Flexible Architecture Research Articles

Convenient synthesis of O-alkylated/N-acylated polyhydroxyazepane based compounds for modulating MD-2-TLR4 complex formation

Toll-like receptor 4 (TLR4) is a critical component of the innate immune system, recognizing lipopolysaccharide (LPS) from Gram-negative bacteria and triggering immune responses. The activation of TLR4 involves several key steps, including interactions with LPS-binding protein (LBP), CD14, and myeloid differentiation protein 2 (MD-2), culminating in the formation of the (LPS.MD-2 TLR4)2 complex. Structural insights show that LPS acyl chains insert into the hydrophobic pocket of MD-2, driving TLR4 activation. Inspired by this understanding, numerous natural and synthetic compounds have been developed to inhibit TLR4 by targeting the MD-2/TLR4 complex. Eritoran 1, is one such illustration. The conformational flexibility of azepane architecture inspired us to visualize O-alkylated/N-acylated polyhydroxyazepane-based compounds toward this objective. The docking studies and molecular simulation studies supported the rationale. Synthesis of O-alkylated/N-acylated polyhydroxyazepane-based compounds 2-4 (a-h) through a key building block is described herein.

Peptide-aware chemical language model successfully predicts membrane diffusion of cyclic peptides.

Language modeling applied to biological data has significantly advanced the prediction of membrane penetration for small molecule drugs and natural peptides. However, accurately predicting membrane diffusion for peptides with pharmacologically relevant modifications remains a substantial challenge. Here, we introduce PeptideCLM, a peptide-focused chemical language model capable of encoding peptides with chemical modifications, unnatural or non-canonical amino acids, and cyclizations. We assess this model by predicting membrane diffusion of cyclic peptides, demonstrating greater predictive power than existing chemical language models. Our model is versatile and can be extended beyond membrane diffusion predictions to other target values. Its advantages include the ability to model macromolecules using chemical string notation, a largely unexplored domain, and a simple, flexible architecture that allows for adaptation to any peptide or other macromolecule dataset.

Digital Storage Oscilloscope with Frequency Control & Real-time Visualization

Project presents a digital storage oscilloscope with real-time visualization capabilities and frequency control. The system is based on the flexible ESP32 microcontroller architecture, which allows for the creation of a wide range of waveforms at tunable frequencies. Quick waveform adaptation is made possible by users' easy control over the oscillator's frequency in real-time through simple input techniques. Additionally, the system has a graphical user interface that allows users to instantly visualize created signals and easily monitor waveform properties. The oscillator is appropriate for a variety of applications in industries like audio synthesis, instrumentation, and telecommunications because it also provides sophisticated control options and signal processing capabilities. This comprehensive solution meets the needs of both enthusiasts and experts in signal production and visualization by providing a practical and versatile tool for those working in

DNA origami drives gene expression in a human cell culture system

Self-assembling DNA nanoparticles have the potential to significantly advance the targeted delivery of molecular cargo owing to their chemical and architectural flexibility. Recently, it has been demonstrated that the genetic code embedded in DNA nanoparticles produced by the method of DNA origami or related techniques can be recognized and copied by RNA polymerase in vitro. Further, sculpted DNA nanoparticles can serve as a substrate for Cas9-mediated gene modification and gene expression in cell culture. In the present study, we further investigate the ability of DNA origami nanoparticles to be expressed in a human cell line with emphasis on the impact of single-stranded DNA (ssDNA) domains and the contributions of the architectural disposition of genetic control elements, namely promoter and enhancer sequences. Our findings suggest that while cells possess the remarkable capability to express genes within highly folded architectures, the presence and relative density and location of ssDNA domains appears to influence overall levels of gene expression. These results suggest that it may be possible to nuance folded DNA nanoparticle architecture to regulate the rate and/or level of gene expression. Considering the highly malleable architecture and chemistry of self-assembling DNA nanoparticles, these findings motivate further exploration of their potential as an economic nanotechnology platform for targeted gene editing, nucleic acid-based vaccines, and related biotherapeutic applications.

Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing

The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven classification-based anomaly detection based on the sensor data collected in manufacturing processes. However, one critical challenge is that newly presented defect category may manifest as the manufacturing process continues, resulting in monitoring performance deterioration of previously trained machine learning models. Hence, there is an increasing need for empowering machine learning models to learn continually. Among all continual learning methods, memory-based continual learning has the best performance but faces the constraints of data storage capacity. To address this issue, this paper develops a novel pseudo replay-based continual learning framework by integrating class incremental learning and oversampling-based data generation. Without storing all the data, the developed framework could generate high-quality data representing previous classes to train machine learning model incrementally when new category anomaly occurs. In addition, it could even enhance the monitoring performance since it also effectively improves the data quality. The effectiveness of the proposed framework is validated in three cases studies, which leverages supervised classification problem for anomaly detection. The experimental results show that the developed method is very promising in detecting novel anomaly while maintaining a good performance on the previous task and brings up more flexibility in model architecture.

