Scalable Structure Research Articles

TiO2 particle agglomeration impacts on radiative cooling films with a thickness of 50 μm

The random micro-particle-embedded thin film is an inexpensive and scalable structure for radiative cooling (RC). However, current investigations on evaluating the performance of such films assume that the embedded particles are perfectly separated from each other. The particle agglomeration effects on the spectral characteristics of the film are dismissed. In this work, by comparing the spectral reflectivity and emissivity of a film composed of perfectly separated TiO2 particles, three-particle aggregations, five-particle aggregations, and ten-particle aggregations, we studied the particle agglomeration impacts on the performance of a disordered radiative cooling film with a thickness of 50 μm. The results show that the agglomeration suppresses the solar reflectivity of the particle-embedded film and harms the temperature drop and cooling power in RC applications. Therefore, particle agglomeration is intended to be industriously eliminated in the preparation of a random particle-embedded thin film for radiative cooling. The results in this paper provide a further explanation of the difference between the theoretical calculations and experimental measurements of the cooling performance of a particle-embedded RC film.

First neutron tomography with the novel ANET Compact Neutron Collimator

This paper presents the first application of the novel ANET Compact Neutron Collimator to a tomographic study. The ANET collimator consists of a structure were collimating and absorbing channels are alternated in a chessboard-like geometry. It is characterised by an elevated collimation power within a limited length and presents a scalable structure both in length and in field of view. The first tomography with this collimator has been performed at the Paul Scherrer Institute, at the BOA beamline, on two small objects chosen for their different scattering properties: an iron fibula, circa X century, and an Afghan lapis lazuli. The result of the tomographic reconstruction is described in this paper.

Fabrication of scalable poly(N-isopropylacrylamide)/gold nanoparticles composite ring array as LSPR sensor for label-free biosensor application

Self-organization facilitates the formation of specific structures as a result of constituent interaction. In this study, the bottom of a 500-nm hole array photoresist template, which was deposited with a hydrophobic atom transfer radical polymerization (ATRP) initiator, was wetted by treatment with oxygen plasma. After the removal of the photoresist template, ring patterns of the ATRP initiator were formed at the interface between the hydrophobic and wetting regions. Poly(N-isopropylacrylamide) (PNIPAAm) was grafted from the ring array of the initiator to immobilize gold nanoparticles (AuNPs) as a uniform ring array on the silicon substrate via repeated swelling/shrinking cycles. The localized surface plasmon resonance (LSPR) peaks of the biotin-anchored AuNP–PNIPAAm composite ring (APCR) were employed to detect streptavidin as biosensors. The coupling of biotin and streptavidin led to asymptotical convergence from rings to disks as well as blue-shifts of the LSPR peaks. Linear correlation between the blue-shift and streptavidin concentration showed a limit of detection of ∼10 nM and a linear range of 10–150 nM for the detection of streptavidin within 30 min. The simple method of combining lithography and plasma technology provides a versatile platform for developing the scalable ring structure of AuNP for highly sensitive and sensitive biosensing.

Thermal shock resistance of additively manufactured gas-permeable SiO2 ceramic structures for HIP-quench applications

The increasing demand for hot isostatic pressing (HIP) means that a reliable and efficient operation of modern HIP units with fast cool capability is indispensable. A key factor for efficient operation is the ceramic crucible used as the load basket. Its task is to keep as much of the HIPed parts as possible effectively within the hot zone and to prevent them touching the furnace wall. This work focuses on designing a gas-permeable ceramic structure with a high thermal shock resistance that can be scaled up to a load basket for future HIP applications. Stereolithography (SL) 3D printing of a ceramic resin is employed to build various scalable framework structures inspired by nature and by existing engineering applications. Thermal shock tests with water quenching reveal that framework structures with evenly distributed triangular bracings offer the highest flexural strength, whereas auxetic structures are best at retaining their flexural strength after thermal shock.

