results in their behaviours being labelled as “naughty” or “challenging” by teachers. Continued dysregulation can lead to periods of suspension and exclusion, impacting both attendance rates for students and their broader families. Previous research has shown that the impacts of poor self-regulation can be wide-ranging, spanning both social and academic outcomes. The broad negative impact of poor self-regulation means that it is important to support families and classroom teachers to effectively improve children’s self-regulation. However, to support families and educators, there is a need to develop and deploy a theoretical framework to suggest why self-regulation may be under-developed and, conversely, how self-regulation may be effectively developed across a wide range of contexts. This paper considers current literature exploring the links between individual experiences of emotions and connections with core abilities of interoception, self-regulation, emotional intelligence, and metacognition. It outlines a hypothesised model of how these abilities intertwine and how supporting core building blocks within educational settings can enable supportive and inclusive educational contexts, providing positive experiences for students and teachers alike.

Cellular force-sensing through actin filaments.

The actin cytoskeleton orchestrates cell mechanics and facilitates the physical integration of cells into tissues, while tissue-scale forces and extracellular rigidity in turn govern cell behaviour. Here, we discuss recent evidence that actin filaments (F-actin), the core building blocks of the actin cytoskeleton, also serve as molecular force sensors. We delineate two classes of proteins, which interpret forces applied to F-actin through enhanced binding interactions: 'mechanically tuned' canonical actin-binding proteins, whose constitutive F-actin affinity is increased by force, and 'mechanically switched' proteins, which bind F-actin only in the presence of force. We speculate mechanically tuned and mechanically switched actin-binding proteins are biophysically suitable for coordinating cytoskeletal force-feedback and mechanical signalling processes, respectively. Finally, we discuss potential mechanisms mediating force-activated actin binding, which likely occurs both through the structural remodelling of F-actin itself and geometric rearrangements of higher-order actin networks. Understanding the interplay of these mechanisms will enable the dissection of force-activated actin binding's specific biological functions.

Exploration of Alkyl Group Effects on the Molecular Packing of 5,15-Disubstituted Tetrabenzoporphyrins toward Efficient Charge-Carrier Transport.

The high design flexibility of organic semiconductors should lead to diverse and complex electronic functions. However, currently available high-performance organic semiconductors are limited in variety; most of p-type materials are based on thienoacenes or related one-dimensionally (1D) extended π-conjugated systems. In an effort to expand the diversity of organic semiconductors, we are working on the development of tetrabenzoporphyrin (BP) derivatives as active-layer components of organic electronic devices. BP is characterized by its large, rigid two-dimensionally (2D) extended π-framework with high light absorptivity and therefore is promising as a core building unit of organic semiconductors for optoelectronic applications. Herein, we demonstrate that BP derivatives can afford field-effect hole mobilities of >4 cm2 V-1 s-1 upon careful tuning of substituents. Comparative analysis of a series of 5,15-bis(n-alkyldimethylsilylethynyl)tetrabenzoporphyrins reveals that linear alkyl substituents disrupt the π-π stacking of BP cores, unlike the widely observed "fastener effect" for 1D extended π-systems. The n-octyl and n-dodecyl groups have the best balance between high solution processability and minimal π-π stacking disruption, leading to superior hole mobilities in solution-processed thin films. The resulting thin films show high thermal stability wherein the field-effect hole mobility stays above 1 cm2 V-1 s-1 even after heating at 160 °C in air, reflecting the tight packing of large BP units. These findings will serve as a good basis for extracting the full potential of 2D extended π-frameworks and thus for increasing the structural or functional diversities of high-performance organic semiconductors.