Nested Neural Networks: A Novel Approach to Flexible and Deep Learning Architectures

It’s easy to get lost in the complexities of sentences like, “Never will I have been in a place whose experiences will have been teaching me how amazing life really is till the beginning of every one of those marvelous whole new advenchallenges for more than 20 years of life by the end of my military service.” These kinds of nested and layered statements are not just the domain of advanced grammar—they have a parallel in cutting-edge technology as well. This paper introduces a novel neural network architecture, termed Nested Neural Networks (NNN), that incorporates nested layers within a more complex overarching structure. Much like the layered conditions in Future Perfect and Future Perfect Progressive tenses, NNNs utilize ‘nested’ conditions to bring flexibility and depth to data processing. This structure captures complex relationships, achieving high performance, computational efficiency, and generalization across tasks, much as understanding the nested clauses of a sentence can reveal deeper meanings. Our experiments on benchmark datasets demonstrate NNNs’ ability to handle intricate data patterns with enhanced memory efficiency and training adaptability, often outperforming traditional models. This fascinating intersection between language and machine learning showcases the powerful potential of structured complexity, whether in communication or computation.

A flexible data acquisition system architecture for the Nab experiment

The Nab experiment will measure the electron–neutrino correlation and Fierz interference term in free neutron beta decay to test the Standard Model and probe Beyond the Standard Model physics. Using National Instrument’s PXIe-5171 Reconfigurable Oscilloscope module, we have developed a data acquisition system that is not only capable of meeting Nab’s specifications, but flexible enough to be adapted in situ as the experimental environment dictates. The L1 and L2 trigger logic can be reconfigured to optimize the system for coincidence event detection at runtime through configuration files and LabVIEW controls. This system is capable of identifying L1 triggers at a rate of at least 1MHz, while reading out a peak signal rate of approximately 2GB/s. During the commissioning phase of the experiment, the system ran at a sustained readout rate of 400MB/s of detector signal data originating from roughly 6kHz L2 triggers, well within the peak performance of the system.

GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study pathology. Nevertheless, how to reserve more unique features from different modal source images on the premise of ensuring time efficiency is a tricky problem. To handle this issue, we propose a flexible semantic-guided architecture with a mask-optimized framework in an end-to-end manner, termed as GeSeNet. Specifically, a region mask module is devised to deepen the learning of important information while pruning redundant computation for reducing the runtime. An edge enhancement module and a global refinement module are presented to modify the extracted features for boosting the edge textures and adjusting overall visual performance. In addition, we introduce a semantic module that is cascaded with the proposed fusion network to deliver semantic information into our generated results. Sufficient qualitative and quantitative comparative experiments (i.e., MRI-CT, MRI-PET, and MRI-SPECT) are deployed between our proposed method and ten state-of-the-art methods, which shows our generated images lead the way. Moreover, we also conduct operational efficiency comparisons and ablation experiments to prove that our proposed method can perform excellently in the field of multimodal medical image fusion. The code is available at https://github.com/lok-18/GeSeNet.

Diverse Semantic Image Synthesis with various conditioning modalities

Semantic image synthesis aims to generate high-fidelity images from a segmentation mask, and previous methods typically train a generator to associate a global random map with the conditioning mask. However, the lack of independent control of regional content impedes their application. To address this issue, we propose an effective approach for Multi-modal conditioning-based Diverse Semantic Image Synthesis, which is referred to as McDSIS. In this model, there are a number of constituent generators incorporated to synthesize the content in semantic regions from independent random maps. The regional content can be determined by the style code associated with a random map, extracted from a reference image, or by embedding a textual description via our proposed conditioning mechanisms. As a result, the generation process is spatially disentangled, which facilitates independent synthesis of diverse content in a semantic region, while at the same time preserving other content. Due to this flexible architecture, in addition to achieving superior performance over state-of-the-art semantic image generation models, McDSIS is capable of performing various visual tasks, such as face inpainting, swapping, local editing, etc.