Normalising practice – moving a technological implementation from project phase to operational phase


 
 
 It is widely acknowledged that successful implementation of new technologies relies on a project-based institutional approach with a dedicated and motivated project team to champion the technology and support professional development. One challenge that arises following project-style implementations is how to transition support structures and project drivers to 'normalisation' phase where technologies are seen as 'business-as-usual'. Griffith University’s PebblePad implementation project team deliberately planned an end-of-project to business-as- usual transition. This planning involved; embedding PebblePad in the institution’s Virtual Learning Environment (VLE); setting up ongoing scalable support structures; and ensuing there were suitable institutional partnerships to sustain the diffusion of the institutional adoption. Griffith’s implementation project was led by the central learning & teaching unit (Learning Futures) and relied heavily on buy-in and support from many institutional stakeholders. Therefore it was essential that stakeholder teams were empowered to maintain the existing users and continue to look for opportunities for targeted and meaningful implementation. Similarly, Learning Futures had to find staffing capacity to continue to support PebblePad as a technology within the VLE. This ongoing work involves maintaining vendor relationships, providing ongoing professional development and continuing to be a centralised point for managing internal and external communications between stakeholders.
 
 

An Efficient Blockchain Transaction Retrieval System

In the era of the digital economy, blockchain has developed well in various fields, such as finance and digital copyright, due to its unique decentralization and traceability characteristics. However, blockchain gradually exposes the storage problem, and the current blockchain stores the block data in third-party storage systems to reduce the node storage pressure. The new blockchain storage method brings the blockchain transaction retrieval problem. The problem is that when unable to locate the block containing this transaction, the user must fetch the entire blockchain ledger data from the third-party storage system, resulting in huge communication overhead. For this problem, we exploit the semi-structured data in the blockchain and extract the universal blockchain transaction characteristics, such as account address and time. Then we establish a blockchain transaction retrieval system. Responding to the lacking efficient retrieval data structure, we propose a scalable secondary search data structure BB+ tree for account address and introduce the I2B+ tree for time. Finally, we analyze the proposed scheme’s performance through experiments. The experiment results prove that our system is superior to the existing methods in single-feature retrieval, concurrent retrieval, and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is decreased by 43.16% under the multi-feature hybrid retrieval. It has better stability in different block sizes and concurrent retrieval scales.

Unifying multi-associations through hypergraph for bundle recommendation

Bundle recommendation, which seeks to recommend a group of items to users, is widely used in real-world applications. Despite the success of current bundle recommendation approaches, there are still significant challenges: (1) Multiple associations (e.g., user-bundle interactions, bundle-item affiliations, etc.) in bundle recommendation. (2) People’s interactions with a single item and a bundle follow different patterns, e.g., users can quickly decide whether to purchase an item, but hard to determine with a bundle regardless of what items are included. (3) The data sparsity of user-bundle historical interactions. This paper proposes a Unified Hypergraph framework for Bundle Recommendation (UHBR) to tackle the aforementioned challenges. Specifically, UHBR first unifies multiple associations among users, bundles, and items into hypergraph, a more flexible and scalable data graph structure. Second, this hypergraph architecture allows both direct and indirect user-bundle relationships through items to be efficiently and comprehensively represented as hyperedges. Third, we leverage the potential of indirect association to diminish the impact of user-bundle interaction scarcity. Experimental results on two real-world datasets show that UHBR outperforms the state-of-the-art baselines by 15.9% on Recall and 19.8% on NDCG. Experiments further indicate that UHBR can alleviate data sparsity dilemma and has the highest efficiency.

High-performance compact athermal panoramic annular lens design with separated radial optical power.

A panoramic annular lens can provide abundant spatial information in real time with its compact and scalable structure. To deal with extreme temperature environments and to improve the utilization of an image plane, we propose a high-performance compact athermal panoramic annular lens with separated radial optical power. By introducing multiple free-form surfaces, the system achieves a field of view of (40∘∼100∘)×360∘, an f-number of 2.2, and a total length of 40 mm. The modulation transfer function of the system's full fields of view is greater than 0.5 at 200 lp/mm, and the distortion is less than 2.5%. It can work stably in the range of -30∘C to 70°C. The proposed system is compact, athermal, and high-performing and has broad application prospects in the fields of visual positioning and biomedicine.