Multisectoral partnerships to tackle complex health issues at the community level: lessons from a Healthy Communities Approach in rural Alberta, Canada

SettingHealth inequities exist in rural communities across Canada, as rural residents are more likely than their urban counterparts to experience injuries, chronic conditions, obesity, and shorter life expectancy. Cooperative and coordinated action across sectors is required to both understand and address these complex public health issues.InterventionThe Alberta Healthy Communities Approach (AHCA) is based on the values and core building blocks of the Healthy Communities Approach, a framework centred on building community capacity to support community-led actions on the determinants of health. Adaptations within the AHCA focused on implementation mechanisms with a 5-step process and supporting implementation and assessment tools for multisectoral team building. Local measurement of change was enhanced and focused on community capacity and multisectoral action stages. Between 2016 and 2019, the AHCA was piloted with 15 rural communities across Alberta with population sizes ranging from 403 to 15,051 people.OutcomesWhile communities piloting the AHCA ranged in the level of diversity of their coalition membership and partnerships, members’ reflections demonstrate that intentional engagement with diverse citizens and sectors is pivotal to collaboratively identifying local assets and priorities and mobilizing cross-sectoral action that will sustainably improve supportive environments for cancer and chronic disease prevention.ImplicationsEngaging across sectors, building partnerships, and establishing a multisectoral team increase diversity and can catalyze community-led prioritization and actions for asset-based community development. An increase in diversity may lead to increased investment and sustainability at the community level.

Bidirectional Deep Learning of Polarization Transfer in Liquid Crystals with Application to Quantum State Preparation

Accurate control of light polarization represents a core building block in polarization metrology, imaging, and optical and quantum communications. Voltage-controlled liquid crystals offer an efficient way of polarization transformation. However, common twisted nematic liquid crystals are notorious for lacking an accurate theoretical model linking control voltages and output polarization. An inverse model, which would predict control voltages required to prepare a target polarization, is even more challenging. Here we report both the direct and inverse models based on deep neural networks, radial basis functions, and linear interpolation. We present an inverse-direct compound model solving the problem of control voltages ambiguity. We demonstrate one order of magnitude improvement in accuracy using deep learning compared to the radial basis function method and two orders of magnitude improvement compared to the linear interpolation. Errors of the deep neural network model also decrease faster than the other methods with an increasing number of training data. The best direct and inverse models reach the average infidelities of $4 \times 10^{-4}$ and $2 \times 10^{-4}$, respectively, which is the accuracy level not reported yet. Furthermore, we demonstrate local and remote preparation of an arbitrary single-photon polarization state using the deep learning models. The results will impact the application of twisted-nematic liquid crystals, increasing their control accuracy across the board. The presented bidirectional learning can be used for optimal classical control of complex photonic devices and quantum circuits beyond interpolation.

Organic molecular crystal with a high ultra-deep-blue emission efficiency of ∼85% for low-threshold laser

Organic crystal with a high emission efficiency and excellent spectral stability is crucial for constructing low-threshold deep-blue organic laser. Here, we synthesized an organic lasing molecule (BPhDPF) with a steric diarylfluorene as the core building block, the single crystal of which has ultra-deep-blue emission with the photoluminescence quantum yield (PLQY) as high as 84.9%, as well as short lifetime and super-stable emission. Meanwhile, C-H ⋯ π interactions are easily observed in single crystal structure, contributing to the formation of BPhDPF polyhedral microcrystals by self-organize in solution. Interestingly, the intermolecular coupling and exited state were well inhibited by the large steric hindrance group at 9-position and 2-/7- flexible side-chain, which is critical to laser applications. Finally, the deep-blue emission organic laser was obtained with a peak located at 418 nm and very low threshold of Pth = 1.62 μJ cm−2. A novel organic lasing molecule has been developed, offering helpful information for further investigating organic lasers.

Big data and data ownership rights: The case of car insurance

Customers’ data points sources are growing, through the Internet of Things (IoT), telematics and interfacing with third parties’ platforms. Third parties provide either by-product data about their clients, or data that are generated for the specific task of a more accurate measurement of behaviour. One of the core building blocks in insurance is understanding risk – therefore, innovation around data points (sources and analytics) to proxy risk is the engine behind its digital transformation. While the value of data points is clear for the providers of insurance products and services, the customers have not only less control over their data, but also hardly benefit from the creation of those data points. In this paper we provide a case study from the British car insurance industry, about a data-driven product that pivoted into a re-designed personal data right architecture, which included transparency, traceability and usability of customers’ digital traces, and enabled customers’ ownership rights over their data.