Microsoft applications of 6G in V2X

Abstract. With the rapid advancement of intelligent transportation systems and autonomous driving technologies, 6G networks, as a key technology for future communications, are set to bring trans-formative changes to V2X (Vehicle-to-Everything) communication. This paper explores the application prospects of 6G in V2X, with a focus on the critical technological support 6G provides to vehicular networks. The high transmission rates and ultra-low latency of 6G will significantly enhance real-time interactions between vehicles and between vehicles and infrastructure, improving traffic safety and efficiency. Additionally, 6G's broad connectivity and flexible network architecture will support a greater number of simultaneous device connections, facilitating the widespread adoption of intelligent transportation systems. However, 6G faces challenges in V2X applications, including security issues, privacy protection, and internet power supply concerns. The paper analyzes these challenges, proposes corresponding solutions, and envisions the future development of 6G technology in the V2X domain.

Cryo-electron microscopy structure of the di-domain core of Mycobacterium tuberculosis polyketide synthase 13, essential for mycobacterial mycolic acid synthesis.

Mycobacteria are known for their complex cell wall, which comprises layers of peptidoglycan, polysaccharides and unusual fatty acids known as mycolic acids that form their unique outer membrane. Polyketide synthase 13 (Pks13) of Mycobacterium tuberculosis, the bacterial organism causing tuberculosis, catalyses the last step of mycolic acid synthesis prior to export to and assembly in the cell wall. Due to its essentiality, Pks13 is a target for several novel anti-tubercular inhibitors, but its 3D structure and catalytic reaction mechanism remain to be fully elucidated. Here, we report the molecular structure of the catalytic core domains of M. tuberculosis Pks13 (Mt-Pks13), determined by transmission cryo-electron microscopy (cryoEM) to a resolution of 3.4 Å. We observed a homodimeric assembly comprising the ketoacyl synthase (KS) domain at the centre, mediating dimerization, and the acyltransferase (AT) domains protruding in opposite directions from the central KS domain dimer. In addition to the KS-AT di-domains, the cryoEM map includes features not covered by the di-domain structural model that we predicted to contain a dimeric domain similar to dehydratases, yet likely lacking catalytic function. Analytical ultracentrifugation data indicate a pH-dependent equilibrium between monomeric and dimeric assembly states, while comparison with the previously determined structures of M. smegmatis Pks13 indicates architectural flexibility. Combining the experimentally determined structure with modelling in AlphaFold2 suggests a structural scaffold with a relatively stable dimeric core, which combines with considerable conformational flexibility to facilitate the successive steps of the Claisen-type condensation reaction catalysed by Pks13.

Micro/nano additives in 3D printing concrete

3D concrete printing has attracted burgeoning interest in the construction industry for its ability to offer cost-effectiveness, architectural design flexibility, efficient use of energy and materials, as well as significant time savings in the construction process. However, conventional cement-geopolymer-based materials cannot be used directly for printing due to their lack of printability. This review explores the utilization of inorganic micro/nanomaterials to modify the rheological and mechanical performance of fresh-state and post-hardening cementitious composites, respectively, offering an in-depth analysis of the mechanisms underpinning their effects. This paper discusses a wide range of inorganic micro/nanomaterials, including carbon-based nanomaterials, silicon-based nanomaterials, metallic oxide nanomaterials, nano-calcium carbonate particles, and other micro/nano-materials. Those materials have been utilized in 3D printing concrete or show considerable potential for future applications in this field. Furthermore, this work provides insights into the multiple applications that arise from the synergistic combination of 3D printing construction techniques with the distinctive properties of different nanomaterials.

Analysis, Modelling and Simulation for the Intelligent Active Vibration Control of a Combined Plant using a New Gyroscopic Combined Actuator Model

There have been several research conducted worldwide on analysis, modelling, and controlling for flexible manipulator architectures. In this paper, one of the most interesting areas in the science of rotational dynamics as the study of spinning solid objects, such as Gyroscopes (G) has been investigated as a new approach for the improvement of the gyroscope non-linear behaviour which controls the magnitude and the direction of the torque separately in the frequency domain by describing a New Graphical Combined Actuator Model (NGCAM) for the Intelligent Active Vibration Control (IAVC) of a Combined Plant (CP) as a linear-nonlinear combined single link Robotics Flexible Manipulator (RFM) structure which rotates in the horizontal plane with two degrees of freedom in order to have a performance assessment and suppressing vibrations. The effect of the parameter variation on the stability and dynamic nonlinear behaviour of the gyroscope in gimbals with a feedback sensor control system has been formed and optimized by the Modified Fuzzy Logic (MFL) algorithm as an artificial intelligence control technique and the Proportional, Integral and Derivative (PID) tuning by the modified Ziegler Nichols (ZN) method. To validate the gyroscope stabilization to be implemented into the RFM for controlling the vibration, the proposed method combines Active Force Control (AFC) technique with Fuzzy Logic (FL) and PID as the (AVC-FL-PID) controller for the NGCAM, CP and RFM by using MATLAB & SIMULINK program is created. For a linear-non-linear combination system, it is better to use a linear-non-linear combination controller. Simulation results illustrate the effectiveness of the proposed strategy which is significantly and quite satisfactory about 25 (%) generally better than compared to the other systems’ performance criteria and is so clear and significant optimization is visible than compared to the other conventional actuators such as PZTs and other control strategies in improvement stabilization and vibration control of RFM structures. The advantages of the proposed method and the possibilities of further improvements are discussed.