Responsive friction modulation of 3D printed elastomeric bioinspired structures

Elastomeric bioinspired structures with desirable friction performances are attractive for engineering applications. However, friction-tuning mechanisms through hybrid designs and shape fabrication techniques remain challenging. Herein, we developed a scalable elastomeric bioinspired structure using 3D printing inspired by claws, toe pads and overhang granules found in nature. Friction modulations are demonstrated with pillar and dimple hybrid structures in dry and wet conditions, including different structure arrangements, mechanical properties (strength ~13.8 MPa, elongation ~410%) and numerical simulations. We identified pillar hybrid design has strong friction features (~2.5 times higher than smooth) on wet surfaces, by evaluating through strong edge boundary and spontaneous liquid splitting effects. Pillar structures are used to mimic grip experiments and simulations to determine gripping strategies.

Core-shell microspheres hybridized membrane for light emitting and radiative cooling

Tremendous effort has been put into radiative cooling for its great potential of alleviating energy consumption and greenhouse gas emission. However, there remains a big challenge to tackle the puzzle of combining the color display and radiative cooling with scalable and simplified membrane structure. Here, we report a novel monolayered hybrid membrane (MHM) using dielectric core-shell microsphere-embedded polydimethylsiloxane to achieve luminescence and radiative cooling bifunctionality. The dielectric shell constructed through the zinc ion diffusion upon the surface of spheric silica serves as both color display and infrared radiative improvement, while the core-shell structure provides high-efficient light scattering pathway, allowing enhancement of solar reflection. We show that the fabricated MHM exhibits a high solar reflectivity of 93 % and infrared emissivity over 0.96. Under typical outdoor environment with the solar intensity over 800 W m−2, such MHM can achieve a sub-ambient temperature drop of 7.8 °C with the mainstream method and 3.9 °C with the Stevenson screen one. More interestingly, the dielectric shell enables the MHM with a brilliant green coloration, potential UV resistance, and certain aesthetic purposes.

Prediction of COVID-19 diagnosis based on openEHR artefacts

Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems.

Intensifying the co-production of vapor and salts by a one-way brine-flowing structure driven by solar irradiation or waste heat

Interfacial evaporation technology holds great promise in seawater desalination with zero liquid discharge. Here, a salt-extraction evaporator enabling high-productivity co-generation of vapor and salts is designed by combining a one-way brine-flowing evaporation structure with solar irradiation or waste heat. A highly scalable thermo-evaporative structure, prepared by growing polypyrrole (PPy) onto glass fiber (PPy-GF), is employed for investigations. Modeling tools are further developed to guide the rational geometric design of the evaporator based on PPy-GF. This merit also allows us to investigate the complex effects of salt concentration and ionic species on the evaporation-salt generation behaviors to achieve intensified site-specific co-production of vapor and salts. Specifically, this evaporator enables the concentrated seawater to evaporate at an average evaporation rate of 1.12 kg m−2 h−1 and simultaneously produces 4.21 g of salts in 24 h under 1 sun. In addition, the stable evaporation rate of 2.58 kg m−2 h−1 and a salt production amount of 12.92 g in the concentrated seawater are obtained under 60 °C in continuous 30 h. This study is believed to provide a novel avenue for stable and efficient seawater desalination with zero liquid discharge.

Enhancing the adjustable range of saturation in color reflectors using a phase-change material as an effective absorption base

Structural color technology has garnered extensive attention in the development of ink-free color technology for applications such as color displays, color reflectors, and colorimetric devices. A Fabry–Perot (F–P) structure formed by stacking a metal base, an interference cavity, and a phase change material layer (MIP) is of significant interest as a lithography-free and scalable color-reflecting structure. Such a structure can selectively reflect interfered light over a range of visible wavelengths, resulting in bright colors. However, obtaining a wide range of saturation regulation spaces has become a challenge. In this study, an F–P color reflector based on a phase-change material (PCM) base is proposed, which consists of a PCM base, an interference layer, and a PCM top layer (PIP). The results of the finite element simulation and experimental measurements demonstrated that the PIP reflector had an adjustable saturation range 10.75 times larger than that of the MIP reflector. The effects of the structure size and phase change of the PCM layer on the structural characteristics were further analyzed. In addition, the performance of laser-induced color change and its application in color printing were demonstrated. The present study sheds new light on color reflectors, and the strategy proposed indicates their potential optoelectronic applications based on saturation modulation.

Numerical optimization of amplitude-modulated pulses in microwave-driven entanglement generation

Microwave control of trapped ions can provide an implementation of high-fidelity two-qubit gates free from errors induced by photon scattering. Furthermore, microwave conductors may be embedded into a scalable trap structure, providing the chip-level integration of control that is desirable for scaling. Recent developments have demonstrated how amplitude modulation of the gate drive can permit a two-qubit entangling operation to become robust against motional mode noise and other experimental imperfections. Here, we discuss a method for the numerical optimization of the microwave pulse envelope to produce gate pulses with noise resilience, considerably faster operation and high energy efficiency.

Recognition of DDoS attacks on SD-VANET based on combination of hyperparameter optimization and feature selection

The aim of Vehicular Ad Hoc Networks (VANETs) is to provide drivers and passengers with various applications and services for comfortable transportation by supporting traffic efficiency and safety. However, the traditional VANETs face various technical challenges in meeting the basic requirements of intelligent transportation systems such as scalability, flexibility and management due to the ever-increasing number of intelligent vehicles. With its flexible, programmable, scalable network structure, Software Defined Networks (SDNs) are candidates for providing solutions to the problems experienced. The architecture, which was created by adapting the SDN paradigm to the traditional VANET is simply called SD-VANET. This new architecture allows easy scaling of the network and flexible network management. Despite the advantages of SD-VANET architecture, it is also vulnerable to cyberattack threats such as Distributed Denial Of Service (DDoS). In this study, different machine learning classifiers were used to detect DDoS attacks targeting SD-VANETs. First, a dataset containing features of normal network traffic and DDoS attack network traffic was obtained from an experimentally created SD-VANET topology. Then the Minimum Redundancy Maximum Relevance (MRMR) feature selection algorithm was used to select the most distinctive features of the dataset. Machine learning classifiers were trained and tested with both original and feature selection applied datasets. Moreover, in the learning phase, hyperparameter optimization for the classifiers was applied using the Bayesian optimization method. According to the experimental results, the highest accuracy score obtained was 99.35% with MRMR feature selection and Bayesian optimization-based decision tree classifier. The results demonstrate that the MRMR feature selection and Bayesian optimization-based classifier approach have been successful for the detection of DDoS attacks on SD-VANETs.

The DeBakey Executive Research Leadership Program Bridges the Depth of Experienced Leaders with the Breadth of Emerging Leaders

Although new investigators leading research operations may be well trained to conduct research, they often have limited practical experience leading research teams. To address this unmet need, the Aggie Research Program (ARP) was developed in 2016 to advance the research careers of graduate students and postdoctoral scholars by connecting them with teams of undergraduates seeking research experiences. The requirements for graduate students and postdoctoral scholars to become “Team Leaders” are simple: they must get permission from their faculty mentors to participate, have an active research project, select and train a team of 3‐8 undergraduates, meet with their team once a week, and submit short weekly progress reports. To earn certification of completion, they must also attend monthly Team Leader meetings where they discuss best practices in leadership and complete a “Best Practices Report” to document their innovations to address challenges. This scalable structure allowed the ARP program to grow exponentially until a critical mass was achieved for Team Leaders to congregate in different affinity groups, which could then be formalized in distinct research leadership programs with different disciplinary or vocational focuses. In 2020, the ARP partnered with the Michael E. DeBakey Institute at Texas A&M University to create the DeBakey Executive Research Leadership program for graduate students, postdoctoral scholars, and junior faculty. This program leverages the combined 50 years of leadership experience of the Director and the Executive Director of the DeBakey Institute to distill a set of “research leadership principles” consistent with successful leadership careers at Texas A&M. These principles are introduced at the beginning of each Team Leader meeting to stimulate discussion of current challenges faced by Team Leaders. By bridging the deep experience of established leaders with the broad experience of a diverse cohort of emerging leaders, the DeBakey Executive Research Leadership Program structures a resilient, mutually supportive community that has grown during a pandemic. In the fall of 2021, it not only provided research leadership training for 47 Team Leaders, but also created research opportunities for 229 undergraduates. This multilevel, experience‐based training program provides a strategy to fill a critical gap in research training that cannot be filled by standard leadership or mentoring workshops which are typically too general, too short in duration, and too removed from the context in which they were originally developed.