Relaxed and flexible – what (else) happens when sofas enter everyday lives of schools?

ABSTRACT School environments in Finland are changing from traditional classrooms towards more flexible architectures. This change invites designers and commercial organizations to take part in re-thinking these environments by creating narratives of desirable educational environments. Their comprehensive furniture solutions promise flexible entities, including components that facilitate sharing and joining together. This article describes the school architecture reform from the perspective of one local school. We focus on an element emblematic of the planned relaxed learning environment: sofas, and discuss these using the philosophical concept of assemblage. We propose that arriving in the everyday life of a school the comfortable sofa – the promise of relaxation – also begins to participate in and to produce negotiations of power. Sofa becomes a participant in an assemblage of school: the sofa brings to surface relations that everyday life school assemblages already foster and sustain, making the negotiations of power in the school visible.

A human-centered intelligent platform for holographic control and management of 3D printers

Under the wave of emerging human-centric concepts such as Industry 5.0 and Human-Centered Intelligent Manufacturing, Mixed Reality (MR) has emerged as a pivotal enabling technology with profound impacts on human–machine interaction. Simultaneously, the affordability of Fused Deposition Modeling (FDM) 3D printers has expanded their accessibility beyond industrial applications to homes and public spaces. However, the operational and management complexities of different 3D printers and their varying human–machine interfaces (HMI) present challenges for users, especially beginners. In this paper, we presented a human-centered intelligent platform for FDM 3D printer control and management enabled by Mixed Reality. A flexible system architecture is developed in the paper with two essential communication protocols, MQTT and WebSocket, for stable data transfer. In addition, a task-oriented HMI is explored on the MR headset to simplify the operation procedure with little training effort and allow parallel supervision of multiple machines. By leveraging the advantages of our architecture, real-time monitoring, failure detection, and hologram visualization were able to be integrated into our platform for intelligent operation and management. Importantly, the flexibility of the architecture via a holographic interface enables our platform to easily expand to various machining operations in the future.

Model-based assembly process planning for flexible aircraft cabin architectures

The configuration and equipment of an aircraft cabin has a significant impact on the passenger’s flight experience. To meet the needs of passengers and improve the flight experience, airlines repeatedly demand customised adaptations to the cabin design. The implementation of these changes requires a high degree of flexibility in production and can lead to difficulties in setting up a fixed final assembly line. In addition, unforeseen events and changes in the OEMs’ supply chain require a rapid response in aircraft production to be able to deliver on time. Digitalisation in product development and production planning makes it possible to overcome these challenges and makes an important contribution to flexibility, time and cost efficiency. This paper presents a digital approach to modelling and flexible planning of assembly processes of the cabin. To this end, aircraft design data is automatically linked to the assembly system to react quickly to design changes in the assembly planning process. The integration is realised in a system architecture model depicted with the systems modelling language (SysML). The architecture model follows the formal process description defined in VDI3682. A planning algorithm then uses the production architecture parameters to optimise the assembly processes, e.g. in terms of time. The approach presented is demonstrated using the example of scheduling the pre-assembly processes of the "crown module". The latter includes all structural and functional components above the window panels, such as overhead bins, electrics and air conditioning. The results show how changes in aircraft and cabin design or in the production plant and resources can be flexibly evaluated. This allows conceptual changes to be evaluated and traded before the cabin design is frozen and transferred to real production. These advantages contribute to the time and cost optimisation of aircraft production. This work thus makes a valuable contribution to the further development of digital solutions in aircraft production.

Regulatory and spectrum policy challenges for combined airspace and non-terrestrial networks

The recent advancements in the aviation and space industries facilitate new types of users in the sky, e.g., electrical vertical take-off and landing (eVTOL) aircraft. To unlock the full potential of these innovations, it is crucial to create interconnected three-dimensional networks that integrate airspace and non-terrestrial networks (NTNs) with their terrestrial counterparts, also known as Combined Airspace and Non-Terrestrial Networks (ASN). Combined ASN needs a flexible and adaptive network architecture with multiple, and partly converging technologies, such as air-to-ground, satellite, and high-altitude platforms, with a focus to cover both ground and aerial users. In addition to technical challenges, spectrum policy and regulatory aspects will affect the wireless network design and management schemes in three-dimensional space. Hence, this paper introduces regulatory and spectrum policy challenges posed by the development of combined ASN. This paper investigates a hypothetical urban air mobility use case with an eVTOL aircraft that operates as a flying taxi.

Efficient SpMM Accelerator for Deep Learning: Sparkle and Its Automated Generator

Deep learning (DL) technology has made breakthroughs in a wide range of intelligent tasks, such as vision, language, recommendation systems, and so on. Sparse matrix multiplication (SpMM) is the key computation kernel of most sparse models. Conventional computing platforms, such as CPUs, GPUs, and AI chips with regular processing units, are unable to effectively support sparse computation due to their fixed structure and instruction sets. This work extends Sparkle, an accelerator architecture, which is developed specifically for processing SpMM in DL. During the balanced data loading process, some modifications are implemented to enhance the flexibility of the Sparkle architecture. Additionally, a Sparkle generator is proposed to accommodate diverse resource constraints and facilitate adaptable deployment. Leveraging Sparkle’s structural parameters and template-based design methods, the generator enables automatic Sparkle circuit generation under varying parameters. An instantiated Sparkle accelerator is implemented on the Xilinx xqvu11p FPGA platform with a specific configuration. Compared to the state-of-the-art SpMM accelerator SIGMA, the Sparkle accelerator instance improves the sparse computing efficiency by about 10 to 20 \(\%\) . Furthermore, the Sparkle instance achieved 7.76 \(\times\) higher performance over the Nvidia Orin NX GPU. More instances of accelerators with different parameters were evaluated, demonstrating that the Sparkle architecture can effectively accelerate SpMM.

Diabetic Retinopathy Detection Using Convolutional Neural Networks with Background Removal, and Data Augmentation

Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have shown promise in automating DR diagnosis, offering faster, more reliable, and cost-effective solutions. Our study employed convolutional neural networks (CNNs) to classify the severity of DR using retinal images from the EyePACS dataset, which includes 35,155 images categorized into five classes. Building on previous research that often classified DR into two classes, such as no DR and varying levels of DR, we found that while these studies typically used models like Inception V3, VGGNet, and ResNet, they focused on simplifying the diagnostic process by reducing the number of classes. However, our approach utilized a smaller, more flexible CNN architecture, allowing for a more detailed classification into five stages of DR. We employed various image preprocessing techniques, including grayscale conversion, background removal, and data augmentation, with our findings indicating that background removal significantly enhanced model performance, achieving a validation accuracy of 90.60%. This underscores the importance of sophisticated data preprocessing in medical imaging, and our study contributes to the ongoing development of automated DR diagnosis, potentially easing the burden on healthcare systems and improving patient outcomes.

In situ anchoring of FeNi nanoparticles into three-dimensional nitrogen-doped carbon framework as a self-supporting electrode for high-performance lithium storage

Constructing free-standing electrodes with superior lithium storage capacity and good mechanical performance are fascinating for next-generation lithium-ion batteries (LIBs). Nevertheless, their practical application is hindered by complicated synthesis procedures and high cost. Herein, a highly stable, self-supporting and flexible electrode material for LIBs was constructed by encapsulating spherical FeNi nanoparticles into one-dimensional N-doped carbon fiber film (FeNi/N- CFM) using the electrospinning technique and subsequent heat treatment. The FeNi/N-CFM can be directly used as anode materials for LIBs without conductive additive, current collector and binder. The synergistic effect of uniformly dispersed small FeNi nanoparticles and three-dimensional (3D) conductive network constructed by CF framework effectively improves the utilization rate of active materials, enhances the transport of both electrons and lithium ions, facilitates the electrolyte penetration, and promotes the lithium-ion storage kinetics and stability, thus leading to a greatly enhanced electrochemical performance. Benefiting from the unique architectural feature and flexibility, the FeNi/N-CFM composites employed as binder-free electrodes for LIBs exhibit good lithium storage performance (an initial discharge capacity of 1031.5 mA h g−1 and a reversible capacity of 481.3 mA h g−1 at 100 mA g−1 after 100 cycles), rate capacity (305.1 mA h g−1 at 1000 mA g−1) and outstanding reversibility (coulombic efficiency of around 100 % after 100 cycles). Furthermore, this work also provides a facile and effective pathway to design and fabricate free-standing electrodes.