KmerKeys: a web resource for searching indexed genome assemblies and variants.

K-mers are short DNA sequences that are used for genome sequence analysis. Applications that use k-mers include genome assembly and alignment. However, the wider bioinformatic use of these short sequences has challenges related to the massive scale of genomic sequence data. A single human genome assembly has billions of k-mers. As a result, the computational requirements for analyzing k-mer information is enormous, particularly when involving complete genome assemblies. To address these issues, we developed a new indexing data structure based on a hash table tuned for the lookup of short sequence keys. This web application, referred to as KmerKeys, provides performant, rapid query speeds for cloud computation on genome assemblies. We enable fuzzy as well as exact sequence searches of assemblies. To enable robust and speedy performance, the website implements cache-friendly hash tables, memory mapping and massive parallel processing. Our method employs a scalable and efficient data structure that can be used to jointly index and search a large collection of human genome assembly information. One can include variant databases and their associated metadata such as the gnomAD population variant catalogue. This feature enables the incorporation of future genomic information into sequencing analysis. KmerKeys is freely accessible at https://kmerkeys.dgi-stanford.org.

Selective broadband absorption by mode splitting for radiative cooling

A plasmonic-photonic structure based on colloidal lithography was designed for a scalable radiative cooling system and its absorption properties were theoretically investigated. The structure comprises a SiO2 core, which is on top of an Au reflector and partially covered by an indium tin oxide (ITO) shell. This simple and scalable structure possesses a strong selective absorption in the primary atmospheric transparency window (8-13 µm). The strong selective absorption is attributed to a mode splitting of the localized surface plasmon (LSP) of the ITO shell. To understand the mechanisms of the mode splitting, a quantitative analysis was conducted using a coupled-oscillator model and a coupled-dipole method. The analysis revealed that the mode splitting is induced by a strong coupling between the LSP of the ITO shell and a magnetic dipole Mie resonance of the SiO2 core.

An adaptive gain based approach for event-triggered state estimation with unknown parameters and sensor nonlinearities over wireless sensor networks

The distributed state and parameter estimation problem is investigated in this paper for discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances over a wireless sensor network. A novel architecture for distributed state estimator is introduced that incorporates adaptive coupling gains to govern the information exchange between the sensor nodes under event-triggering mechanism. The aim of this paper is to provide a scalable structure for unknown parameter identification independent of sensor networks’ complexity. The boundedness of estimation error is ensured in the framework of uniformly ultimately bounded stability by developing an algebraic connectivity based criterion. The estimator gains including proposed coupling gains are then presented as solution to matrix inequalities. Finally, two simulation examples are presented to demonstrate the effectiveness of proposed estimation architecture.

Diverse classes of constraints enable broader applicability of a linear programming-based dynamic metabolic modeling framework

Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework’s success are the linear kinetics and regulatory constraints imposed on the system. However, while the linearity of these constraints reduces computational complexity, it may not accurately capture the behavior of many biochemical systems. Here, we developed three new classes of LK-DFBA constraints to better model interactions between metabolites and the reactions they regulate. We tested these new approaches on several synthetic and biological systems, and also performed the first-ever comparison of LK-DFBA predictions to experimental data. We found that no single constraint approach was optimal across all systems examined, and systems with the same topological structure but different parameters were often best modeled by different types of constraints. However, we did find that when genetic perturbations were implemented in the systems, the optimal constraint approach typically remained the same as for the wild-type regardless of the model topology or parameterization, indicating that just a single wild-type dataset could allow identification of the ideal constraint to enable model predictivity for a given system. These results suggest that the availability of multiple constraint approaches will allow LK-DFBA to model a wider range of metabolic systems.